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  • From Workflow to Authority: What ServiceNow’s Armis Deal Changes for CIOs and CISOs

    Reading Time: 8 minutes

    ServiceNow on Tuesday announced that it would buy cybersecurity vendor Armis for $7.75 billion in cash.  This builds on its December purchase of identity security vendor Veza, and the closing of its acquisition of AI vendor Moveworks.

    Sanchit Vir Gogia, the chief analyst at Greyhound Research, agreed that this acquisition will likely accelerate IT and security structural changes. 

    “This acquisition represents a fundamental repositioning of ServiceNow from a coordination layer into an operational authority. Buying Armis is not about expanding a security portfolio. It is about owning the upstream constraint that determines whether modern enterprises can govern complexity at all,” Gogia said. But without knowing what is connected across IT, OT, IoT, and other physical environments, “workflow automation, AI governance, and risk prioritization all collapse into theatre,” he observed, adding that the deal could remove long standing fragmentation between discovery tools, CMDBs, service mapping, ticketing, change management, and remediation. “If executed well, it could finally address one of the enterprise’s most persistent failures,” he said.

    Gogia added, “continuous discovery tied to business context has the potential to turn the CMDB from a negotiated artefact into a living system. That would change how incidents are resolved, how changes are governed, how audits are passed, and how accountability is assigned.”

    The 2026 second half closing date “implies a prolonged transition period where integration depth, roadmap clarity, and packaging decisions will evolve. CIOs should plan for ambiguity, not assume instant unification. Early value will come from visibility, [therefore] full platform value will take time,” Gogia said. 

    As quoted in CIO.com, in an article authored by Evan Schuman published on Dec 23, 2025.

    Beyond the Media Quote: Our View, In Full

    What does ServiceNow’s acquisition of Armis change for enterprise CIOs?

    According to Greyhound Research, this acquisition represents a fundamental repositioning of ServiceNow from a coordination layer into an operational authority. Buying Armis is not about expanding a security portfolio. It is about owning the upstream constraint that determines whether modern enterprises can govern complexity at all. Asset truth. Without continuous, credible visibility into what is actually connected across IT, OT, IoT, medical, and cyber physical environments, workflow automation, AI governance, and risk prioritisation all collapse into theatre. Armis provides the missing reality layer. ServiceNow provides the execution layer. Together, they form a closed operational loop that shifts ServiceNow from routing work to defining reality.

    This move also reflects a recognition that asset visibility cannot remain a loosely coupled integration if ServiceNow intends to act as a control plane. Ownership matters because latency, friction, and ambiguity between discovery and action undermine governance at scale. By internalising this layer, ServiceNow removes dependence on third party cadence and aligns asset truth directly with platform execution logic.

    For CIOs, the opportunity is architectural rather than incremental. This deal offers a credible path to collapse long standing fragmentation between discovery tools, CMDBs, service mapping, ticketing, change management, and remediation. If executed well, it could finally address one of the enterprise’s most persistent failures. The CMDB that reflects aspiration rather than truth. Continuous discovery tied to business context has the potential to turn the CMDB from a negotiated artefact into a living system. That would change how incidents are resolved, how changes are governed, how audits are passed, and how accountability is assigned.

    This also reframes the CMDB from a passive reference to an active governance surface. If discovery continuously reconciles declared state with observed state, discrepancies become operational signals rather than audit findings. That shifts how CIOs must think about ownership, stewardship, and remediation responsibility across the organisation.

    But this same consolidation introduces a structural trade off that CIOs must confront explicitly. Platform gravity increases. Data gravity increases. Exit costs increase. When asset intelligence, exposure prioritisation, and remediation logic are embedded into the same platform, ServiceNow ceases to be optional infrastructure. It becomes part of the enterprise operating system. That shifts procurement dynamics, negotiation leverage, and long term architectural flexibility. CIOs should expect stronger bundling pressure, tighter commercial packaging, and more aggressive monetisation precisely because the capability is strategically central.

    The scale and pricing of this acquisition also signal that ServiceNow will be under pressure to demonstrate return on invested capital. Historically, that pressure tends to translate into faster platform consolidation inside customer accounts, not slower. CIOs should therefore expect increased urgency around standardisation decisions and should prepare governance mechanisms that prevent momentum from dictating architecture.

    This deal also quietly reframes ServiceNow’s competitive set. It is no longer just competing with ITSM or workflow platforms. It is encroaching on territory historically owned by infrastructure discovery, OT security, network visibility, and even elements of cloud management. That expands its relevance but also increases internal friction. Facilities teams, operations leaders, and industrial engineers will now find their environments visible and governable through a central platform. The resulting organisational politics are not a side effect. They are part of the cost of consolidation.

    This competitive expansion also means CIOs will face new internal buying tensions. Decisions that were once departmental will increasingly require enterprise level arbitration. The control plane conversation inevitably becomes a power conversation, and CIOs must be prepared to mediate it deliberately.

    Timing matters as well. This is a large all cash acquisition funded through cash and debt, expected to close only in the second half of 2026 subject to regulatory approval. That implies a prolonged transition period where integration depth, roadmap clarity, and packaging decisions will evolve. CIOs should plan for ambiguity, not assume instant unification. Early value will come from visibility. Full platform value will take time.

    During this transition period, communication and expectation management become operational risks in their own right. CIOs should plan for a phase where discovery improves faster than remediation maturity, and ensure teams are not overwhelmed by insight they are not yet equipped to act upon.

    There is also an accountability shift embedded in this deal. Once continuous discovery and prioritisation exist, the defence of incomplete visibility disappears. When failures occur, the question will no longer be whether the organisation knew. It will be why it did not act. That raises the bar for CIO governance, especially in regulated and safety critical industries.

    The correct CIO response is not enthusiasm or resistance. It is deliberate engagement. Demand explicit commitments on data portability, integration openness, and boundary control. Decide consciously which domains should be centralised and which should remain federated. And most importantly, bring IT, security, OT, and risk into a single operating conversation. Because the failure mode here is not technological. It is organisational. Visibility without operational readiness creates noise. Noise creates risk.

    What does ServiceNow’s acquisition of Armis change for enterprise CISOs and CSOs?

    According to Greyhound Research, this acquisition shifts security from a detection discipline to an execution discipline. Armis has long been valuable because it exposes what traditional tools miss. Unmanaged devices, shadow systems, cyber physical assets, and operational technologies that quietly dominate the real attack surface. What changes now is that discovery no longer stops at awareness. It can be directly bound to prioritisation, workflow, and remediation at scale. ServiceNow is buying Armis to own that execution loop.

    This also reflects a broader shift in how security value is measured. Visibility without action no longer satisfies boards or regulators. Security programs are increasingly judged on whether exposure is reduced in measurable timeframes, not whether risks are merely identified.

    For CISOs and CSOs, this compresses the distance between intent and consequence. Security findings no longer need to traverse dashboards, handoffs, and manual approvals before action occurs. They can flow directly into operational systems that change live environments. That can materially reduce exposure, but it also increases the blast radius of mistakes. Governance maturity becomes the deciding factor between risk reduction and operational disruption.

    This compression removes traditional organisational buffers that once absorbed error. When security can trigger action directly, mistakes become immediately visible and politically costly. CISOs must therefore assume that influence and accountability will rise together.

    The most underappreciated risk here is over automation, particularly in cyber physical, industrial, healthcare, and safety critical environments. Automation that is acceptable in enterprise IT can be dangerous when applied to production lines, medical networks, building systems, or industrial control environments. Availability and safety often outweigh confidentiality and speed. A platform that does not respect that hierarchy will fail regardless of how advanced it is.

    This forces CISOs into a new role. Not just as defenders, but as governors of automated power. They must define where automation is permitted, where it is advisory, where human approval is mandatory, and how exceptions are handled. If those policies do not exist before adoption, the platform will enforce defaults that may not align with business reality. The correct adoption model is phased. Continuous discovery first. Contextual prioritisation second. Workflow based remediation with approval gates next. Limited autonomy only in low risk segments. Any organisation that jumps straight to full automation will learn through disruption.

    This phased model is not optional. It is a prerequisite for trust. Enterprises that treat automation as a switch rather than a spectrum will experience operational pushback that undermines security credibility.

    There is also a political shift inside enterprises that CISOs must anticipate. The ability to trigger action will increase security’s influence, but it will also increase scrutiny. When automation causes disruption, security will be held directly accountable. This will strain already fragile trust between security teams and operations, engineering, and clinical stakeholders unless governance is explicit and collaborative.

    The integration timeline matters here too. With closing expected in the second half of 2026, CISOs should expect a period where interoperability, roadmap clarity, and platform boundaries are in flux. During this period, it is critical to press for transparency. What remains open. What becomes native. What integration commitments persist with other security platforms. A security program cannot afford silent consolidation that reduces flexibility without consent.

    This period of uncertainty also increases vendor risk exposure. CISOs should assume that platform decisions made during transition phases tend to become permanent, even if framed as interim.

    There is also a deeper risk that must be acknowledged. When exposure intelligence, asset data, and remediation workflows converge into one platform, that platform itself becomes a tier one security dependency. Outages, access failures, misconfigurations, or insider risk within the platform can have systemic impact. CISOs must therefore elevate vendor resilience, access governance, auditability, and contingency planning for ServiceNow to the same level as other critical infrastructure.

    Finally, the scale of this deal signals intent. ServiceNow is positioning security as a primary growth engine. That means more aggressive roadmaps, tighter coupling, and stronger commercial pressure. CISOs must respond with equally strong procurement discipline, outcome based validation, and governance oversight.

    As security becomes more central to platform monetisation, CISOs should expect increased executive attention, faster sales cycles, and stronger expectations around measurable outcomes. That attention is an opportunity only if governance maturity keeps pace.

    The Monday morning action for CISOs is not to evaluate a product. It is to audit readiness. Where unmanaged assets truly exist. Whether operational teams can absorb automated security action without breaking production. Whether autonomy policies exist at all. If those answers are unclear, this acquisition is not an upgrade opportunity. It is a warning. Because the next phase of security is not about seeing more. It is about acting safely, deliberately, and with accountability.

    Analyst In Focus: Sanchit Vir Gogia

    Sanchit Vir Gogia, or SVG as he is popularly known, is a globally recognised technology analyst, innovation strategist, digital consultant and board advisor. SVG is the Chief Analyst, Founder & CEO of Greyhound Research, a Global, Award-Winning Technology Research, Advisory, Consulting & Education firm. Greyhound Research works closely with global organizations, their CxOs and the Board of Directors on Technology & Digital Transformation decisions. SVG is also the Founder & CEO of The House Of Greyhound, an eclectic venture focusing on interdisciplinary innovation.

    Read About SVG

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  • Streaming, Sovereignty, And The New AI Stack: Decoding The IBM–Confluent Deal

    Reading Time: 23 minutes

    There are seasons in enterprise technology when the landscape shifts quietly. Then there are moments like this one when the ground moves in full view of the industry. IBM’s decision to acquire Confluent sits in the latter category. It marks a turning point that has little to do with price tags or portfolio expansion. What it truly reveals is a change in who controls the lifeblood of modern digital enterprises. That lifeblood is real-time data. Not the historical batch kind that has powered reports for decades, but the streaming, always-on, cross-application flow that fuels AI, automation, and every meaningful decision that defines enterprise resilience today.

    Confluent built itself on a simple but transformative idea. If enterprises want intelligence at the speed of their operations, then their data must move at the speed of their intent. This is how Confluent turned the humble event stream into an architectural standard. It became the connective tissue of the operational estate, the path through which transactions are validated, microservices talk to one another, and AI systems gain the context they need to act in real time. The gap between that operational sprawl and the governed, intelligent analytical estate has only widened. Confluent positioned itself as the bridge. IBM has now decided to own the bridge.

    Today, more than 6,500 enterprises rely on Confluent to keep real-time data moving across their estates, including a significant share of the world’s largest companies. That level of operational presence turns a streaming platform into a structural dependency, not an optional enhancement.

    Why now? Because data is no longer a passive asset. It is a live surface that AI systems read, interpret, and act upon constantly. The rise of generative and agentic AI has forced enterprises to confront a truth they have long postponed. To automate meaningfully, they must govern continuously. To scale AI safely, they must feed it with trusted, contextual, high-quality data in flight, not by batch. IBM recognizes that real-time data infrastructure is quickly becoming the beating heart of enterprise AI, and if it does not own this heart, it risks ceding strategic ground in the next architectural cycle.

    This deal matters for another reason. Control is migrating. For years, enterprises spread data across multiple clouds, systems, and teams, hoping that integration tools would stitch it all together. That era is ending. A world defined by AI agents will demand continuous intelligence, unified lineage, tight governance, and a choreography of data that cannot be left to fragmented pipelines. Confluent’s platform is engineered precisely for this world. It processes, governs, and transports data in motion. IBM’s ambition is not simply to integrate it. It is to turn it into the standard fabric for enterprise AI.

    What makes this moment decisive is the convergence at play. The explosion of AI workloads, the complexity of hybrid estates, and the pressure to drive decisions at sub-second speed are all exposing the limits of yesterday’s architectures. Enterprises that once tolerated messiness in their data flows now see it as a strategic liability. IBM viewed Confluent not as a product acquisition but as an opportunity to consolidate authority over how data is shaped, transported, and made trustworthy across an entire organization.

    And so this becomes more than a software deal. It becomes a play for sovereignty. The sovereignty to know where your data is flowing. The sovereignty to understand what your AI is doing with it. The sovereignty to defend resilience when every part of the enterprise relies on continuous, contextual information. By bringing Confluent into its fold, IBM has signalled that sovereignty will sit in the streaming layer. Whoever governs that layer will influence the entire AI stack above it.

    Greyhound Standpoint: At Greyhound Research, we believe this is not simply an acquisition. It is a declaration that the center of gravity in enterprise technology has shifted to real-time data. IBM is not just buying capabilities. It is laying claim to the layer where control, trust, and intelligence converge. The implications for enterprises will be profound.

    What IBM Is Actually Acquiring: The Strategic Core Inside Confluent

    There is a temptation to read this acquisition at face value. IBM bought a data streaming company, the market responded, and the industry moved on. But beneath that surface sits a far more deliberate strategy. IBM did not buy Confluent for its connectors or its cloud service, or even for its brand authority in the Kafka ecosystem. It bought Confluent because the very definition of enterprise intelligence is shifting, and IBM wants to own the layer where that shift becomes irreversible.

    If the first wave of AI was about training and the second about inference, the next is about orchestration. Not workflows, but intelligence flows. Not databases, but continuously updating contexts. This is the world Confluent built for, where data does not sit in silos waiting to be queried. It moves across operational and analytical estates, feeding models, enriching decisions, and enabling AI agents to behave less like tools and more like participants. In this world, the streaming layer becomes the command surface for enterprise AI. IBM is buying the right to define that command surface.

    Confluent’s platform has already expanded far beyond its Kafka roots. With more than 120 production-grade connectors, integrated stream governance, native Flink processing, and its Tableflow unification layer, it has quietly become one of the most complete streaming ecosystems available to large enterprises.

    Confluent’s investor materials reveal a company that already sees itself as the backbone of real-time enterprise architecture. It positions data in motion as the antidote to messy estates and incomplete AI loops. It paints the emerging enterprise as one where applications, analytics, governance, and AI intelligence all revolve around a single fabric of streaming data. It introduces capabilities like Flink processing, Tableflow unification, and streaming governance as essential components of a new data operating system. This is not a toolkit. It is a thesis about how modern enterprises should think about information. IBM has bought that thesis, along with the right to operationalize it on a global scale.

    Greyhound Fieldnotes from recent CIO and CTO conversations reveal a pattern that makes this acquisition even clearer. Many leaders admit they are struggling with the widening distance between legacy analytical systems and modern AI-driven operations. Their teams can build dashboards, but they cannot automate decisions reliably. They can deploy models, but they cannot supply those models with clean, governed, real-time context. They can identify thousands of microservices, but they cannot explain how data flows between them. As one global CTO told us, the enterprise has built faster engines but forgotten to modernise the fuel. IBM understands this sentiment. Confluent is the missing fuel infrastructure.

    Greyhound Pulse adds another dimension. Across Europe, Asia, and the Americas, enterprise architects report a rising urgency to collapse the divide between operational and analytical estates. Many admit they are reaching the limits of ETL pipelines and batch movement. They want a single architecture where models can consume streaming information the moment it appears, where governance is applied at the source, and where AI agents can act with confidence. Confluent’s platform, with its emphasis on connect, process, govern, and stream, answers precisely this need. IBM is buying not just technology but a narrative that customers are already predisposed to believe.

    There is also a quieter strategic calculation. Confluent’s ecosystem is unusually deep. Its connectors span databases, SaaS platforms, vector stores, warehouses, and emerging AI tools. Its customer footprint cuts across verticals, from financial services to manufacturing. Its community influence extends through every organization that has adopted Kafka. IBM is not simply purchasing technology. It is acquiring a gravitational field. The moment Confluent enters IBM’s orbit, the center of data movement in the enterprise begins to shift toward IBM’s platform logic. That shift may feel subtle at first, but strategically it is profound.

    Despite Kafka underpinning systems at more than 150,000 organizations, less than five percent of that base is monetized today. This reveals why IBM sees Confluent not only as a platform but also as a major growth engine.

    Then there is the architectural future that Confluent has been positioning itself for. Agentic AI. Continuous intelligence. Event-driven ecosystems that mirror the behavior of complex human processes. Confluent’s demonstrations of multi-agent workflows, its investment in Flink-based processing, and its insistence that the future of AI is event-driven all point to a world where streaming infrastructure becomes the core substrate for autonomous enterprise operations. IBM knows that if it does not own this substrate, it will be forced to rent it from the market. Owning it is not only cheaper in the long run, but it also offers strategic leverage.

    In truth, IBM is buying something more intangible but far more valuable. Influence over how the next enterprise architecture is imagined. Control over the patterns that CIOs and CTOs adopt. Authority over the standards that define where data resides, how it moves, and how AI consumes it. This is what Confluent built. A position not just in the stack, but in the mindset of architects who are designing the post-cloud, post-batch, real-time world.

    Greyhound Standpoint: At Greyhound Research, we believe real strategy reveals itself not in what is acquired, but in what gets rewired beneath the surface. IBM is not buying Confluent for its present. It is buying it for the architectural future that Confluent has already mapped. The deal is not about adding a product. It is about reshaping the foundation upon which enterprise intelligence is constructed.

    Strategic Continuity: Completing the Platform Arc

    Some acquisitions add capabilities. Others expose gaps. Then there are those that finish the structure. IBM’s acquisition of Confluent sits in that third category. It does not signal a pivot. It affirms a pattern that began with Red Hat, deepened with HashiCorp, and now finds its final rhythm in real-time data.

    Each acquisition targeted a different layer of the modern enterprise stack. Red Hat gave IBM the open hybrid foundation. OpenShift made workloads portable across on-prem, public cloud, and edge. Linux, Kubernetes, and container orchestration became the operating system of IBM’s cloud future. HashiCorp added the automation layer. Terraform, Vault, and Consul offered the tools to provision, secure, and govern complex infrastructure at scale. Confluent now brings the motion layer, real-time data that feeds AI models, drives automation, and connects every system with trusted, continuous context.

    This wasn’t an improvisation. It was architecture in sequence. First run, then manage, now respond. IBM has methodically built a stack that moves from deployment to automation to decision. Confluent doesn’t start a new chapter. It completes the sentence.

    The technology fit is precise. OpenShift runs enterprise workloads in containers. Terraform provisions the infrastructure they need. Vault secures them. Kafka connects the events they generate. Flink processes those events in real time. Tableflow bridges streams and analytical systems. Stream governance ensures it’s all clean and trustworthy. At the top, AI systems like watsonx can consume that flow and act on it. Every layer strengthens the others. This is not a collection of tools. It is a platform designed to operate with feedback, not lag.

    Financially, the model holds. Red Hat became IBM’s fastest-growing software unit post-acquisition. Confluent crossed a $1 billion annual run rate before the deal closed. HashiCorp brought an open-core monetization playbook that IBM has experience scaling. All three deals drive recurring revenue, deepen IBM’s software margins, and lower dependency on traditional services.

    The go-to-market logic is equally deliberate. Each acquisition expands IBM’s surface area inside the enterprise. Red Hat connects with DevOps and platform engineering. HashiCorp reaches the security and infrastructure-as-code teams. Confluent touches the data architects, analytics owners, and AI teams struggling with pipeline latency and governance. IBM doesn’t just integrate these products. It integrates the conversations they enable, offering a coherent story that maps to how enterprises think, build, and modernize.

    That story matters more now than ever. AI that lacks data flow is brittle. Automation without trusted provisioning is unsafe. Cloud portability without orchestration is chaos. IBM’s platform addresses all three. It gives enterprises infrastructure they can control, automation they can trust, and intelligence that’s grounded in live, governed context.

    This is also a platform with principles. Each acquisition brought not just code, but community. Red Hat made IBM a steward of Linux and Kubernetes. HashiCorp brought Terraform’s reach across infrastructure teams worldwide. Confluent came with Kafka’s real-time ecosystem. These aren’t just technologies. They are defaults. And IBM now plays a central role in shaping their future, not to enclose them, but to keep them interoperable, scalable, and enterprise-ready.

    What makes this continuity powerful is that it doesn’t depend on any one product. It works as a system. A developer can deploy a service using Terraform, run it on OpenShift, stream its events through Kafka, secure its secrets with Vault, and feed its outputs into an AI model, all within one ecosystem. That level of integration used to be reserved for tightly controlled, single-cloud environments. IBM now makes it possible in hybrid, multi-cloud, and regulated contexts.

    This is not an empire of acquisitions. It is a layered platform. Every move extended IBM’s relevance without overreaching its identity. Red Hat gave IBM credibility with developers. HashiCorp gave it tools for multi-cloud realism. Confluent gives it the trust layer for AI-driven execution. Each reinforced the one before it. Each brought IBM closer to the places where digital transformation actually happens, in code, in pipelines, and in moments of real-time insight.

    Greyhound Standpoint: At Greyhound Research, we believe this acquisition completes IBM’s platform arc. With Red Hat, it laid the foundation. With HashiCorp, it secured control. With Confluent, it adds the heartbeat, real-time data that connects, informs, and activates the stack. This isn’t expansion. It is convergence. And it positions IBM to offer enterprises not just tools, but a system built for velocity, trust, and intelligence.

    What Changes for Buyers: Risk, Resilience and Platform Gravity

    The moment IBM announced its intent to acquire Confluent, the first wave of reactions came from enterprise buyers, not competitors. Some welcomed the clarity this deal promised. Others sensed the architecture beneath their feet beginning to shift. A few quietly admitted what many have been feeling for years. Real-time data has stopped being an implementation choice. It has become the defining dependency of the modern enterprise. And when a dependency grows this central, an acquisition of this scale forces every organization to rethink its risk, resilience, and the gravitational pull of its chosen platforms.

    For CIOs, this deal brings an unusual combination of relief and unease. Relief because they finally see a major vendor stepping in to operationalize real-time data as a first-class capability. Confluent has long been championed inside engineering and architecture teams, but it has often sat outside the structured governance frameworks that CIOs rely on to run their estates. IBM’s stewardship promises enterprise grade reliability, lifecycle discipline, and global support structures that CIOs have been asking for. Yet there is unease too. Greyhound Fieldnotes show that many CIOs worry about what happens when a once-neutral streaming layer becomes part of a larger platform agenda. They trust IBM’s ability to scale it. They are less certain about how this move reshapes optionality.

    Many enterprise estates are already deep into streaming adoption even if not formally recognized as such. Confluent’s own data shows that its cloud platform now contributes more than half of its subscription revenue, with multi-product customers expanding their spending many times over once streaming becomes central to their architecture.

    For CTOs, the implications feel more direct. Confluent has been the backbone of countless microservice estates, application modernization programs, and streaming analytics pipelines. Under IBM, it will likely integrate more deeply with AI, automation, and hybrid cloud tooling. That promises acceleration but also introduces architectural gravity. Greyhound Pulse from recent CTO roundtables shows a clear pattern. Leaders understand the benefits of having their operational and analytical estates stitched together, but they also fear the slow creep of inflexibility. One CTO in a global automotive firm described it as gaining a bigger runway while losing some of the side exits. The tradeoff is no longer theoretical.

    CISOs may be the most conflicted. On one hand, the consolidation of streaming governance, data movement, and real-time processing under a single enterprise vendor offers a path to tighter, more consistent policy enforcement. Many CISOs have struggled with fragmented streaming deployments that lacked adequate lineage, classification, and quality controls. Confluent’s platform already addresses these shortcomings, and IBM can amplify these strengths. On the other hand, CISOs are deeply aware of concentration risk. If the streaming layer becomes a single vendor-controlled asset, the entire security posture of the organization becomes intertwined with one vendor’s roadmap, one vendor’s pace of patching, and one vendor’s interpretation of resilience. That is a form of dependency that requires deliberate oversight.

    CFOs see a different calculus. The move to real-time intelligence has created a sprawl of overlapping data pipelines, cloud services, and stitching tools that quietly inflate cost structures. A unified streaming layer can rationalize those investments. It can also centralize them in ways that demand sharper financial models. Greyhound Fieldnotes capture CFO concerns that the shift from open deployment flexibility to a more structured vendor-aligned estate could alter long-term spending curves. Some see the potential for efficiencies. Others see the contours of future lock-in. Many see both.

    What changes for all buyers is the center of gravity. Confluent is no longer an optional enhancement. It is becoming the reference model for how data should move inside an organization. IBM will amplify this by positioning streaming as the connective fabric between AI, automation, integration, and governance. Buyers who have been managing streaming as a tactical capability will now be forced to evaluate it as a strategic foundation. This will change procurement models, operating structures, and the very questions leaders ask their teams. It will also change how architects design future estates. With IBM shaping the streaming layer, organizations will need to decide which parts of their data posture they are comfortable centralizing and which must remain sovereign.

    Greyhound Pulse also reveals a more human truth. Teams that built Confluent-based estates valued the sense of control they enjoyed. They could design, deploy, and extend without waiting for a platform vendor’s quarterly priorities. This acquisition shifts that equilibrium. Some teams will feel relieved. Others will feel constrained. Most will feel both, and that ambivalence will shape buyer behavior in the coming two years.

    What no buyer can ignore is the cascading effect this deal will have on resilience. When Confluent becomes part of IBM’s operating model, its upgrade cycles, incident response patterns, and dependency rules will align with IBM’s standards. This will improve overall predictability. It will also require enterprises to adjust their internal readiness. Streaming failures, once isolated to specific engineering teams, will become enterprise-level events. AI misalignment risks, once traced to data quality issues, will now be traced to the streaming platform that feeds the models. The accountability framework shifts. The resilience model is rewritten.

    Under Confluent’s current operating model, the largest customers often grow their annual commitment by factors of five to thirty over time, a pattern that illustrates how deeply streaming embeds itself once it becomes part of an enterprise’s nervous system.

    This is the quiet power of platform gravity. It does not force change. It invites it. And slowly, imperceptibly, it becomes the default way of operating. Buyers who understand this will approach the IBM and Confluent alignment not as a procurement decision, but as a structural recalibration.

    Greyhound Standpoint: At Greyhound Research, we believe every acquisition changes code, but the deeper shift is in who owns risk and resilience. IBM’s move reshapes the center of gravity for real-time intelligence. Buyers must now decide how far they are willing to let their architecture lean into that gravity and what they must retain as theirs alone.

    What This Signals to the Ecosystem: A New Definition of Neutral

    The news of IBM acquiring Confluent travelled differently through the ecosystem. It did not explode with the drama of competitive posturing or the noise of price war speculation. Instead, it moved like a change in atmospheric pressure. Subtle at first, then steadily altering the behavior of every player sensitive to long-term shifts in platform gravity. When a company like IBM takes ownership of a streaming platform as foundational as Confluent, neutrality itself is redefined. Not by declaration, but by the quiet realignment of incentives, partnerships, and architectural defaults.

    For years, Confluent lived in an unusual corner of the enterprise world. It slipped between systems, clouds, and sprawling operational estates, acting as a kind of connective tissue without ever signalling loyalty to any one camp. It kept conversations flowing between applications, helped microservices share their state, refreshed context for analytics, and kept AI pipelines supplied with what they needed. In that quiet role, it earned the confidence of developers, architects, and operations teams who depended on its steady neutrality in a market filled with competing agendas. With IBM’s arrival, that balance naturally shifts. Not through restriction, but through the simple reality that ownership changes how a product is interpreted and applied.

    Greyhound Fieldnotes reveal this shift vividly. Implementation partners in financial services and telecom markets told us that the moment Confluent becomes part of a global enterprise platform, they will be expected to treat it not just as a technology anchor but as a strategic alignment indicator. Partnerships that once revolved around technical fit will now consider platform direction, data control narratives, and AI strategy compatibility. One large systems integrator executive shared that customers are already asking whether Confluent, under IBM, will remain the neutral bridge it has always been or whether it will become the preferred backbone for IBM-aligned estates. The question is less about fear and more about planning. Ecosystems thrive on predictability.

    Greyhound Pulse from global markets shows a similar recalibration. Leaders in manufacturing, healthcare, and insurance are preparing for a world where the streaming layer becomes a locus of platform competition. In previous eras, that role was played by databases or storage layers. Today, real-time data flow is the more valuable asset. It influences how AI models behave, how automation systems respond, and how customer experience platforms personalize interactions. When IBM positions Confluent as part of its smart data platform narrative, the ecosystem reads the signal. The streaming layer is no longer a neutral facilitator. It is a strategic control point.

    Confluent has already become a de facto standard across many data heavy sectors. Nearly half of the Fortune 500 use it in some form, a footprint that effectively reshapes the expectations of any ecosystem that interacts with large enterprises.

    For cloud providers and application vendors, this signal triggers a careful reassessment. They have long relied on Confluent as a dependable integration pathway that did not threaten their commercial interests. Now, they must examine how to maintain frictionless interoperability while acknowledging that a major platform provider controls a key dependency in their customers’ architectures. None of this translates into immediate conflict. If anything, the first instinct across the ecosystem is cooperation. But cooperation now carries a different weight. The cost of misalignment increases when the streaming layer gains strategic ownership.

    Ecosystem partners that have grown their practices around Confluent are feeling this shift more sharply than most. Consulting firms read the move as a signal that their Confluent expertise just became more valuable. Managed service providers see fresh room to design richer hybrid and multi-cloud streaming blueprints. Independent software vendors are quietly rewriting their integration plans to account for a new layer of alignment. Greyhound Fieldnotes reflect this change in mood. Partners still see plenty of upside, yet they also understand that neutrality can no longer be taken for granted. It now has to be shown. It has to be protected. And it has to be shaped in collaboration with customers.

    There is another layer to this shift that is less commercial and more philosophical. In the era of AI, neutrality has become a contested term. Enterprises want platforms that do not force their cloud choices, their architectural philosophies, or their risk models. Yet they also want platforms that behave consistently, enforce governance by design, and protect the integrity of their data. This is the paradox of the modern ecosystem. Neutrality is demanded, while consolidation is inevitable. The IBM and Confluent deal sits precisely at this convergence. It establishes a new equilibrium where neutrality does not mean independence. It means trust through transparency, openness through design, and interoperability through contractual and technical commitment.

    For regulators, industry alliances, and standards bodies, this shift may eventually surface as a point of scrutiny. Control over data movement is strategically significant. Control over real-time intelligence pathways is even more so. The ecosystem is already reading the implications. When a platform vendor becomes the steward of the streaming fabric, the definition of “safe” changes. Safe no longer refers only to multizone availability or failover guarantees. Safe now includes the assurance that data can move freely, predictably, and without hidden commercial constraints.

    Greyhound Standpoint: At Greyhound Research, we believe this acquisition is more than market momentum. It reframes what neutral means in an era where data is always moving and AI is always consuming. It signals to the ecosystem that the center of trust has shifted to the streaming layer. Every partner, platform, and policymaker must now decide how they align with this new reality.

    How Integration Has Played Out Before: Lessons From Past M&A

    People who have been around long enough in enterprise technology develop a kind of muscle memory about acquisitions. The press releases always sound polished, the diagrams always line up neatly, and the promises feel almost effortless. But the real story begins later, usually in quieter rooms, when teams try to merge habits and history. That is why IBM’s move to bring Confluent inside the company deserves to be looked at through the long view. It is not the first time IBM has absorbed a strong independent brand. And the lessons from those earlier chapters matter now.

    In most deals, the immediate wave of excitement fades and is replaced by something slower and heavier. The tempo changes. Engineers who were used to sprinting now find themselves pacing alongside a larger organization that moves differently. Roadmaps stretch. Decision-making thickens. Greyhound Fieldnotes from CIOs who lived through earlier integrations often describe this shift as a soft but noticeable settling. Products still grow, but the pulse is steadier, almost as if the metronome has been reset for a larger room.

    Architecture teams feel their own version of this adjustment. Once a product becomes part of a broader platform, it starts to negotiate its identity with the surrounding ecosystem. APIs are fine-tuned so they sit cleanly inside the parent company’s conventions. Release cycles become shared events instead of self-directed pushes. Features once framed around the needs of a specific community get reframed as threads in a larger narrative. Greyhound Pulse conversations with senior architects reveal a recurring theme. The more a product is integrated, the more its individuality becomes something that must be protected consciously rather than expressed naturally.

    Yet IBM’s track record is not uniform, and one example stands out because it reshaped the industry’s expectations of how a major acquisition can be handled. The Red Hat chapter is remembered differently, almost fondly, by customers and partners who watched it unfold. Red Hat was not swallowed. It was supported. Its culture stayed intact. Its leadership remained autonomous. Its open-source worldview did not dissolve inside a larger corporate story. That balance did not come from luck. It came from a clear decision, championed early by Arvind Krishna, that Red Hat’s magic depended on independence. He argued for that view long before he became chief executive, and the years since have shown how right that instinct was. IBM let Red Hat breathe, and because of that, the hybrid cloud story matured without losing the authenticity that made Red Hat credible in the first place. Customers looking at the Confluent deal will remember that precedent. They will want to see the same respect applied to a platform that also grew by cultivating trust.

    Support is another place where integration leaves fingerprints. Smaller firms often build support cultures that feel almost personal. If something breaks, someone familiar picks up the issue. Once the product enters a large enterprise support system, the experience changes. Tickets move through layers. Escalations follow a structured path. Some customers appreciate the predictability. Others miss the immediacy that once defined the product’s support DNA. A CISO in one of our Fieldnotes described it well. The answers were still correct, still thorough, but they no longer carried the urgency of a team that built the product with its own hands.

    Commercial models shift too. Not abruptly, but steadily. Licensing rarely survives an acquisition untouched. Over time, contracts reflect the shape of the broader platform. Renewal cycles widen. Bundles appear. What once felt simple begins to coexist with a more complex commercial architecture. CFOs notice these transitions quickly. They are neither surprised nor alarmed, but they are rarely passive. Their first question is always the same. Does the value of the product grow in line with the change in pricing posture, or is it drifting into a new category without delivering new returns?

    Culture, of course, remains the most difficult thread to weave. Independent teams carry a sense of purpose and tempo that does not always fit easily inside a larger frame. Some thrive when given more resources. Some hesitate when confronted with more structure. Greyhound Fieldnotes show both outcomes. Teams that feel protected often become more ambitious. Teams that feel constrained can lose the spark that made them unique. Confluent’s teams will enter this liminal space next. The outcome will depend on whether IBM chooses to repeat its Red Hat philosophy or attempt a more tightly bound integration.

    Customers watch all of these signals quietly. They read documentation closely. They pay attention to whether release cycles feel natural or orchestrated. They listen for tone in community conversations. They notice subtle changes in product personality. CTOs track whether the architecture remains coherent. CIOs watch for delivery reliability. CISOs watch for operational resilience. CFOs look for predictable economics. These observations accumulate slowly, but once formed, they shape long-term procurement and architectural decisions.

    Which is why this moment carries weight. Confluent does not arrive empty-handed. It comes with a clear identity, a strong community and a well-defined philosophy about how real-time data should shape the modern enterprise. That identity deserves protection. IBM has done this before. Red Hat is proof that independence can be preserved inside scale. The question now is whether the same approach will guide the Confluent journey.

    Greyhound Standpoint: At Greyhound Research, we believe every acquisition carries the memory of those that shaped it. Ambition introduces a deal, but integration decides its legacy. IBM has shown it can create room for an acquired company to retain its essence while still delivering enterprise scale. If that discipline is brought to Confluent, the streaming layer of the AI era may gain not only strength but also stability.

    Post-Acquisition Toolkit: Defending Enterprise Control as the Stack Expands

    1/ Begin by mapping your architecture with complete honesty. Not the polished diagrams with tidy borders, but the real picture of your data flows, shadow services, forgotten integrations, and dependencies that only emerge when teams draw what they actually run. CIOs who do this early avoid being surprised when a newly integrated platform begins to shift its center of gravity. Once that map is visible, decisions about what to align, what to isolate, and what to keep sovereign become clearer. This is the foundation for preserving long-term control before vendor momentum begins to influence the estate.

    2/ Separate your licensing strategy from your technology strategy. They may sound like the same discussion, but in the wake of any acquisition, they tend to drift apart. CFOs know the pattern. Pricing models evolve. Packaging shifts. Incentives tilt toward the broader platform. If you anchor negotiations in capabilities rather than categories, you retain leverage. Define what you need in terms of outcomes and volume, not in terms of vendor constructs. That separation protects you from the subtle pressure that comes when commercial logic begins steering architectural decisions.

    3/ Create internal boundaries for governance and security before the platform sets them for you. CISOs often inherit the consequences of consolidation without having shaped its terms. A newly combined platform can look cleaner but also concentrate control points in ways that alter risk exposure. Establish which governance rules must remain within your organization and which can safely rely on the vendor’s controls. Draw these lines early. If the boundary is left undefined, platform defaults will quietly become enterprise policy, even when they do not match your internal expectations.

    4/ Strengthen your exit strategies while relationships are still warm. This is not an act of mistrust. It is an act of discipline. CTOs often regret how difficult it becomes to renegotiate flexibility once multiple workloads are dependent on a newly integrated platform. Build pathways that allow you to pivot if direction shifts, performance changes, or economics drift. That preparation does not weaken the vendor partnership. It strengthens your ability to stay engaged on your own terms and signals that you are operating with foresight rather than dependency.

    5/ Rebalance your talent strategy to match the new shape of the platform. Acquisitions alter skill requirements, often faster than job descriptions reflect. Some teams may need deeper expertise in data flow mechanics. Others may require stronger architecture governance skills. CIOs and CTOs who invest early in retraining or reassigning talent avoid the lag that follows when the platform matures but the organization has not. Treat talent planning as part of your risk management. A platform can only offer control if the people using it can interpret its behavior and shape its boundaries.

    Greyhound Standpoint: At Greyhound Research, we believe enterprise sovereignty is not defended after the contract is signed. It is established in the questions buyers ask, the boundaries they draw, and the preparations they make long before integration is complete. A strong post-acquisition posture is not an act of resistance. It is the foundation of long-term confidence.

    Enterprise CXO Playbook: Five Points to Ponder

    1/ Consider what it means when the streaming layer becomes part of a larger strategic centre. For years, data in motion lived at the edge of architectural diagrams, treated as a delivery mechanism rather than a seat of control. Now it stands closer to the core. CIOs and CTOs must reflect on whether their current architecture grants them real influence over that core or whether control has slowly migrated outward. The question is not about loyalty to a vendor. It is about understanding who now shapes the organisation’s intelligence loop and whether that aligns with the enterprise’s long term posture.

    2/ Think about how your definition of resilience may need to expand. Enterprises once viewed resilience through the lens of infrastructure stability or disaster recovery. But in a world built on continuous streams of context, resilience has a new dimension. It includes the integrity of data flows, the predictability of models that depend on those flows, and the governance structures that sit above both. CISOs will need to ask whether their current frameworks reflect this shift or whether they still guard an older, more static version of risk that no longer maps to the way modern systems behave.

    3/ Reflect on the relationship between simplification and sovereignty. Many CXOs appreciate the appeal of a platform that smooths complexity. Yet those same leaders know that each layer of simplification carries its own form of dependency. CFOs in particular recognize the tension. Simplification can improve cost predictability while narrowing options. Sovereignty, on the other hand, can feel heavier operationally but offers greater leverage. The balance between the two is not fixed. It changes as the enterprise matures and as platforms begin to exert their own gravity. The harder question is which future your current choices make inevitable.

    4/ Revisit how your organization interprets neutrality. Neutrality once meant compatibility. Today it has a more strategic meaning. It describes the freedom to steer architecture without hidden constraints and the ability to interrogate platform behavior without fear of unseen trade-offs. CTOs and CIOs have both learned that neutrality cannot be inferred from a vendor’s intention. It must be tested through patterns in roadmap commitments, pricing evolution, and integration pathways. The challenge is not to demand neutrality but to understand how much of it the enterprise truly requires to remain agile.

    5/ Ask whether your enterprise has the cultural readiness to operate in a world where data in motion becomes the organizing principle of intelligence. Tools and platforms matter, but culture determines whether the organization can think in continuous cycles rather than periodic reviews. CXOs often speak about operational tempo in abstract terms, yet the move toward real-time intelligence demands a culture that thrives on immediacy, iteration, and coordinated action. Cultural readiness is not a soft concept. It shapes architecture more than any technology choice.

    Greyhound Standpoint: At Greyhound Research, we believe reflection is a strategic discipline. These questions are not distant hypotheticals. They are already shaping boardroom discussions as enterprises confront a world built on continuous intelligence rather than delayed understanding.

    The Strategic Reset: What This Acquisition Teaches Us About the Future

    There are moments in enterprise technology when the story stops being about a product or a platform and becomes something larger. IBM’s decision to bring Confluent into its orbit marks one of those moments. It signals that real-time data has moved beyond its role as a supporting layer and has become the organizing force behind how modern enterprises will think, decide, and act. It also most definitely shows that the future of AI will not be shaped by algorithms alone. It will be shaped by the systems that feed them, govern them, and protect them.

    This shift is not theoretical. Confluent’s business has already crossed the one billion dollar annual run rate threshold, a milestone that only a handful of modern data platforms have achieved. The market has spoken clearly about where value is concentrating.

    This acquisition reveals a broader shift in the industry’s priorities. Enterprises are beginning to recognize that the architecture of intelligence depends on continuous streams of trusted context. The organizations that control those streams will carry extraordinary influence over how digital ecosystems evolve. Confluent built a foundation for that evolution. IBM now holds the responsibility to advance it without erasing the qualities that made it integral to so many architectural blueprints. The future will judge this move not by the announcement, but by the steadiness of the stewardship that follows.

    For CXOs, this moment serves as a reminder that the shape of enterprise power is changing. Control no longer resides only in infrastructure or applications. It lives in the choreography of data. It lives in the choices that govern how information moves, how quickly it reaches intelligent systems, and how reliably those systems behave under pressure. The acquisition of Confluent is therefore not just a transaction. It is a statement about where the next decade of enterprise strategy will be written.

    Greyhound Standpoint: At Greyhound Research, we believe this acquisition does more than redraw the map. It redefines the mandate. It calls on enterprises to rethink how they build trust, how they design intelligence, and how they preserve sovereignty in a world shaped by data in motion. This is the strategic reset that will influence the architecture of the future.

    Analyst In Focus: Sanchit Vir Gogia

    Sanchit Vir Gogia, or SVG as he is popularly known, is a globally recognised technology analyst, innovation strategist, digital consultant and board advisor. SVG is the Chief Analyst, Founder & CEO of Greyhound Research, a Global, Award-Winning Technology Research, Advisory, Consulting & Education firm. Greyhound Research works closely with global organizations, their CxOs and the Board of Directors on Technology & Digital Transformation decisions. SVG is also the Founder & CEO of The House Of Greyhound, an eclectic venture focusing on interdisciplinary innovation.

    Read About SVG

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  • What IBM Power11 Really Means for Enterprise Architecture and Operations

    Reading Time: 10 minutes

    For decades, Power servers stood as quiet enablers of core enterprise operations, rarely questioned, rarely credited. But with Power11, IBM isn’t nudging the platform forward. It’s recasting the role of infrastructure itself. This isn’t a performance leap. It’s an operational ultimatum. At the heart of Power11 is a commitment to zero planned downtime, underpinned by live firmware patching, workload migration, and self-healing diagnostics, all operating within a design envelope of six nines availability, or just 31 seconds of unplanned outage per year.

    While Power11 has only just entered commercial release, the transition risks are familiar. With Power10, we repeatedly saw patch automation underused, workload mobility left idle, and clients defaulting to traditional maintenance windows despite capabilities to avoid them. In multiple sectors, especially financial services, the hardware outpaced the operations. That pattern is unlikely to vanish with Power11; it will intensify. Because while the platform removes excuses for downtime, it also removes tolerance for immaturity.

    This pressure is compounded by energy governance. With regulators now viewing power budgets as enforceable sustainability metrics, infrastructure isn’t just expected to perform. It’s expected to justify. Power11 introduces a new Energy Efficient Mode, delivering up to a 28% reduction in power consumption while maintaining service-level guarantees. These trade-offs aren’t optional anymore; they’re infrastructure policy.

    Yet buyers must remain clear-eyed: Power’s reliability has always come with a hardware premium. Even Power11’s entry-level configurations are positioned above commodity pricing, with IBM’s custom memory modules and scale-up architecture requiring a willingness to pay for consolidation. This is not a “start small” system and never has been. Buyers looking for bursty elasticity or cloud-native economics may find Power’s architecture overbuilt for their actual needs.

    And that calculus now extends to licensing. With Power11, IBM has phased out perpetual IBM i licenses in favor of subscription-only models. For enterprises accustomed to CAPEX-based licensing strategies, this shift introduces budgeting friction. For new workloads, the economics are flexible. For legacy customers, especially those with dozens of LPARs amortized over a decade, the change is more abrupt. Licensing posture has become as dynamic as the platform itself. Not everyone is ready for that.

    It’s also worth noting that Power11’s “zero downtime” posture places new demands on the full stack, particularly OS levels. Power10 previously required enterprises to upgrade to AIX 7.3 or IBM i 7.5 to unlock native features. Many did not and ended up running in compatibility mode. That trade-off is back. Power11 will run legacy OSes but with degraded functionality. For a full return on investment, buyers must align infrastructure, software, and policy in one motion.

    Greyhound Standpoint: Power11 doesn’t eliminate downtime. It eliminates the excuse for it. The platform delivers autonomy, but it assumes orchestration. For those without the discipline to keep pace or the justification for premium architecture, Power11 will be less a solution and more a confrontation.

    Greyhound Research recommends using Power11 in enterprise scenarios where data integrity, latency predictability, and auditability are non-negotiable. Ideal workload profiles include:

    • Tightly coupled, I/O-bound platforms (e.g. SAP HANA, DB2, Oracle RAC)
    • High-volume transaction engines (core banking, telecom, claims)
    • Embedded AI decision systems (fraud scoring, underwriting)
    • Regulated infra (central banks, entitlement systems)

    Avoid deploying Power11 for ephemeral, stateless, or burstable microservice patterns where cost elasticity outweighs RAS depth. This platform serves where fragility is unacceptable, not where flexibility is the primary constraint.

    Inference Without Drama: AI That Belongs in the Middle of the Process, Not on the Sidelines

    Enterprise AI has long been siloed; trained in one place, inferred in another, and often detached from core decision systems. Power11 challenges that detachment with infrastructure where inference becomes local, secure, and operational. On-chip matrix math engines handle machine learning at the point of data. IBM’s AI orchestration via watsonx.data and OpenShift AI turns Power11 into an inference platform, not a training rig, but a decision machine.

    This alignment is further enhanced by the upcoming Spyre accelerator, a native AI inference engine promising 32 AI cores and 1TB of memory per module. But buyers should note: Spyre will not ship until Q4 2025. And while its design is promising, its field performance remains unproven. For now, Power11’s on-chip AI and inferencing stack offer immediate value, but enterprises should calibrate expectations: this is AI for process intelligence, not for training billion-parameter models.

    That line matters because Power10 paid the price for drifting away from deep AI hardware partnerships. It dropped support for Nvidia NVLink, which had enabled high-speed CPU-GPU coupling in Power9. Without that interface, Power10 lost its edge in AI training environments, and Power11 hasn’t reclaimed that space. Enterprises running GPU-centric AI models will still rely on external systems or hybrid designs. Power11’s AI stack is strategic but bounded.

    In one session, IBM i leaders shared how an MR Williams technologist resolved a customer issue in just 20 minutes using WCA for i, a task that had taken a senior developer six hours the previous day, underscoring real productivity gains. Similar internal pilots suggest feasibility of rapid model‑scoring capabilities in weeks, not quarters, when Power11’s embedded inference is activated.

    But inference locality does not erase integration debt. Power11’s AI integration is tight, especially for inferencing workloads that reside near structured data, but that tightness doesn’t guarantee ease of adoption. Enterprise DevOps teams still report friction integrating Power systems into standard automation pipelines. While IBM has made significant progress publishing Ansible roles, Terraform modules, and Red Hat OpenShift support for Power, many core DevOps tools remain optimized for x86 by default. Popular container runtimes and orchestration frameworks, such as Rancher, lack full support for the ppc64le architecture, and CI/CD platforms often require custom runners, rebuilt container images, or architecture-specific exceptions to deploy consistently. This means that even with IBM’s modernization stack in place, developer convenience and plug-and-play flexibility remain asymmetric, and organizations should budget for the engineering effort required to integrate Power11 cleanly into x86-dominant toolchains.

    And while Power10’s ecosystem constriction, via closed firmware components, has cast a long shadow, Power11 offers few public guarantees of reversal. Open computing momentum elsewhere (Ampere, RISC-V) has trained buyers to expect transparency as default. Power11 may regain that trust, but as of today, openness is still a promise, not a practice.

    Greyhound Standpoint: Power11 doesn’t chase AI hype. It internalizes AI logic. But buyers must ground their expectations: this is AI at the heart of operational systems not a silver bullet for every model or method. And for developer-first teams, it still demands adaptation.

    Greyhound Research believes if AI operations are focused on training large models, embedding with cloud-native inference APIs, or GPU-intensive vision/NLP workloads, Power11 should not be the core stack. Where it excels is proximity to structured data, inference co-residency with ERP/claims logic, and low-latency decision enforcement.

    Power11 is an architectural bridge for enterprises that want real AI without AI sprawl. But it is not a dev-first ecosystem. It is a platform-first assertion.

    Autonomy Is Not Optional: Security That Starts Below the OS

    Power11’s security design is not layered; it’s embedded. With the introduction of Power Cyber Vault, IBM has shifted ransomware protection into the architectural core of the system. Detection now occurs in under one minute, using telemetry integrated at the system firmware level, not dependent on operating system agents or third-party endpoint tooling. This design allows threats to be identified before they escalate, operating beneath the OS where attackers can’t reach. When paired with IBM’s safeguarded copy capabilities, originally developed within FlashSystem, enterprises can initiate rapid rollback from immutable snapshots. While IBM’s sub‑60‑second recovery guarantees apply formally to its storage platforms, Power Cyber Vault enables compute-side enforcement of the same recovery logic. The result is not just containment; it’s time compression. The infrastructure doesn’t just alert. It acts. And for enterprises under ransomware pressure, that compression could be the difference between continuity and crisis.

    That promise builds on a decade of IBM RAS leadership. But Power10 taught us that detection speed without response choreography is a false signal. In multiple healthcare and financial deployments, Cyber Vault delivered perfect signals, yet the recovery lagged. SOC teams weren’t connected to infra. Runbooks were written, not rehearsed. The platform bought time. Teams squandered it.

    In the Greyhound CIO Pulse 2025, 73% of global enterprises claim to have ransomware response protocols. But only 24% have integrated those protocols with infrastructure automation. Power11 doesn’t bridge that gap. It surfaces it faster. If your team still restores from backups manually, Cyber Vault’s detection will feel less like a lifesaver and more like a silent judgment.

    And while IBM has hardened much of the firmware stack since Power10, the scars remain. Early Power10 adopters reported firmware stalls during provisioning, partition corruption, and, in some cases, systems that required manual ASMI resets out of the box. These weren’t design failures, but they were reminders. Buyers of Power11 will be watching its first 90 days closely. Not for theoretical limits but for operational polish.

    Greyhound Standpoint: Cyber Vault is fast. But it’s not forgiving. Power11 assumes choreography where many organizations still operate in crisis mode. Detection is hardware. Recovery is muscle memory. If you haven’t trained for it, this platform won’t wait.

    Greyhound Research believes security maturity on Power11 is not a license checkbox. It’s a response speed test.

    • Build out pre-assigned rollback triggers tied to your SIEM and MDR workflows.
    • Test failover from Cyber Vault snapshots as you would a fire drill.
    • Don’t defer tooling modernization: many recovery misfires come not from infra but from unchanged organizational behaviors.

    Cloud, But Not Like Before: Hybrid That Doesn’t Need a Translator

    Hybrid has become architecture by necessity, not choice. But most hybrid deployments remain half-measured: two stacks, two teams, and two versions of the truth. Power11 changes that equation by launching its entire stack (hardware and cloud) on the same day. IBM Cloud’s Power Virtual Server isn’t an emulation. It’s a replica. And for enterprises bound by latency, sovereignty, or licensing, that parity matters.

    The architecture proves itself in real deployments. A global logistics firm migrated its SAP workload to Power11 in IBM Cloud and reported a 25% faster cutover versus prior x86-to-cloud migrations. There was no need for workload reauthoring or API realignment. Just an LPAR move and a DNS change. That’s what parity looks like.

    IBM has also secured RISE with SAP certification for PowerVS, making Power11 the only non-x86 platform available from a hyperscaler for SAP cloud landscapes. That’s not a marketing badge. It’s a deployment unlock for CIOs juggling SAP modernization and data residency in the same breath.

    Still, buyers must acknowledge limits. IBM Cloud is the only hyperscaler hosting Power. If your cloud estate is multicloud by mandate, Power workloads will remain in a parallel lane, connected but distinct. And while IBM has improved tooling for workload portability, hybrid automation in the Power ecosystem still lags behind AWS-native equivalents. This isn’t a blocker. But it is friction.

    And it sharpens the contrast with ARM-based platforms that now dominate the open compute conversation. AWS Graviton, Ampere, and other cloud-native ARM architectures offer not only elastic scaling and transparent pricing but also the reassurance of ecosystem openness. In contrast, Power remains a closed-loop system, tightly engineered but tightly controlled. If your architecture mandates openness by design—firmware visibility, modular ISAs, multi-vendor availability—Power11 will force a philosophical exception.

    Greyhound Standpoint: Power11 doesn’t just make hybrid possible. It makes it equivalent. But only if you accept that “cloud” here means IBM Cloud and only if your architecture accepts separation as a design choice, not a flaw.

    Per our experience at Greyhound Research, if your multicloud strategy depends on portability above all, Power11 may add complexity. But if your strategy demands workload integrity, architectural parity, and regulatory parity between on-prem and cloud, Power11 offers the tightest vertical alignment in the market today.

    The Real Transformation Is Cultural

    Every feature in Power11 is real. And every promise is operational. But the platform carries a message few others dare articulate: infrastructure is not your bottleneck; your organization is.

    Power10 made this truth visible. Too many enterprises bought the system, skipped the training, and limped through patch cycles like it was still 2010. Power11 demands better. It expects patch automation, not deferral. It expects workload mobility without downtime. It expects AIX, IBM i, and Linux admins to collaborate across fault domains, not escalate across ticket queues.

    That reality is compounded by architecture. Power has never been a casual purchase, and Power11 doesn’t pretend to be. Its price-to-capability ratio is unmatched in the right workload. But if you’re buying it to solve an infrastructure problem instead of an operational one, you’re misusing the system.

    This platform doesn’t scale with complexity. It punishes it.

    Meanwhile, legacy friction hasn’t vanished. The proprietary firmware decisions made in Power10 strained trust in the ecosystem. The requirement to be on the latest OS release caught some CIOs off guard. The shift to subscription-only licensing for IBM i has rattled budgeting models long anchored to capex. And the hardware itself, while world-class, demands processes that reflect its design intent. Power11 doesn’t walk those issues back. It builds over them. And demands more.

    Greyhound Standpoint: Power11 doesn’t handhold. It benchmarks. It’s not here to save your infrastructure. It’s here to reflect its discipline. If you’re ready, it’ll run without excuses. If you’re not, it’ll show you why.

    Greyhound Research believes if an enterprise is still debating whether they are a “power shop”, they must frame the decision around this:

    • Do you have workloads that require constant uptime, deterministic latency, and embedded security?
    • Do you have teams that understand partitioned infrastructure and live workload orchestration?
    • Are your operational processes mature enough to support autonomy without ceremony?

    If the answer to all three is yes, you’re ready. If not, Power11 will become an expensive teacher.

    Final Decision Grid: Where Power11 Wins And Where It Doesn’t

    PriorityIf You Need…Power11 Verdict
    Resilience99.9999% uptime + zero planned downtimeUnmatched – Power11 is built for it
    ElasticityEphemeral, dev-heavy cloud workloadsMisaligned – Use x86 or ARM
    Embedded AIReal-time inference inside ERP/claims systemsStrong fit – Built for data locality
    AI TrainingFoundation model development on GPUsNot the target – use DGX or cloud GPUs
    Ecosystem OpennessOpen firmware, vendor choice, ISA modularityWeak – Power remains a closed loop
    Multicloud InteropCross-cloud, no provider lock-inLimited – Tied to IBM Cloud only
    Capex OptimisationPay once, own foreverChanging – IBM shifting to subscription
    Audit-Grade RecoveryImmutable snapshots, rollback, forensicsBest-in-class – Cyber Vault + rollback

    Greyhound Standpoint: What Power11 Actually Represents

    Power11 is not a platform for the faint-hearted. It is a high-assurance, high-demand system engineered for workloads where excuses have no place, whether those excuses are about downtime, threat response, tooling maturity, or cultural inertia. Its capabilities are immense, but its tolerance for fragility is zero.

    Enterprises that meet Power11 on its own terms with architectural discipline, operational readiness, and the willingness to modernize not just infrastructure but also internal behavior will find it delivers extraordinary value. It compresses recovery time, embeds AI into business logic, and collapses hybrid complexity into a coherent execution fabric.

    But for those looking to shortcut governance, dodge hard decisions, or deploy without redesigning the process, Power11 will not bend. It will only reflect. And in that reflection, it will surface every gap you’ve chosen not to close.

    This is what makes Power11 not just a new system but a strategic filter. It won’t ask if you’re ready. It will show you whether you are.

    Analyst In Focus: Sanchit Vir Gogia

    Sanchit Vir Gogia, or SVG as he is popularly known, is a globally recognised technology analyst, innovation strategist, digital consultant and board advisor. SVG is the Chief Analyst, Founder & CEO of Greyhound Research, a Global, Award-Winning Technology Research, Advisory, Consulting & Education firm. Greyhound Research works closely with global organizations, their CxOs and the Board of Directors on Technology & Digital Transformation decisions. SVG is also the Founder & CEO of The House Of Greyhound, an eclectic venture focusing on interdisciplinary innovation.

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  • IBM’s Q3 2025 Results – Delivery Strength Holds, But Integration Demands Intensify

    Reading Time: 12 minutes

    IBM closed Q3 2025 with 16.3 billion dollars in revenue, up 7 percent in constant currency and 9 percent year over year, its fastest pace in several years. All three core segments grew sequentially, and operating discipline showed up clearly in the numbers. Gross margin expanded to 58.7 percent, pre-tax margin climbed to 18.6 percent, and adjusted EBITDA rose 22 percent to 4.6 billion dollars. Free cash flow of 2.4 billion dollars for the quarter brought the year-to-date total to 7.2 billion dollars, a record nine-month margin for the company. IBM raised full-year guidance again, now targeting more than 5 percent revenue growth and roughly 14 billion dollars in free cash flow.

    MetricQ3 2025 Result
    Revenue$16.3bn, up 7% (constant currency), 9% YoY
    Gross Margin58.7%
    Pre-tax Margin18.6%
    Adjusted EBITDA$4.6bn, up 22%
    Free Cash Flow$2.4bn Q3; $7.2bn YTD (record nine months)
    Full-Year Guidance>5% revenue growth, ~$14bn free cash flow

    This quarter’s results were not about surprise; they were about reliability. For the first time in recent memory, every business line moved in the same direction. Software rose 9 percent, Infrastructure grew 15 percent, and Consulting increased 2 percent. Automation led the software portfolio with 22 percent growth, while Red Hat bookings advanced 20 percent and OpenShift ARR passed 1.8 billion dollars. IBM Z revenue jumped 59 percent, marking the strongest two-quarter mainframe launch in two decades. Yet, even with this breadth of performance, investors were unmoved. The market response was flat because predictability, once IBM’s weakness, has now become its expectation.

    Business LineGrowthHighlights
    Software+9%Automation +22%, Red Hat bookings +20%, OpenShift ARR >$1.8bn
    Infrastructure+15%IBM Z revenue +59%, strongest launch in 20 years
    Consulting+2%Consistent growth across all lines

    The composition of growth continues to define IBM’s challenge. Automation and Z-hardware remain the biggest accelerants, while Transaction Processing fell another 3 percent, softening the software narrative just as HashiCorp and watsonx integration began to take hold. IBM’s ability to convert hardware momentum into recurring software monetisation is still the hinge between short-term strength and long-term valuation. The question for investors and enterprise buyers alike is no longer whether IBM can execute, it is whether it can scale that execution without fragmentation.

    Operationally, the company is proving leaner and more productive. Client Zero, IBM’s internal transformation programme, now automates more than 70 workflows and has delivered 4.5 billion dollars in annualised savings. Those efficiencies are feeding both margin expansion and client credibility. But even with this progress, IBM faces a perception gap. Its operating consistency has improved faster than its storytelling. What was once a turnaround has turned into a grind, and markets are waiting for IBM to show that its AI-led growth model can sustain beyond cycles.

    Greyhound Standpoint –  IBM has entered a phase of controlled acceleration. The fundamentals are solid, with growth across all units, rising profitability, and credible AI execution, but cohesion is now the test. The company no longer needs to prove that it can deliver; it needs to prove that its parts can move as one. For CIOs and investors, this quarter signalled continuity rather than inflection, and that may be precisely what IBM needs before its next pivot.

    Regional Performance: Steady in the West, Cautious in the East, but Balance Is Returning

    IBM’s Q3 2025 results reflected a company finding its regional balance again. The Americas and Europe, Middle East and Africa remained steady growth engines, each posting a 9 percent revenue increase year on year, while Asia Pacific held flat at constant currency after several uneven quarters. Together, these results show that IBM’s expansion is broadening, even if performance across regions still varies in speed and texture.

    RegionPerformanceRevenue Growth YoY
    AmericasSteady growth+9%
    EMEASteady growth+9%
    Asia PacificFlat (constant currency)0%

    In the Americas, performance was driven by the full-stack pull of IBM’s platform strategy. Strong demand for IBM Z systems in U.S. federal agencies, financial institutions, and telecom operators combined with sustained Consulting momentum in Canada and Latin America. Greyhound Fieldnotes indicate that enterprise buyers in North America now view IBM as an operational partner rather than a supplier of components. Projects increasingly link infrastructure, software, and services into long-term, outcome-based engagements. For most CIOs in the region, the question has shifted from whether to use IBM to how fast IBM can scale its delivery against hyperscaler alternatives.

    Across Europe, the Middle East, and Africa, growth matched the Americas but reflected a different texture. Germany and the UK continued to lead adoption of z17 systems, while Red Hat and Automation gained traction in Northern Europe’s public sector. In France and Southern Europe, procurement cycles remained slow, with several government renewals delayed by extended audit and pricing reviews. IBM’s watsonx.governance continues to resonate with regulators and enterprise compliance teams, giving the company a competitive edge in markets where explainability is now as important as performance. The opportunity is clear, but speed remains the constraint.

    In Asia-Pacific, IBM’s business was stable but uneven. Japan and South Korea continued to show conservative infrastructure spending, while Australia’s Consulting pipeline softened slightly due to delayed transformation budgets. On the upside, early Power11 projects across Southeast Asia, particularly in Singapore and Malaysia, are driving new conversations around AI-enabled infrastructure. Interest in watsonx Orchestrate for procurement and HR automation is growing, but buyers still cite fragmented post-sale support as a key friction point. Greyhound Fieldnotes suggest that IBM’s regional success will depend on localising delivery ownership, rather than running Asia from global centres.

    Greyhound Standpoint – IBM’s regional picture this quarter reflects steady progress rather than breakout performance. The Americas and EMEA remain dependable growth anchors, underpinned by high client retention and expanding AI-led engagements. APAC is stabilising, but delivery cohesion and regional autonomy are still limiting factors. For global CIOs, IBM now represents a predictable partner — one capable of consistent delivery, but still working to make that consistency feel local.

    Segment Highlights: Strength Across the Stack, but Balance Still a Work in Progress

    IBM’s third-quarter results showed solid progress across all its major businesses. Software, Infrastructure, and Consulting each contributed to growth, although the pace and sources of that growth varied. Software revenue rose 9 percent, Infrastructure climbed 15 percent, and Consulting increased 2 percent. On paper that looks balanced; in practice, the story is more nuanced.

    Software stayed the largest and most profitable part of IBM, bringing in 7.2 billion dollars. Automation was again the standout, up 22 percent, helped by steady demand for workflow orchestration and tighter integration with HashiCorp’s tools. Red Hat bookings grew roughly 20 percent, and OpenShift’s recurring revenue passed 1.8 billion dollars after growing more than 30 percent in the past year. Data-related offerings also expanded 7 percent, while Transaction Processing slipped 3 percent as customers focused spending on newer Z hardware.

    Key AreaPerformance Highlights
    Software Revenue$7.2bn, largest and most profitable segment
    AutomationUp 22%, strong workflow demand, HashiCorp integration
    Red HatBookings up ~20%
    OpenShiftRecurring revenue over $1.8bn, up 30%+
    Data OfferingsExpanded by 7%
    Transaction ProcessingDown 3%, focus shifting to newer Z hardware

    Greyhound Fieldnotes show that many CIOs now treat Red Hat as the backbone of IBM’s hybrid-cloud design. With HashiCorp folded into the automation stack, IBM is pitching a single control layer that can manage infrastructure and application policy across clouds. Buyers like the simplicity, but they also note that software monetisation still lags behind the success of the Z platform.

    Infrastructure had another strong quarter, reaching 3.6 billion dollars in revenue. IBM Z grew 59 percent and delivered its best third-quarter showing in almost twenty years. The new z17 systems are proving that mainframe computing and AI can coexist comfortably. Financial-services, government, and telecom clients are already running inference directly on Telum II and are preparing to extend that with the upcoming Spyre accelerator. Distributed Infrastructure revenue rose 8 percent, supported by renewed storage demand and the first meaningful Power 11 deployments for SAP RISE and AI workloads.

    Key AreaHighlights
    Infrastructure RevenueStrong quarter, $3.6B revenue
    IBM Z GrowthUp 59%, best Q3 in 20 years
    Mainframe & AIz17 systems show coexistence
    Client AdoptionFinance, government, telecom using Telum II, preparing for Spyre
    Distributed InfrastructureRevenue up 8%, driven by storage, Power 11 for SAP RISE & AI

    Greyhound Research sees this as part of IBM’s quiet transformation of its infrastructure stack into an AI execution layer. The z17 cycle is redefining where regulated enterprises run mission-critical AI, while Power 11 is starting to gain traction as an energy-efficient option for data-sovereign computing.

    Consulting produced 5.3 billion dollars in revenue, up 2 percent from a year earlier and reversing three flat quarters. Intelligent Operations grew 4 percent, while Strategy and Technology held steady. Generative-AI work now represents about 12 percent of Consulting revenue and more than 20 percent of its backlog. Greyhound Fieldnotes suggest that clients are shifting from broad transformation programmes to targeted, short-cycle projects that deliver visible productivity gains. IBM’s ability to standardise and scale these AI-driven engagements will decide whether Consulting becomes a growth multiplier or stays an add-on to the product business.

    Greyhound Standpoint –  IBM’s portfolio is advancing, but not evenly. Software is healthy, Infrastructure remains the growth engine, and Consulting is showing early momentum. The pieces work, yet true balance will depend on how well IBM synchronises rapid innovation in Automation and z17 with the slower rhythm of software renewals and services delivery. For CIOs, the message is reassuring: IBM has regained stability. The next challenge is making that stability feel seamless.

    AI-Led Momentum: Proof Points Multiply, but Expectations Are Rising

    IBM’s AI story moved from concept to conviction this quarter. The company’s generative AI book of business has now passed 9.5 billion dollars since inception, adding nearly 2 billion dollars in new signings during Q3 alone. About 1.5 billion dollars came from Consulting engagements, while the rest flowed from software demand linked to watsonx, Red Hat AI, and governance solutions. The numbers confirm what IBM has been promising for over a year: its AI engine is not theoretical, it is commercial.

    IBM’s approach to AI remains distinct from the hyperscaler playbook. Instead of building ever-larger models, IBM is focused on how those models operate inside regulated enterprises. watsonx Orchestrate and watsonx.governance are now at the heart of this strategy, turning AI from a productivity experiment into an operational framework. The company’s newest Granite 4.0 models, launched this quarter, use 70 percent less memory and run inference at twice the speed of conventional architectures. Partnerships with Groq and Anthropic extend IBM’s reach into high-performance inferencing and conversational AI, creating new paths for hybrid deployments.

    Client adoption is beginning to reflect this maturity. Deutsche Telekom is using watsonx.ai to manage network reliability through predictive analytics. S&P Global has embedded watsonx Assistant into its reporting workflows, and State Street is using watsonx Code Assistant for Z to modernise mainframe applications directly on-platform. Each of these examples highlights IBM’s focus on bringing AI to where data already lives, not where it is cheapest to process.

    Under the surface, IBM’s internal transformation continues to validate its own technology. The Client Zero initiative now covers more than seventy automated workflows across finance, HR, and supply chain, producing productivity gains of about 4.5 billion dollars. These practices are being packaged into client-ready blueprints that demonstrate measurable efficiency gains. IBM is not just selling AI software; it is selling its own experience in using AI to simplify operations at scale.

    The ecosystem strategy has also become clearer. Rather than competing with hyperscalers, IBM is embedding watsonx within them, integrating with AWS, Azure, Oracle, Salesforce, and ServiceNow. The goal is to make watsonx the orchestration layer that keeps enterprise AI compliant, auditable, and connected across multiple clouds.

    Greyhound Standpoint – IBM’s AI story has moved from experimentation to execution. The momentum is real, and the client proof points are credible. Yet expectations are rising just as fast. The next stage of growth will depend on how well IBM can simplify its AI stack, clarify its commercial structure, and make watsonx the default governance layer for enterprise AI. For CIOs, IBM now offers something the hyperscalers do not: an AI platform built for accountability rather than scale. The question is whether IBM can keep that lead before the rest of the market catches up.

    What Clients Are Saying and Experiencing

    Across more than 800 enterprise technology leaders surveyed in Greyhound CIO Pulse 2025, the tone this quarter is more measured but also more pragmatic. The majority of CIOs told us that their focus has shifted from AI adoption to AI accountability. Seventy-eight percent said they are now concentrating on how AI integrates into existing systems of record and whether it can operate under the same governance standards that regulate their core data. This change marks the difference between early curiosity and operational trust.

    IBM’s hardware-led platforms are beginning to align with that shift. The z17 and Power11 systems are now being evaluated less for their technical specifications and more for their policy compliance and efficiency in regulated industries. In financial services and the public sector, the ability to perform AI inference directly on Telum II remains a major attraction, as it eliminates the need to move sensitive data off core systems. Power11 is gaining visibility for similar reasons, especially among clients running SAP RISE or hybrid cloud workloads that require controlled compute environments.

    Greyhound Fieldnotes from Q3 reveal that most IBM clients are no longer assessing the individual merits of Red Hat, watsonx, or Consulting in isolation. Instead, they are looking at IBM’s ability to coordinate these elements into one coherent experience. The strongest projects are those where Consulting, Software, and Infrastructure act as a single team. Where that alignment breaks, clients report friction. As one CIO put it, “IBM’s architecture is integrated; its delivery still isn’t.”

    In Europe, demand for watsonx.governance continues to climb, especially in banking and government. Clients in Germany, the UK, and the Nordics praise the product’s technical strength but often express concern about overlapping modules and complex licensing structures. These buyers want IBM to simplify how they purchase and manage AI governance tools, not just expand their capabilities.

    In Asia-Pacific, IBM is seeing a rise in automation pilots moving to full production, particularly in manufacturing and logistics. watsonx Orchestrate is gaining traction, but buyers across India, Singapore, and Malaysia still describe project timelines as longer than expected. The delays, they say, often stem from coordination issues between global and regional delivery teams. IBM’s decision to expand local partner enablement programs in Malaysia and Australia could help reduce this gap in the quarters ahead.

    In North America, the discussion is less about capability and more about scale. Financial institutions and federal agencies experimenting with z17 for confidential inferencing are satisfied with performance but want clearer answers on how Spyre, OpenShift AI, and watsonx will work together in hybrid environments. Confidence in compute has been earned; confidence in integration still needs to follow.

    Greyhound Standpoint –  IBM’s clients have moved beyond the stage of testing AI to the harder work of making it dependable. They are not questioning IBM’s technology but its ability to deliver it as one seamless experience. Buyers are looking for continuity — in pricing, in ownership, and in delivery. IBM has the architecture and the trust; what it now needs is rhythm. In Q3 2025, the company’s credibility is unquestioned. Its consistency, however, remains the true measure of progress.

    Strategic Takeaways for Enterprise Buyers

    For CIOs and enterprise architects looking at IBM this quarter, the story feels different. The company is not chasing momentum anymore; it is learning how to sustain it. Execution has become consistent, but the next challenge is knitting its progress together. The z17 and Power11 systems, once hardware headlines, are now real tools in production. z17 is handling inferencing for major banks and government workloads, and the upcoming Spyre accelerator is expected to expand those capabilities into generative use cases. Power11, meanwhile, is starting to show up in client discussions about data-sovereign AI and SAP RISE environments where energy costs and regulatory controls are shaping design decisions. These systems are no longer product cycles; they are the physical anchors of IBM’s AI strategy.

    IBM’s software platform is also settling into form. The integration of Red Hat, watsonx, and HashiCorp is giving enterprises a clearer path to unify automation, governance, and hybrid operations. Clients tell us the technology works; what they want next is simplicity. They want one contract, one license model, and one ownership structure that stays consistent through deployment and support. IBM’s success here will depend less on new features and more on how well it removes friction from buying and running its platform.

    In Consulting, IBM continues to shift its centre of gravity. The firm’s own AI playbooks from Client Zero are now showing up in client engagements, and its consulting backlog reflects a steady move toward higher-margin, shorter-duration projects focused on measurable efficiency gains. Buyers see the difference. They are engaging IBM Consulting earlier in design phases, using its reference models as proof that AI and automation can coexist with compliance and cost control. What remains is scale — ensuring that quality stays intact as Consulting expands these templates across industries and regions.

    For enterprise buyers, IBM is beginning to resemble a systems company again — one that connects chips, code, and governance into a single architecture. The edges still need smoothing, but the foundation is solid. For organisations that care about control, auditability, and predictability in AI, IBM now stands apart. The coming quarters will test whether that discipline can translate into speed, but the direction is finally clear. IBM is no longer trying to prove that it can deliver. It is working to show that it can deliver seamlessly.

    Greyhound Standpoint: Execution Is Holding, Integration Will Decide What Comes Next

    IBM’s third quarter was not about surprise; it was about staying the course. The company’s ability to grow across all major segments while improving margins shows a discipline that has been missing for years. The z17 and Power11 systems have turned from launch stories into working proof points of what IBM now represents — a technology firm that builds where regulation and reliability matter most. These products are no longer about compute cycles; they are about control, and that is where IBM’s strength lies.

    The software business continues to mature, even as its complexity occasionally slows it down. Red Hat, watsonx, and HashiCorp now share a common direction, and clients are beginning to see how the pieces connect. The next test is execution in the field. Buyers are asking for fewer handoffs, fewer licenses, and one IBM that owns the outcome from design to delivery. If the company can meet that expectation, its platform will finally feel as integrated as it looks on paper.

    Consulting has become IBM’s proving ground for that idea. It is where the firm’s AI tools, governance frameworks, and automation practices come together in client settings. The quality of the backlog is improving, but clients still notice uneven execution across regions. IBM has the expertise; now it must translate that knowledge into speed and consistency. The company’s internal playbooks, refined through its own Client Zero experience, give it a head start — but that discipline needs to become habit, not initiative.

    Strategically, IBM is in a position that few of its peers can claim. It has credible AI technology, a hardware backbone, and an enterprise audience that values both. What will determine its next phase is not innovation but coherence. IBM has proven it can execute. The question now is whether it can make that execution feel effortless to the clients who depend on it.

    Analyst In Focus: Sanchit Vir Gogia

    Sanchit Vir Gogia, or SVG as he is popularly known, is a globally recognised technology analyst, innovation strategist, digital consultant and board advisor. SVG is the Chief Analyst, Founder & CEO of Greyhound Research, a Global, Award-Winning Technology Research, Advisory, Consulting & Education firm. Greyhound Research works closely with global organizations, their CxOs and the Board of Directors on Technology & Digital Transformation decisions. SVG is also the Founder & CEO of The House Of Greyhound, an eclectic venture focusing on interdisciplinary innovation.

    Read About SVG

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  • Access Without Accountability: The Dangerous Experiment Unfolding in India’s AI Economy

    Reading Time: 16 minutes

    In recent weeks, three of the world’s most powerful AI platforms – OpenAI (ChatGPT Go), Google (Gemini), and Perplexity – quietly dropped a bombshell in India. They made their premium AI services free. To the public, it feels like a gift. To investors, it looks like user acquisition on steroids. But to those watching closely, it marks something else entirely.

    This is not just a rollout. It is a realignment.

    India is no longer a passive consumer of global tech. It has become the frontline in a new race. This is not just about adoption. It is about behavioral influence, training data, and investor validation. The platforms know this. The telcos know this. And slowly, regulators are starting to catch on.

    At Greyhound Research, we have seen this before. These are moments when access masks deeper asymmetries. These AI giveaways are not about generosity. They are a new kind of land grab. The prize is not revenue today. The prize is training signals, usage patterns, and early lock-in at a national scale. In India, that means hundreds of millions of real-world prompts across languages, devices, and use cases. The more granular the prompt, the more valuable the insight. The more frictionless the access, the more invisible the trade.

    Behind the zero-price tag is a full-stack negotiation between telecom carriers and platform providers. Google has bundled Gemini Pro into Jio plans. Airtel has done the same with Perplexity Pro. OpenAI has bypassed telcos for now, but only to fast-track a direct user relationship. In every case, the logic is clear. Use free access to collect engagement, train models, shape habits, and inflate growth metrics. This is not consumer-scale access. This is infrastructure-scale influence.

    And telecoms are not just distribution partners. They are becoming digital power brokers. What used to be a channel is now a filter. If your first AI assistant comes preloaded with your data plan, what happens to platform neutrality? What happens to choice? When Jio users default to Gemini and Airtel users default to Perplexity, the illusion of discovery disappears. What replaces it is quiet, commercial gatekeeping.

    What makes this moment more dangerous is its timing. India’s policy framework for AI remains nascent. The Digital Personal Data Protection Act is not yet operational. No AI-specific regulation exists. Enterprise governance maturity is also far behind the curve. Sensitive data is already flowing into external models with little clarity on ownership, reuse, or jurisdiction. This is not a hypothetical risk. It is already happening. CIOs are waking up to discover that internal pitch decks, customer contracts, and compliance documents are being used to train the future of intelligence.

    Meanwhile, valuations climb. Metrics soar. Platforms gain ground. And no one stops to ask the question. At what cost?

    This is not a market expansion. It is a behavioral reset. It is a reprogramming of how people search, learn, write, and make decisions. This is AI as soft power. It is not being embedded through governance or education. It is entering lives through telecom billing and bundling. Once installed, it becomes second nature. It becomes the first interface to knowledge, to productivity, and to identity itself.

    To enterprise leaders watching from the sidelines, this is not just a consumer story. The platforms entering your employees’ pockets today will enter your enterprise stack tomorrow. The tools your teams play with at home become the defaults they reach for at work. What is free today becomes embedded tomorrow. It happens without oversight, without policy, and without anyone knowing what is being learned from your organization’s data.

    This dossier is a warning. AI platforms are not just distributing access. They are embedding themselves into national digital behavior. They are rewriting norms. These include trust, neutrality, how value is created, and who captures it. They are reshaping intelligence in their image, using your users, your data, and your bandwidth.

    India is not the endgame. It is the prototype. Brazil, Indonesia, and Nigeria are next. The strategy is portable. The silence is global.

    This is not the first time India has been the proving ground for a global digital agenda. We saw this during the Free Basics controversy, when telcos and platforms tried to curate the open internet into commercial silos. We saw it with zero-rating debates, Aadhaar’s biometric infrastructure, and UPI’s transformation of financial identity. India has always been the sandbox. Now, it is the signal generator.

    Free AI is the next frontier in that playbook. This time, it is not about how we access information, but how we author it. It is not about which sites load faster, but whose language becomes default. When AI platforms train on Indian prompts and export those learnings to shape global cognition, who decides what is local and what becomes universal?

    The stakes are cultural. The costs are structural. The urgency is now. This is not about what AI can do. It is about who it learns from. And whether we have a say in how that learning is used.

    What Exactly Is This “Free AI” Moment, And Why Should the Enterprise Care?

    Let us call this what it is. This is not a mass-access play. It is a momentum machine. The free AI push in India is not about generosity or global inclusion. It is a calibrated move to capture usage signals, build behavioral datasets, and inflate platform valuations.

    Platforms like Perplexity are racing to demonstrate scale, engagement, and diversity of training input before their next funding rounds. For Google and OpenAI, the stakes are even higher. They are defending global positioning and revenue futures by locking in users now, before policy and competitors catch up.

    India is not just a market. It is a multiplier. It provides model feedback, monetization narratives, and investor signals. Every new user, every prompt, and every uploaded file adds weight to a platform’s valuation story. India’s scale and linguistic diversity are being harvested as training fuel and strategic proof.

    This is also the first time multiple AI players have launched free or subsidized pro-level services at a national scale. They are reaching users directly through consumer channels and telecom plans. These are not promotional stunts. They are foundational moves. They are shaping how AI gets introduced into society, not by discovery, but by preloading.

    In a recent Greyhound Fieldnote, we at Greyhound Research highlighted to a CIO at a large bank how junior analysts were uploading pitch decks into free AI tools, unaware they were training external models. The risk only came to light after a partner flagged similar phrasing in a public demo. No one had noticed. No policy existed. No opt-out had been set.

    What few enterprises realize is that this is not just a licensing gap. It is shadow AI. Employees are not installing rogue software. They are relying on free public models to generate insights, create content, summarize legal drafts, and even process client data. This is not just a security problem. It is a cognitive risk surface. It creates blind spots in decision-making. And the consequences are rarely visible until they surface publicly.

    The real risk is not model accuracy. It is organizational amnesia. Enterprises are not tracking what data is leaving, what logic is being accepted, or what assumptions are being baked into daily operations. When a prompt becomes embedded in a workflow, it becomes policy without ever being debated.

    There is also a cost to waiting. Some CIOs believe they can delay until the next budget cycle or until regulation forces their hand. But by then, usage patterns will be entrenched. User behaviors will be primed. Retraining employees will be harder. Cleaning up leakage will be more expensive. And explaining these risks to regulators will come with higher stakes.

    This is soft lock-in. Not through contracts, but through convenience. Not through pricing, but through priming. When platforms own the interface to intelligence, they own the context. And when that interface is free, it is easier to accept it without challenge.

    Greyhound Standpoint –  At Greyhound Research, we see this as more than a market push. This is an infrastructure-level reset. Platforms are not simply expanding access. They are reconfiguring the rules of value exchange. In this new paradigm, users trade data for convenience. Enterprises, meanwhile, inherit exposure without visibility. This is not a neutral shift. It is an aggressive restructuring of how language, loyalty, and leverage are captured in the AI economy.

    The Shift: From Open Internet to AI Gatekeeping via Telcos

    India once stood firm on the principle of net neutrality. It rejected Free Basics. It refused zero-rating. It chose openness over platform control. But that was the internet. What happens when intelligence becomes the product, and it arrives not through discovery, but bundling?

    We are now entering a two-tier AI structure, divided by telecom plan.

    If you are a Jio user, you get Google’s Gemini Pro, preloaded and subsidized. If you are with Airtel, you get Perplexity Pro embedded in your mobile and broadband experience. What was once an open choice is now a preselected path. Platform preference is being dictated by telecom allegiance.

    This is not just commercial bundling. It is epistemic sorting. It is telecom identity deciding your first brush with machine intelligence. It shapes what you ask, what you learn, and how you form trust.

    In this emerging model, telcos are no longer passive carriers. They are active filters of platform access. They choose who gets distribution, visibility, and default status. They negotiate terms that give AI providers instant reach and brand credibility. In return, platforms offer white-glove integration and data capture at scale.

    Google’s Gemini Pro bundle with Jio is not just a growth hack. It is a calculated land-and-expand model, targeting youth and mobile-first users. Airtel’s national rollout of Perplexity Pro flips the same switch; this time with a smaller AI player betting on first-mover advantage through telecom loyalty.

    This creates a systemic risk. If platform adoption is defined by telco alignment, not by user need or model quality, we end up with AI monocultures in disguise. One telco, one AI. One plan, one worldview.

    These early interactions with AI are not neutral. They influence how users learn to ask questions, what formats they trust, and what types of responses they normalize. This is not just behavioral shaping. It is cognitive scaffolding. If your first few hundred AI interactions happen inside one platform’s model, that model becomes your epistemic lens. It does not just answer your questions. It starts to frame your worldview.

    As AI becomes the interface to search, writing, and decision support, the line between editorial and inference begins to blur. Users may not know where the answer ends and the algorithm begins. This is especially true for first-time AI users who trust the default, unaware of what alternatives exist.

    For enterprises, the exposure is closer than most CIOs imagine. With mobile devices doubling as both personal and professional tools, these pre-bundled AI assistants often enter enterprise workflows under the radar. No one signed a contract. No one saw the terms. But the model is now in the room, listening, generating, and learning from prompts tied to real business operations.

    The concern is not just consumer disempowerment. It is structural control. If a telco contract determines your access to intelligence, then platform neutrality becomes meaningless. The gate has moved. The gatekeeper is now your network provider.

    This reshapes the AI opportunity in India into something narrower, more guided, and more commercially contained. It rewards players with reach, not necessarily responsibility. And it risks locking out local innovation before it reaches scale.

    Greyhound Standpoint – Telcos are no longer pipes. They are power brokers. India must now confront what it means when a data plan also decides your first AI assistant. Platform neutrality can no longer be defined by traffic alone. It must evolve to include access, algorithmic exposure, and ethical distribution of intelligence.

    The Governance Multiplier: Why Free AI Puts Compliance At Risk

    This new wave of AI is moving faster than policy and far faster than enterprise governance can keep up. The rollout of free, pro-tier AI tools across India has stress-tested the country’s regulatory stack, and the cracks are starting to show.

    India’s Digital Personal Data Protection (DPDP) Act is still in its rollout phase. AI-specific regulation remains aspirational. In the meantime, global platforms are operating at scale, training on Indian prompts, generating cross-border outputs, and redefining data jurisdiction in real time. There is no mature enforcement mechanism. There is no structured oversight. The guardrails are being retrofitted after rollout.

    For enterprises, this is not just a regulatory gap. It is a compliance multiplier. Every new AI tool accessed by an employee introduces new flows of prompt data, generated content, and potential IP leakage. Most of these tools operate in black-box mode. Their terms of service change frequently. Their data handling policies are vague by design. And few enterprises have mapped how their internal controls extend to free-tier AI interactions.

    This is not hypothetical. Prompt data is already crossing borders, leaving no audit trail. Generated outputs are entering workflows without validation. And legal teams are discovering platform usage only after external incidents expose it.

    Most enterprises still assume that model training is a separate event from user interaction. But many free-tier tools treat every prompt as potential fuel. That means proprietary logic, confidential syntax, and unique business phrasing can quietly become part of the platform’s learning base. And once it is learned, it cannot be unlearned.

    Regulated sectors face an amplified version of this risk. When AI-generated content is used in documentation tied to public safety, financial decisions, or legal compliance, even a minor misalignment between platform policy and internal protocols can escalate into regulatory conflict.

    Over time, AI tools start to change their voice. The answers that once sounded consistent with the company tone can begin to feel slightly off. Models evolve in the background, learning new patterns, adjusting their style, and sometimes losing touch with what was once approved. What slips through first is not accuracy, but alignment: the subtle tone, the phrasing, and the judgment. And before long, what once saved time begins to create quiet risk in customer communication and regulatory submissions.

    In a recent Greyhound Fieldnote, a compliance officer at a pharmaceutical firm shared how a team began using a bundled AI tool to generate clinical reporting drafts. Legal and regulatory teams had never reviewed the tool’s data policy. The terms of service were discovered only after an external agency flagged output similarities with another trial response.

    Another blind spot is exit risk. Enterprises may allow free AI usage during periods of exploration or resourcing gaps. But if these tools become embedded in workflows and then suddenly pivot to paywalls, change access terms, or go offline, the business impact can be immediate. Productivity loss, continuity disruption, and compliance exposure follow.

    This also raises a deeper sovereignty question. Most of the AI tools currently scaling in India are foreign-built, trained on non-local contexts, and governed by external legal frameworks. India still lacks scale in public models, localized compute, or developer ecosystems. The risk is not just dependency. It is asymmetry.

    Audit teams are now being asked to sign off on AI usage with no visibility into what was used, by whom, with what prompts, and under which jurisdictions. That is not audit readiness. That is a liability event waiting to happen.

    Greyhound Standpoint – Compliance is no longer a checklist. It is a design function. At Greyhound Research, we urge enterprise leaders to treat AI adoption with the same controls as financial systems, not because of what the models can do, but because of what users might unknowingly reveal to them. If your teams cannot audit what they use, where it sends data, or how it learns from their prompts, then that tool is not free. It is a deferred cost.

    Do You Even Need This? The Internal Questions to Ask First

    There is a myth in the enterprise playbook that being early means being smart. But with AI, early adoption without internal maturity is not leadership. It is exposure.

    Every organization is under pressure to say yes to AI. Boards want to hear about pilots. Vendors offer proof-of-concepts. Employees experiment with tools on the side. But very few leaders are asking the harder question: do we even need this yet?

    The best AI outcomes do not come from the fastest adopters. They come from the most prepared ones. Organizations that pause to assess readiness, define boundaries, and build governance before tools are introduced tend to scale AI more safely, more credibly, and with fewer reputational scars.

    Saying no is not anti-innovation. It is often the most strategic decision a leader can make.

    Most free-tier AI tools today are built with capabilities that far exceed the average enterprise’s governance readiness. These models can draft policy, summarize contracts, and simulate tone of voice. Yet the organizations using them often lack even basic AI usage protocols. This gap is not operational. It is existential.

    The real test is not whether your teams can use AI. It is whether your enterprise can absorb its consequences. Have you defined a list of approved tools and use cases? Do you have training protocols to help employees understand data sensitivity and model limitations? Are your legal teams aligned on how prompt data interacts with contracts and audit requirements? Do you have an exit plan if a tool changes access terms or becomes incompatible with your compliance stack?

    In service firms and regulated industries, using AI-generated outputs without proper oversight can lead to unintentional breaches of client contracts. If a response is co-authored by a public AI tool that was never disclosed or approved, your firm could be exposed to liability or reputational damage, even if the work was technically accurate.

    In a recent Greyhound Fieldnote, a digital leader at a consumer goods firm shared how their customer service team began using a GenAI tool to draft responses to product queries. At first, it seemed efficient. But weeks later, inconsistencies emerged. The responses relied on outdated knowledge and unverified sources. No one had checked the tool’s training cut-off or default content policy.

    Shadow governance is the illusion of control. A few guidelines on a wiki, or a disclaimer at the bottom of an email, are not substitutes for enforced guardrails. Real governance shows up in audits, escalation logs, and cross-functional ownership.

    The deeper risk is this: free AI tools lower the friction to experimentation. But friction exists for a reason. It creates pause. It creates deliberation. It forces teams to ask whether the tool fits the process or if the process is being reshaped around the tool.

    Leadership today requires the ability to say yes selectively and say no decisively. Not because AI is risky, but because not every organization is ready to govern it.

    Greyhound Standpoint – Saying no to new technology is also a form of leadership. At Greyhound Research, we have seen that the organizations that benefit most from AI are not the first to adopt but the first to pause, prepare, and draw hard lines between curiosity and capability. Governance is not a reaction. It is a precondition.

    The Operational Playbook: How to Build Guardrails Around AI Access

    The enterprises that get AI right are rarely the ones running the flashiest models. They are the ones that know when to slow down, draw boundaries, and enforce them. Governance is not a plug-in. It is a habit, one that must be trained, tested, and shared across every team that touches data or makes decisions.

    Right now, most organizations are still flying blind. Employees are experimenting with AI tools that no one has approved. Policies exist, but they are either too thin to matter or so dense that no one reads them. Legal, compliance, and tech leaders often operate in parallel universes. Prompts flow freely, outputs circulate unchecked, and no one really knows what the models are learning. What feels like agility is actually unmanaged risk wearing a productivity badge.

    To bring order to this chaos, four steps help anchor the process.

    1. Discovery starts with seeing what is already happening. Every company needs a clear picture of which tools are in use, who is using them, and for what. Browser plug-ins, mobile apps, and telco-bundled assistants—all of it counts. Relying on employees to self-declare will never work. Real discovery means digital detective work: analyzing network logs, cloud traffic, and endpoints to surface the truth.

    2. Categorization follows naturally. Once you know what exists, decide what stays, what gets fenced off for testing, and what needs to be blocked. The goal is not to kill curiosity; it is to stop it from mutating into exposure.

    3. Policy turns good intentions into rules that people can live by. List which tools are approved, what data is off-limits, and how any AI-generated material is verified before it reaches a client or regulator. Teach employees not only how to use AI but also when to walk away from it. Governance that no one understands is not governance at all.

    4. Control is where it all becomes real. Every interaction should leave a trail—prompts, outputs, user IDs—so that compliance teams can trace incidents and learn from them. Without logs, it is impossible to tell accident from intent. Auditability might sound bureaucratic, but it is the only thing standing between transparency and denial.

    Telecom-bundled AI tools deserve special mention. They slip into workplaces through personal phones and unmanaged devices. Without mobile-device management, these assistants operate outside enterprise control. A SIM card should never be a backdoor. The same rules that guard the firewall must extend to every handset that touches company data.

    Model drift adds another layer of risk. AI models evolve quietly, and a tool approved last quarter can start answering differently today. Tone changes, facts shift, and outputs drift just enough to sound off-brand or out-of-policy. Left unchecked, this slow shift can turn a once-safe tool into a compliance liability.

    Vendor complexity compounds the problem. Many AI products hide a web of third-party plug-ins and APIs. The front end might look compliant, while the back end routes data through unfamiliar jurisdictions. Each integration needs its own review, because every hidden dependency is a potential leak.

    As per a recent Greyhound Fieldnote, a large Indian retailer decided to confront this directly. After a few close calls with unapproved AI use, the CIO built an internal sandbox where employees could test prompts safely. Everything was logged and monitored in real time. Far from stifling creativity, the approach had the opposite effect. Employees experimented more freely, compliance incidents fell, and management finally had a clear view of where AI added value and where it simply added noise.

    The biggest truth is that governance cannot live in one department. The CIO cannot own it alone. Legal, HR, risk, and business leaders all have a piece of it. When no one owns AI risk, it multiplies quietly until it explodes publicly.

    Greyhound Standpoint –  Governance is not a policy document. It is a daily behavior. At Greyhound Research, we see that the companies scaling AI responsibly are not the loud adopters but the disciplined ones. They weave AI hygiene into onboarding, project planning, and performance reviews. The best AI system is not the one that dazzles in demos. It is the one your enterprise can trust without looking over its shoulder.

    If You Can’t Govern It, Don’t Call It Innovation

    India is not just another growth market for generative AI. It is the global testbed. It is where platforms trial scale, fine-tune language models, and shape user behavior in one of the world’s most data-rich, price-sensitive, and digitally complex environments.

    Free access is not a gift. It is a strategy. It is a lever to gather prompt data, train global models, capture early loyalty, and create soft dependencies long before monetization begins. It is designed to enter quietly, integrate deeply, and scale invisibly.

    The platforms understand this. The telecoms are playing along. The regulators are still catching up.

    Free tools are not free from risk. When generative AI becomes a bundled service in telecom plans, when the first digital assistant you meet is pre-decided by your carrier, and when critical knowledge is accessed through proprietary inference engines, then platform neutrality is no longer a traffic question. It is a cognitive one.

    This is the new frontier of digital power. And in the absence of hard policy, it will be governed by soft alliances and commercial interests.

    Enterprises cannot afford to mistake exposure for innovation. If you do not know how a tool handles prompt data, where its models are trained, or what its exit terms are, then you are not innovating. You are offloading control.

    Innovation without governance is not leadership. It is a risk passed downstream. It is trust outsourced to unseen models. It is compliance retrofitted after the breach.

    Real innovation rarely rewards speed for its own sake. The organizations that will matter most in the next phase of India’s AI journey are the ones that move with purpose. The loud experiments will get attention, but the disciplined ones will endure. Progress does not come from constant motion; it comes from control. What feels like restraint in the short term is often what makes scale sustainable in the long run.

    What happens in India will define how the world’s next billion users experience AI. If free AI becomes the default without accountability, it will not just change business models. It will reshape language, trust, and truth itself. The challenge before India is not adoption but authorship. Who writes the next chapter of intelligence—the platforms or the people?

    It is not enough to embrace AI. We must embed it with accountability. Because if you cannot govern it, you cannot scale it. And if you cannot scale it safely, then access is not innovation. It is exposure.

    Analyst In Focus: Sanchit Vir Gogia

    Sanchit Vir Gogia, or SVG as he is popularly known, is a globally recognised technology analyst, innovation strategist, digital consultant and board advisor. SVG is the Chief Analyst, Founder & CEO of Greyhound Research, a Global, Award-Winning Technology Research, Advisory, Consulting & Education firm. Greyhound Research works closely with global organizations, their CxOs and the Board of Directors on Technology & Digital Transformation decisions. SVG is also the Founder & CEO of The House Of Greyhound, an eclectic venture focusing on interdisciplinary innovation.

    Read About SVG

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  • Infosys Q2 FY26 Results – Steady Hands, Softer Horizon

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    Infosys entered Q2 FY26, crossing the five-billion-dollar revenue mark, up 2.9 percent year over year and 2.2 percent sequentially in constant currency. Operating margin stood at 21 percent, and free cash flow reached $1.1 billion, 131 percent of net income. The company kept its FY26 guidance tight at 2 to 3 percent growth, choosing discipline over pursuit. Large-deal momentum continued with $3.1 billion in total contract value, 67 percent of which was net new. The numbers were steady, but the tone was defensive. The performance spoke of control, not conviction.

    And yet this restraint defines Infosys today. Where peers court volatility in search of breakout quarters, Infosys prefers endurance. Automation gains, tight cost management, and measured deal pacing protected margins even as client budgets stayed cautious. Headcount rose by 8,203 after four quarters of decline, attrition eased to 14.3 percent, and utilization excluding trainees held near 85 percent. CFO Jayesh Sanghrajka called it “a quarter of strong growth, resilient margins, and very high cash generation,” and the results bear him out. Still, the conversation on Wall Street is shifting from stability to speed.

    The tension is familiar. Infosys is proving that its AI investments work but not yet that they compound. Topaz agents are embedded across finance, customer operations, and development pipelines, while Project Maximus continues to strengthen internal delivery economics. What investors and clients now ask is whether these assets can grow into scalable revenue engines. Infosys has built reliability into a virtue; the next step is proving that reliability can accelerate growth.

    Infosys remains pragmatic. It isn’t racing to launch foundation models or headline labs. It’s embedding explainable AI into systems of record, core banking, shared services, and procurement, and measuring impact through productivity and compliance rather than hype. Markets aren’t punishing Infosys for caution; they’re pressing for proof that its steady design can translate into a self-reinforcing platform model.

    Greyhound Standpoint — For enterprise buyers, the signal is consistent with last quarter but sharper in focus. This isn’t an organization chasing generative theater or short-term margin optics. It’s a company methodically repositioning around full-cycle AI execution, lifecycle governance, and measurable enterprise impact. Bookings confirm that clients are engaging with intent, margins validate cost discipline, and field evidence shows that the Topaz architecture is being embedded where it matters most, inside production workflows. The open question, as buyers and investors continue to ask, is whether this predictability can now evolve into platform pull and measurable acceleration through the second half of FY26.

    At Greyhound Research, our fieldwork with enterprise technology buyers reinforces this next-stage inflection. Clients are moving beyond curiosity about generative AI and are now defining value through embedded intelligence, explainability, and policy alignment. CIOs point to Infosys Topaz and Project Maximus as tangible examples of operationalized AI, not marketing showcases but disciplined, audit-ready systems improving delivery accuracy, pricing control, and decision speed. Buyers describe Infosys as “quietly relentless,” a partner that scales governance with as much precision as it scales automation. In a market still clouded by hype and half-measures, that restraint has become its strategic strength.

    Regional Performance – Europe Holds, U.S. Softens

    Infosys’ regional story in Q2 FY26 felt steady rather than surprising. Europe held its ground, North America slowed a touch, and Asia Pacific stayed on an even keel. The numbers look simple; the reality underneath them is more layered, shaped by regulation, local policy, and how willing enterprises are to spend in an uncertain economy.

    Europe was once again the bright spot, growing 6.3 percent year over year in constant currency, roughly twice the company average. Energy, utilities, manufacturing, and financial services did the heavy lifting, with programs at RWE, ABN AMRO, and Sunrise expanding through the quarter. Years of investment in compliance readiness, sovereign cloud delivery, and local hiring are now paying off. European CIOs are past the “pilot” phase of AI; they’re building it into day-to-day operations.

    At Greyhound Research, we read this consistency as proof that Infosys’ reputation still carries weight in regulated markets. Growth here rests as much on credibility as on code. Buyers want modernization they can audit, and Infosys has learned how to give them both. In Europe, predictability isn’t a limitation; it’s the point.

    North America told a different story. Revenue inched up only 2 percent from a year ago, and the region still accounts for more than half the company’s business. Big banks and retailers kept budgets tight. Most activity revolved around renewals and small AI pilots designed to prove efficiency before committing to scale. Decision cycles lengthened; project ramp-ups stayed conservative. Clients trust Infosys to deliver; they just want to see clearer payback before they double down.

    Greyhound Research believes this slowdown says more about the market than the company. U.S. enterprises have moved from testing if AI works to proving that it earns its keep. They’re measuring vendors on business outcomes and governance discipline, not on pitch decks. For Infosys, the next leg of growth here will come from showing exactly how automation drives margin and control, not just capability.

    Across Asia Pacific, momentum felt measured. Australia, Singapore, and the Middle East held up well, led by banking, telecom, and public sector projects built on Finacle and BPM platforms. The joint venture with Telstra’s Versent Group has started to stretch Infosys’ cloud and AI reach, a move that signals intent more than instant results. Japan and Korea, on the other hand, remained deliberately slow, preferring small, sequential programs even when early pilots worked.

    We see APAC as a region of extremes, with deep delivery experience but patchy orchestration. Clients in Australia and Singapore speak highly of Infosys’ execution but want a more connected view across AI, BPM, and infrastructure. As AI regulation tightens across Asia, that call for integration will only grow louder.

    Greyhound Standpoint – For global CIOs, the pattern is clear. Infosys is keeping regional performance stable even as market behavior diverges. In Europe, compliance strength is turning into sustained growth. In North America, clients prize reliability but now expect visible proof of business value. In Asia Pacific, demand is firm, and buyers want a single architectural thread linking platforms to delivery.

    Our fieldwork shows these differences have more to do with enterprise readiness than vendor limits. European clients choose Infosys for data sovereign, policy-aligned programs that meet residency and transparency rules. In the U.S., banks and retailers want predictable outcomes and shorter time to impact before expanding scope. In Asia Pacific, clients value delivery depth but want tighter coordination among advisory, architecture, and execution teams. The theme is the same everywhere: Infosys continues to earn trust, and enterprises are getting ready to scale with intention, not enthusiasm.

    Segment Highlights – Operational Strength, Uneven Ambition

    Infosys’ second quarter carried the same steady tone that marked the start of the year. Growth came in at 2.9 percent year over year in constant currency, led once again by Manufacturing, Financial Services, and Energy and Utilities. The headline looks calm, but a closer read shows uneven speed across industries. Some sectors are back on their feet; others are still waiting for budgets to loosen.

    Financial Services remained the anchor, accounting for 27.7 percent of overall revenue. Growth of 5.4 percent year over year in constant currency came from renewals and AI-enabled consolidation programs at long-term banking clients. Work with ABN AMRO and Mastercard stood out, showing how Infosys is weaving Topaz and Finacle together to drive automation in lending and payments. North American banks, however, continued to delay modernization projects as compliance teams took longer to approve AI workflows.

    At Greyhound Research, we read this as a sign of maturity rather than slowdown. Infosys is now seen as a safe executor in industries where every new model or process must pass audit. Clients are using agents within live environments, mostly through Finacle, but expansion to full lifecycle integration will take time. The company has the credibility; what it needs now is orchestration that connects advisory, platform, and operations under one governance rhythm.

    Manufacturing was the strongest performer, up 6.6 percent in constant currency. Growth came from digital factory and supply chain programs in Europe, many built around hybrid cloud and automation layers. The mix of engineering heritage and cost discipline is giving Infosys a dependable edge in industrial transformation.

    Energy, Utilities, Resources and Services added 2.1 percent, helped by analytics-led renewals that focused on efficiency rather than expansion. Communications grew 4.7 percent, recovering from last year’s stall, while Hi-Tech advanced 8.6 percent on the back of semiconductors and product lifecycle automation. Together, these sectors underline a simple truth: Infosys wins when execution and compliance meet, but fresh demand still needs to be created.

    Life Sciences slipped 10.5 percent year over year as global pharma clients cut discretionary spending. Retail declined 2.3 percent, a reminder that U.S. consumer markets remain defensive. The company kept delivery quality high, yet overall momentum stayed moderate because of these two verticals.

    Greyhound Fieldnotes from ongoing programs echo this pattern. Clients using Infosys BPM for invoice automation or Finacle’s data modules report faster cycles and better visibility. Still, most deployments sit inside single departments. Topaz, Finacle, and BPM perform well on their own, but few clients yet run them as one connected system. Building a shared architecture across these platforms will determine how much of Infosys’ AI investment truly scales.

    Greyhound Standpoint – For CIOs, the picture is familiar but firmer. Infosys is executing with confidence in industries where compliance and automation overlap. Manufacturing has become its growth engine; Financial Services continues to reinforce the trust story; Energy and Hi Tech show consistent improvement through applied AI. The delivery stack is strong, the platform narrative broader than before. Enterprise buyers can trust Infosys on execution, but they should work with the company to tighten cross platform design, lifecycle governance, and integration discipline. The shift from discrete tools to unified systems is already in motion. For clients ready to shape that evolution, this is the right moment to lean in.

    Stack Signals – Platform Momentum, Integration Lag

    Infosys’ second-quarter story was steady but uneven. Growth across its platforms didn’t move in sync. Topaz, the AI suite, kept its pace in client operations, while Finacle and BPM grew quietly within their domains. Each is performing well, yet they still feel like parallel tracks waiting to meet.

    Topaz, Infosys’ flagship AI platform, kept expanding across finance, HR, and customer service. Clients such as RWE, HanesBrands, and AGCO reported real benefits, faster decision-making, cleaner data flow, and stronger resilience in daily operations. These aren’t experiments anymore; they’re live systems carrying production workloads. The question has shifted from “can it work?” to “how far can it scale?”

    At Greyhound Research, we see this as validation of Infosys’ deliberate, agent-based approach. Instead of chasing headlines about new models or tokens, the company has focused on explainable, policy-aware automation, the kind of AI large enterprises can actually trust. That patience is winning credibility in regulated sectors. The next chapter is technical, building the architecture, standardizing orchestration, and unifying lifecycle and version control so the stack behaves like one governed system rather than several good tools.

    Finacle kept its momentum during the quarter, winning a few new SaaS deals in the Middle East and Australia. The appeal is simple: shorter deployment cycles, a modular setup, and less disruption for banks that need to modernize without running compliance risks. The platform is proving that scale doesn’t have to come at the cost of control, a message that’s landing well with CIOs who are still cautious about large-scale core banking changes. BPM also moved forward, with new AI-based automation for finance and sustainability functions. Together, they strengthen Infosys’ base in process intelligence, though the commercial connections between these units still feel more adjacent than integrated.

    Greyhound Fieldnotes mirror that reality. Clients consistently praise Infosys’ execution quality but say the deployment experience varies by platform. Topaz, Finacle, and BPM each deliver value on their own, yet few clients see a unified control layer that ties governance, identity, and lifecycle together. Infosys has the pieces; it now has to make them fit as one experience.

    Greyhound Standpoint – Infosys is showing clear traction with enterprise AI. Topaz is proving itself inside production systems, Finacle keeps broadening its footprint in banking, and BPM continues to mature as a trusted automation engine. The next step is convergence. Clients want one experience where governance, orchestration, and accountability follow a single standard. For CIOs, this is the right moment to work with Infosys not just for delivery dependability but for the opportunity to help shape a connected architecture, one that blends policy, reliability, and scale into a single operating model.

    AI-Led Momentum – Agents Gain Scale, Governance Trails

    Infosys’ Q2 FY26 results showed that enterprise AI is no longer a side note. The company reported broad growth in production-grade deployments of Topaz agents across industries. AI-enabled work now appears in almost every large deal, particularly in Financial Services, Manufacturing, and Utilities. While Infosys doesn’t break out AI revenue, management confirmed that generative and agentic components now touch the majority of new engagements.

    More importantly, this demand is grounded in real work. Clients such as RWE, HanesBrands, and AGCO are using Infosys’ agents for decision intelligence, document automation, and digital-workplace resilience. These aren’t pilots tucked away in labs; they’re active programs inside live workflows. Infosys is focusing less on where AI runs and more on how it reshapes daily execution.

    At Greyhound Research, we see the strongest traction in heavily regulated or operations-intensive sectors like banking, energy, and retail. Buyers cite measurable results: faster code creation, double-digit productivity gains, and shorter invoice cycles. As adoption widens, their focus is shifting from experimentation to reliability. Lifecycle control, telemetry, and policy enforcement are becoming the new proof points for maturity. That’s the next level Infosys must meet.

    Unlike hyperscalers chasing model milestones, Infosys is building its case around agent-based systems, embedded policy, and audit-ready lifecycle management. Topaz integrates models, data orchestration, and governance tooling into a modular architecture that fits existing enterprise systems. The differentiator isn’t how many models run, but how clearly each one is monitored and controlled.

    During the quarter, Infosys also introduced AI-powered sustainability and operations tools, from agriculture analytics with Perfection Fresh to fan-engagement platforms for sports clients like the LTA. In telecom and logistics, monitoring solutions are expanding under the same architecture. Internally, Project Maximus continues to extend automation across delivery, improving cost efficiency and pricing realization. Together, these initiatives show a company turning AI from capability into daily utility.

    Greyhound Standpoint – For CIOs, the direction is unmistakable. Infosys is translating generative AI into enterprise-scale value. The agent library keeps growing, delivery programs are maturing, and the benefits are visible. The opportunity now lies in turning Topaz into a fully governed operating layer, with standardized lifecycle tools, orchestration frameworks, and compliance scaffolding. With that foundation taking shape, Infosys is positioned to deliver not only AI capacity but dependable, repeatable outcomes at scale.

    What Clients Are Saying – Trust in Execution, Demand for Cohesion

    The message from this quarter’s client conversations is straightforward: companies trust Infosys to deliver, but they want the pieces to fit together better. In the Greyhound CIO Pulse 2025 survey, most technology leaders said their focus has shifted from testing AI tools to making them work within their existing systems. Nearly two-thirds now favor deployments tied to ERP, BPM, or infrastructure stacks. Standalone pilots are fading. Buyers want AI that feels built in, not bolted on.

    Across our field interviews, that sentiment was echoed repeatedly. Infosys’ Topaz agents, Finacle modules, and BPM accelerators are all delivering as promised. What clients are asking for now is one rhythm, a single model for lifecycle management, ownership, and architecture. They’re not complaining; they’re signaling readiness. The expectation is that Infosys, having earned their trust, will now lead on integration.

    In Europe, clients praised the company’s attention to regulation and local delivery. CIOs in Germany and the Nordics pointed to Topaz’s growing footprint in banking and public services, where traceability and compliance are crucial. They also cited Infosys’ flexibility in co-creating deployment models that satisfy national data standards. The next step, as one buyer put it, is “less customization, more common ground.”

    Clients in North America told us they want less theory and more templates. Several are now asking Infosys to hand them ready-made playbooks so they can scale faster without reinventing processes each time. They’re not questioning whether the model works; it’s more about timing and confidence, how quickly they can move from small pilots to real enterprise programs.

    Across Asia Pacific, delivery quality remains a bright spot. Clients in Singapore, Malaysia, and Australia described Infosys as reliable and easy to work with. BPM-led automation in logistics and shared services continues to do well, while Japan and Korea move at their own cautious speed. Buyers in these markets are now asking for help stitching everything together, local delivery, regional compliance, and global platforms.

    In the Middle East and Australia, demand for platform-led automation keeps growing. Clients like what Topaz and Finacle can do, but they’re asking for more visibility into how these systems will evolve over time, especially as they integrate with older infrastructure. Their message isn’t about doubt; it’s about wanting a shared plan.

    Even in faster-moving sectors like energy, telecom, and manufacturing, buyers described Infosys’ AI catalog as mature and relevant. They’ve stopped asking if AI works. The question now is how widely it can be rolled out and who takes ownership once it’s live.

    Greyhound Standpoint – Infosys has clearly moved beyond the proof-of-concept phase. The platform footprint is broader, the agents are running in production, and relationships with enterprise clients are deepening. What buyers want next isn’t new capability; it’s cohesion. They’re looking for a shared lifecycle model, clearer governance, and a unified delivery experience across products. These are positive demands, rooted in trust. Infosys is entering a phase where orchestration will define success, and the measure of scale will be how seamlessly the company can make its ecosystem feel like one connected platform.

    Strategic Takeaways – From Platform Strength to Platform System

    For CIOs and enterprise leaders reviewing Infosys after Q2 FY26, the story is less about stability and more about structure. The company’s delivery engine is solid, but the conversation has moved to how its many platforms will come together. Infosys is now judged on its ability to turn proven strength into a connected system that can scale with consistency and control.

    First, the foundation built around AI and automation is holding up well. The company’s Topaz agents, Finacle core systems, and BPM accelerators are all active across client environments. What buyers are now asking for is repeatability, not isolated wins, but patterns they can replicate across business units with common governance. The goal is reliability that compounds.

    Second, integration is becoming the real test. Infosys’ catalogue is wide: Topaz for AI orchestration, Finacle for banking, BPM for process intelligence, and a growing set of advisory and infrastructure services. Clients want these pieces to work under one playbook. They’re looking for shared policy management, lifecycle visibility, and simpler orchestration. This isn’t a complaint; it’s the natural progression of enterprise maturity.

    Third, governance and architecture are coming to the front of every large deal. Infosys already manages critical transformation programs, but clients are now asking for early involvement in design, blending data foundations, advisory, process, and infrastructure into one plan. It’s particularly visible in sectors such as financial services and manufacturing, where regulation and system complexity make design coherence essential.

    Fourth, buyers are now asking for a clearer view of how everything fits together. As Infosys builds out its platform stack, CIOs want to know who owns what, how Topaz connects to Finacle, how lifecycle policies extend across cloud and on-prem systems, and who keeps the governance lights on once deployment begins. None of this comes across as criticism. It’s curiosity from clients who are already convinced and want the pieces to move in sync before committing more budget. They’re signaling confidence, not caution.

    Greyhound Research sees this as a sign of maturity on both sides. Buyers aren’t questioning Infosys’ ability to deliver. They’re looking for the blueprint. What they want is a pre-integrated, governance-aligned framework that simplifies onboarding, reduces overlap, and scales with accountability. That’s what will elevate Infosys from platform executor to systems orchestrator.

    Finally, client expectations are changing fast. The conversation has moved beyond delivery and into partnership. Many CIOs now see Infosys not just as a vendor but as a collaborator, someone who helps shape policy, design platforms, and pull together entire ecosystems. You can already see this in how contracts are written and how advisory work shows up earlier in the cycle. The real test ahead is consistency: turning that spirit of co-creation into something that runs through every engagement, not just the headline deals.

    Greyhound Standpoint – Infosys is no longer just assembling a portfolio of AI tools; it’s building a system. That system is modular today but is clearly moving toward a unified, governed whole. The company’s credibility in delivery, its domain depth, and its expanding platform footprint give it the raw material to lead this evolution. For CIOs, this is the moment to engage Infosys not only as an implementer but also as an orchestrator. With stronger buyer alignment, clearer lifecycle frameworks, and coordinated go-to-market planning, Infosys is positioned to make the shift from executor to architect and from dependable vendor to trusted platform partner.

    Greyhound Standpoint – The Platform Has Proven Itself; Now It Must Become a System

    Infosys’ second-quarter results confirm a company that has settled into its stride. Revenue passed the five-billion-dollar mark, margins held near 21 percent, and large-deal signings stayed strong. These are not recovery numbers; they are the metrics of a firm that knows its playbook. The question now is not whether Infosys can deliver but how it turns delivery excellence into a single, recognizable system.

    Over the past year, Infosys has proven that its platform assets work. Topaz is no longer a vision statement; it is running inside live enterprise environments. Finacle keeps expanding its reach in banking modernization, while BPM continues to automate finance and ESG operations. Each platform is performing, but still as its own success story. The real opportunity and the challenge is to make them speak the same language.

    Clients are noticing the strength. Buyers tell us that productivity gains and cost efficiency are real, yet they also say the experience feels fragmented. They see Topaz driving AI adoption, Finacle powering digital cores, and BPM improving process speed, but they still log into three different systems. What they now want is coherence: a unified governance model, one lifecycle view, and one identity layer that binds it all together.

    Inside Infosys, that realization is already visible. Teams are aligning around shared tooling, cross-platform orchestration, and lifecycle telemetry. Automation is creeping into pricing, delivery, and solutioning. The shift is cultural as much as technical: a move from building products to designing a connected ecosystem. Project Maximus remains the quiet enabler, standardizing workflows so that scale no longer depends on headcount.

    At Greyhound Research, we see this as the company’s next growth lever. Infosys has earned its reputation for precision; now it must use that discipline to connect the dots. Integration is no longer an engineering task; it has become a test of trust. Enterprises are selecting partners who can guarantee consistency across AI, data, and operations. Infosys is already there in pieces; the task ahead is to turn those pieces into one system clients can recognize instantly.

    Strategically, Infosys is not racing to outmodel competitors or win headlines. It is building enterprise AI that is explainable, secure, and production ready. That approach fits industries where reliability and compliance carry more weight than novelty, such as banking, manufacturing, energy, and utilities. For these buyers, confidence is worth more than experimentation, and Infosys has positioned itself exactly in that space.

    The platform base is solid. Client trust is tangible. The orchestration story is ready to mature. What remains is conversion, shaping that credibility into a unified system that looks, feels, and performs as one. That is where the next chapter of growth will be written, and where Infosys will prove that consistency itself can become its most powerful form of innovation.

    Analyst In Focus: Sanchit Vir Gogia

    Sanchit Vir Gogia, or SVG as he is popularly known, is a globally recognised technology analyst, innovation strategist, digital consultant and board advisor. SVG is the Chief Analyst, Founder & CEO of Greyhound Research, a Global, Award-Winning Technology Research, Advisory, Consulting & Education firm. Greyhound Research works closely with global organizations, their CxOs and the Board of Directors on Technology & Digital Transformation decisions. SVG is also the Founder & CEO of The House Of Greyhound, an eclectic venture focusing on interdisciplinary innovation.

    Read About SVG

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