#3 Beyond the Hype: A Practical Framework to Evaluate AI Opportunities

AI is everywhere. Value isn’t!

We’re in a gold-rush moment: 95% of companies have a generative AI project on the roadmap, yet only one in four can show real, measurable impact (Bain & Company 2025; BCG 2025)

Over the past 1.5 years, as I’ve worked on GenAI tools, listened to demos and pitches, and had countless hallway conversations, one question keeps surfacing: Is this AI idea worth pursuing?

To help answer that, for myself and my team, I developed a 7‑Pillar AI Opportunity Scorecard. It breaks that BIG question down into something more actionable. The framework balances problem clarity, strategic alignment, and stakeholder readiness with a dose of realism around AI fit, speed to value, opportunity cost, and risk.

Whether you’re a PM under pressure to “add AI,” a data scientist evaluating the next request, or a leader prioritizing resources, this framework is designed to help you make more confident, grounded decisions.

Let’s dive in…

📌 The 7-Pillar AI Opportunity Scorecard

Not every AI idea deserves the same level of investment. The 7-Pillar Scorecard is a quick gut-check, a way to pressure-test whether an idea is worth building before committing time and resources.

1. Problem Clarity: Is the problem clear, impactful, and validated?
2. Strategic Alignment: Does the idea align with current business priorities and timing?
3. Stakeholder Investment: Do we have clear stakeholder buy-in or a strong case to quickly prototype and validate interest?
4. Data & AI Fit: Is the data available and reliable? And is AI genuinely the right solution?
5. Speed to Value: Can we demonstrate incremental value quickly?
6. Opportunity Cost: What are we saying no to? Is this the best use of our time and resources?
7. Risk Check: What could go wrong, and are we ready to handle it?

🧪 Sample Case Study: Finance Assistant for Internal Stakeholders

The Problem: A finance team at a large enterprise was overwhelmed. Each week, internal stakeholders across product, marketing, and sales flooded them with ad hoc requests.

Despite having dashboards, most people defaulted to Slack or email. They didn’t know where or how to find the right data. The finance team found themselves answering the same questions over and over: “What’s the budget vs. spend for Q2?” “What’s the headcount projection?” The work was interrupt-driven and repetitive.

The work was interrupt-driven, manual, and repetitive, slowing down more strategic analysis and frustrating everyone involved. It was a clear opportunity for an AI-powered solution.

How was the opportunity evaluated using the AI Opportunity Scorecard?

1. Problem Clarity: The team had a clear understanding of the pain point: repetitive, interrupt-driven reporting work that slowed down analysts and frustrated stakeholders.
2. Strategic Alignment: The solution directly supported their goal to improve operational efficiency and scale support without increasing headcount.
3. Stakeholder Investment: Rather than waiting for a formal ask, the team took initiative. They believed they could build a prototype using internal data and a chat-based interface, and planned to test it with a few reporting-heavy teams to validate interest.
4. Data + AI Fit: They had structured data, documented FAQs, and the tools to build a retrieval-augmented generation (RAG) model, making it possible to fetch contextual answers from existing financial reports.
5. Speed to Value: A proof of concept could be built in about four weeks. Their goal wasn’t to replace analysts, but to automate the top 20 most frequent requests, covering roughly 60% of incoming volume.
6. Opportunity Cost: The team was deliberate: “If we don’t solve this, we’ll keep burning time on low-leverage work.” They deprioritized a less critical dashboard redesign to focus here.
7. Risk Check: They surfaced a few key risks: potential exposure of sensitive financial data and user trust issues due to inaccurate responses. These risks were clear, but manageable.

The Outcome: Based on the evaluation, the team determined this was a strong opportunity worth pursuing. The scorecard helped clarify tradeoffs, align priorities, and move forward with a focused, high-confidence proof of concept.

What this Framework helps You Do and What It Doesn’t

✅ It is:

• A practical way to evaluate early-stage AI opportunities before investing significant effort
• A shared lens to align decisions across product, data science, engineering, and design
• A tool to compare multiple ideas and highlight the most promising directions
• A living guide — meant to be tested, refined, and adapted to your team’s context

🚫 It Isn’t:

• A rigid gatekeeper meant to slow down innovation — think of it as a compass, not a checkpoint
• A full project plan or execution roadmap
• A detailed feasibility study that accounts for every potential risk, resource, or edge case

This framework helps you ask the right questions early, so you can move forward (or pause) with greater intention and clarity.

How to Use this Scorecard in Practice?

Use this framework early, during backlog grooming, idea pitches, or roadmap planning, to filter and compare competing AI opportunities. It helps align team conversations, surface red flags, and spotlight high-leverage bets before you invest in detailed work.

When multiple ideas are on the table, the scorecard provides a structured, side-by-side way to guide prioritization and decision-making.

Score each idea using a single 1–5 rating per pillar:

• 5: Strong fit
• 4: Good fit, minor questions
• 3: Moderate fit, needs discussion
• 2: Weak fit, known risks
• 1: Serious concerns, not viable now

Once an idea scores well, move into focused planning:

• Draft a short project charter outlining the problem, objectives
• Sketch a lightweight proof of concept with a clear hypothesis, key features, and feedback loops
• Define your “Definition of Done”, including success metrics and adoption signals

This approach keeps momentum high and helps ensure promising ideas turn into real, testable outcomes.

From Scoring to Action

In an AI-saturated landscape, intentionality is your sharpest edge. The most impactful work doesn’t come from chasing every new tool — it comes from clearly understanding the problem, validating early, and prioritizing deliberately.

This scorecard isn’t just a decision aid. It’s a mindset — and one that scales when your whole team embraces it.

✅ Empower Everyone in Your Team

Too often, good ideas never surface simply because someone hesitates to speak up. This framework gives every team member a way to confidently identify opportunities, clarify problems, and advocate for ideas worth testing.

Opportunity evaluation shouldn’t be reserved just for leaders. In fast-moving environments like AI, the more people who think critically, the stronger your team becomes.

✅ Think Proactively

If your team isn’t spotting and addressing meaningful problems, someone else eventually will. The key is to stay a few steps ahead:

• What repetitive tasks can your team automate?
• What new capabilities could your team unlock by offloading lower-value work?
• How can you disrupt your own workflow before someone else does it for you?

✅ Shift Your Mindset

AI-first doesn’t mean replacing people. It means freeing them up to focus on higher-value work. Ask yourself:

“If my team automates repetitive, low-impact tasks, what kind of higher-value, creative, or strategic work could we pursue?”

AI-first teams will increasingly be expected to deliver greater impact without significant resource expansion. Adopting an intentional, opportunity-first mindset helps your team thrive, not just survive, in this new era.

Ready to Put It Into Practice?

Try the 7-Pillar Scorecard with your team this week. Use it during a planning session, pitch review, or async doc to evaluate an idea you’re already considering.

📥 Want to try it with your team? Download the Google Sheets version of the 7-Pillar AI Opportunity Scorecard to score, compare, and prioritize ideas in real time.

Start small. Pick one opportunity. Walk through the pillars. See what surfaces.