This week’s two topics are deeply connected — and they show up in nearly every advisory engagement I take on. Choosing the right AI use cases. And building the kind of culture where responsible AI use becomes the default, not the exception. Let’s get into how I coach clients through both.

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⚡ Quick Wins This Week

A Simple Framework for Evaluating AI Use Cases. Most organizations pick their AI projects the way they pick a movie on a Friday night — quickly, based on what looks interesting, and with very little real evaluation. Then they wonder why the project stalled six months later. This week I’m walking through the use case evaluation framework I teach clients — five categories, ten criteria, and a weighted scoring system that takes the gut feel out of the decision. Creating a

Culture of Responsible AI Use. A policy document is not a culture. You can have the most thorough AI governance framework in the industry and still end up with employees making shortcuts that put your organization at risk — because culture is what people do when nobody’s watching. This week I’m sharing the practical steps I help leaders take to build a culture where responsible AI is the norm, not the exception.

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A Simple Framework for Evaluating AI Use Cases

When a client comes to me with a list of AI ideas — and most of them do — the first conversation isn’t about which one to build. It’s about how we’re going to decide. Because here’s the pattern I see consistently: organizations evaluate AI use cases on enthusiasm. The most interesting idea wins. The use case championed by the loudest executive wins. The one that sounded best in the vendor demo wins.

None of those are evaluation methods. They’re tie-breakers at best. What actually works is a structured, weighted scoring approach. Five categories. Ten criteria. A clear interpretation of what your final score means. Here’s the framework I use with clients:

Business Value (30% weight): This is the most important category, because impact matters most. It breaks into two criteria:

1. Impact Potential (15 pts) — How much does this project actually move the needle on a metric people care about? A 50%+ improvement on a key metric scores a 5. A 5–15% marginal improvement scores a 2 and probably isn’t worth doing as a first project.
2. ROI Calculation (15 pts) — Can you confidently calculate the return? Hard, quantifiable savings or revenue gain scores high. Speculative or hard-to-measure benefits score low.

Technical Feasibility (25% weight): If it can’t actually work, nothing else matters.

1. AI Capability Maturity (15 pts) — Is this a proven solution with established vendors, or are you a beta tester for experimental technology? First projects should never use unproven AI.
2. Data Readiness (10 pts) — Honest assessment: is your data AI-ready right now (5 pts), or does it need three to six months of cleanup (2 pts) before this can even work?

Organizational Readiness (20% weight): Even technically perfect projects fail if the organization isn’t ready to adopt them.

1. Stakeholder Support (10 pts) — Enthusiastic champions actively pushing for this scores a 5. Active opposition or apathy scores a 1. The team begging for the solution is in a fundamentally different place than the team being told it’s coming.
2. Change Capacity (10 pts) — Does the affected team actually have the bandwidth right now? A team in the middle of a reorganization with three other major initiatives scores a 2. A team with capacity, energy, and no competing changes scores a 5.

Risk Profile (15% weight): First projects should be low-risk experiments, not high-stakes gambles.

1. Implementation Risk (10 pts) — Proven approach with minimal unknowns scores high. Pioneering something new scores low. Save the pioneering for Year 3.
2. Brand/Reputation Risk (5 pts) — Internal-only initiatives score 5. Customer-facing, highly visible AI scores 1 — and shouldn’t be your first project under any circumstance.

Strategic Alignment (10% weight): Weighted lowest because for first projects, proving AI works matters more than perfect strategic fit. But it still matters.

1. Supports Current Priorities (5 pts) — Does this directly enable one of leadership’s top three goals this year, or is the connection a stretch?
2. Creates AI Foundation (5 pts) — Will success here enable two or three future AI projects, or is this a dead-end deployment that doesn’t open any doors?

How to interpret your score:

• 45–50 points (90–100%) — Excellent first project candidate. Move forward with confidence.
• 40–44 points (80–89%) — Good candidate with fixable concerns. Address the weak areas before launching.
• 35–39 points (70–79%) — Marginal. Significant challenges exist. Consider alternatives.
• Below 35 points — Not suitable as a first project. Save it for Year 2 once you’ve built credibility and capability.

A note from experience: the use case that scores highest on this framework is almost never the one clients walk in expecting to build. The flashy one usually scores in the low 30s — because data isn’t ready, change capacity is thin, or it’s customer-facing and high-risk. The boring one — invoice automation, document processing, internal knowledge search — scores in the mid-40s. And that’s the point.

The framework tells you to start with what’s most likely to succeed and build credibility, not with what’s most likely to wow leadership in a kickoff meeting. Better to do a 45-point project excellently than a 48-point project poorly.

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Creating a Culture of Responsible AI Use

Here’s a question I ask every leader I work with: if your team had a question about whether they should use AI for a specific task tomorrow morning — and your AI policy document was 30 pages long — would they actually go look it up?

Responsible AI use isn’t enforced through documentation. It’s modeled, reinforced, and woven into how teams already make decisions. Here’s how I help leaders build that:

• Lead by example, visibly. If you want responsible AI use to become the norm, your team needs to see you practicing it. Talk openly about an AI output you double-checked. Mention when you decided not to use AI for a particular task and why. Share a moment where AI got something wrong and you caught it. Leaders who only talk about AI wins create a culture where people hide AI mistakes. Leaders who model thoughtful, occasionally imperfect use create a culture where people surface problems early.
• Create safe reporting channels for AI concerns. When a team member notices an AI output that seems off, biased, or wrong, where does that concern go? If the answer is “they raise it in their next 1:1, maybe,” you don’t have a reporting channel. You have a hope. Build a clear, low-friction way for people to flag concerns — and make sure those flags are reviewed and acted on. If the first three concerns get ignored, no one will raise the fourth.
• Reward the behaviors you want, not just the outcomes. Most organizations celebrate the AI win — the time saved, the revenue generated. Few celebrate the team member who paused a deployment because they spotted a fairness issue. Or the manager who slowed an implementation timeline to do proper change management. If only outcomes get rewarded, people will optimize for outcomes — including by cutting the corners that responsible AI use depends on.
• Integrate AI ethics into your existing values, not as a separate track. If your organization already has a value like “we do right by our customers,” responsible AI use is an expression of that value, not a separate concept. Tying AI ethics into the language and frameworks your team already uses makes it feel like an extension of who you are — not a compliance burden bolted on top.

The leaders who build this culture deliberately spend less time managing AI risk than the ones who rely on policy alone. Because culture catches what policy misses — and culture scales as your AI footprint grows. Policy doesn’t.

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🔍 Deep Dive: Connecting the Two

These two topics — use case evaluation and responsible culture — connect in a way that most leaders don’t see until it’s too late.

The use case you choose shapes the culture you build around it.

If you choose your first AI project on enthusiasm and skip the structured evaluation, you signal to your organization that AI decisions are made on feel. That signal carries. The next 50 AI decisions in your organization will get made the same way — and most of them will be made by people far less senior than you, with even less context.

Conversely, when you walk through a structured evaluation framework openly — when you show the trade-offs you considered, the criteria you used, the use cases you decided not to pursue and why — you teach your organization how AI decisions should be made. You build the muscle for responsible AI use through the way you make your own decisions visible.

This is the through-line I see in every successful AI advisory engagement: the leaders who build sustainable AI programs don’t separate “what to build” from “how we operate.” They use the early decisions to model the operating principles they want the rest of the organization to adopt.

That’s how culture actually gets built. Not in a document. In the decisions everyone watches you make.

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✅ Action Item

This week, pick the next AI decision your organization is facing — even a small one. Before making the call, write down:

1. The five evaluation categories above
2. Your honest weighted score for each
3. The decision you’re leaning toward
4. Whether the framework supports that decision

Then make the decision openly. Walk your team through how you got there. That single moment of transparency does more for building responsible AI culture than a quarterly all-hands on the topic.

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💡 NRM Spotlight

If you’re sitting on a list of AI use cases and not sure which one to start with — or if your organization has launched something and you’re not confident it was the right pick — the AI Strategy Assessment is the structured starting point I’d recommend.

It’s the same readiness framework I use with my advisory clients: a personalized 15-page roadmap based on your specific organization’s readiness across five dimensions, with prioritized use cases that make sense for where you actually are.

→ Take the Assessment

If you want to learn the full use case evaluation framework above — and the rest of the foundational AI leadership skill set — Module One of AI Leadership Essentials is free, no credit card required. The complete use case evaluation lesson is in Module Three.

→ Start Module One free here

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👀 Coming Next Week

Next week we’re getting into one of the most important — and most overlooked — questions in AI strategy:

What do you actually do with the time AI gives back? Most organizations treat efficiency gains as a cost-cutting opportunity. The leaders building real long-term value treat them as something else entirely — and that distinction will define the next decade.

And we’re tackling what “productivity” actually means in an AI-augmented organization. Spoiler: the old definition is going to get expensive.

Both in your inbox next week.

Until then — make your decisions visible! Nikki