If you haven’t checked out our previous blog post on why your AI strategy will actually matter in 2026, do yourself a favor and start there. I breakdown the AI gap, the two types of AI that matter heading into the New Year, and what a modern AI strategy should look like.
This second part is about the how.
- How to decide where AI should actually help your team
- How to turn real business problems into clear use cases for AI
- How to choose tools, roll them out, and know if they are working
We’ll walk through a simple, step-by-step framework – starting with a real problem, then moving through how to define outcomes, evaluating tools, mapping out adoption and finally, measurement tracking. The goal is to give you a tactical guide to building a strategy your team can actually run that helps people do better work, faster.
The 2026 AI Strategy Framework
Step 1: Start With a Real Problem
Most AI initiatives fail because the problem is weak.
If your strategy sounds like:
- “We want to use AI more.”
- “We need automation.”
- “Everyone else is adopting AI.”
You don’t have a strategy. You have anxiety.
A real problem statement is specific, measurable, tied to performance, and grounded in outcomes.
Example:
- Bad: “We want sales to be better.”
- Good: “It takes five months to ramp reps and top performers behave differently than the rest.”
Having spent 25 years in product I can confidently say: the best strategies, roadmaps, and products are obsessed with the problem and hold the solution lightly. When teams fall in love with tools instead of outcomes, they optimize in the wrong direction. When they fall in love with (and fully understand) the problem you are trying to solve, the right solutions tend to reveal themselves, sometimes with AI, sometimes without it.
Step 2: Define Outcomes Before You Choose Tools
If you can’t measure success, AI can’t deliver it.
In 2026, winning AI strategies won’t be built on experimentation, they’ll be built on execution discipline. That starts by defining clear goals and outcomes and tying AI directly to the business metrics that drive growth.
Good outcomes sound like:
- Reduce ramp time by 40%
- Increase close rate by 12%
- Improve forecast accuracy to ±5%
- Raise proficiency from 62% to 80%
Avoid vague, unmeasurable goals, like:
- “Improve productivity”
- “Increase efficiency”
- “Adopt AI”
- “Automate workflows”
AI is not the goal. Business results are. Huge fan of “Measure What Matters” by John Doerr if you want to go deeper. I can talk about setting meaningful goals for hours.
Step 3: Map the Skills That Drive Results
AI doesn’t improve businesses on its own, people do. Technology only creates impact when it changes behavior, strengthens capability, and improves decision-making. That’s why the most important question isn’t “Where can we apply AI?” but rather “Which skills actually move our business forward?”.
Organizations don’t fail because they lack tools; they fail when execution breaks down at the human layer.
Skill gaps become performance gaps.
Performance gaps become revenue gaps.
But the deeper truth most teams avoid is this: no meaningful improvement, whether in a product or a team, happens without getting honest about two things:
- What “great” actually looks like
- An honest and humble view of where you truly are today.
Not where you hope you are. Not what your dashboard suggests. But the reality of how work is getting done when no one is watching. Real change starts by clearly defining the desired state and mapping the current state. Without that gap analysis, transformation is just theater. You can’t improve what you won’t name, and you can’t mature what you refuse to measure.
When leaders can’t clearly define excellence, when they don’t understand where behavior breaks down in practice, or when they can’t pinpoint the actions that truly move the needle, AI doesn’t fix the problem, it simply accelerates it. Automating without understanding the skills that drive outcomes doesn’t lead to better performance; it leads to faster mediocrity.
You end up scaling whatever already exists, whether that’s excellence or dysfunction.
Step 4: Evaluate Tools Through Impact and Security
Choosing AI tools for your team has never been easier OR riskier.
Demos are seductive. Dashboards are polished. Generative outputs are impressive. And every vendor promises transformation. But if you’re serious about performance, not appearances, your filter has to be brutal and objective.
AI buying decisions aren’t about features. They’re about impact. What you add to your stack will either sharpen your team or slow it down.
When you evaluate any AI tool, don’t ask if it looks impressive. Ask if it actually changes the way your team works:
- Does it reinforce better behavior—or just analyze it?
- Does it save time—or quietly create more “hidden” work?
- Does it integrate—or isolate?
- Does it live where users live—or is this another adoption problem waiting to happen?
If a product can’t answer those questions clearly, it’s not leverage. It’s friction.
Also remember that choosing AI isn’t just a product decision, it’s a data decision. Every tool you bring into your revenue stack is touching sensitive conversations, performance insights, and customer information, which means leaders must ask hard questions about privacy, model training, and data storage from day one.
- How is data encrypted?
- Where is it stored?
- Is customer data used to train models?
- Who can access it?
Too many teams assume “enterprise-ready” without verifying it. And the truth is, not all AI vendors are equally mature when it comes to security. Some are building with rigor; others are patching trust on later.
Red flags show up quickly if you know where to look: vague answers about data usage, unclear retention policies, no formal compliance framework, or a lack of documented controls. If a vendor can’t clearly explain how your data is protected, they’re not ready to be trusted with it.
At Luster, data protection isn’t a checkbox—it’s part of our culture. Choose vendors whose leadership you trust with the data behind the humans who generate it.
Step 5: Design for Adoption
AI doesn’t fail because the technology falls short. It fails because it gets treated like an add-on instead of a capability. Too many teams roll out tools with the assumption that value appears automatically once software is “live.” In reality, that’s where the work actually begins.
For the last half of my career after 10+ years in product design and user research, I’ve intentionally only built contextual products that bring experiences to users where they already are - inside their calendars, inboxes, chats, workflows, and systems of record. Because context is everything. A tool buried in another tab is ignored; a tool embedded in daily life is used. The closer AI gets to the real work, the more valuable it becomes.
What drives adoption:
- Design for the Flow of Work: Embed AI where teams already operate (email, chat, CRM, calendar). Tools outside daily workflows don’t get used. Context drives adoption.
- Enable Leaders First: Managers must model usage, coach with the tool, and reinforce it weekly. If leaders don’t use it, teams won’t either.
- Create Fast Feedback Loops: Launch with a pilot group, review usage data weekly, and actively collect user input. Optimize quickly or kill what’s not working.
Adoption isn’t a vanity metric. It’s the clearest signal of value you have.
Step 6: Measure, Learn, Update
You don’t measure AI. You measure impact. If you aren’t tracking what’s changing because of AI, you’re not measuring strategy, you’re telling a story. Real AI measurement starts and ends with performance.
Look at whether behavior is actually changing.
- Track improvements in proficiency and decision-making.
- Measure whether teams are moving faster and producing higher-quality work.
- Pay attention to risk reduction, time saved, and revenue movement.
Then go one step further: move beyond vanity metrics and anchor measurement in outcomes. That means returning to your original goals and building a cadence around leading and lagging indicators. I personally look every two weeks, every month and every quarter at various metrics where appropriate. Decide what makes the most sense for your team.
Are you getting closer to what you set out to achieve, or drifting further away? Is the problem you set out to solve shrinking or just shifting shape? Is the goal the right goal or have you learned something new and need to adjust?
AI strategy isn’t static. It’s a system of rapid learning. Encourage experimentation, pay attention to what works, and adjust quickly when it doesn’t.
Why We Built Luster This Way
We built Luster around a single belief: AI should make skill measurable, behavior predictable, and improvement inevitable.
We believe behavioral skill improvement, not software, is the real competitive advantage. Behavior is the earliest signal that something is working or breaking. And measurement is what turns learning into leverage instead of noise.
That’s why Luster doesn’t stop at analysis. It’s built to diagnose performance, predict risk, prescribe action, and reinforce execution—systemically, not once. We designed Luster so AI doesn’t just observe work. It actively helps teams do better work, faster.
Final Thoughts
AI does not create impact on its own. People do. A real AI strategy is one that helps them show up better and faster in the moments that matter.
When you get clear on the problem you are solving, define the outcomes that count, understand the skills that drive results, choose tools that truly support the work, design for adoption, and measure what changes, AI becomes more than technology. It becomes part of how your team performs.
If you are looking for AI that actually supports measurable improvement and not just another logo in the stack, get in touch with Luster and see how Predictive Enablement™ can move the numbers that matter.
