Copilot Catalyst Lesson 8: Curate a Business-First AI Strategy, Not an AI-First Business Strategy
You know the feeling. You get a new tool and suddenly you feel like you have the solution to every problem in your hands. The pressure washer means the driveway needs cleaning. Then the deck. Then the siding. Then, inevitably, you’re eyeing the neighbor’s fence.
The tool starts creating the tasks. It’s human nature.
When a new capability shows up, we go looking for places to use it. It’s not hard to find examples of this pattern in every technology wave from CRM systems to cloud migrations to new communication upgrades. The organization gets access to something powerful, and the instinct is to find applications for it.
AI adoption at a lot of enterprise companies is following a similar script. A company gets access to Copilot or a similar LLM and the first question is: “Where can we use this?” The team brainstorms applications, pilots spin up wherever someone has enthusiasm, and the technology goes looking for problems to solve.
This isn’t wrong, exactly. But for established enterprises, it almost always leads somewhere familiar: scattered experiments, unclear ROI, and the kind of budget conversations we explored in the last lesson.
We at FlexPoint Consulting have found the better question isn’t “Where can we use AI?” It’s “What’s broken, slow, expensive, or frustrating in how we work today?” If we can ask that question first, we can better answer whether AI is the right fix.
The Tool-First Trap
New technology triggers excitement. That excitement often obscures the operational discipline required to actually use it. For organizations, it comes back to a basic question: do you know what you’re targeting and how it will impact your business?
We saw this in our Copilot Catalyst rollouts. Early on, when we asked teams to identify their AI use cases, they would brainstorm features. “Copilot can summarize emails. It can draft documents. It can create presentations.” This kind of exploration is fine when people are first learning the technology. In fact, it’s helpful, but those are capabilities, not business problems. Before you start rolling out pilots, it is useful to get a handle on what those business problems actually are independent of the technology.
One VP told us her team spent four hours every Monday morning compiling a multi-source status report that executives skimmed for thirty seconds and put in archive. It was an important historical document with KPIs that leadership thought should be captured, but the repetitive, multi-person, copy/paste/reformat effort was disrupting key work that could be done so early in the work week. Chief AI Officer at Dell Technologies, John Roese found something similar: their salespeople spent 40 percent of their time preparing for meetings, not meeting with customers, because content was fragmented across multiple tools.
These weren’t AI problems. They were business problems. AI happened to be one possible solution.
Put on the AI Blindfold
When you look at AI use cases without looking at business problems first, you see all the things AI can do. Whether those things are what the business actually needs is a completely different question.
We once saw a suggestion to use an AI feature that automatically updated someone’s Outlook calendar when their vacation approval request was approved. It was a simple feature that would have been easy to build, and it would be nice to have. The problem was that nobody asked for it.
Meanwhile, in similar organizations, departments can have workflows collapsing under the weight of technical debt, historical precedent, and vestigial steps from processes modified long ago. These are the friction points that actually slow organizations down. They’re also harder to see, harder to fix, and harder to demo in a meeting. The calendar sync is easy to show. It’s the three-department, monthly report build that requires explaining history, politics, and process debt that tend to get pushed to the back burner.
That’s when we started running the AI Blindfold exercise.
We ask leadership teams to forget AI exists. No Copilot. No ChatGPT. No automation. No agents. We ask them to look at their operations as if it were 2016 and these tools hadn’t arrived yet. Then we ask three questions:
Where does work get stuck? Not slow. Stuck. Where are the handoffs that create bottlenecks? Where do approvals sit for days or weeks? Which are the processes that depend on one person who’s always overloaded?
Where do smart people do work beneath their capability? These are the tasks that don’t require judgment but consume time. It’s the controller manually copying numbers from one spreadsheet to another because the systems don’t integrate, or the analyst reformatting the same chart for the fifth executive who wants it slightly different.
Where does quality suffer because there’s no capacity? It’s the report that goes out without a second set of eyes because there wasn’t time for review. It’s the customer who waits an extra day for an answer because the queue is too deep.
These questions produce a different kind of list. Not a catalog of AI features, but a map of pain points. Only after that map is drawn do we take off the blindfold and ask: are these problems AI can help solve?
The answer is often yes, but not always. Sometimes the bottleneck is a governance issue. Sometimes it’s a bad process that technology will only automate faster. Sometimes it’s a staffing problem masquerading as an efficiency problem. The blindfold exercise reveals what’s actually worth solving before you get distracted by what’s technically possible.
Why This Works for Enterprises
“AI-first” is a viable approach for startups. When you’re building from scratch, it makes sense to design around AI’s capabilities. You don’t have legacy processes. You don’t have thousands of employees with established workflows. You don’t have quarterly earnings reports going out to Wall Street. A new business can architect the whole operation around what the technology enables.
Established enterprises are in a different situation. They have existing infrastructure, regulatory obligations, institutional knowledge encoded in how things currently work, and employees whose jobs are defined by processes that evolved over years. These aren’t just obstacles that can be bulldozed; they are a company’s operating reality. True transformation doesn’t ignore this foundation.
A business-first AI strategy doesn’t ignore technology. It refuses to let technology drive the conversation before the business problem is clear.
It starts with the pain map. Run the blindfold exercise with your leadership team and get specific. “Reporting is inefficient” doesn’t tell you a lot. “Our monthly variance analysis takes three days because controllers manually pull data from four systems, then spend another day reformatting everything for the CFO’s preferred layout” tells you exactly where intervention could help.
Once you have the pain map, evaluate solutions without playing favorites. Look at each problem and ask: what’s the simplest fix? Maybe it’s AI. Maybe it’s better integration between existing systems. Maybe it’s a process change that doesn’t require technology at all. Maybe it’s just good old-fashioned automation. The answer that survives scrutiny is the one that solves the problem most effectively, not the one that uses the shiniest technology.
When you do choose AI solutions, let the business outcomes drive your portfolio. The projects that should get prioritized are the ones that address real friction and have measurable outcomes. That’s what it means to have a business-first AI strategy.
The Takeaway
When you are rolling out a new AI product, remember the goal is not “use AI.” The goal is to solve business problems.
AI is just one tool among many. Sometimes it’s the right one. Sometimes it’s overkill for what you’re trying to fix. The balance can be found by when you put on your AI blindfold, find the friction, map the pain points, then take off the blindfold and ask what solves each one.
Up Next
This wraps the eighth and final lesson from our Copilot Catalyst rollouts. Next, we’ll step back and look at how all eight lessons connect, what patterns emerged across the series, and what we’d tell someone starting from scratch today.
If you’re in the middle of your own AI rollout, take what fits and leave what doesn’t. Every organization is different. What matters is building the capability to adapt as the technology and your business evolve together.
If something here resonated, or if your experience tells a different story, drop it in the comments. We learn from those conversations as much as from our own work.
And if you want help navigating this, the team at FlexPoint Consulting is always happy to compare notes. Reach out anytime.