The Disconnect

When companies ask "what should we do with AI?" they usually ask the wrong people. Their technical teams know the technology but not the pain points. Their executives have seen impressive demos but can't really evaluate the claims.

This mismatch kills more AI initiatives than bad technology or insufficient budgets. The people generating AI ideas and the people who understand where AI could actually help are rarely the same. They don't talk to each other, and most of the time they don't speak the same language.

AI ideas flow through predictable channels. IT reads about new capabilities. A vendor gives a compelling demo. Someone attends a conference and comes back excited. A board member asks why the company isn't "doing more with AI." These are reasonable starting points, but they share a common flaw: they start with technology and work backward toward a problem.

Meanwhile, somewhere else in the organization, an operations manager manually reconciles data between two systems for three hours every week. A customer service lead knows exactly which calls waste the most time. A sales director can pinpoint the moment in the pipeline where deals stall. A finance analyst rebuilds the same report every month because nobody trusts the automated version.

These people understand problems worth solving. They see inefficiencies and patterns that cost real money. But they don't know how AI could help; or if they suspect it might, they can't articulate it in terms that would get funded.

Technical people have seen what AI can do, but haven't lived with the operational pain. Operational people live with the pain but haven't seen what's possible.

When AI ideas originate from technical teams, they tend to be solutions looking for problems. The team learns about a new capability and searches for somewhere to apply it. This occasionally produces something useful, but more often creates projects that are technically impressive but operationally irrelevant.

Technical teams optimize for what's interesting to build, not what's valuable to solve. A recommendation engine is more exciting than automating manual data entry. Building a custom model feels more significant than configuring an off-the-shelf tool. And technical teams rarely have direct exposure to the friction points that cost money. They see data flows on architecture diagrams, not the three-hour workarounds when those flows break.

The people who understand operational problems rarely propose AI solutions. They don't know what's possible. AI capabilities have expanded dramatically, but that knowledge hasn't penetrated most of the organization. The operations manager doesn't know that AI can handle messy, inconsistent inputs. The customer service lead doesn't realize that conversation summarization is now reliable. The finance analyst doesn't understand that AI can handle the judgment calls that make their monthly report tedious.

There's also a vocabulary problem. Business teams describe problems in operational terms: time spent, errors made, and customers frustrated. Technical teams describe solutions in capability terms: models, APIs, training data. Without a shared language, the connection never gets made.

And there's a credibility barrier. When someone from operations suggests a technical solution, they risk dismissal. "That's not how it works" is a typical response from IT. After a few failed attempts, business teams stop suggesting solutions. They describe problems and hope someone else figures out the answer.

Even when the right problems surface, translation failures block progress. "We spend too much time on customer inquiries that don't go anywhere" is a real problem with real costs. But it lacks the specificity a technical team needs. What kind of inquiries? Through which channels? What does "don't go anywhere" mean? Technical teams need to understand the exact workflow, the data involved, the decision criteria, the edge cases. Getting this requires sustained conversation. In most organizations, it doesn't happen. Technical teams get a brief description, make assumptions, and build something. Months later, the business team says, "That's not what we meant."

Vendors make this worse. They pitch to executives who control budgets, showing demos that work perfectly under controlled conditions and citing case studies from companies with different data, different processes, different problems. Executives struggle to evaluate these claims. The demo looks impressive. The ROI projections seem reasonable. What's missing is anyone who can say, "That won't work here because of X", where X is some specific detail about actual operations. The people who understand those details aren't in the room. They learn about AI initiatives when they're told to participate in implementation, by which point the direction is set.

Getting technical and business people talking is harder than it sounds. They report through different hierarchies with different priorities and metrics. Cross-functional collaboration requires effort neither side is rewarded for. The operations manager doesn't have hours to explain problems to technical teams. The data scientist doesn't have hours to shadow business processes.

Language barriers persist even in the same meeting. Technical teams speak in capabilities. Business teams speak in outcomes. Without translation, meetings produce frustration instead of alignment.

Organizations that bridge this gap share certain characteristics. They create structured forums for business teams to surface problems without specifying solutions—building a library of problems worth solving, described in enough detail to evaluate. They embed technical people in business processes for months, not days, developing operational intuition. They require problem validation before any AI project gets funded—demonstrated by people who live with the problem, not people who heard about it secondhand. They build translation capacity, either through dedicated roles or distributed cross-functional experience. And they accept that most ideas won't work. The goal is finding the few problems where AI is the right solution and the organization is ready to implement.

Executives set the conditions. When leadership measures AI success by the number of projects launched, teams launch projects regardless of value. When leadership rewards technical complexity, teams build complex solutions. When leadership delegates AI strategy entirely to IT, business problems never enter the conversation. The alternative: measure problems solved, costs reduced, decisions improved. Reward cross-functional collaboration. Insist that AI investments start with validated business problems, not vendor demonstrations.

Most companies are sitting on AI opportunities they can't see; not because the technology isn't ready or the budget isn't there, but because the people who know what's possible and the people who know what's needed aren't connected.

Closing this gap doesn't require new technology. It requires organizational rewiring: pathways for problems to surface, translation capacity, and the ability to measure what actually matters.

The best AI ideas are already inside the organization. They're just trapped in the heads of people who don't know they're AI ideas.

If your company is wrestling with AI or data decisions and you need someone who can figure it out, not just advise on it, let's talk: ericbrown.comI

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