Don’t Be Great at the Wrong Thing
In 2015, Steph Curry broke the NBA record for three-pointers in a season. In 2016, he would go on and break the record again. The Warriors went 73-9. And across the league, something quietly shifted. Mid-range specialists who had been among the most valuable players in basketball started watching their contracts shrink.
Not because they got worse. Because the game’s understanding of where value was created changed underneath them.
For decades, the mid-range jumper was the backbone of NBA offense. Reliable, repeatable, and rewarded. Then the math caught up. Analytics showed that a three-pointer, even at a lower shooting percentage, generated more points per possession than a mid-range two. Teams that understood this restructured their offense around the arc. Players who had built entire careers on a skill that was genuinely excellent found that excellence was no longer the question. Relevance was.
Professional services firms are standing in a similar moment. The model has worked for years: large enterprise engagements, multi-year transformations, teams of consultants delivering complex implementations. That work requires real expertise and creates real outcomes. And firms are investing in AI to get even better at accelerating delivery, automating documentation, and compressing project timelines. The skill has never been higher. The efficiency has never been sharper.
But that efficiency is pointed at a category of work the market is repricing. As AI collapses the cost of creation, it is not just changing how firms deliver. It is changing what clients need delivered. The largest pool of unaddressed demand is not at enterprise scale. It is emerging at the individual and team level, in problems that were never large enough to justify a traditional engagement.
Firms are using AI to get better at the mid-range jumper while the game is moving to the three-point line.
The Pattern Nobody Expects
There is an economic concept called Jevons Paradox that keeps showing up in technology. When something gets cheaper to use, we do not use less of it. We use more. A lot more.
You have already seen this play out. Access databases held together with formulas became the backbone of departments IT never planned for. Excel files turned into mission-critical systems overnight. Power Platform apps bypassed procurement entirely. Now natural language prompts produce working software in minutes. Every wave made creation cheaper. Every wave made more people creators.
When creation gets cheaper, the number of creators grows. When the number of creators grows, the range of problems worth solving expands. And when that range expands, the people who used to be the only ones capable of doing the work have to answer a harder question: where does our expertise matter now? In technology, this cycle has repeated with every generation of tools.
AI is the steepest wave yet. The cost of building something useful is collapsing faster than any previous cycle. That does not mean less gets built. It means more gets built, by more people, in more places, solving problems that were never worth the cost of a formal project.
What Abundance Actually Creates
When cameras got cheap enough to put in phones, the value moved. Not gradually. Almost immediately. Instagram, cloud storage, the creator economy. All of it was built on top of what cheap creation made possible. The opportunity was not in making better cameras. It was in what people did once photos were everywhere.
New markets form downstream from the cost collapse, not inside it.
That is the pattern. When creation gets cheap, new markets appear in the space between what people can now build and what they need to run it at scale. The value does not stay with the creation. It migrates to what comes after.
AI is making it possible for people inside organizations to build solutions that used to require a team of consultants. Microsoft’s 2024 Work Trend Index found that 78% of AI users are bringing their own tools to work, often without their company’s knowledge or guidance. The downstream markets are already forming. The question for professional services firms is whether they will play a role in them.
Where the Value Compounds
The real unmet need lives at the individual and team level. Not enterprise scale.
Think about the analyst spending forty-five minutes formatting a weekly report. The team lead manually routing approvals through email. The operations manager copying data between systems every Monday morning. None of these problems would justify a statement of work. And every one of them is a drag on a KPI someone is already accountable for.
These are the five-minute saves. One person figures out how to automate a repetitive task and gets back a small piece of their week. Individually, they look like rounding errors. But they compound. One person shares the approach with three teammates. Within a quarter, a team is recovering hours per week. Not because anyone mandated an AI initiative, but because the savings stacked along a metric that already mattered.
Five-minute saves do not show up on an AI adoption dashboard. They show up on the KPIs of the organization. Making them easy to miss and hard to argue with once you start counting.
Those saves do not stay small. An automation that works for one person spreads to a team because the director sees it and wants it running across a division. Nobody greenlit an AI project. A KPI that was already on the dashboard just started moving.
This is also the moment those solutions hit a wall. The scaling cliff: the gap between something that works for an individual and something hardened enough to run reliably at organizational scale. Security, compliance, uptime, integration. These are not skills the average knowledge worker has or should be expected to develop. But they are the difference between something that helps one person and something an organization can depend on.
The demand is real. The KPI impact is real. And the gap between a working prototype and a production-grade tool is exactly where professional services expertise belongs. Not building the initial solution. Helping it survive contact with the rest of the organization.
That is not implementation work in the traditional sense. It is the next practice area.
What to Do Now
The instinct is to dismiss this as work that is too small to matter. But your clients are not calling you about these problems precisely because they have already half-solved them, which is why the opportunity is invisible from inside your current pipeline.
Three moves to make:
1. Find what your clients stopped calling you about
People in your accounts are building things that work for one person and struggling to make them work for a team. Find those scaling cliffs. Connect them to the KPIs those teams already report on. That is your signal.
2. Run an engagement where you don’t build anything
Look at what your last implementation left on the table. The problems too small to scope are now compounding across teams. Those are KPIs waiting to move. Help the client scale what they have already started building on their own.
3. Design the model that replaces your best one
Demand is moving to problems your current model does not reach. Pull your last four quarters and sort by engagement type. If more than 80% is enterprise implementation, that is a concentration risk that healthy revenue makes easy to ignore. Design one engagement this quarter that serves the individual and team level profitably.
This changes how you get paid. Five-minute saves do not justify six-figure statements of work. But five-minute saves compounding across a division, tied to KPIs with executive attention, justify something more durable, recurring relationships priced on outcomes, not hours. You do not need the model fully formed. You need one proof point where the commercial structure follows the value.
The largest pool of unaddressed demand is not at enterprise scale. It is in the five-minute saves your clients are already making without you.
The Game Already Changed
Steph Curry did not kill the mid-range jumper. He just made the scoreboard impossible to ignore. AI is doing the same thing to professional services. People inside your client organizations are already building, already saving time, and already hitting the wall when they try to make it work beyond themselves. The firms that help them cross that wall will define what professional services looks like next. The rest will be waiting for enterprise engagements that get smaller every quarter.