Someone on my team asked me recently where business intelligence goes when most of its work can be automated. They had already worked out the premise. When a dashboard that took two weeks takes an afternoon, and soon a sentence, the value stops living in the dashboard. If your job description is a list of artifacts, that’s a reasonable thing to lose sleep over.
My answer was that the field is about to grow, by a lot. Business intelligence moves from reporting to understanding, at a fundamental level, how the business works and how leadership’s strategy shapes its outcomes. That has no ceiling. We will need far more people creating understanding than we ever needed producing calculations on a report.
That answer sounds like the thing a manager says to keep people calm, so it needs defending. And the question is bigger than BI. Swap in a junior lawyer and a research memo, an accountant and a variance analysis, a consultant and a deck, a marketer and a campaign brief. It’s the same question, and almost nobody is answering it for them.
The conversation has been about code
Nearly everything written about AI and the future of work is written about software developers. That makes sense as a starting point. Developers adopted these tools first and hardest. In Anthropic’s January 2026 Economic Index, computer and mathematical tasks made up about a third of Claude.ai conversations, against 15% for education. The commentary follows the usage.
The software conversation has also produced some sharp thinking. Camille Fournier’s The Manager’s Path in the Age of AI puts the shift in four words: “Nowadays? Code is cheap.” When code was expensive, working code was evidence that someone had thought hard about the problem. Cheap code stops being evidence of anything, and what becomes scarce is alignment between the people doing the building.
Replace “code” with any other knowledge-work artifact and the sentence still holds. A contract draft, a financial model, and a forty-slide deck with a clean storyline are all cheap now. Most of the people producing those things don’t have a Fournier writing about their profession, and they’re working out the answer on their own, usually by watching the tool do in ten minutes what used to fill their Tuesday.
A job description is a list of artifacts
Knowledge work has always defined itself by what it hands over. Lawyers produce memos, opinions, and agreements, and accountants produce statements and reconciliations. Consultants hand over decks, analysts hand over reports, and HR hands over policies and job descriptions. Those are the things that get billed, reviewed, filed, and counted. Ask someone in any of those roles what they did this week, and they’ll answer with a list of documents.
The artifact became the unit of work because it was expensive. It took real hours, and you couldn’t produce one without doing the thinking behind it, so the artifact stood in for the thinking. I wrote earlier this year about what that did to hiring: every résumé and portfolio reads output and infers the judgment behind it, and that inference stopped holding once output got cheap.
The same substitution runs through the rest of working life. We plan, staff, and price work in artifacts. So when the artifact gets cheap, the instinct is to conclude the job got smaller. That conclusion only holds if the artifact was the purpose of the work, and in most knowledge work the artifact was the bottleneck.
The queue was the ceiling
Every knowledge-work function runs a queue. In BI, a request means modelling data, writing measures, building visuals, and reconciling totals against what finance already reports. That takes weeks, so teams ration. They build what was asked for and rarely reach the question behind the request: why margin slipped after a pricing change, or whether the strategy leadership announced in January shows up anywhere in the data by June.
Other functions have the same shape with different nouns. A legal team answers the question the client asked, because answering the question behind it would take another forty hours nobody approved. A finance team spends the first part of every month closing the books and much of the rest explaining last month, so the analysis that would change next quarter waits. Production cost has set the limit on how far anyone could go into the substance of their own profession.
Remove that cost and the limit moves. It moves to how well people understand the system they work in, and that limit has no top. There is always another layer, whether it’s which levers move which outcomes or how a client’s commercial position shapes the risk they should accept. The deepest is how a decision made in a leadership meeting travels through operations until it shows up, or fails to, in a quarter’s results.
History has run this experiment before. Spreadsheets took the arithmetic out of accounting, and the US still projects the profession to grow. The Bureau of Labor Statistics expects accountants and auditors to grow 5% between 2025 and 2035, while bookkeeping and accounting clerks shrink 6%. It expects the clerks who remain to shift “toward analysis and advisory roles.” ATMs are the other well-worn example. James Bessen found that tellers per urban branch fell from 20 to 13 between 1988 and 2004. Branches got cheaper to run, banks opened 43% more of them, and teller jobs did not decrease as ATMs rolled out. The work moved from counting cash to selling and serving customers.
David Autor gave the reason in a 2024 NBER paper: information “is merely an input into a more consequential economic function, decision-making.” The artifact is information. The decision was always the point.
What fills the space
Some of the best evidence on what happens when you hand knowledge workers these tools comes from outside software, and it gets less attention than the coding studies.
Noy and Zhang gave 453 marketers, grant writers, consultants, data analysts, HR professionals, and managers realistic writing tasks. With ChatGPT, time fell 40% and rated quality rose 18%. Nobody fact-checked the outputs, so read the quality number generously, but the speed result has held up across study after study.
The BCG study is more useful, because it found the edge. In Navigating the Jagged Technological Frontier, 758 consultants worked on realistic tasks with and without GPT-4. On tasks inside the model’s capability, consultants with AI completed 12% more tasks, 25% faster, at more than 40% higher quality. On one task deliberately set just outside it, consultants with AI were 19 percentage points less likely to get the right answer than those without. On a problem the tool couldn’t solve, using it made them worse.
That result describes the new job better than any framework I could draw. The work that grows is knowing where the frontier is for your own domain. You can only know that if you understand the domain well enough to tell a plausible answer from a correct one, which is the understanding the queue never left time to build.
People are already spending the capacity on scope. In Anthropic’s June 2026 survey, 82% of respondents said AI had widened the scope of what they could do, close to the 86% who reported speed gains. Microsoft’s 2026 Work Trend Index found 58% of AI users producing work they couldn’t produce a year ago. A study of P&G professionals, The Cybernetic Teammate, found individuals with AI matched the performance of teams without it. R&D and commercial staff with AI also produced more balanced solutions, where without it each leaned toward its own department. People are reaching past their old boundaries. That’s what a raised ceiling looks like in practice.
The knowledge nobody wrote down
There’s a second kind of work hiding in this shift.
Organizations run on knowledge that was never written down. Autor called the general version Polanyi’s paradox, after Michael Polanyi’s line that “we know more than we can tell.” Inside a firm it looks mundane. Finance knows March always looks bad and why. A senior lawyer knows which clauses a particular client will fight and which they’ll accept without comment. The BI team knows which of three revenue figures leadership steers by. None of it lives in a system, so none of it is available to an AI.
The legal research shows why this matters. In a 2025 study by Schwarcz and colleagues, law students worked through legal tasks with either a reasoning model or a retrieval tool built on legal sources. Both improved the quality of the work, which earlier GPT-4 studies hadn’t found. The reasoning model also introduced some hallucinations. The retrieval tool, which answered from documents, didn’t. The difference is a model answering from memory against a model answering from the record.
An AI can only read what someone has written down. So a large share of the new work is making the organization legible: writing down how the business behaves, in a form a machine can read and a person can correct.
The platforms are already building for this. Microsoft describes its Fabric IQ ontology, still in preview, as “a shared, machine-understandable representation of your business.” It can be generated from the Power BI semantic models analysts have spent years building, so that agents “use the organization’s defined calculations rather than recreating them.” Microsoft’s Work Trend Index gives the general idea a name, “owned intelligence,” meaning the institutional know-how that compounds over time. In law, it’s the clause library with the firm’s actual negotiating positions and the reasons behind them. In finance, it’s the close procedure with the judgment calls written in.
I think of this as a true semantic layer of knowledge that AI can feed on. Consistent definitions are the start. The rest is a written model of how the organization works, kept current, that every agent reads from. That work never finishes, because the business keeps changing, and it can’t be done by anyone who doesn’t understand the business. AI can draft the layer. Checking the draft against how the business behaves takes the people who have spent years living inside it.
Where the optimism breaks
The growth has a shape, and it isn’t kind to everyone.
Stanford’s Digital Economy Lab has been tracking payroll data for workers in AI-exposed occupations. Its August 2026 revision found employment for 22- to 25-year-olds in the most exposed jobs about 19% below where it would otherwise be, driven by less hiring rather than layoffs. Employment fell where AI automates the work and held flat or rose where it augments it, especially for experienced people. The authors call the findings descriptive, not causal, but the pattern matches what BLS projects for the support layer. Paralegals are projected at 0% growth to 2035, with BLS naming AI as a limit on demand. Even the ATM story has an ending: the World Economic Forum’s 2025 Future of Jobs report lists bank tellers among the fastest-declining roles. The precedent held for a few decades and then didn’t.
The studies show the same split at the level of skill. In the customer support study by Brynjolfsson, Li, and Raymond, AI raised productivity 15% on average, with the largest gains for novices and little effect on top performers. Law students in Choi and Schwarcz’s experiment showed the same pattern: the weakest gained the most, and the strongest got slightly worse. AI compresses the skill premium on producing the artifact. That’s good for anyone who struggled to produce one, and it removes the gap that used to tell a promotion committee who was good.
The harder problem is how people learn. In most professions, producing artifacts is how people came to understand the work. Junior associates learn the law by drafting the memo a partner will rewrite. Junior analysts learn the business by reconciling numbers that won’t tie. The tedium did the teaching. In Thomson Reuters’ 2026 Future of Professionals report, 48% of respondents worried about AI’s effect on developing judgment, and 71% said juniors need structured mentoring. In law, the billable hour makes it worse, because the firm has every incentive to keep that work slow.
Autor and Thompson’s paper on expertise gives the rule for sorting this out. Whether automation helps the people who remain depends on whether it removes tasks that need little expertise or tasks that need a lot. Where it removed the inexpert tasks, wages rose and employment fell. Most knowledge-work artifacts sit in the middle: expert enough to have taught people, routine enough for a model to produce.
So the honest version of my answer has conditions attached. The work grows, but the old way into it narrows, and teams will have to design a new way in on purpose. My candidate is a first year spent explaining variances, deal outcomes, and campaign results to the people whose decisions caused them. And freed time doesn’t turn into understanding on its own. The same Thomson Reuters report found 91% of professionals describing a gap between their AI use and the value it delivers. Those working under a named AI strategy were three times as likely to say it met expectations, 66% against 22%. Capacity goes where the organization points it, and most organizations haven’t pointed it anywhere.
Direction stays with people
The ceiling stays high for one more reason. An AI working from a well-built semantic layer can explain what happened and, increasingly, why. Deciding what the organization should want next means placing a bet and answering for it. Fournier borrows the old IBM line for this: “Machines cannot be held accountable, so they cannot make management decisions.”
The people using these tools already sense where the line sits. In Microsoft’s 2026 survey, half ranked quality control of AI output as the most important human skill, and 86% said they stay responsible for the thinking. Microsoft also found that organizational factors explained twice as much of AI’s impact as individual ones, while only 26% of respondents said their leadership was aligned on AI. Direction is the scarce input, and by that measure most leadership teams haven’t settled on one.
That’s where the understanding work pays off. Strategy is a set of bets about what will matter. Knowledge workers who understand how the business works can show leadership how its last bets played out and what the next ones would have to overcome. A lawyer who understands a client’s business can tell them which risk is worth taking, which is worth more to them than a longer memo. An accountant who understands the operation can say which cost will move if the plan changes. The same holds for a marketer deciding which campaign not to run, or an HR lead deciding which role to create before anyone writes the job description. All of that was possible before, but it was rationed, because the people who could do it were busy producing the artifact someone had asked for in the meantime.
AI can widen the options, test them against the knowledge the organization has written down, and do the production once a choice is made. Choosing what the organization is for, and answering for the choice, is the core of the job, and it was the core of the job before any of this started.
For most of the history of knowledge work, the artifact was the only part of the job anyone could see. Now it’s the part that needs us least.
If you lead a team outside software and you’re working through the same question, reply and tell me how you’re answering it. If you’d like the next essay, subscribe to AndrewLewisWasHere.