AI is doing something odd to professional work. It isn't only automating tasks. It is exposing what those tasks were really for.
For years, many knowledge workers have been rewarded for producing things. Reports and briefs, decks and memos, meeting notes, market scans, prospect lists, the recommendation at the end. A lot of that work looked like analysis because it arrived in analytical packaging. It had the headings and the charts and the right tone, and it moved through the machinery of government or business with enough polish to be taken seriously.
But much of it was never analysis in the deeper sense. It was collection, compilation, formatting, and the steady translation of one bureaucratic dialect into another. AI is now very good at that layer. It can summarize a report, draft a briefing note, compare two markets, build a first version of a trade mission agenda, turn meeting notes into follow-up actions, sketch a stakeholder map, and write the polite email nobody wanted to write. It does in minutes what used to take the better part of an afternoon.
That does not make people less important. It makes the actual human contribution harder to fake.
We are all analysts now. Not because everyone needs to retrain as a data scientist, and not because every economic development officer should be building machine-learning models or memorizing prompt techniques. The point is simpler than that. In an AI-enabled workplace, the premium moves from producing material to interpreting it. What matters is judgment: knowing whether the output makes sense, knowing what has been left out, and understanding the institution, the market, the politics, the company, the relationship, the timing, and the cost of getting it wrong. The job is shifting from "make me a document" to "tell me what this means."
That shift will land hard in economic development, trade, and investment attraction, and in the diplomatic side of this work, because these fields have always run on a mix of information and judgment. A good practitioner needs facts, but the facts rarely answer the real question on their own. Should we prioritize this market? Is this investor credible, or just fishing for incentives? Is this mission worth the minister's time? Does this university partnership have any commercial value, or only a nice photo? Is this export opportunity real, or a pleasant conversation that goes nowhere? AI can help prepare the material. It cannot own the judgment.
The AI sandwich
Picture the workflow as three layers: human, then AI, then human. The first layer is human judgment: deciding what to ask, what context to bring, what sources to trust, and what the real question actually is. The old programmer's warning applies — garbage in, garbage out. The AI is the filling, doing the heavy lifting of gathering, drafting, building, and polishing. Then human judgment closes the sandwich: assessing what came back, catching what was missed, and testing whether the output holds against the political, commercial, and institutional realities that no model fully holds.
The trouble is that a lot of organizations are fixated on the bread. They want to know which tools to buy, which prompts to write, which subscriptions to pay for, and how many hours they can save. Those are fair questions, but they are not the central one. The central question is whether the human layer is strong enough. If the person in the middle has poor judgment, AI will not rescue the work. It will help produce weak thinking faster.
What the evidence shows
We now have evidence that this is how the technology behaves in practice. Researchers at BetterUp Labs and Stanford have started using the term "workslop" for AI output that looks polished but carries little substance, and their finding is that AI doesn't simply level performance. It amplifies whatever is already there. Skilled people can use it to extend their expertise, while less-skilled people are more likely to generate passable-looking work that hides shallow reasoning and slows their own development. In their survey, 41 percent of workers said they had received workslop from a colleague, and each instance cost roughly two hours to sort out.
The same pattern turns up where you can measure it precisely. Salesforce's own engineers, writing about the surge in AI-generated code, found that their file-by-file review process started to break down: reviewers could no longer reconstruct the intent behind changes that touched backend logic, configuration, tests, and the interface all at once. Separately, a study by CodeRabbit across hundreds of open-source projects found that AI-assisted pull requests contained about 1.7 times as many issues as human-written code, with the sharpest rise in logic and correctness errors, even as the raw volume of work went up. More output, more polish, and more problems buried underneath.
AI is going to make mediocre work look better. The surface improves while the substance gets riskier.
A thin market scan can now be formatted beautifully. A generic investment pitch can sound confident. A vague strategy can be written in clean, persuasive prose. A briefing note can have exactly the right structure while quietly skating past the hard question. The output looks professional without being good.
Risk and opportunity
For economic development professionals, this cuts both ways. The risk is easy to describe. Teams get overconfident because the first draft already looks finished. Junior staff skip the slow work of actually understanding a market. Senior leaders receive slicker documents with thinner thinking inside them, and agencies mistake a productivity gain for a strategic one.
The opportunity is larger. AI can strip a lot of low-value friction out of the work, the repetitive drafting and formatting and summarizing that eats time without producing insight. It can help a small team reach further, give a regional agency better first drafts, let a trade officer prepare faster, help an investment team organize messy information, and help a diplomat get up to speed across an unfamiliar sector.
But that value only shows up if the human spends the recovered time on better analysis, which mostly means asking better questions. What is actually changing in this market? What does this investor need that we can credibly offer? Where is the gap between our public narrative and our real assets? Which relationship matters most right now? What is the next move that would build momentum? What would make this opportunity more investable, more exportable, or more politically credible? Those are human questions, and they are the ones that will define the profession.
Economic development is often presented as a toolkit: business retention, export promotion, investment attraction, workforce partnerships, site readiness, incentives, trade missions, aftercare, cluster development. The toolkit still matters, but AI will make many parts of it easier to reach. The differentiator becomes the practitioner's ability to decide which tool fits, when to use it, and what the output actually means. That is analysis. Not analysis as a forty-page report, but as disciplined interpretation: connecting information to action, knowing when the obvious answer is wrong, and knowing what cannot be found in any document.
Where judgment still wins
A trade mission is a good test. AI can help build the country brief, identify stakeholders, draft the meeting requests, summarize sector trends, prepare company profiles, and set up a follow-up tracker. That is genuinely useful. But the real value of the trade professional sits in a different place: deciding whether the mission has a reason to exist at all. Is the timing right? Are the companies ready? Will the right buyers actually be in the room? Does a policy barrier need to be cleared first? Will anyone follow up once the reception ends? AI can help run the mission. It cannot care whether the mission is worth running.
Investment attraction works the same way. AI can help build a target investor profile, summarize sector trends, draft outreach, and assemble a value proposition. What it cannot fully judge is whether a region is genuinely credible for that investor. It does not know from experience that a particular company is unlikely to move, that a ministerial meeting is premature, that a promising "lead" is really a consultant shopping jurisdictions for the best deal, or that the workforce story reads far better in a slide than it holds up on the ground. The practitioner still has to tell the difference between a lead and a distraction.
The diplomatic side of this work may be where the line is sharpest. AI can produce a competent summary of a bilateral relationship, point to areas of possible alignment, and draft talking points. But the work that Foreign Service Officers, Trade Commissioners, and embassy-based trade and investment officers actually do is not mostly about content. It runs on timing, trust, hierarchy, protocol, competing interests, and the ability to hear what is being signaled without being said outright. The human layer matters here because relationships are not databases. We map that craft in Commercial Diplomacy and Relationship Tradecraft.
This is why the practitioner of the next decade needs to become more analytical, not less. The job is not to outwrite AI, which is a losing game. The job is to direct it, question it, correct it, and apply it. The practitioner shifts from producing documents to editing reality, deciding what matters, what is noise, what is missing, what is credible, and what should happen next.
Building the muscle
That calls for a different kind of training. Most organizations do not need another generic AI webinar. They need to build analytical muscle: people who can use AI without handing their judgment to it, and who can get from output to insight. In practice that means learning to frame a problem clearly, separate facts from assumptions, recognize weak evidence, read markets and institutions, spot the missing stakeholder, test a strategic narrative, connect local assets to global trends, write for the person making the decision, and know when not to use AI at all.
That last point matters more than it first appears. The strongest professional will not be the one who uses AI for everything, but the one who knows where it belongs in the workflow and where it does not. Some work should never go into external tools: confidential commercial information, sensitive government advice, personal data, cabinet material, and the relationship context that depends on discretion. And there are moments when the fastest AI-assisted answer is simply not the best one. Judgment includes restraint.
The goal is not to make every professional more machine-like. It is to use the machines to make room for the more human parts of the work: interpretation, context, ethics, relationship judgment, strategic imagination, and accountability for the call that gets made. That is not automation replacing the profession. It is automation raising its standard — but only if the people in the middle are good.
Picture a small regional team producing sharper market scans, stronger investor briefs, cleaner stakeholder maps, and more reliable follow-up without adding headcount, then spending the time it saved talking to more companies, learning the local assets more deeply, building real university relationships, testing opportunities with investors, and briefing leaders with more confidence.
The scarce ingredient is the bread: the human judgment that knows what to ask going in — and what the answer is actually worth coming out.
AI is becoming a commodity, and everyone will end up with access to similar tools. The future does not belong to the professionals who simply know how to prompt, because prompting will soon be ordinary. It belongs to the ones who know what they are asking for, and what to do with what comes back.
These fields are entering a period where the baseline output gets better quickly. More people will produce an acceptable first draft. More organizations will look sophisticated. More strategies will read as polished. The real difference will sit underneath: who has judgment, who can see the whole board, who can catch the weak assumption, who can connect the university to the investor and the ministerial priority to the market opportunity, and who can tell activity apart from progress. Those people become more valuable, not less.
AI will change the work, but it will not remove the need for professionals who can think. If anything, it makes them easier to spot. We are all analysts now.
Sources & notes
Figures cited above are drawn from the studies named in the text. This feature applies Doyen interpretation to draw out what they mean for economic development, trade and investment teams.