Key takeaways:

• • • The dulling effect is real, but the root cause is older than AI — Wherever gatekeeping institutions reward a narrow formula, their output converges long before any chatbot enters the picture. AI then accelerates optimization toward your already selected criteria.

    • Using AI for efficiency alone leaves the creative upside on the table — While most organizations deploy AI for simple drafting tasks, the bigger payoff comes from using it as a discussion engine — a sort of sparring partner that pressure-tests ideas and pushes thinking past the first plausible answer.

    • The highest-leverage intervention is reforming what you reward — The fix for this is upstream of the technology, and it comes from giving people time to make sure unconventional ideas actually survive your organization’s sorting mechanisms.

A tension sits at the center of nearly every serious conversation about AI and organizational strategy, and most leaders can feel it even if they haven’t named it yet.

On one side is the promise that AI can make teams more creative. That it can accelerate brainstorming, provide deeper research, identify hidden connections, and pressure-test ideas before they reach a client or a boardroom. When used well, AI is not a replacement for thinking but an amplifier of it.

On the other side, of course, is the fear that regular AI use is quietly dulling the creativity it’s supposed to enhance. That real fear is that the more people lean on these tools, the more their thinking converges toward the same polished, plausible, and fundamentally safe middle ground — and that the less people work their creative muscles, the more they atrophy without them realizing it. This trade-off, swapping originality for efficiency, is a losing exchange.

Both of these intuitions are reasonable and both are, to varying degrees, correct. However, they aren’t equally weighted. The purely cautious camp is taking the bigger gamble, because any competitor that cracks the problem by figuring out how to capture AI’s creative upside while managing the dulling effect gets both the innovation edge and the efficiency gains. The cautious organization doesn’t just miss the upside, it falls behind on both fronts.

The catch is that cracking the problem requires correctly diagnosing what’s actually killing your creativity — and a prominent recent essay on this exact topic gets it instructively wrong.

A good question, poorly tested

Rebecca Winthrop, a senior fellow at the Brookings Institution and director of its Center for Universal Education, recently published in The New York Times arguing that AI is constricting creative thinking. Her central claim is that while chatbots produce polished language, they’re masking a narrowing range of underlying ideas — and this is especially dangerous for students, whose creative development is still taking shape.

The piece is worth reading, and not just as a foil. Winthrop draws on from Georgetown neuroscientist Adam Green, whose team has been tracking the range of ideas in college application essays before and after ChatGPT’s release. Green’s findings related to the before/after tracking study (which have not yet been peer-reviewed) are striking, finding that while post-ChatGPT essays used more diverse and colorful vocabulary, the ideas beneath that language converged. Human judges rated the AI-era essays as more creative, even though the substance had narrowed. In a separate study by Green’s team, cited by Winthrop, human-written essays contributed up to eight-times more novel ideas than AI-generated ones.

The fear is that regular AI use is quietly dulling the creativity it’s supposed to enhance, and the real fear is that the more people lean on these tools, the more their thinking converges toward the same polished, plausible, and fundamentally safe middle ground — and the less people work their creative muscles.

And Winthrop flags serious concerns that deserve far more attention than they typically get. For example, AI’s homogenizing pressure falls hardest on those students who sit farthest from the mainstream, including neurodivergent students and those from racial and linguistic minorities. That finding alone should be shaping education policy conversations and acting as a warning for innovation-conscious reformers.

Here’s where Winthrop’s piece stumbles, however, and where it becomes a cautionary tale for organizations that may be thinking about their own AI and innovation strategies. The evidence Winthrop chooses to build her case on — the college admissions essay — is possibly the worst genre in American education for measuring whether AI is killing creativity. Because the creativity in college admissions essays was already dead.

I should know. I’m one of its murderers.

The most templated genre in America

The college admissions personal statement has been reverse-engineered for decades. Well before any large language model existed, applicants had cracked the code: Be damaged, but not too damaged; be resilient but make it look like you did it yourself; and be whole now, because the institution wants guaranteed successes, not risky projects. And all of this must be delivered in a tone that makes the committee feel good about their institution’s role in a meritocratic society. Deviate from this formula and you’re taking a risk, but hit every beat and you’re in the pile that moves forward.

I know this because I lived it recently enough to still remember the specific frustration of trying to fit my own experiences into that template at the age of 17, twisting and contorting experiences I’d actually lived through into the shape I knew admissions readers were looking for while sanding away the human beneath when it didn’t fit. The authentic version of my story wasn’t what they wanted, the version that hit the beats was.

And there’s a further detail conspicuously absent from Winthrop’s essay: The college admissions consulting industry. It’s enormous, it’s been around for decades, and its entire business model is teaching applicants to write to the template. Some of these consultants charge $5,000 or more, and their product isn’t creativity, it’s optimization. They teach students to identify what the admissions committee rewards and deliver exactly that, with the rough edges smoothed away and the personal experiences torqued into the right emotional shape.

My family took this seriously enough to invest in help, and I was fortunate enough they had the means to do so. I had one of those consultants. Mine cost $2,000, and my parents had to sell my mom’s pinball machine to pay for it. I think sometimes about what it says that the path to higher education ran through a professional who taught me, essentially, to write to a formula rather than to present myself in a way that would have given the committee a more honest, unique portrayal of just who they were letting into their institution. The consultant didn’t make me less creative; the system that made the consultant necessary did.

And this is the blind spot in Winthrop’s argument. She treats pre-ChatGPT essays as the baseline for authentic creative expression, but that baseline was already shaped by an industry dedicated to template optimization. So when Green’s research finds that post-ChatGPT essays use richer vocabulary but converge on familiar ideas, the question worth asking isn’t just whether AI caused a measurable shift (Green’s controlled experiments suggest it did) but whether the underlying ideas were already converged at a more fundamental level that the metrics don’t capture. In essence, all AI may have done is make that convergence more visible while democratizing the surface polish.

There’s an entirely different version of Winthrop’s essay waiting to be written — one in which the same data tells a democratization story rather than an erosion story. Where a free chatbot gives a first-generation college student the same surface-level advantage that a $5,000 consultant gave wealthier applicants for years. That’s not a comfortable reframe for institutions already invested in the idea that their selection processes brings forth authentic individuality — but it’s the reframe the data actually supports.

The template always comes first

This isn’t unique to college admissions. Wherever institution rewards a narrow formula, it gets gamed — and the gaming predates whatever technology that has made it easier.

The video essayist Sarah Z traced exactly this pattern in , which makes the gap in Winthrop’s argument clearer. When the publishing industry rewarded a specific shape of trauma narrative in the 1980s and ’90s — suffering resolved through individual resilience — the template grew so predictable that fabricators outcompeted honest writers. Laurel Rose Willson sold a satanic-ritual-abuse memoir and, years later, a Holocaust-survival story, citing the same self-inflicted wounds as evidence for both. Publishing houses weren’t fooled just because they were careless, they were fooled because they’d built a machine that searched for formula — and the system that rewards a narrow pattern is the same one that makes it exploitable.

AI doesn’t create that convergence, it just accelerates the optimization toward whatever you’re already selecting for. Blaming AI for homogenized output in an already-homogenized system is like blaming your GPS for traffic on the BQE — the bottleneck was there long before the tool arrived.

If your organization has ever received a stack of pitch decks, strategy memos, or RFP responses that all hit the same beats in the same order, congratulations! You’ve built your own admissions committee, but don’t blame AI.

AI doesn’t create that convergence; it just accelerates the optimization toward whatever you’re already selecting for. Blaming AI for homogenized output in an already-homogenized system is like blaming your GPS for traffic on the BQE. The bottleneck was there long before the tool arrived.

Threading the needle

Of course, none of this means the concern about AI and creativity is unfounded. The dulling effect is real, and anyone who uses these tools regularly has probably felt its subtle gravitational pull toward the center. Or in the way a chatbot’s first suggestion can quietly foreclose any other directions you might have explored on your own, or how it may produce something that sounds polished but carries none of your voice

A different research team — Anil Doshi and Oliver Hauser, behind the , Winthrop herself points to — put a name to the mechanism, anchoring. Handed an AI-generated idea, writers locked onto it, narrowing the range of what they produced before they’d really begun.

However, the solution on an organizational level isn’t to restrict the tool; rather it’s to address the institutional and behavioral factors that determine whether the tool narrows thinking or expands it.

In this determination, three things matter most:

First, use AI as a discussion engine, not just an automation tool — There’s a meaningful difference between asking a chatbot to draft something for you and using it to create something with you. This article is a case in point. I didn’t read Winthrop’s essay and immediately decide to write a response. Instead, I spent almost half an hour talking to Claude about the article, debating the argument, testing my objections, diving into Green’s research more deeply, and connecting the piece to ideas I’d been thinking about from completely different contexts, such as the Sarah Z essay. This article emerged from that conversation unintentionally, and it would not have existed without it.

Further, the ideas emerged pressure-tested and sharpened through a process that felt more like sparring than delegation — and that’s exactly the kind of process organizations should be targeting. Most organizations deploying AI are using it for efficiency — drafting, summarizing, formatting — and that’s fine. However, if that’s all you’re doing, you’re leaving the creative upside untouched, and your people are feeling the dulling effect without the compensating benefit. It takes an intentional push from leadership to get teams using AI as a thinking partner rather than a shortcut.

Second, give people time — This sounds obvious, but it matters specifically because of how AI interacts with time pressure. When people are rushing, they take the first adequate output and move on. With traditional workflows, shortcuts save time at the cost of quality or risk. With AI-assisted workflows, however, shortcuts save time at the cost of originality, because the first output from a chatbot is almost always the most conventional one. It’s the statistically average response, and reaching the edges takes iteration, pushback, and follow-up prompts that challenge the initial direction. That takes time, and if your people don’t have it, they’ll use AI the way a stressed applicant uses a college essay consultant, producing the safest possible version of whatever the system rewards rather than the innovative one which could change the game.

Third, reform what you reward — This is the intervention that actually addresses the root cause, and it’s the one most organizations will resist because it requires examining their own sorting mechanisms. If your evaluation criteria, your promotion structures, your review processes, and your RFP scoring rubrics all select for the safe and conventional, then AI will only turbocharge that selection.

You’ll get the template faster and more polished than ever, much like the admissions committee that rewards a narrow emotional arc and gets 300,000 identical essays. Or, if your firm rewards the pitch deck that hits every expected beat and takes no risks, AI will produce that pitch deck beautifully — and you’ll wonder why innovation has stalled.

Again, the intervention is upstream of the tool. What does your organization actually do when someone brings in an unconventional idea? What happens to the proposal that doesn’t fit the template? If the answer is that it gets smoothed out in review or tossed altogether, that’s not an AI problem.

The old traps didn’t disappear

Winthrop is right that creative thinking is something to protect and nurture. She’s also right that AI introduces new pressures that deserve serious attention. And she’s right that the stakes are highest for the people whose perspectives are already farthest from the mainstream.

But the college admissions essay wasn’t homogenized by ChatGPT, it was homogenized by decades of institutional selection pressure that rewarded a single template and penalized everything that didn’t fit. AI didn’t create that problem, it just made the template accessible to everyone, including the families that couldn’t previously afford $2,000 and a pinball machine to get their kid across the threshold.

Similarly, your organization’s creative output won’t be determined simply by which AI tools you adopt. It will be determined by what your leadership rewards, what your processes select for, and whether your people have the time and incentive to push past the first plausible AI-supplied answer.

The technology is new, but the traps are very old. And if you want to use AI to make your organization more innovative, the place to start isn’t the tool — it’s the template.

You can find moreĚýĚýfrom the Thomson Reuters Institute here

You can read TRI’s latest feature article, The AI adoption board game: Why law firm leaders can’t afford to play it safe here

Let’s be honest: most law firms know AI is a big deal. They’ve read the headlines, attended the conferences, and nodded along when someone says, “AI will change everything.” The problem? Knowing that AI matters and actually doing something strategic about it are two very different things. And according to our latest feature article, that gap is where many law firms are starting to lose ground.

In our latest feature, author Michelle Nesbitt-Burrell, Marketing Strategy Director for ¶¶ŇőłÉÄę (TR), frames AI adoption as a boardgame that’s already underway. Some law firms are moving confidently across the board, while others are stuck on the starting square, not because they don’t see the future, but because they’re hesitating. The latest TRI research shows that while the majority of lawyers say they believe AI will fundamentally transform the legal industry within the next few years, far fewer expect real change inside their own firms anytime soon. That disconnect is risky — especially when competitors and clients aren’t waiting around.

Here’s what should concern every law firm partner — corporate legal departments aren’t just playing the same AI adoption game, they’re winning it.

 

One of the most uncomfortable truths the article reveals is that corporate legal departments are further often ahead on AI adoption and utilization than their outside counsel. In fact, many corporate legal teams are investing in AI faster and using it more deeply in their day‑to‑day legal work. That means clients are reviewing contracts faster, doing more work internally, and increasingly judging their outside law firms on their technological sophistication. In a world like that, the excuse that We’re still experimenting stops sounding reasonable pretty quickly.

The article breaks law firms into three players on the game board:

1. 1. 1. 1. 1. The laggards — Those firms with no meaningful AI plans and very little ROI to show for it.
            2. The adopters — Thos firms that are experimenting with tools but don’t really have a clear strategy. These firms see some efficiency gains but too often hit a ceiling.
            3. The innovators — Those firms with visible, intentional AI strategies. These firms are far more likely to see ROI, revenue growth, and long‑term competitive advantages.

So, what separates the winners from everyone else? The article details the PLAYERS framework: pilot with purpose, leadership that sets the pace, action over perfection, strong ethics, serious education, good data, and — most importantly — strategy before tools. In other words, those law firms that want to become innovators should stop asking, What AI should we buy? and start asking What are we actually trying to achieve?

Clearly, AI isn’t a side project anymore. Law firms that treat it like one may save some time, but as the article fully explains, those firms that approach AI adoption and implementation strategically will reshape how legal work gets done. The game is already moving — the only question is whether your firm is playing to win or quietly falling behind.

You can find more Ěýfrom the Thomson Reuters Institute here

You can read TRI’s latest feature,ĚýThe human side of AI: The growing risks of ubiquitous use of AI on talent here

It’s no exaggeration to say that AI is everywhere in our workplaces right now. It writes our emails, summarizes our meetings, generates slides, and even helps us think through problems. On the surface, this may sound like progress — and in many ways, it is.

However, our , The human side of AI: The growing risks of ubiquitous use of AI on talent by Natalie Runyon, Content Strategist for Sustainability and Human Rights Crimes for the Thomson Reuters Institute, makes a clear and timely point: When AI use becomes excessive and unchecked, it can quietly undermine the very people it’s meant to help.

One major consequence of cognitive decay is the weakening of the brain’s capacity to engage deeply, question systematically, and — somewhat ironically — resist the potential manipulation of AI.

As the article goes into in much greater detail, these harms caused by AI overuse can include a slow erosion of human connections, a loss of a professional’s sense of purpose, and a general sense of feeling overwhelmed in the workplace.

Of course, the solution isn’t to reject AI, it’s to use it better. To this end, the article makes a strong case for organizations to foster hybrid intelligence, a process by which human judgment and creativity work alongside AI capabilities.

In today’s workplace, AI can be a powerful advantage; however, that is only if organizational leaders can remember that technology should enhance the human experience, not replaces the parts of professional life that workers value.

To examine this and many more situations, the Thomson Reuters Institute (TRI) has launched that leverages our expert analysis and supporting data to tell some of the most compelling stories professional services today

You can read TRI’s latest feature article,ĚýPremortem: Your 2028 agentic AI pilot program failedĚýhere

Picture this: It’s 2028, your law firm spent real money on an agentic AI pilot, and now it’s quietly been shut down. No press release, no victory lap — just a post‑mortem that nobody wants to read. In our , we see that such a future is very likely unless firms start preparing for agentic AI in a way that’s very different than how they think they should.

The big idea is simple but uncomfortable: Success with generative AI (GenAI) does not mean your organization is ready for agentic AI. GenAI works because it’s forgiving. You can paste text into a tool, get a decent answer, and move on — even if your data is messy and your workflows live in people’s heads. Agentic AI doesn’t work that way. It expects clean data, documented processes, and clear rules. If your firm runs on institutional memory, workarounds, and a kind of just ask Linda problem-solving process, then the system will eventually break down.

To examine this and many more situations, the Thomson Reuters Institute (TRI) has launched that leverages our expert analysis and supporting data to tell some of the most compelling stories professional services today.

Our latest feature, Premortem: Your 2028 agentic AI pilot program failedĚýby Bryce Engelland, Enterprise Content Lead for Innovation & Technology for the Thomson Reuters Institute, walks us through two fictional but painfully familiar failure stories of how two separate firms handled their agentic AI pilot programs.

The author explains how the first firm moves fast after crushing their GenAI rollout and assuming agentic AI is just the next logical step. Everything looks great in a sandbox; but then the system hits real‑world chaos: Undocumented exceptions, fragmented document storage, and conflict checks that only work because humans intuitively know when something feels off. One bad intake decision later, client trust is damaged and the pilot is frozen. In this example, the tech didn’t fail — the organization did.

The second firm goes the opposite direction. They’re cautious, thoughtful, and obsessed with governance. They build guardrails, limit risk, and launch a perfectly reasonable pilot. And then… nothing happens. Attorneys ignore the system — not because they hate AI, but because using it only adds risk with no reward. If it works as it’s supposed to, nothing changes; but if something goes wrong, they’ll be blamed. So, unsurprisingly, the rational choice is to nod in meetings and quietly keep doing things the old way until the project dies of inertia.

The challenge is that “preparing” doesn’t mean what most people think. It doesn’t mean buying early, and it doesn’t mean waiting for maturity. Rather, preparing means understanding now why these systems fail, and building the institutional capacity to avoid those failures when the technology arrives in full.

The feature article points out the common thread here: These failures have very little to do with AI capability; rather, they’re about incentives, documentation, and institutional honesty. Firms that succeed with agentic AI won’t be the ones that buy in early or wait patiently. The winners, the piece explains, be the ones doing the boring, unsexy work now: Writing things down, fixing information architecture, identifying hidden dependencies, and aligning rewards so adoption isn’t all risk and no upside.

In short, this article isn’t a warning about technology. It’s a warning about pretending your organization is ready when it’s not — and mistaking optimism or caution for preparation.

So, dive a little deeper behind the headlines about AI adoption and how to make agentic AI work for your organization. Click through and read today’s feature. It might help you see more clearly than before whether the path your organization is pursuing with agentic AI will carry it over the goal line and into the next decade… or leave your team watching from the sidelines.

You can find more Ěýfrom the Thomson Reuters Institute here

You can read TRI’s , The 4 scenarios: Which law firm business model are you building? here

If you’re running a law firm right now, you’ve probably felt it — the ground is shifting fast. AI isn’t some future consideration; indeed, it’s already shaping how clients choose their firms, how work gets done, and which business models are built to last.

To examine this situation — and many more in the future — more deeply, the Thomson Reuters Institute (TRI) has begun publishing a , that will allow our expert analysis and supporting data to more fully tell some of the most important stories in the legal, tax, accounting, corporate, and government areas.

Our first feature, The 4 scenarios: Which law firm business model are you building? by Elizabeth Duffy, TRI’s Senior Director of Client Engagement and Raghu Ramanathan, President of ¶¶ŇőłÉÄę Legal Professional business, cuts through much of the noise around AI adoption in the legal industry. The piece lays out a clear, uncomfortable truth: in the age of AI, strategic clarity isn’t optional — it’s survival.

For years, many law firms have tried to hedge their bets. A little bespoke work here, a little efficiency there. Premium pricing mixed with cost pressure. The result? A mushy middle that feels safe but is actually the most dangerous place to be. As the feature makes clear: Law firms without a distinct position — neither elite, automated, scaled, nor protected by regulation — are the ones most exposed to existential risk.

And here’s the kicker that the authors point out: Clients aren’t waiting for firms to catch up.

Corporate legal departments are already evaluating firms based on their AI maturity — how effectively they use technology to deliver speed, consistency, and quality. This is happening even as many clients are still figuring out their own AI strategies. In other words, law firms are being judged by their clients right now, whether they’re ready or not.

Client pressure is building faster than internal capabilities can keep pace.

Even corporate legal departments new to AI are asking pointed questions of outside counsel.

The authors don’t argue that there’s one correct model. Instead, the piece lays out four distinct scenarios that law firm leaders can choose from, making the case that choosing something deliberately is far better than drifting. Of course, each scenario demands different investments in talent, technology, pricing, and client engagement. What they all share, however, is intention.

That’s what makes this piece so valuable. It’s not full of hype, and it’s not fear‑mongering. Rather it’s offering a strategic framework for leaders who know that AI is reshaping professional service markets but are still wrestling with what that actually means for their organization.

If you’re a managing partner, innovation leader, or industry watcher who’s tired of vague predictions and wants a clearer map of what’s ahead, the feature articles will be well worth your time. The questions they will raise — about positioning, differentiation, and the cost of standing still — aren’t comfortable, but they’re exactly the questions firms need to be asking now.

So don’t just skim the headlines about AI in law. Click through and read today’sĚýfeature. It might help you see, more clearly than before, which business model you’re actually building in your law firm — and whether it’s the one that will be able to carry your firm into the next decade.

You can find more from the Thomson Reuters Institute here