Key insights:

• • • The duty of competence now includes technology 鈥� Rules first written for legal knowledge and diligence are being read to cover the tools lawyers use, not just the arguments they make.

    • The rules didn’t anticipate AI, but they still apply 鈥� Formal opinions and updated guidance make clear that generative and agentic AI fall squarely within lawyers鈥� existing ethical obligations.

    • What reaches the court carries the highest stakes 鈥� A lawyer’s technology missteps are most consequential, and most visible, the moment they show up in a filing, an exhibit, or an argument made to a judge.

Every attorney holds a duty of competence, and that duty has always included legal knowledge and sound judgment. Today, however, it now extends to technology as well. Often called 鈥�technical competence鈥�, today鈥檚 duty of competence requires understanding the benefits and risks of the tools used in practice, not just the law itself.

The American Bar Association’s Model Rules of Professional Conduct describe an attorney’s duty of competence as the obligation to provide representation using the (Rule 1.1). Comment 8 of the rule states explicitly, in part: 鈥淭o maintain the requisite knowledge and skill, a lawyer should keep abreast of changes in the law and its practice, including the benefits and risks associated with relevant technology鈥� .鈥�

Indeed, competence matters well beyond a lawyer’s own file. Every filing, brief, and exhibit is, in effect, a lawyer’s communication to the court, and the court relies on the presumption that what it receives has been prepared with care. When a lawyer misunderstands or misuses technology, whether that means citing an AI hallucination as precedent or mishandling e-discovery output, the failure lands directly on the judge’s desk.

Rules of professional conduct are written broad and durable, so they do not need constant revision. Even so, few could have anticipated the pace of technological change now reaching the legal field.

A lawyer cannot simply plead ignorance when technology goes wrong, and failing to understand the tools being used can lead to ethics complaints, sanctions, or malpractice claims. Perhaps even more damaging, it can just as easily undermine a judge’s confidence in everything else that lawyer submits.

Rules built to last meet a technology no one predicted

Each state and territory maintains their , but most draw heavily on the ABA’s Model Rules as a foundation. Precedent is also shaped by disciplinary proceedings and case law, including a growing number of cases in which judges have sanctioned lawyers for submitting AI-generated content that was never checked for accuracy.

Rules of professional conduct are written broad and durable, so they do not need constant revision. Even so, few could have anticipated the pace of technological change now reaching the legal field. E-discovery was an early example, already raising real questions about what a lawyer could certify to a court in good faith. The more significant shift today is generative AI (GenAI) and agentic AI, capable of producing legal analysis or even taking action with minimal human oversight, often producing material that looks polished and authoritative right up until a judge or opposing counsel checks it.

Recognizing this shift, the ABA issued in July 2024 on the use of GenAI tools in legal practice, confirming that the duty of competence applies squarely to AI. The Formal Opinion requires lawyers to understand these tools well enough to use them responsibly, supervise their output, and avoid overreliance on unverified results 鈥� a standard that matters most the moment a document is filed, or an argument is made in open court.

5 ways to stay ahead of the curve

Given how quickly legal technology is evolving, and how little margin for error exists once something reaches a judge, there are five practical ways that lawyers can maintain their technical competence, including:

1. 1. 1. Pursue CLEs and structured education 鈥� Continuing legal education courses focused on AI, e-discovery, and cybersecurity offer a reliable, credentialed way to stay current. Many state bars now offer, and some require, CLE credits specifically on legal technology, exposing lawyers to real-world case studies that include those involving AI missteps that have drawn judicial sanctions.
      2. Build personal familiarity through hands-on use 鈥� Reading about a tool is no substitute for using it. Treating AI as a “thought partner,” for drafting, brainstorming, or spotting issues, helps professionals develop an intuitive feel for what these tools do well and in which ways they fall short, long before a document ever reaches a court. This low-stakes experimentation on matters that don鈥檛 impact client confidentiality or sensitive data can help build practical AI fluency.
      3. Practice in sandboxes and controlled environments 鈥� Before relying on a new platform in an active matter, test it in a sandbox first. Many vendors and firms now offer walled-off spaces to explore a tool’s features and failure points without exposing real client data.
      4. Review organizational guidelines 鈥� Firms, courts, and bar associations increasingly publish their own AI use policies that cover permitted tools, disclosure requirements, and data security. A growing number of courts also require lawyers to certify or disclose any AI use in filings, making familiarity with local rules as important as firm policy.
      5. Choose the right tools for the task 鈥� Free consumer-grade AI tools may suit general research, but should not be used in matters involving privileged or sensitive information, let alone a court filing. Premium and fiduciary-grade platforms, built with legal-specific safeguards around data handling and auditability, are better suited for substantive casework. Matching the tool to the sensitivity of the task, rather than convenience, is critical.

Used together, these five habits can help lawyers build a durable foundation in AI literacy. While technology will keep evolving, of course, any lawyer who has internalized this approach will be far less likely to be caught off guard once their work is tested in front of a judge.

Why this is about more than just the rules

Maintaining technical competence is not simply about avoiding disciplinary consequence. It is about preserving what the legal system is meant to provide. When lawyers understand the technology they use, including AI, they can apply it to research and review in ways that benefit clients and courts alike.

Just as importantly, technical competence protects the public’s trust in the legal profession, and something more immediate: a judge’s ability to rely on what lawyers put in front of them. In the end, technical competence is not a burden imposed by the rules; rather, it鈥檚 what allows the relationship between lawyers and the court, and the profession itself, to keep working.

For more on AI in the courts, check out the 听鈥� a joint effort by the National Center for State Courts听(NCSC) and the 成人VR视频 Institute (TRI)

Key insights:

• • • AI adoption is now a talent retention and recruitment issue 鈥� Law firms that lack professional-grade AI tools risk losing both current and prospective talent.

    • Client relationships are increasingly tied to AI-driven value 鈥� Corporate legal departments expect their outside counsel to use AI to improve productivity, quality, and innovation; however, few believe most of their law firms are meeting those expectations.

    • Law firms must rethink their business and pricing models 鈥� Although many firms feel financial pressure to accelerate AI adoption, most have not adjusted their pricing structures to reflect AI-driven efficiencies.

Law firms are experiencing unprecedented pressure from the rapid advancement of AI, which is affecting their talent recruitment, client relationships, and business models, according to deeper analysis of the recent 成人VR视频听2026 Future of Professionals Report.

To help law firms navigate this AI-driven disruption, 成人VR视频 has published a new action paper, Future of Professionals Report 2026: Actionable insights for law firm leaders, drawing on insights from 736 law firm professionals and 203 corporate legal professionals.

Indeed, the new paper highlights that almost one-quarter of law firm professionals will refuse a job offer if the prospective firm lacks professional-grade AI tools. Further, any perceived misalignment between a professional鈥檚 AI preferences and the firm鈥檚 strategy increase the risk of attrition, especially among those professionals who value mentorship and skill development.

You can download your copy of the听2026 Future of Professionals Report听here

In addition, almost one-third of corporate legal professionals say they are reconsidering relationships with outside law firms that do not demonstrate how they鈥檒l offer clear AI-enabled value within the next 12 months, the paper notes. And clients increasingly expect their outside counsel to deliver efficiency, quality, and innovation through AI; however, only between 3% and 6% say they believe most of their outside firms are meeting each of these expectations.

Finally, almost 4-in-10 law firm professionals say they are feeling financial pressure to act faster on AI, yet almost two-thirds say their firm鈥檚 pricing structure remains unchanged despite clients鈥� demand for new models that reflect AI-driven efficiencies and increased value.

Dealing with AI-driven challenges

The paper notes that firms with approved AI tools are more attractive to talent, while the use of unauthorized “shadow AI” by more than one-third of professionals creates security and compliance risks. To address this, firms should provide transparent AI solutions and invest in training. While AI may reduce demand for some junior roles, it may increase the need for others, especially hybrid tech-legal roles.

On the client relationship front, many corporate legal departments are facing internal pressure to adopt AI and expect their outside law firms to keep pace. In-house legal teams increasingly expect AI-enabled productivity, quality, and innovation, yet many see a significant gap between expectations and delivery. For example, 70% say they expect productivity gains, while only 6% say they believe most of the firms they work with are delivering them.

Clients, for their part, also expect pricing models that reflect AI-driven efficiencies through greater cost certainty and transparency. Outside law firms that fail to adapt may risk fee pressure, ultimately losing business to more agile competitors.

Only half of professionals see their firm鈥檚 AI strategy reflected in their daily work, and this potential misalignment could cause talent and AI adoption problems.

Fortunately, amid all these challenges for law firm leaders, the paper identifies three strategic paths law firms can take, including:

• • • Using AI to elevate by automating routine tasks that would then allow professionals to handle complex, high-value work.
    • Using AI to scale by prioritizing productivity and efficiency and handling high volumes of routine work with AI and human oversight.
    • Using AI to reimagine by rebuilding the firm around AI and offering new models like outcome-based pricing and embedded partnerships.

Unfortunately, some firms are choosing to defer this crucial decision, which increases their risk of client and talent attrition as the market evolves.

Whichever path law firms take, however, the paper makes clear that firm leadership must clearly communicate their AI strategy.听The paper notes that only half of professionals see their firm鈥檚 AI strategy reflected in their daily work, and this potential misalignment could cause talent and AI adoption problems.

The paper encourages firms to move quickly to close the gap between client expectations, talent needs, and operational realities by defining a clear AI strategy, investing in training and tools, and adapting pricing models for an AI-driven market.

Using the guidance from this action paper, firm leadership can navigate these challenges and move their law firm into a more responsive, profitable, and sustainable AI-enabled future.

You can read a full copy of the听Future of Professionals Report 2026: Actionable insights for law firm leaders听paper here

Key highlights:

• • • Build on internal momentum rather than top-down mandates 鈥� Dean Kalb backed faculty who were already experimenting with AI, embedding shared learning outcomes into the legal writing program first before expanding to other courses.

    • Empower students to shape the school’s AI policy 鈥� Dean Kalb formed a 15-person student advisory group that surveyed one-third of the student body and produced AI principles that directly influenced school policy.

    • Create opportunities to get students collaborating with faculty 鈥� Efforts by Dean Kalb uncovered shared concerns of faculty and students, underscoring that students often know AI tools better than faculty and creating a co-learning opportunity in the classroom.

In her first six months as dean at the University of San Francisco (USF) School of Law, Johanna Kalb heard the same message from alumni across sectors: Those students entering law school today would step into a profession that looks meaningfully different from the one that exists now.

So, in her first move to translate that urgency into institutional change, Dean Kalb got behind those faculty members who had already started building toward that future.

Start with what is already in motion and invite others in

Dean Kalb started with the efforts that Profs. Nicole Phillips and Megan Hutchinson had already been doing by conducting their own experiments in their classrooms and building their own tools.

Dean Kalb鈥檚 first step mattered as a strategic choice. Rather than convening a task force or commissioning a study, she identified the faculty who had credibility with their peers and gave them resources and institutional backing. In this way, USF was able to embed shared AI learning outcomes across its legal research, writing, and analysis program in the second semester of the 2024-鈥�25 academic year.

The decision to focus on this program was deliberate because it built upon existing internal momentum and fit into the course鈥檚 existing goals. The structure of the legal research and writing program 鈥� with faculty having autonomy while supporting each other 鈥� also made the integration work by providing natural support.

Expand through optional workshops before adding mandates

Over the following summer in 2025, Profs. Phillips and Hutchinson ran optional hands-on workshops for the broader faculty. “Faculty learn from other faculty,” explains Dean Kalb. 鈥淭hey don’t want a vendor to come in and sell them. They’re not interested in having somebody from central administration try to tell them how they can teach better. But listening to a colleague who really understands the work that they do is very helpful.”

Indeed, some faculty showed up, were excited by the possibilities, and began integrating AI learning outcomes into their elective courses. This voluntary uptake created a visible proof of concept before any additional requirements or mandates were introduced.

Alongside the workshops, Dean Kalb also expanded AI learning outcomes into two required courses on evidence and professional responsibility. The professional responsibility inclusion was straightforward given the ethical dimensions of AI use in legal practice. One colleague, Prof. Tiffany Li, had already been building those outcomes into her section and was willing to share her approach with other faculty members who were teaching the same course.

Give students a formal role in shaping the direction

When USF rolled out access to the AI platform Claude across students and faculty, the response was more complicated than Dean Kalb anticipated. Feedback from students at USF 鈥� a Catholic Jesuit institution with a strong social justice identity 鈥� raised questions about AI鈥檚 social, environmental, and democratic impacts.

Dean Kalb intentionally chose to use the students鈥� feedback to involve them. With the help of another alum, who has deep experience in evaluating and implementing emerging technologies, Dean Kalb convened a student group to develop a set of draft principles for AI use at USF Law. The students conducted structured interviews with faculty, staff, and students, resulting in the creation of a survey in which approximately one-third of the student body participated. The student group drew on these results to draft a series of AI principles and presented them to faculty, staff, and other students. Ultimately, the principles were adopted by the faculty.

What came out of that process has already begun to shape the law school鈥檚 AI practices in concrete ways. For example, a faculty technology advisory committee with student representation has been formed to implement the principles to ensure transparency and ongoing oversight. The school also has begun exploring ways to engage with AI that reflect and enhance its social justice mission.

We now have a shared sense of where the community is and what our concerns are. That allows us to speak in a common language as we talk about why and how we’re doing this.

The more significant outcome, Dean Kalb says, was the discovery of shared concerns among faculty and students that AI would erode critical thinking rather than develop it. “That was probably the most helpful part of the whole process,” she says. “We now have a shared sense of where the community is and what our concerns are. That allows us to speak in a common language as we talk about why and how we’re doing this.”

Commit to sharing in the learning

Dean Kalb鈥檚 suggestion for her peers and faculty is to integrate AI tools into their own lives, which would allow them to better 听听听听听keep pace with technology that is moving faster than any curriculum committee can match. 鈥淚t鈥檚 hard to regulate and teach these tools in the abstract,鈥� she explains. 鈥淚鈥檝e found that playing around with them in my personal life 鈥� where the stakes are low 鈥� has helped me come up with ideas for their use at work, and that in turn, means that I notice their evolution.鈥�

For a profession built on expertise and the authority that comes with it, this mindset requires a particular kind of intellectual honesty. Some students are beginning to arrive at law school with more familiarity with AI tools than their professors, Dean Kalb adds, and this may offer an opportunity to shift the classroom dynamic toward co-creation, in which faculty and students are building knowledge together rather than transmitting it in one direction.

This change in perspective can, turn the stress of 鈥渒eeping up鈥� into the more enjoyable experience of collaboration, she says.

You can find out more about the impact of AI on legal education here

Key findings:

• • • Workloads grow, while staffing stays flat 鈥� Many government legal department professionals say their work keeps increasing while staffing remains stagnant; and many are turning to AI tools to improve capacity.

    • AI adoption is surging 鈥� Over the past year, AI adoption among government legal departments has spread rapidly, with federal and state agencies leading the way.

    • Unfortunately, AI oversight hasn鈥檛 surged 鈥� Many legal departments report that their AI governance is lagging behind adoption, with 20% of agencies having no AI use policy in place at all.

Government legal departments are facing an all-too-familiar problem: more work, more complexity, and the same number of staff to do the job, according to the 成人VR视频 Institute鈥檚 2026 Government Legal Department Report, which captures the insights from 200 government legal department professionals at varying levels.

Jump to 鈫�

2026 Government Legal Department Report

Threaded through these insights, some clear trends emerged. For example, technology 鈥� especially AI and other advanced tools 鈥� is increasingly serving as an extension of staff, expanding agencies鈥� capacity to manage rising workflow demands.

Increasing pressures across all levels

More than one-third of respondents report that their workload increased by more than 10% in the past year, with many handling between 21 and 50 legal matters per week. At the same time, workloads are becoming more complex, with more than one-third of respondents saying that more than half of the legal issues they face are complex, which is particularly notable at the state and federal levels.

Staffing shortages, a top concern in recent years, continue to persist. Three-quarters of respondents say their agencies experienced staffing shortages over the past two years, and almost two-thirds say they anticipate shortages into 2027.

Indeed, despite an increase in complexity and workload, attorney staffing levels have stayed the same for almost 40% of agencies, the report shows. And at the federal and state level, departments were more likely to have experienced a reduction of more than 10% of their staff.

AI adoption skyrockets, making governance more necessary than ever

More than one-quarter of respondents say their agency or department is now using AI tools, up from a meager 5% last year, with this increase taking hold at the federal and state level much more quickly. Among the different groups of respondents, one-third of federal and state government legal professionals report using AI tools compared to just 19% of those at county and city departments. Resistance to AI is diminishing, too; however, more than one-third of county and city legal departments still report having no plans to use AI.

Optimism toward AI is rising alongside implementation, the report shows. More individuals at the federal and state level feel optimistic than pessimistic about AI technology, which is an inversion of last year鈥檚 sentiment. Among county and city legal professionals, pessimism still remains more common. Among all respondents, confidential data exposure remains the top evaluation criterion when assessing these advanced tools.

The report underscores that this all points to a need for the establishment of strong governance models before adoption. Nearly two-thirds of government agencies and departments have an AI use policy in place or are developing one, respondents say. However, 1-in-5 departments and agencies are still without an AI use policy, risking unofficial use of prohibited AI tools.

Those agencies hesitant to implement AI technology are encouraged to view AI technology as a way to increase staff capacity amid flat staffing, rising workloads, and growing matter complexity. AI tools can help reduce strain on employees, contributing to better-managed workloads while reducing employee burnout. When appropriately vetted, however, AI technologies can reduce administrative burdens, increase legal research efficiency, and help those organizations facing trying to manage more work with the same staffing levels.

An actionable path forward

As the report makes clear, AI is no longer a future challenge; rather, it鈥檚 a present reality in a rising percentage of government legal departments. Indeed, the report outlines ways departments and agencies can move forward in this space, by beginning with lower-risk foundational tools like legal research and case management systems; and then investing time in developing thoughtful AI use policies and evaluation protocols. With responsible staff training and a thoughtful evaluation process, AI technologies can protect the valuable time and work-life balance of government legal professionals.

Increasing workloads are not optional for government legal departments, but how department leaders empower their staff to manage these workloads is becoming the differentiator.

You can download

a full copy of the 成人VR视频 Institute’s “2026 Government Legal Department Report” by filling out the form below:

Key highlights:

• • • AI can help remove interpersonal friction during feedback conversations 鈥� Attorneys have taken naturally to soliciting substantive critique from AI that covers tone, argument strength, and clarity in ways they might hesitate to request from a colleague or supervisor.

    • The role of feedback-giver remains irreplaceable 鈥� The developmental core of feedback still requires someone who knows both the work and the person receiving the feedback, even if AI is used as a sounding board.

    • Feedback culture can be accelerated using human-AI mentorship 鈥� AI makes the conditions for receiving feedback easier and can spark shared human conversations about the work rather than replacing those conversations.

It鈥檚 one of those things upon which most people on any legal team would agree 鈥� feedback matters. It鈥檚 essential to professional development, to mentorship, and to the long-term growth of attorneys at every level. And yet, for all the emphasis placed on its importance, feedback remains one of the more nuanced and inconsistently experienced dimensions of working in the legal profession.

At my firm, Seyfarth Shaw, in particular, there has long been a deliberate focus on building high-performing teams and developing legal talent in a way that is both structured and intentional. We have designed systems that measure and reinforce feedback because we believe it is central to how attorneys grow and how high-performing teams consistently deliver exceptional client service.

Within the legal profession, however, there are dynamics and circumstances that can often make feedback more complex to navigate in practice.

At its core, a lot of feedback still relies on the comfort level of both supervising and junior attorneys to proactively provide it and seek it out, and that is often easier said than done. Time pressures, the structure of legal teams, and the challenge of balancing candor with constructiveness all contribute to making this an often-difficult task.

One of the most common use cases we began seeing was attorneys taking their own drafts and asking AI to critique them 鈥� not just for grammar or formatting, but for more substantive reasons such as tone, argument strength, clarity, and impact.

Attorneys are trained, as an actual skill set, to question everything and that can naturally extend to how feedback is processed, depending on how it is delivered and by whom. The amount of effort, intelligence, and judgment that goes into legal work is significant, so when that work is challenged, it can feel personal. And on the receiving end, actively seeking out substantive feedback is not a muscle that gets consistently developed in most educational settings leading up to working within a law firm.

The behavioral shift

This is the backdrop against which something genuinely interesting started happening when generative AI entered the picture. Almost immediately, one of the most common use cases we began seeing was attorneys taking their own drafts and asking AI to critique them 鈥� not just for grammar or formatting, but for more substantive reasons such as tone, argument strength, clarity, and impact. Traditionally, this is the kind of feedback many of these same professionals might hesitate to request directly from a colleague or supervisor.

What struck me was how quickly and naturally asking this of AI became a default behavior. There was an almost instinctive willingness to let AI review and analyze work product in a way that felt qualitatively different from how feedback had traditionally been experienced in interpersonal settings.

Landmark, meta-analysis research on found that feedback interventions actually decreased performance roughly one-third of the time, particularly when that feedback shifts attention to the self and triggers anxiety rather than a renewed focus on the work. More identify three triggers that cause people to reject feedback, including the relationship trigger, in which the reaction is not to the substance but rather to the person delivering it.

And 鈥檚 Dr. Larry Richard鈥檚 adds a profession-specific dimension: Lawyers tend to score lower on resilience, defined as how one reacts to criticism or rejection, while also ranking high in skepticism, an instinct to question assertions rather than accept them at face value. The combination can make feedback both more essential and, at times, more complex to deliver effectively.

Using AI neutralizes many of these dynamics 鈥� there is far less perceived pressure, no interpersonal dynamic to navigate, and no relationship to manage in the moment. We are constantly giving AI feedback about what it did right, what it did wrong, and how to improve. Indeed, we often are already flexing that muscle without thinking twice about it.

What the paradox reveals

This is the part to which I keep coming back. The problem was never that lawyers cannot handle feedback; if that were true, they would not be seeking it so readily from AI. The opportunity lies more in the conditions under which feedback is delivered and received. AI did not make people more open to feedback, rather it removed some of the perceived barriers that can accompany feedback in traditional settings.

What AI has done, perhaps unintentionally, is create a clearer line of sight into how feedback could work even better.

However, removing that friction is not the same as providing what lawyers actually need to grow. AI can tell you that your argument has a structural gap, but it cannot tell you why that gap matters in the context of a particular client relationship, a judge鈥檚 known preferences, or the broader strategy of a case. It cannot replace the judgment that comes from a supervising attorney explaining not just what to change, but why it matters to change it, and how to think about it differently next time. The developmental core of feedback still requires a person who knows the work, knows you, and is invested in your growth.

What AI has done, perhaps unintentionally, is create a clearer line of sight into how feedback could work even better. It has identified behaviors 鈥� such as seeking input early, iterating quickly, engaging with critique 鈥� that firms like ours have long been working to encourage, and made such behaviors easier to access in the flow of work.

Yet, there is also something more subtle happening. With AI, attorneys retain a clear sense of autonomy over the feedback itself. They can take it or leave it, focus on it, or set it aside without any interpersonal consequence.

AI creates a different dynamic: It functions more as a sounding board, essentially another set of eyes on the work. That makes it easier to engage with feedback in a more exploratory way by incorporating what resonates, questioning what is unclear, and testing ideas before bringing them back into a human conversation. In that sense, AI can help build the muscle not just of receiving feedback, but of engaging with it more thoughtfully 鈥� including developing the confidence to ask the 鈥渨hy鈥� that is often where the real learning happens.

At Seyfarth, this is where we see a meaningful opportunity to build on an already strong foundation. Our focus has long been on creating a culture in which feedback is expected, measured, and part of how work improves. What AI allows us to do is take that a step further, making feedback more continuous, more immediate, and easier to engage with as part of the workflow.

In the next part of this series, we will look at what we call SEYmultaneous Advancement, our way of intentionally bringing AI into the feedback process 鈥� not as a replacement for human interaction but as a progression 鈥� and in true AI form, iterating with the human-in-the-loop along the way.

You can find out more about

Key insights:

• • • Successful AI implementation requires a solid foundation 鈥� Strategy, leadership, and the impact on operations and individual users are key elements to any successful implementation.

    • AI success is a skills strategy, not a technology strategy 鈥� AI creates a whole new set of skills that are required for both legal department attorneys and department leadership.

    • AI changes how legal work is conducted 鈥� If implemented correctly, AI not only improves the end work product, but it also changes how lawyers perform their jobs.

Corporate legal departments are already experiencing the benefits of AI, including improved productivity, and reduced costs and errors, the 成人VR视频 Institute鈥檚 recent shows. So it鈥檚 not surprising that AI is increasingly becoming a strategic priority for general counsel (GCs).

The report cautioned, however, that success with AI is not a given. AI is not a silver bullet which guarantees improvements across the department. Instead, AI adoption and implementation must be carefully planned in order to realize those benefits.

Crucially, successful AI implementation is not simply about the technology; rather, it鈥檚 a reflection of the department itself and often can signal whether the department has the right elements in place to enable that success.

AI enhances successful legal departments 鈥� it does not create them

AI implementation is like any other law department strategy 鈥� it does not live on its own but instead advances as a direct result of everything that has come before it, including the work of the department鈥檚 attorneys and professionals, its daily operations and processes, and the GCs who are guiding the overall vision.

Overall, it鈥檚 about having a solid foundation upon which to build AI adoption and implementation.

The Pyramid of AI Success

AI may be one of the most impactful and transformational technologies to come on the scene in recent years, but it鈥檚 important to remember that it is still simply one tool among many. And its ultimate success will be determined not only by its capabilities, but by how it integrates with and augments the work that corporate legal department attorneys perform daily.

The technology itself does not perform the work 鈥� it enables more efficient work. This means that the rise of AI creates a whole new set of necessary skills for both legal department attorneys and department leadership.

With that in mind, GCs should focus on a few key areas to improve their department鈥檚 chances of AI success. The essential steps can be viewed as a pyramid 鈥� every step that you take builds, each upon another, creating a solid foundation. Establishing a top-level AI strategy means setting the tone from leadership, which then permeates down through operations and ultimately transforms how individual users work every day.

• • Learning 鈥� Most departments have a basic AI understanding and a culture to encourage change, but they often do not have the depth of understanding to move from AI literacy to AI fluency. Be sure to determine where your team is on this learning curve.
  • Empowerment 鈥� Empowering your professionals is crucial to drive experimentation and identify new use cases. Ask yourself, does my team feel encouraged to explore new ways of working and empowered to make changes?
  • Ownership 鈥� The legal team should feel they have significant input into how AI will be used in the department and throughout the organization. AI can be a major transition, and team members should feel that they can freely share ideas, concerns, and insights.
  • Accountability 鈥� Team members with personal goals that are linked to AI adoption are more likely to become top learners and regular users, our research shows, and this leads to greater overall benefits for the department.
  • Usage 鈥� Regular use drives adoption, so you should build AI into your team鈥檚 daily habits, monitor how many legal team members have tried AI, and how many are using it regularly.
  • Expectations 鈥� Balance encouraging uptake with clear expectations around adoption. Offering open encouragement along with access to tools and training to build momentum can be key first steps. As team members become more proficient, set formal expectations around AI usage. Be clear that when targets are set, usage will be tracked and individuals will be held accountable. Then, follow up with low- or no-usage individuals to determine causes, such as difficulty with training.

For GCs, today鈥檚 top challenge is how the department can develop needed AI skills in a way that will best augment how lawyers work. If implemented correctly, AI will not only improve the end work product, but it will also better enable lawyers to perform the work they do best.

AI success with outside counsel

The same principles of strategy and leadership that contribute to AI success within the department also extend to working with outside counsel. Currently, more than half of corporate counsel say they believe their outside law firms should be using AI, according to the report; however, two-thirds also say they do not know how their outside firms are approaching their use of AI.

This creates a communication gap, in which some GCs attribute to hesitance or caution. 鈥淲e do not ask and they are shy to provide answers because they are already under a lot of pressure because their rates are so high,鈥� reports one GC.

About three-quarters of corporate counsel also say they expect their outside law firms to take the lead in AI conversations between the department and the firm. However, that does not mean that GCs should simply accept a lack of conversation if firms are not forthcoming. Those GCs that want their outside firms to embrace AI should be open and transparent, conveying that they believe AI can assist firms with most work tasks, while placing a strong emphasis on output verification and the authority of attorney expertise. Indeed, GCs need to understand how their outside firms are using AI, especially how and when it is being applied, how it鈥檚 being supervised, and, perhaps most importantly, how it impacts fees.

Without detailed and regular discussions, GCs could develop a blind spot in this area. 鈥淐onversation has been only high level,鈥� another GC explains. 鈥淲e generally know what AI they are using but not how they are using it.鈥� What鈥檚 surprising, the GC adds, is that 鈥渢he billing has remained the same as it did before 鈥� so either they are not using AI tools efficiently, or they are just doing double work.鈥�

You can download a fully copy of the , from the 成人VR视频 Institute here

Key highlights:

• • The AI strategy-execution gap is an organizational problem, not a tech one 鈥� Among professionals whose firm or department has a stated AI strategy, more than half say that either the strategy is not visible on a daily basis or that the organization has no strategic AI direction at all.

  • The human cost of inaction is building faster than most leaders recognize 鈥� More than 90% of professionals say they are experiencing some degree of this AI-value disconnect, and among them, one quarter is considering leaving their current organization within two years if things don鈥檛 change 鈥� at an estimated replacement cost of $232,000 per employee.

  • Disrupted development compounds talent risk for the next generation 鈥� The flight risk of experienced professionals creates a compounding multiplier effect on the ability of entry-level talent to develop critical skills.

The greatest barrier to AI transformation in professional services is the widening human gap between what organizations promise their people about AI and the spoken and unspoken messages that professionals see and observe in their workplace every day, according to the 成人VR视频 recent , which surveyed more than 1,800 professionals in 62 countries across areas of law, tax, audit, accounting, compliance, risk, and global trade.

Establishing a visible AI strategy

While our research showed that most organizations have a stated AI strategy, the data suggests that it is not translating evenly to employees in their day-to-day work. Among professionals whose firm or department has a formal AI strategy, 35% say that strategy is not visible in their day-to-day experience, and another 17% say their organization has no strategic direction on AI at all. That means that more than half of professionals with fiduciary commitments are working in an environment in which the AI strategy on paper does not match the reality of how their work gets done.

The reasons that professionals say AI strategies are stalling suggest an absence in AI transformation and change agility across the organization. Indeed, professionals say that drivers of their organizations’ struggles to translate AI ambition into measurable results include the fact that the right tools are not yet in place (with 47% of respondents saying this), people are not equipped or trained to work in the intended way (43%), the strategy has not been translated into clear operational priorities (32%), and there is no shared understanding of the AI strategy across the organization (30%).

When strategic clarity around AI exists, it translates into more visible value. In fact, more than two-thirds of professionals in firms and departments with a stated strategy say AI is meeting or exceeding expectations for creating value at work. When there is no understandable AI strategy, less than one-quarter say this.

The quiet accumulation of human costs

The talent consequences of the gap between stated AI strategy and its daily execution are building faster than most leaders recognize. More than 90% of professionals say they are experiencing this gap to some degree. Among them, 1-in-4 is considering leaving their current organization within the next two years if things don鈥檛 change. This can result in an estimated replacement cost of $232,000 per employee, which means the quantifiable financial outlay of this talent flight can add up quickly.

The greatest vulnerability of flight risk sits with mid-career professionals, who often are the most embedded AI users, the most influential in day-to-day operations, and the most impatient with slow adoption. Almost 30% of these professionals would change jobs within two years if AI fails to deliver the value they expect, and 14% say they are considering leaving within the next 12 months.

Of course, the financial risks extend beyond mid-career professionals and could in fact disrupt a generation of early career talent. When experienced professionals leave, they take with them the mentorship and oversight upon which the development of early-career employees depends.

The report shows that 71% of professionals say they believe early-career roles need structured support from experienced peers to develop the skills that are at risk of being displaced by AI. Moreover, nearly half say they are concerned about AI鈥檚 impact on the development of independent judgment and learning through experience. In fact, legal professionals specifically say they expect the timeline for early-career lawyers to develop a level of trusted judgment could be stretched by nearly two years.

Taken together, these factors create a compounding multiplier effect that will almost certainly have a negative impact on organizational performance in the near future, the report suggests.

Recommended actions for employers and professionals

The Future of Professionals Report 2026 details three distinct paths for how organizations can deploy AI:

1. 1. 1. Elevate, which allows AI to handle rote tasks while keeping human expertise at the center;
      2. Scale, which uses AI primarily to increase capacity without increasing headcount; and
      3. Reimagine, which puts AI at the core as it rebuilds operating models and service propositions from the ground up.

The data also makes clear that the organizational and human costs of inaction are multifaceted. There are several concrete steps that both employers and professionals can take now, as outlined in the report, which include:

For employers:

• • • Choose a path and make it visible 鈥� The aforementioned three paths represent genuinely different futures with different commercial models, talent strategies, and definitions of professional value. Organizations must choose one deliberately and make it easily visible at the individual and leadership levels.
    • Close the rift in alignment before it results in talent departure 鈥�听More than one-third of professionals say they are working somewhere where the AI approach does not match their preference. These professionals are almost twice as likely to consider leaving within the next 12 months 鈥� and organizational leaders need to be aware of that.
    • Let strategy drive investments听鈥� Professionals working in organizations with a stated AI strategy are 3-times more likely to say AI is meeting or exceeding expectations for creating value at work compared to those at organizations without a stated AI strategy. This makes the value of establishing a stated AI strategy and ensuring its visible on a daily basis, a clear step for organizational leadership.

For professionals:

• • • Know which future you are working toward听鈥� Almost all professionals say they can see a future in one of the three paths. Understanding which one fits you is the first step to having a productive conversation about where the inconsistency is between your preference and your organization’s direction.
    • Invest in judgment as well as AI tool fluency听鈥� Judgment will always be a human differentiator in regard to AI, and the judgment that professionals apply on top of AI competency is what builds lasting professional value.

Professionals and organizational leaders need to make decisions in regard to their relationship with AI 鈥� and these decisions will determine the future of professional services. Those employers and professionals that choose their path deliberately, work to ensure alignment between strategy and experience, and invest in human judgment as seriously as they invest in technology will be the ones that can turn AI’s promise into a competitive advantage that compounds over time.

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Key insights:

• • • Unauthorized practice of law doctrine is a poor fit for regulating AI and legal technology 鈥� UPL remains important when people represent others in court or affect legal proceedings, but it does not translate cleanly to software. Extending UPL to software moves the doctrine away from its original purpose and toward regulating the existence of tools rather than the risk of harm.

    • Legal technology should be regulated through consumer protection principles, not tool-based restrictions 鈥� Rather than asking whether an AI product or software platform “practices law,” regulators should focus on whether the tool misleads users, performs as advertised, provides adequate transparency, or causes demonstrable consumer harm.

    • A harm-based framework can protect consumers while expanding access to justice 鈥� Because most legal needs remain unmet, AI and justice tech can provide meaningful support to people who would otherwise receive no legal help. Safe harbors, disclosures, and accountability for fraud, negligence, or false advertising would better balance innovation with consumer protection.

This is the second of a two-part blog series examining how regulators, the legal profession, and individual litigants are looking at the unauthorized practice of law (UPL). We first looked at the history of UPL, and now this installment suggests a consumer protection-based method of regulation to replace today鈥檚 supplier-based regulations.

The legal industry has reached an inflection point in which the unauthorized practice of law (UPL) doctrine is no longer the right framework for regulating modern legal services. As technology reshapes how legal help is delivered, the focus must shift from policing who can provide legal support to ensuring that consumers are protected from harm.

This does not mean dismantling the doctrine entirely. UPL continues to serve an important function in which individuals represent others in court or otherwise engage in conduct that affects the integrity of legal proceedings 鈥� because courts need enforceable boundaries around who may appear before them.

Those considerations, however, do not translate cleanly to today鈥檚 AI-driven advanced technology. Extending UPL to software may shift the doctrine away from its original purpose and toward regulating the existence of tools rather than the risk of harm. A more effective approach is to apply existing consumer protection principles 鈥� such as fraud, negligence, and false advertising 鈥� to evaluate whether legal technologies are delivering accurate, transparent, and reliable support for users.

UPL continues to serve an important function in which individuals represent others in court or otherwise engage in conduct that affects the integrity of legal proceedings 鈥� because courts need enforceable boundaries around who may appear before them.

Over the past several decades, we have bent UPL law to fit emerging models of legal assistance, but with AI we have reached the point at which we should not attempt to stretch it further. AI has expanded the capabilities of legal technology beyond document automation to include research, summarization, and guided analysis. These tools are now widely available and are being used by individuals who would otherwise navigate legal issues without assistance.

At the same time, familiar regulatory arguments are being applied to these new tools. Assertions that such systems constitute UPL rely on assumptions that are increasingly difficult to reconcile with how these technologies function and are used.

Users generally understand that these systems are not lawyers. Engagement with AI tools is driven by accessibility, cost, and convenience, particularly in a landscape in which a substantial portion of legal needs go unmet. The more relevant inquiry is whether these tools provide a meaningful improvement compared to having no assistance at all.

Shifting from UPL to consumer protection

When consumers turn to technology, outcomes will vary, as they do across all forms of legal support. When issues arise, existing legal frameworks already provide mechanisms for accountability. Questions of fraud, negligence, and misleading representations can be addressed through established consumer protection laws without relying on an expanded interpretation of UPL.

Focusing on demonstrable harm rather than the mere existence of a tool aligns regulatory efforts with their intended purpose. It also reduces the risk of limiting innovation that could address persistent gaps in access to legal services encountered by so many individuals today. When liability is tied to the mere provision of technology, rather than to harmful conduct, the result is often reduced investment and slower development of potentially beneficial solutions.

Some jurisdictions have begun to adopt approaches that reflect this distinction. Non-prosecution policies, disclosure requirements, and clearly defined safe harbors for AI-driven tools can provide a framework in which innovation can proceed alongside appropriate safeguards. Indeed, these models emphasize transparency and consumer awareness while allowing for continued experimentation and improvement.

Legal doctrine in transition

Whether state regulators drive the change or watch from the sidelines, there are broader legal considerations on the horizon. On the federal level, the U.S. Federal Trade Commission (FTC) has already signaled that a change towards harm-based regulation as opposed to tool-centered regulation is coming.

In his concurrence in , which had billed itself to consumers as 鈥渢he world鈥檚 first robot lawyer,鈥� :

鈥淢y vote should not be taken as support for the State Bar of California鈥檚 claim that DoNotPay was engaged in the unauthorized practice of law. The Commission does not enforce state occupational-licensing laws like California鈥檚 unauthorized-practice-of-law prohibition. And if a company were to create a computer system capable of giving accurate legal advice and drafting effective legal documents, or honestly advertise a system that provides something less, I doubt that the aggressive enforcement of lawyers鈥� monopoly on legal service would serve the public interest.鈥�

This aligns with remarks from U.S. Supreme Court Justice Neil Gorsuch鈥檚 confirmation hearing when he stated: 鈥淲hy is it that every time certain companies that provide online legal services for basic things get sued every time they move into a new State?鈥�

While approaching it from different angles, courts are arriving at similar positions. In , an Oregon court of appeals case about fabricated citations, the court stated: 鈥淩egardless of provider, a generative artificial intelligence program is not, itself, a lawyer.鈥� While perhaps not intended, the logical extension of this is that a tool cannot be engaged in UPL 鈥� only the human using it can.

Finally, recent judicial reasoning, such as in the Supreme Court case of , suggests a closer examination of attempts to regulate speech by categorizing it as professional activity. The Court found professional speech protected by an 8-1 vote, suggesting bipartisan questions about whether professional licensing can continue to restrict speech.

Aligning regulation with reality

Within this evolving landscape, the central policy question is how best to protect consumers while enabling meaningful improvements in access to legal support. Frameworks designed for earlier models of service delivery can be difficult to apply effectively to new forms of technology without producing unintended consequences.

Instead, a consumer-focused approach can direct attention to the quality, accuracy, and transparency of services. It can evaluate whether users are misled, whether tools perform as described, and whether harm can be identified and addressed.

While AI-driven technology offers a way to extend the reach of legal support systems and to develop solutions that operate at a scale not previously achievable, the concept of UPL is still a valid one, albeit within a more limited and clearly defined scope.

As the legal system rapidly evolves, aligning regulatory approaches with current realities allows for both the protection of consumers and the development of the tools that expand access to justice in practical and sustainable ways.

You can find more about the challenges around issues of access justice here

Key highlights:

• • • How AI-native firms redefine the lawyer’s role 鈥� AI-native firms like Paralex are gravitating toward a “technician” archetype, which puts less emphasis on the trusted-advisor dynamic that has long defined the attorney-client relationship.

    • The profession may be heading toward a two-tier split 鈥� As AI-native firms grow and normalize this operating model for a new generation of attorneys, the legal profession may bifurcate into a smaller cohort of relationship-driven advisors who provide deep, context-rich counsel; and a larger pool of proficient, AI-assisted technicians working at high volume.

    • AI firms can highlight how future lawyers learn 鈥� AI-native law firms are elevating a long-standing mentorship gap that threatens to erode how the next generation of lawyers develop independent judgment; and addressing it will require both creative AI-assisted solutions and more deliberate frameworks for deciding which cognitive tasks should remain done by humans.

The opportunity of starting a native AI law firm to test an idea is intriguing to some lawyers, especially those with an entrepreneurial instinct and determination to see the idea through. When founded , he aspired to democratize legal services for small businesses and leveraged AI to do so. His 29 years of practicing law had shown him the inefficiency and costly downsides of the billable hour; and he hypothesized that if the workflow could be automated with an attorney in the loop and could charge one-tenth of what it normally cost, demand would follow.

The reality has been more complicated and more instructive for the future of legal practice, Candelmo explains, as he offered a candid accounting of what Paralex has learned in practice.

Building for underserved small business owners

Paralex was built around a tiered service model covering everything from verified legal Q&A to AI-assisted contract drafting. AI handles the intake and first drafts at every stage, and the attorney handles the judgment. The small business transactional law vertical was a deliberate bet because the practice area is most amenable to pattern recognition and workflow automation. In addition, small business represents one of the largest pools of underserved legal clients.

Candelmo has learned that affordability alone does not unlock demand. The long-cited statistic that 鈥�60% of small businesses never use a lawyer because of cost鈥� overstates how much of that gap is price-driven. Indeed, a meaningful portion of business owners appear to not want legal counsel at any price. Free AI tools have compounded this learning because ChatGPT, Claude, and Gemini can produce a plausible contract or answer a legal question at zero cost. 鈥淧eople feel that maybe it鈥檚 just good enough,鈥� Candelmo says.

How AI-native firms redefine concept of a lawyer

AI-native firms like Paralex are discovering they need to develop exclusively the 鈥渢echnician archetype鈥� among its lawyers. The attorneys who thrive in Paralex鈥檚 workflow are those most comfortable operating at volume, untroubled by the absence of ongoing client relationships, and motivated by clean execution rather than the slower cultivation of client relationships. Candelmo describes them as comfortable with gig work because they want to be paid for what they produce rather than chasing invoices.

Young attorneys need to master the tools but not outsource their judgment to them. And they should seek out senior attorneys and cultivate human relationships that will make them more than a technician.

Candelmo shares that the trusted-advisor attorney who deeply knows a client鈥檚 business, anticipates problems before they arise, and provides counsel grounded in years of accumulated context is largely absent from the Paralex experience. He describes AI-native firms鈥� role as taking out the unnecessary back-and-forth that occurs in traditional law firms鈥� practices. At the same time, AI-native firms start out narrowly servicing a vertical by providing legal services that are optimizing for efficiency and relatively less complex.

The implication is significant for lawyers and their professional identity. As native AI firms grow and attract a generation of attorneys for whom this model is normal, the profession might be more likely to bifurcate between a smaller cohort of relationship-driven advisors on the one hand, and a larger pool of technically proficient, AI-assisted attorneys working at volume on another.

A generation of lawyers with no one to learn from

What Candelmo says he worries most about is who will teach the next generation of lawyers how to think. In AI-native environments, a junior attorney working at high throughput may review AI-generated output quickly, trust it, and move on. The output looks complete 鈥� but there is no obvious signal that something important was missing and no senior attorney to say why it matters.

Candelmo’s proposed solution is a second layer of AI tools, such as simulation tools, that can function like a senior lawyer. It reviews the initial output, flags gaps, and provides the kind of annotated feedback that would have come from a partner review in a traditional law firm.

His advice to young attorneys is to master the tools, but do not outsource your judgment to them. And they should seek out senior attorneys and cultivate human relationships that will make them more than a technician, Candelmo adds. “Ensure that your humanness, your human relationship skills make you stand apart.”

As native AI firms grow and attract a generation of attorneys for whom this model is normal, the profession might be more likely to bifurcate between a smaller cohort of relationship-driven advisors on the one hand, and a larger pool of technically proficient, AI-assisted attorneys working at volume on another.

In addition, , Partner at Foley and Gardner and adjunct professor at the teaches at the University of Wisconsin Law School, goes one step further and advocates for adding a conscious step before instinctively turning to AI tools. He suggests each lawyer first ask themselves, 鈥淲hat cognitive function is being delegated to GenAI at each step in the workflow?鈥�

In the current state, the AI conversation within the legal ecosystem continues in a good-or-bad binary rather than simply asking when AI use is beneficial and when it is risky, which is increasingly what law students are asking for. For example, the announced a policy that bans students from using AI for class assignments and during exams, although students can still use AI for research to identify sources.

The experiences of Candelmo and Paralex, alongside the broader debate playing out across the legal ecosystem, make it clear that the legal profession is being forced to make deliberate choices about what lawyers are for, which cognitive tasks should remain human, and how professional judgment is developed and passed on.

The law firms and legal institutions that build thoughtful frameworks for when and how AI should be used will create a profession that is both more efficient and more capable of producing the kinds of lawyers that clients and society will continue to need.

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Key takeaways:

• • • The dulling effect is real, but the root cause is older than AI 鈥� 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 鈥� While most organizations deploy AI for simple drafting tasks, the bigger payoff comes from using it as a discussion engine 鈥� 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 鈥� 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 鈥� 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 鈥� 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鈥檙e masking a narrowing range of underlying ideas 鈥� 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 鈥� 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鈥檚 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 鈥� the college admissions essay 鈥� 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鈥檓 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鈥檛 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 鈥� 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 鈥� 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 鈥� 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鈥檚 argument clearer. When the publishing industry rewarded a specific shape of trauma narrative in the 1980s and 鈥�90s 鈥� suffering resolved through individual resilience 鈥� 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 鈥� 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 鈥� 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鈥檛 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 鈥� Anil Doshi and Oliver Hauser, behind the , Winthrop herself points to 鈥� 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 鈥� 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鈥檚 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 鈥� and that鈥檚 exactly the kind of process organizations should be targeting. Most organizations deploying AI are using it for efficiency 鈥� drafting, summarizing, formatting 鈥� 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 鈥� 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 鈥� 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 鈥� 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 鈥� it’s the template.

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