Tag Archives: Robert MacKenzie

Offloading, not Surrendering, to AI

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Robert MacKenzie and I published a column in Corporate Compliance Insights based on a recent eCornell workshop we taught. It reads,

We have been teaching lawyers how to use generative AI in their actual work — the drafting, reviewing and decision-making that fills their days. But when we designed our workshop, “Generative AI for Business Transactions,” we built it for a broader range of professionals: the healthcare compliance officer who had never opened ChatGPT, the corporate counsel whose legal department had recently deployed Harvey, the financial analyst running queries through Gemini and the operations manager who had heard the buzz but didn’t know where to begin. What we found confirmed what we suspected: the gap between professionals experimenting with AI and those waiting on the sidelines is widening fast. The ones who will thrive are not those using AI most aggressively but those using it most deliberately.

AI is transforming professional work

Generative AI is reshaping professional workflows in every industry we have encountered. In our workshop, we organize its everyday applications into four areas: communication, such as turning bullet points into polished emails and summarizing meeting transcripts; ideas and content, such as brainstorming and adapting material for different audiences; people and careers, such as preparing for interviews and difficult conversations; and money and numbers, such as building budgets, comparing costs and translating dense financial or legal language into plain English.

The best use cases are for time-intensive tasks. A transactional lawyer compares indemnification clauses across a dozen precedent agreements. A healthcare administrator turns regulatory guidance into a compliance checklist. A finance team compares top holdings across multiple fund prospectuses. The common thread: AI tools excel at quickly doing first-pass, high-volume work that used to consume hours.

A practical framework for responsible use

Every industry carries confidentiality obligations. Privilege in law, HIPAA in healthcare, fiduciary duties in finance, trade-secret protections in business. AI introduces a new exposure vector for professionals who are not careful about which tools they use. A key distinction we identify is the level of control and protections granted by enterprise AI tools versus consumer or free-tier tools. Enterprise tools are provided under negotiated contracts that typically commit the vendor not to train on your inputs and to keep your data confidential Consumer or free-tier tools often are packaged with settings permitting the provider to train on whatever information you input into the tool, undercutting confidentiality obligations you may be subject to. Vendor policies and features change, so verify that your expected protections are in place rather than assume.

We summarize this verification discipline in three words: pause, read, protect. Pause before entering data and ask whether it is safe to share and whether your workplace policies or professional obligations permit use of the tool for the intended purpose. Read the tool’s terms, and your workplace policies or guidance regarding the tool, to understand how your information will be treated. Protect by changing default settings, anonymizing confidential details and ensuring your cybersecurity and IT teams are in the loop when seeking to use new tools or approving use of updated features.

For task-level decisions, we recommend users adopt a red/yellow/green triage system. Red tasks are high importance and high risk and never get delegated to AI (e.g., strategy, high-stakes judgment calls and final approvals). Yellow tasks are lower importance and lower risk and may be delegated because they benefit from AI’s speed, but require competent human oversight and verification (e.g., research, first drafts and issue analysis). Green tasks are low importance and low risk and may, and sometimes, should, be delegated to AI, with minimal required human oversight (e.g., document reformatting, routine correspondence preparation and generation of ideas). If you supervise a team, you should be thinking about how you triage and how you want your team to triage matters. A breakdown in expectations can produce a “garbage-in, garbage-out” cycle.

Evaluating AI outputs critically

Our key takeaway is that AI’s greatest value lies in refining professional judgment, not replacing it. Generative AI is probabilistic, not deterministic. This means that the same prompt can produce different outputs in the same tool across different sessions. Models predict the next likely word in a sequence; they do not understand your question or verify their own answers.

Our recommendation to be effective with this technology: tell the tool what you need and be dynamic in your approach to prompting and task execution. We teach a simple prompting framework that is easy to recall and apply: RCTF—role, context, task, format. R: assign the AI a role. C: provide relevant context. T: define the task precisely. F: specify the output format. We think of this framework in the same way as ordering at a drive-thru. You would not pull up, say “food,” and expect to get what you want. You need to say what you are ordering, how you want it and where to hand it to you.

Other effective strategies we recommend professionals are:

    • Chunking. Breaking tasks into smaller pieces to keep tools on task.
    • Few-shot prompting. Provide examples of good work products to the tool before commencing a task.
    • Iterative refining. Adopting a “the first answer is a first draft” mindset.
    • Flipping interactions. Ask the tool to guide you on how to use it for a particular task.
    • Perspective switching. Assign the tool competing perspectives to pressure-test your work.

Managing hallucinations & overreliance

AI tools are known to generate plausible-sounding outputs that contain errors and invented citations. They also misread sources and silently drop items from long documents. These “hallucinations” are not bugs that will be patched away; they are inherent to how large language models work.

A deeper risk for inexperienced users of AI tools is what Wharton researchers Steven D. Shaw and Gideon Nave call “cognitive surrender.” In their 2026 study spanning three experiments and more than 1,300 participants, they found that participants were highly susceptible to following incorrect advice from AI tools. Access to an AI chatbot during the experiments appeared to inflate participants’ confidence in their answers, even when the answers were wrong. Observations like these point to a broad human tendency towards cognitive surrender: When a fluent, confident-sounding tool delivers a coherent answer, the pull to accept it is powerful.

We want to draw a sharp distinction between cognitive surrender — letting AI do your deliberate thinking and accepting its output uncritically — and “cognitive offloading” — handing defined steps to AI while retaining control of the overall analysis. The first is a professional hazard. The second is a legitimate productivity strategy. After every substantive AI-assisted task, ask yourself: Have I thought this through as fully as I would have without the tool? If not, dig back in.

Building reusable templates & checklists

One of the highest-value applications of generative AI is converting complex source documents into workflows a team can reuse, such as checklists, trackers and comparison matrices. In our workshop, we demonstrate how to take a dense document and instruct AI tools to produce a structured checklist to capture desired variables, like task status, assigned parties, deadlines, source references and risk flags.

We also teach benchmarking: uploading a set of similar documents and directing the AI tool to create a comparison matrix of key terms among the documents. AI tools offer value in their continually improving (but imperfect) ability to accurately extract and categorize information from new documents based on historical templates. For professionals with high accuracy needs, this skill can offer considerable leverage by accelerating the manual steps in these types of workstreams (initial review, identification and extraction or summarization of terms).

The bottom line

Whether you work in law, healthcare, finance or any field built on complex documents and careful analysis, the starting point is the same: Develop your own judgment first, verify before you rely and triage every task before you hand it off.

Generative AI for Business Transactions

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I am teaching an online course with Robert MacKenzie on July 9th about Generative AI for Business Transactions: Practical Applications for Professionals through eCornell. The Workshop Overview reads,

Generative AI is transforming how business transactions are conducted, from drafting and reviewing contracts, to summarizing due diligence materials, to benchmarking contract terms across industries. Many professionals, however, lack a practical framework for integrating these tools responsibly into their workflows.

This Workshop offers a hands-on introduction to applying AI in real-world transactional work. Participants will draft and refine communications, review and benchmark contract terms, and build compliance checklists and workflow playbooks, all while comparing outputs across AI tools to understand strengths, limitations, and potential errors.

Throughout the session, we focus on ethical, legal, and practical considerations, helping participants use AI as a complement to professional judgment rather than a substitute. By the end, participants will leave with reusable workflows, practical experience, and a clear approach to integrating AI responsibly into business transaction processes.

The Key Workshop Takeaways include,

  • Apply AI to core transactional workflows, including contract drafting, precedent comparison, and due diligence summaries
  • Evaluate outputs across multiple AI tools to understand their strengths, limitations, and differences
  • Manage key risks of AI use, such as hallucinations, confidentiality exposure, and overreliance, while integrating AI responsibly
  • Build workflow templates and checklists to structure AI-assisted tasks for consistent, reliable outcomes

 

Law Schools Should Teach How to Integrate AI Tools Into Practice

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The Cornell Law Forum republished an article that I wrote with Robert MacKenzie, Law Schools Should Teach How to Integrate AI Tools Into Practice. It opens,

Now that artificial intelligence tools for lawyers are widely available, we decided to integrate them for a semester in our Entrepreneurship Clinic. We have some important takeaways for legal education in general and the transactional practice of law in particular.

First, employers and educators need to account for law students who already are using AI tools in their legal work and guide new lawyers about how to use such tools appropriately.

Second, different AI products lead to wildly different results. Just demonstrating this to law students is very valuable, as it dispels the notion that AI responses can replace their independent judgment.

Third, AI’s greatest value may be in refining legal judgment for lawyers in ways that can help new and experienced lawyers alike.

Cornell is Hiring a Transactional Clinician

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By Claude-Étienne Armingaud – Claudé, CC BY 2.5

Cornell Law School is hiring! We are looking for a clinical professor of entrepreneurship law who will work with our Entrepreneurship Law Clinic and our Blassberg-Rice Center for Entrepreneurship Law. Our students work with clients with a diverse range of entrepreneurial efforts, and in the process gain valuable skills for their legal careers. If you are interested in helping to train the next generation of entrepreneurs and the lawyers who will serve them, please consider applying. Or if you know of other suitable candidates, please let them know of this great opportunity in Ithaca.

The full job posting is here.

Incorporating AI Tools Into Your Legal Practice

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Image Generated by ChatGPT

I published Advice for Incorporating AI Tools Into Your Legal Practice along with Celia Bigoness and Robert MacKenzie in the National Law Review. It reads,

We have been speaking with many lawyers and law students about using generative artificial intelligence (AI) tools in their legal practice. We are struck by the fact that many of them have not been experimenting much, if at all, with the tools that are available to them – although many acknowledge that their clients are increasingly integrating generative AI into their businesses. We have been integrating a lot of these tools into our own professional lives, and here are some tips to help lawyers and law students get comfortable with AI tools that can help them, in big ways and small, with their job.

Put it on Your Home Screen

Put your preferred AI app (ChatGPT, Claude, etc.) onto your phone’s home screen and be sure to allow it to access your phone’s microphone. You will be surprised by how often you get the urge to ask the app slightly complex questions that a basic web search would not answer. (Hat tip to one of our kids for this idea.)

Start with the Familiar

Trusting the output of an AI tool without having the ability to verify its accuracy is okay if you are choosing a movie to stream tonight. It is not okay if you are using it to provide legal advice to a client. To get comfortable with AI tools, start using it for tasks that you have experience executing and reviewing. One simple way to start: explain a familiar task to the AI tool and ask it for guidance on how you can use it to complete the task.

As you use AI tools in newer areas, you want to review the cited sources in the AI output to confirm that you agree with the AI model’s interpretation of them. Sometimes they are plain wrong, sometimes the AI model misinterprets the cited documents, and sometimes those documents are out-of-date.

When the stakes are greater than your personal entertainment, you need to do a lot of due diligence before you adopt an AI tool’s findings.

Use Multiple Tools

Different AI models are built on different training documents and have different algorithms that they apply to those documents. There is nothing more edifying than running the same queries through a few general AI models and a few specialized ones (like those geared to lawyers, in particular). You will see a range of answers, from non-answers to highly specialized and accurate ones. You will start to become a more sophisticated consumer of the different models, understanding each of their strengths and limitations.

Tell It Your Needs

Most AI tools will tailor their responses to your preferences. In some cases, we created a prompt to instruct the AI tool that responses should be of the type that a lawyer would like to receive—providing sources, explaining its analytical steps, and what it did and did not consider. The AI tool responded that it would be precise, answer “above a lay level,” and “be candid about uncertainty.” This has improved its answers and had the side effect of reducing sycophantic language (“That is a very good question!”).

Use it for Your Pain Points

We all have some routine tasks that we find irritating. They are usually the ones we procrastinate on. For some, it is preparing slide decks. For others, it is drafting certain kinds of emails (unpaid bills, anyone?). Just getting a first draft from the AI tool often helps you to finish the work up. But for some tasks, like preparing presentation slide decks, you can save hours and hours of your time.

We have experimented with both general AI tools and those that specialize in slide deck preparation. They have pros and cons, but are generally very helpful. In all these cases, the AI tool’s time savings are in large part due to the fact that the AI tool is optimizing a task that you are capable of doing yourself. You are able to quickly verify and edit the output.

However, if you were asking the tool to analyze a topic with which you are unfamiliar, or perform a task that you’ve never done before—if you’re learning from scratch—you will still need to go through the painstaking process of checking sources and confirming output.

Play in Vaults

One game-changing use of AI tools is to upload documents to a secure location in the cloud (sometimes referred to as a “vault”) and hone the tool’s focus on only those documents. A transactional lawyer can upload hundreds of documents and quickly identify commonly appearing terms for comparison or inconsistencies among them. A litigator can upload thousands of pages of litigation documents and create a draft chronology of events. Again, the output cannot be taken at face value due to the functional limitations of these tools, but it can provide an extraordinary first draft that can then be verified and edited to the form you prefer. This can be a game-changing use of AI for lawyers, as long as you have verified the vault’s security in advance.

Use it as a Second Set of Eyes

This is a great and scalable tip for those who are skeptical of AI tools. After you have completed a written task, ask an AI tool to critique for clarity, coherence, and accuracy. Even an experienced attorney will get at least a couple of suggestions that will ring true. And of course, you can reject all of the suggestions that you disagree with. This is a great way to see if an AI tool can provide you with real value with very little investment of your time.

Along the same lines, for more advanced experimentation, you can use the AI tool to issue spot and offer counterarguments to your work to complement your own analysis. Again, this is very low stakes because you can reject anything you find wrong-headed or irrelevant. Of course, you need to be careful about sharing privileged information (see vault security above).

Preserve Confidentiality

We have spent more time than many of you would like looking at the Terms of Use of the AI tools we have used. Except for certain tools that are developed for legal work in particular, we believe that the attorney-client privilege can be compromised when using many AI tools because of how the tools use your input information.

We have had students and clients who wanted to use AI transcription tools to compile meeting notes. We have advised them that confidential information can be compromised by such tools and that we do not use them in our practice, at least at this time.

If you begin to use a tool with client-identifying information, be sure to confirm that you are complying with your professional responsibilities to preserve client confidences.

Don’t get Lazy!

We all read the headlines about lawyers who use AI to draft legal documents and do not check to confirm that the work product is correct. Those lawyers rightfully face professional discipline and reputational consequences. We can all say that we would never do that, but a new term has arisen to describe an unthinking reliance on AI: “cognitive offloading.” This offloading occurs when we reduce our own deep research and thinking because of an unhealthy reliance on AI tools.

Every time we complete a substantive task with AI, we need to ask if we have thought through the task as fully as we would have if we did it without the tool. If the answer is no, we need to dig into it again. Cognitive offloading is a particular concern for law students and younger generations of lawyers, who have grown up with technology and tend to be more comfortable using AI tools – and therefore more susceptible to this unthinking reliance.

Conclusion

From our discussions with lawyers in private practice, it is clear that AI tools are being used in the ways we have mentioned above. No doubt, more specialized tools are in development. It’s clear that AI will transform the practice of law in the coming years. Those who are new to AI can use these pointers to begin exploring how AI works. We think they can amplify their effectiveness to the benefit of their clients and themselves, so long as the risks that AI tools pose are thoughtfully addressed.

 

Consumer Law Awardee, alongside Senator Warren

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David J. Reiss (right), clinical professor of law and research director of the Blassberg-Rice Center on Entrepreneurship Law at Cornell Tech and Kara Bruce (left), head of the AALS Section on Commercial and Consumer Law and the Graham Kenan Distinguished Professor of Law at the University of North Carolina School of Law.

Via Cornell Law School:

David J. Reiss, clinical professor of law and research director of the Blassberg-Rice Center on Entrepreneurship Law at Cornell Tech, was among the distinguished law professors honored at an awards ceremony held January 9 during the Association of American Law Schools’ (AALS) Annual Meeting in New Orleans.

Reiss was recognized with the Section on Commercial and Consumer Law Juliet Moringiello Mentorship Award, which was also given to Senator Elizabeth Warren, Harvard Law School (Emeritus). “I am extremely honored to share this award with Senator Elizabeth Warren, who is a hero to many of us who work on consumer protection issues in the financial sector,” said Reiss.

Kara Bruce, head of the AALS Section on Commercial and Consumer Law and the Graham Kenan Distinguished Professor of Law at the University of North Carolina School of Law, presented Reiss with the award.

“In his twenty-three-year career in law teaching, David has founded and directed a variety of law clinics, merging discrete areas of law such as real estate, consumer, and small business law into programs that offer broad support to the communities they serve. Along the way, he has guided and supported many clinicians, adjuncts, and fellows as they found their footing in legal academia,” said Bruce.

Praised for his mentorship-focused activities at Cornell, Reiss spent the past semester co-teaching with Robert MacKenzie, the Davis Polk & Wardwell LLP Clinical Teaching Fellow at New York University School of Law, as he entered the academy. “Watching his excitement as he figures out how he wants to approach teaching, scholarship, and service over the decades to come is a joy in its own right. And to the extent I can offer any advice that he might find useful, I am very happy to do so,” said Reiss.

Reiss remains connected with the practicing bar as a fellow of both the American College of Real Estate Lawyers and the American College of Mortgage Attorneys. He also has a forthcoming book, Paying for the American Dream: How to Reform the Market for Mortgages, that will be published by Oxford University Press.

At the award ceremony, Reiss said, “Receiving this award drove home to me how we are a community of scholars who work with each other and rely on each other to make sense of the immense complexity of commercial law and to understand the implications of its structure for consumers and businesses.”

Integrating AI Tools Into Law School Teaching

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Robert MacKenzie and I have an article in Bloomberg Law, Law Schools Should Teach How to Integrate AI Tools Into Practice. It reads,

Now that artificial intelligence tools for lawyers are widely available, we decided to integrate them for a semester in our Entrepreneurship Clinic. We have some important takeaways for legal education in general and the transactional practice of law in particular.

First, employers and educators need to account for law students who already are using AI tools in their legal work and guide new lawyers about how to use such tools appropriately.

Second, different AI products lead to wildly different results. Just demonstrating this to law students is very valuable, as it dispels the notion that AI responses can replace their independent judgment.

Third, AI’s greatest value may be in refining legal judgment for lawyers in ways that can help new and experienced lawyers alike.

Legal AI Prep

As we were planning our syllabus over the summer, we provided formal training in AI tools designed for lawyers. A librarian provided us an overview of products from Bloomberg Law, Lexis, and Westlaw early in the semester.

Before the training, we asked students how they were using AI in the legal work. Their responses ranged from “not at all” to “I start all of my case law research on ChatGPT.”

We were confident that our students would be better off operating somewhere between those extremes. Over the semester, we demonstrated how AI could enhance the speed and quality of legal work, as well as the dangers of outsourcing research and judgment to an AI tool.

AI Tool Differences

Perhaps the training’s most valuable takeaway was that each tool had access to different databases of materials and had different constraints. We designed simulations that required groups of students to complete the same transactional tasks (drafting, researching, benchmarking market terms, and crafting effective client emails) using various AI tools.

In one exercise, students acted as counsel to a small business owner. The “client” emailed them asking about standard-form contracts relevant to their industry and what pricing mechanics such contracts use.

For the research stage of the task, all teams located a standard-form construction contract, but only half of them found the industry-accepted standard form that we contemplated. The others located this form later by modifying their search approach. This helped to demonstrate some limitations of AI tools.

For the client communication stage, some teams failed to answer the “client’s” questions. This isn’t something the AI tool could address on its own, and it reminded students to constantly refocus on the big picture in addition to individual tasks.

We found that AI tools built on widely available AI platforms such as ChatGPT produced the most responsive outputs and were most forgiving of haphazard prompting. But certain specialized legal AI tools often failed to answer the prompt.

This is a double-edged sword. Although the generally available tools were more likely to generate an answer, they also were more prone to providing unreliable outputs. By contrast, the specialized tools hallucinated much less frequently but regularly stopped short of fulfilling a request if it required work beyond their guardrails.

Delegating Work

Our final takeaway was that AI was surprisingly good at issue-spotting and double-checking a lawyer’s work product. These uses can help both new and experienced lawyers.

We used the idea of delegation to make this point to our students. AI is fast, adaptable, and always available, so it’s a great resource. But you should only delegate work to it when you can verify its output.

In one exercise, students had to issue-spot risks and approaches after a “client” described a business opportunity. Students brainstormed in small groups. There was a lot of overlap, but some groups thought of items that others had not. We added the items to a collective list, relying on our years of practice to guide the students through gaps that remained.

Once we had a strong collective list of items, a team asked an AI product to issue-spot the same scenario. It generated most of the items in our list, some that weren’t relevant, and—most importantly—a couple that no one had raised.

This was a valuable lesson: AI had something to add to our analysis, but we had to exercise independent judgment to determine whether its contributions merited further thought.

Important Takeaways

We asked students for feedback on our use of AI throughout the semester. The most valuable feedback was that they wanted to develop their own legal judgment and learn how and why certain tasks are completed before relying on AI.

This echoes the transition from book-based legal research to electronic legal research. There was some value in searching the law reports in the library, but electronic legal research won out because it was so much more efficient. Yet even with this enhanced efficiency, a responsible lawyer must understand how to build a strong research plan and actually read the cases they cite.

In the clinic, our goal is student learning. It was for this reason that we liked to deploy the AI tools at the end of our exercises: You do the work and then interrogate it with the AI tools of your choice.

Such an approach ensures law students get the benefit of struggling through first repetitions of new tasks while allowing them to generate superior work product with fewer drafts. This process requires discipline. Legal education and legal employers need to clarify the line between AI as a tool versus AI as a crutch.

We learned a lot about how AI tools can help law students develop into good lawyers. As those tools are integrated into legal practice, lawyers of all experience levels should take a self-conscious approach to using them.