Another day, another announcement of an incredible new capability from a large language model (LLM). This month, we saw the release of ChatGPT 4.5, OpenAI’s new “experimental” model tailored specifically for writing, along with a massive leap forward in OpenAI’s image-generating capabilities.
But, in my opinion, the most interesting development in AI isn’t about the technology itself — it’s on the human side of the equation. It’s not just that LLMs are getting better; it’s that we, as humans, are becoming smarter about leveraging them. Here, I want to focus on a recent study from Harvard Business School on working alongside LLMs, drawing out key lessons for lawyers and other professionals who want to leverage AI effectively in our own practices.
The benefits of AI-assisted collaboration
Much of the conversation around AI presumes solitary tasks—coding, drafting, researching. But what about collaborative work? That’s precisely the question posed by Harvard Business School researchers, working together with Procter and Gamble.
In their study, the researchers conducted workshops at Procter and Gamble, tasking professionals with typical business challenges: developing product ideas, packaging designs, and business strategies for units like baby products, feminine care, grooming, and oral care.
Participants were split into two categories—some working as teams, others as individuals. Half of each category had access to ChatGPT, while the other half didn’t. Researchers evaluated the participants’ outputs on criteria such as quality, efficiency, and emotional response.
Their findings were illuminating. Teams (and individuals) who worked alongside AI did much better than those who didn’t. Specifically:
- People working alone (but with the help of AI) did better than better than teams without AI.
- Teams with AI outperformed teams without AI.
- Teams working with AI did about the same as individuals with AI — but the teams were more likely to get results in the top 10 percent.
Together, these results suggest a few things, some more obvious than others. Using AI seems to give an advantage over not using it. And someone working alone with AI can do the work of a team that doesn’t use it.
Collaborating with AI doesn’t mean asking it to do your work for you
There’s a crucial aspect to highlight here: the professionals were coached on how to effectively integrate ChatGPT into their workflows, and provided with targeted, practical prompts. Specifically, these prompts covered:
- Basic research, e.g., “You are an incredibly smart and experienced research assistant. Gather information to help analyze the following problem. Request relevant documents from the team and ask clarifying questions to narrow your research focus.”
- Alternative structuring of problems, e.g., “Introduce yourself to the team and let them know that you are here to help them analyze the problem. Explain that reframing a problem can be helpful because it can help shift the focus and help the team look at the problem from different angles and because it can encourage creative thinking. Then, given the framing of this problem, suggest 3 to 4 different ways to frame the problem. Tell the team they can pick any framing they like and work through this with you.”
- General ideation, e.g., “Generate new product ideas with the following requirements: [Insert problem statement]. First, generate a list of 20 ideas (short title only). Second, go through the list and determine whether the ideas are different and bold, modify the ideas as needed to make them bolder and more different. Next, give the ideas a name and combine it with a product description. The idea should be expressed as a paragraph of 40-80 words.”
(I’ve included the full prompts at the end of this blog post).
Notice that none of these prompts asked the AI to complete the project itself. Instead, they leveraged AI to enhance and support human thinking.
Consider where LLMs excel:
- Summarizing information
- Rewriting or rephrasing ideas
- Suggesting creative ideas (even if imperfect)
These aren’t inherently creative activities, but they certainly can support the creative process. The Proctor and Gamble groups used AI on routine tasks—summarizing, rewriting, generating preliminary ideas—that traditionally occupied human bandwidth. By offloading these tasks to AI, humans were freed up to contribute their own more creative, insightful, and integrative thought to their efforts.
Consider also LLMs’ well-known and notable flaws, including hallucinations, not knowing where to stop digging, jumping right to solutions, and not knowing their own limits — all areas that can introduce subtle errors and uncertainty into AI output. All of those problems are easier to spot and correct when multiple team members review the AI-generated outputs.
What you can do
- Replicate the experiment! I’ve consistently advocated for a curious, exploratory approach to AI. Why not run your own small-scale hackathon?
- Choose a common task or challenge, divide your participants into two groups—one using AI, the other not—and clearly define how you’ll measure outcomes.
- Evaluate and discuss the results. You’ll gain invaluable insights into effectively integrating AI into your workflow.
- Reframe your thinking of AI as a supporting player. AI is not human and comes with inherent limitations and flaws.
- Don’t assign entire tasks to AI. Instead, delegate specific components—summarizing, ideating, rephrasing—allowing humans to focus on higher-order thinking and decision-making.
Example prompts
These prompts are taken from Fabrizio Dell’Acqua, et al, “The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise” (March 28, 2025), Harvard Business School Strategy Unit Working Paper No. 25-043, available at SSRN: https://ssrn.com/abstract=5188231
Problem Definition
Basic research
You are an incredibly smart and experienced research assistant asked to gather information to help analyze the following problem: [Insert Problem Statement] First introduce yourself to the team and let them know that you want to help the team begin their research process. Second ask them for any documents they might have to help you with research. Then ask the team a series of questions 2-3 about the problem (ask them 1 at a time and wait for a response). You can also suggest responses or offer up multiple-choice responses if appropriate; if applicable, provide an all or none of the above option. The goal is to narrow down your research focus. Then gather what information you can to try and answer those questions using the documents and what you know. Actually do it. Dont just say youll do it. You can also suggest other avenues for exploration to help analyze the problem.
Consumer Simulation
For five different consumers that have [Insert PROBLEM] provide the following in a succinct way:
Describe your consumer (WHO) and their Job To Be Done (JTBD), Problem to Solve (WHAT)
Describe the consumers current habit & how they solve the problem today.
Alternative Structuring of the Problem
You are an innovation specialist and helping a team work on the following problem: First introduce yourself to the team and let them know that you are here to help them analyze the problem. Explain that reframing a problem can be helpful because it can help shift the focus and help the team look at the problem from different angles and because it can encourage creative thinking. Then, given the framing of this problem, suggest 3 to 4 different ways to frame the problem. These can include 2×2 graphs, Porter’s Five Forces, Root Cause Analysis, the 3 Ps for positive psychology, and more. Number those and actually frame the problem in italics within the frame. Tell the team they can pick any framing they like and work through this with you. You should work with the team, ask questions, make suggestions, and help them analyze this problem. Your role is not to find a solution but to analyze the problem.
Ideation
General Ideation
Generate new product ideas with the following requirements: [Insert problem statement]. The ideas are just ideas. The product need not yet exist, nor may it necessarily be clearly feasible. Follow these steps. Do each step, even if you think you do not need to.
First, generate a list of 20 ideas (short title only). Second, go through the list and determine whether the ideas are different and bold, modify the ideas as needed to make them bolder and more different. No two ideas should be the same. This is important!
Next, give the ideas a name and combine it with a product description. The name and idea are separated by a colon and followed by a description. The idea should be expressed as a paragraph of 40-80 words. Do this step by step!
Five Vectors
Generate new product ideas for [INSERT PROBLEM] using the 5 vectors of superiority from P&G. The vectors are: Superior Product, Superior Packaging, Superior Brand Communication, Superior Retail Execution, and Superior Customer and Consumer Value. Generate 5 ideas for each vector. No ideas should be the same.
Constrained Ideation
Pick 4 random numbers between 1 and 11. Then, for each number, look at the appropriate lines on the list below and use the constraint you find for that number to generate an additional 3 ideas that solve the question but adhere to the constraints. Take the constraint literally.
List:
1 Must rhyme
2 Must be expensive
3 Must be very cheap
4 Must be very complicated
5 Must be usable by an astronaut
6 Must be usable by a superhero
7 Must be very simple
8 Must appeal to a child
9 Must be scary
10 Must be related to a book or movie
11 Must be made only of natural products
Selection
Read all the ideas so far. Select the ten ideas that combine feasibility, uniqueness, and the ability to drive a competitive advantage for the company the most, and present a chart showing the ideas and how they rank.
For each idea in the chart, describe the main features and functionalities of the proposed solution and how we might drive category growth (i.e., # of users, usage occasions, premiumization).