e51 Discovery artifacts are vacation photos, and agents were never there
Teams build shared understanding and intuition through product discovery. Discovery artifacts are an imperfect representation of what was learned, but help the team remember what they did together. Since agents don’t participate in the work, those imperfect artifacts need to get better to help them move in the right direction.
Photo by Jakob Owens on Unsplash
☕ Discovery artifacts are vacation photos, and agents were never there
You’ve almost certainly been there before. A friend comes back from a holiday and excitedly shows a photo on their phone of a sunset or a city, talking with clear energy about their experience. The photo is nice, but doesn’t particularly stand out, and somehow their excitement doesn’t transmit to your mood. You were not there to feel it as they do.
A few years ago I heard the “vacation photo metaphor” from Jeff Patton, and it made me realize why I often felt the same about discovery artifacts, like maps, decks or rationale documents. Artifacts, just like vacation photos, help people relive and recall the details, but they don’t give people who were not there the same amount or quality of information. This sets a boundary on how we do product discovery.
Product discovery is a team effort, and while it needs different expertise to work with the four product risks (usability, value, business fit, feasibility), the main goal is to build a shared understanding of both the problem space and the solution space for the team.
Developing the shared understanding is what develops the product intuition. Team members can then solve the problems in their domains, provide a coherent solution, and make decisions that ultimately lower the product risks. The outcome of discovery is to know what is the right thing to build, both for the current project and, through the better intuition, for future ones.
Of all the activities in product discovery, the most important is drawing conclusions together. This is why it’s important to broaden discovery beyond user research, so everyone also understands the product constraints and technical options. That understanding is externalized by shared artifacts.
Shared artifacts are helpful, as making something together is better than just talking about things: it gives embodiment to the ideas in everybody’s head, lets those ideas get richer with additional information, and shapes them into a matching form among the team members.
There is a difference between a well made, designed, polished artifact that somebody made on their own, so pretty we could frame it and hang it in the corridor, and a messy whiteboard with post-its everywhere and some messy rectangles obviously drawn by a non-designer, done together by the team. The difference is in how team members absorb the conclusions afterwards. While socializing results can be helpful, working the insights directly is far more effective.
To make something together, you have to be there. Working with the details directly is what helps develop the intuition that answers the questions not yet asked, the unknown unknowns.
This also tells us something about the quality of discovery artifacts. In the past I’ve argued that most discovery artifacts, just like photos, don’t need that much care, as they are intended for the team working with them. If teams worked together, they developed their intuition together and almost any artifact would have worked for them. Artifacts never carried the shared understanding, the people did.
To make a vacation photo work well for others, it has to be really good, much better than what you would usually do. You need to think about framing, lighting, composition, details and colors, wait for the right moment, and tell the story in a way that helps the viewer connect to the experience more closely. This helps the photo transcend the individual’s experience and transmit richer information.
With more and more teams enhancing their discovery with agentic tools, the expected quality of artifacts changes. Teams now use agentic tools that can’t be there in the shared sessions (and transcripts are a poor proxy), and that need richer context than average to make decisions of the same quality a team member would.
Agents can never participate. They rely on context from us and don’t do the work of embodiment, which is part of why AI findings are so often described as shallow and generic. For agents to absorb the context properly, the new artifacts need rigor and inspiration that goes beyond the transcript or the final artifacts.
This rigor also means agents are better suited to some parts of the discovery process than others. Outsourcing synthesis to agents both keeps the team from developing the intuition they need and removes some of the rigor the agents would need.
So where does this leave us? While agentic tools promise to speed things up, they also take more time to accommodate. We have to budget for both: time for the team to work together, and time for making artifacts good enough for whoever wasn’t there. Cutting the first to pay for the second is a trade that looks efficient and isn’t.
🚲 Questions to consider
- Who on this team has the intuition, and how did they get it?
- Which of our artifacts were made together, and which were made alone?
- If an agent only had our written record, what would it get wrong?
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🍪 Things to snack on
Why Documents Fail And What You Can Do About It by Jeff Patton (jpattonassociates.com, 10 November 2014)
The origin of the vacation-photo metaphor. The argument is specifically about asymmetry. Documents reinforce for those who were present and substantially under-serve those who weren’t.
Product design documents are like vacation photos. They help the people that were there relive and recall details, but fail to give people who weren’t there the same amount or quality of information.
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AI Can Synthesize Data for You—But Should It? by Christina Wodtke
Names the loss precisely, as in, the issue isn’t whether AI synthesis is accurate, it’s that wrestling with raw information rewires the brain of the person doing it, and that’s where product intuition comes from. This is grounded in embodied cognition and there is a case for physically moving ideas around.
When AI does the synthesis, you skip an essential part of the process. You don’t inhabit the data.
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Description, Depiction, Delivery by Dan Ramsden
Categorizes discovery modes based on what kind of evidence they produce (alignment, response, outcome) and argues teams falter when they ask one mode to prove what another is for. The most operational counter to treating a cheap prototype as validation.
Accountable discovery means being explicit about the evidence we have, what evidence is missing, and why we’ve made the decisions we have.
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Discovery Hand-Offs Kill Momentum: Here’s What to Do Instead by Teresa Torres
Frames every role-to-role transfer as a game of telephone in which context and nuance are lost, and locates the value of the product trio in removing transfers rather than improving them. Useful because it makes participation, not documentation quality, the variable that matters.
Good discovery establishes a direct communication line between the team who is building the product and the customer.
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AI Made Product Teams Faster, Not Better by Janna Bastow
How things are changing in 2026, agents accelerate shipping without strengthening problem validation. Teams already building the wrong things now do it more efficiently. Directly names polished AI-generated artifacts as a source of false confidence, and puts the fix at the incentive level: reward validated learning, not throughput.
AI doesn’t eliminate bad product decisions. It amplifies them.
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Continuous Discovery at Scale: What Changed with AI by Joca Torres
With the new stack agents can do automated planning, recruiting, conducting and analyzing, which removes the barriers that made continuous discovery impractical at scale rather than removing the need for contact. This is the new boundary we can now assume.
This does not replace an in-person conversation or a deep research session.
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Fast Path to a Great UX – Increased Exposure Hours by Jared M. Spool
There is a direct correlation between hours of direct team exposure to users and design quality, with the explicit finding that reported results are not a substitute. Also dissolves the sample-size debate: two hours with one participant can beat eight participants at fifteen minutes each.
Teams that have dedicated user research professionals, who watch the users, then in turn, report the results through documents or videos, don’t deliver the same benefits.