We're All in the Lab Now
AI is full of unknowns, product development requires "labs-thinking"
For over a decade, I have made my career inside corporate innovation labs. If you have ever worked in one, you know the specific energy of that environment. When they are run correctly, leadership provides the political cover for elite teams to obsess over what comes next. We operated on informed hunches, high-velocity, and a healthy disregard for the status quo. We tracked global shifts, interviewed customers to understand their deepest pain points, experimented with tech that barely worked until it worked, and built prototypes that essentially served as continuous experiments on the future.
Lately, it seems the corporate innovation lab has lost some of its luster in the traditional enterprise. I believe that is generally an oversight. With the right leadership and a protected environment, these teams can be transformative. However, as I look at the landscape in 2026, I realize something many have missed. While corporate innovation groups seem to be disappearing, the mindset is more essential than ever. Because of AI, an experimental “labs” approach is now the baseline requirement for survival for product development. Companies that miss this will miss out on commercializing AI.
We are all in the lab now.
The Jagged Frontier of Probabilistic Products
The need for this mindset is driven by a concept known as the Jagged Frontier. Originally coined by researchers from Harvard, MIT, and Wharton, it describes the unpredictable landscape of AI capabilities. For certain complex tasks, AI is superhuman; for others, it fails in ways that are difficult to anticipate.
In a traditional “corporate factory,” the frontier of tech capability is a smooth, predictable line. You know exactly what a Senior Engineer or a Junior Designer can contribute and how long a task will take building deterministic technology. This predictability allowed for the classic, largely predictable “Assembly Line” of software: Specs to Build to QA. We became experts at building deterministic products because we knew exactly what the output should look like.
LLMs have changed that. We are now building probabilistic products, which makes the old linear process much harder to manage. An AI agent might architect a complex backend in seconds but then fail to center a button on a UI or hallucinate a security vulnerability. Because you do not know where these “dips” in capability are until you hit them, you can’t rely on a linear factory process. You have to use a lab process of constant, rapid fire exploration. This is not just building a CRUD app on a schedule; it is a journey of continual learning.
Navigating the Exploration of Market and Technology
In this new reality, the speed of exploration is a survival metric rather than a luxury.
Historically, customer development was built on a foundation of scarcity. Writing code was expensive and slow, so you obsessively interviewed customers to gather every possible scrap of info before committing a single line to production. You had to be right the first time because being wrong was a capital offense.
But in 2026, code is cheap and fast. You can build a prototype in a few hours to show someone basic functionality. The bottleneck is no longer the “build cost”; it’s the “context cost.”
Because every layer of the AI stack is changing simultaneously, customers often don’t know what they need until they touch it. If you spend three months interviewing them, you’ll hear about problems that won’t even exist in four months due to market shifts.
In addition, you don’t know what exactly what will work and won’t work, technically. What models should you use? What scaffolding should you use? It’s another layer of exploration and discovery.
To survive this, we have to move from a “Measure Twice, Cut Once” philosophy to an OODA loop + rapid experimentation model. * In the Corporate “Factory”: You plan for the average. You know what works, so planning a repeatable process is straightforward. And, you know what customers want, you just talked to them.
In the new “Lab”: You build multiple “micro-bets,” then use high-frequency feedback to re-evaluate them quickly. Your main job is managing the jaggedness of the technology against the volatility of the market. You don’t just build a roadmap; you manage a portfolio of experiments. Some executives might not like this, but you’re making educated bets.
The lab is the correct organizational structure flexible enough to contour to a frontier that is unpredictable.
De-Risking through Discovery
The core of labs thinking is de-risking. Like a startup, a lab spends its time with emerging tech and unknown markets. Early in any project, you must decide if you are de-risking for the market (do people want this?) or de-risking the tech (can we actually build and scale this?).
With probabilistic products/ LLM-based products, you often do not know if a feature will work until you try it. This requires iterating between the use cases you think people want and what you can actually execute with current LLMs. This impacts product commercialization in at least two major ways:
Beachhead Market Selection: In a deterministic world, you just pick the best market because you knew you could make it work. In an AI world, you may need to experiment with multiple use cases simultaneously to see where the AI actually performs best before committing to a market.
Feature Planning: Your favorite feature might not be technically viable yet. You need to be willing to pivot to a lower priority feature that actually works. You only find these answers through exploration.
Taste and Exploration as the Compass
Because the frontier is jagged and the market is moving - but software development is faster, you can no longer rely solely on process to guarantee quality. This is where intuition and taste become critical. Can you make good bets?
In the lab, the team is not just checking boxes against a spec. They are navigating the frontier and using their judgment to decide when the output is “magic” and when it is “trash.” You can’t legislate taste in a handbook; you can only cultivate it in an experimental environment. Companies that try to operate like a rigid factory in 2026 will be overrun by competitors who operate as organic learning machines.
The Campfire and the Hive Mind
In a high-performing lab, you do not build from a spec; you build around a campfire. You start with a living prototype and the team gathers around to sculpt it together. This organic approach relies on two shifts that can be uncomfortable for traditional corporate cultures:
The Death of Ego: There is no hiding behind polished presentations. Work is a continuous stream of messy ideas and real time correction. You have to be comfortable looking “wrong” on the way to finding what is right.
The Hive Mind: These teams are driven by collective intelligence reacting to telemetry and high frequency feedback loops. When aligned, the team moves with the fluidity of a flock of birds rather than a rigid hierarchy.
The Survival Playbook
The transition from a rigid machine to a breathing organism is challenging, but it is the only way to stay relevant. To embrace the lab mindset, consider these steps:
Build Fast for Feedback: You can jump right into multiple prototypes and test them with customers faster than you could have asked them what they wanted in the past.
Experimentation: Move toward AI assisted workflows immediately. See what breaks and fix it in the lab. Plan on rapid experimentation as part of your development process. If you’re keeping the agile process, plan on spikes for discovering what works.
Accelerate Compliance: If Legal or Finance takes six months to approve a tool or product, the tech will have changed three times before you start.
Match Fidelity to Process: The earlier you are in the experiment, the lower the fidelity should be. Don’t slow down or kill projects by over designing them too soon. Allow lower bars earlier, but expect and delivery directionally positive results over time.
Build Prototypes, Question Big Specs: Knock down the silos and get your teams building together around a shared vision. Favor building over process.
Hire actual labs and startup people: There are people who have been developing these de-risking/ experimental and taste instincts for a while. Hire people who can help guide and run these things - the natural experimenters that worked in labs and startups in the past.
Re-launch that dedicated, protected lab with the right leadership that understands the above.
In the age of AI and AI-enabled rapid development, experimentation is a mandate for every department that is developing something new that runs on AI. The lab is no longer just a department. It’s the whole company.