We're All in the Lab Now
AI is full of unknowns, which 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. 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 Hidden Dips
In this new reality, the speed of exploration is a survival metric rather than a luxury.
In the Corporate “Factory”: You plan for the average. You know what works, so planning a repeatable process is straightforward. Your roadmap is very predictable.
In the Lab: You plan for the jaggedness. Your main JOB is managing jaggedness. In AI, in markets, and in distribution. It’s all jagged. For AI/ LLMs, you don’t know which parts of LLMs will work with which models (if at all), and also if new technical scaffolding around the LLM will get you where you want to go. You might need to compromise on features to get to what will work. It’s iterative. You have to be organic with your planning.
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, you can no longer rely solely on process to guarantee quality. AI can follow a process perfectly and still produce the wrong result. This is where intuition and taste become critical.
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:
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.
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.