The Third Loop

AI can optimize bad targets all day; your people still have to question them.

I almost fell for a scam. Silly me.

The editor of a publisher reached out. They’d found my novel, loved my writing, and recommended I contact their preferred literary agent.

I was skeptical at first, so I consulted an AI, and we ran some background checks. Everything checked out: the editor, the publisher, all legitimate. Except that the email was sent from a private Yahoo account, not a company account. Still feeling doubtful, I forwarded the exchange to the editor’s official company address, which I’d pulled from their website.

Bingo!

The real editor replied: “Sorry, that’s not me. They’ve been using our names for weeks. Thank you for checking. I reported the abuse to Yahoo.”

I felt both sad and proud of myself. The scammers had studied my public profile better than most recruiters study a CV. Only my skeptical self saved me from further embarrassment.

It’s so easy to fall for a scam. Especially when it validates what you already believe. For sure, I’m a good writer. Of course, my work needs recognition. Hell yes, I deserve a publishing contract!

But in the age of AI, the number one skill is critical thinking. The talent to question everything, most importantly yourself.

In the age of AI, the number one skill is critical thinking.

What saved me was a habit of doubting my own conclusions (even the ones an AI had just confirmed for me). The AI background check endorsed the scam. My own dubiety caught it, not my tools.

Organizations need that same critical habit on a much larger scale, and most of them never build it.

Single-Loop, Double-Loop, and Triple-Loop Learning

The “learning organization“ became a thing when Peter Senge popularized the term in The Fifth Discipline in 1990. However, few people understand or consider the three different levels of learning. As a result, some kinds of learning happen a lot, while other kinds don’t happen at all.

Single-Loop Learning

In single-loop learning, an entity (worker, team, department, business unit) detects a mismatch between expected and actual results and changes its actions to close the gap.

When inventory falls below the minimum level, we crank up production. When the AI agent mis-routes support tickets, someone tweaks the prompt and moves on. When the delivery team risks missing its deadline, they work extra hours to catch up with the plan.

Single-loop learning is often compared to a thermostat detecting a temperature change and activating or deactivating a heater to balance around a pre-set target. In this process, nobody asks whether that target (threshold, deadline, or inventory level) still makes sense in the current environment.

Double-Loop Learning

In double-loop learning, a term business theorist Chris Argyris introduced in 1974, a team or organizational unit evaluates the mismatch between expected and actual results by examining its governing variables: the goals, beliefs, and assumptions that were used to set the target in the first place.

When faced with reality, an organization might want to change its minimum inventory level, extend the delivery team’s deadline, or ask whether that support queue should even exist now that agents answer most tickets. The organization can adjust the threshold, objective, or inventory level to reflect what is truly desirable and feasible given its circumstances.

With double-loop learning, the entity acknowledges that a goal is a hypothesis that needs continuous validation. The governing variables are always subject to critical examination to check whether the targets are still viable.

Triple-Loop Learning

At the third level, we find deutero-learning, or “learning how to learn.” This is the meta-process that a team or organization needs to figure out if the single loop and double loop are both working as intended.

At this level, the team decides whether to introduce after-action reviews, blameless postmortems, or agile retrospectives. It checks whether its red teaming, action learning, or scenario planning practices are working. And it discusses the pitfalls of rewards and incentives, power dynamics, or a non-transparent organizational culture.

At the meta-level, an entity analyzes its learning history, examines its learning patterns, and diagnoses counterproductive feedback cycles that hinder single- and double-loop learning. In this third loop, a team, department, or business unit deliberately refines its communications and behaviors to make future learning interventions more systematic.

• Note: There is no consensus on the term “triple-loop learning”, which various scholars and authors have defined differently. I use it when “the third loop” is easier to understand and visualize than meta-learning or deutero-learning.

I’m a founder, intrapreneur, and former CIO who helps leaders diagnose and redesign their operating models for the age of AI—informed by plenty of scar tissue. This article offers the same lens I bring to keynotes, workshops, and advisory engagements, from a single team to a multinational. Want that lens on your organization? Let’s talk. And if you’re just here for the maps, subscribe—they’re free, always.