Your Instincts Are Data Too, And It's Time We Treated Them That Way

When Russell Mears opens a conversation with the promise that business analysis, history, and philosophy will all come up (and adds that Dagenham and Redbridge FC might make an appearance too), you know a couple of things right away. First, this is going to be a wide-ranging conversation. Second, whoever's on the other side of it isn't going to leave with only surface-level takeaways.

Russell is an IIBA board member and a 15-year business analysis veteran. He joined me on Business Analysis Live! to make what turned out to be a quietly radical case. The thing we've been trained to distrust in this profession—the gut feeling, the sense that something is off—is actually a form of data (yes, data) that business analysis professionals should be learning to work with, not against.

Here are a few of the ideas that have stayed with me.

Instinct is compressed expertise, not the absence of rigour

Russell opened by pulling out Michael Polanyi's The Tacit Dimension, a slim philosophy book from 1966 whose central premise fits in eight words: "We can know more than we can tell." Polanyi's example is a chemist who takes one look at a piece of apparatus and knows it's about to fail, without being able to articulate why. 

Far from magic, it’s what’s otherwise known as compressed knowledge, expertise running faster than conscious reasoning can keep up with. Russell paired it with research psychologist Gary Klein's research on high-stakes decision-making. In moments where firefighters or emergency room nurses must act fast, they don't weigh options but rather pattern-match against everything they've seen before. 

That same mechanism, Russell argued, is at work when experienced business analysis professionals sense that a stakeholder answer isn't quite right, or that a requirements set is missing something critical. We've been trained to believe that if we can't put a number on something, we can't bring it to the table. Russell's point is that the profession has been paying a real cost for that belief.

The trap of confident-looking AI output

Russell told a story from a Building Business Capability workshop in Toronto. His group used generative AI to plan out an entire hotel launch in three hours: location scouting, brand identity, marketing campaign, even a composed song. Everyone in the room was impressed.

Then a woman who had spent 20 years in the travel industry spoke up. The location was wrong. The timelines were impossible. The staffing plan was absurd. None of it was going to work.

The AI had produced something that looked completely convincing to everyone in the room who didn't know the industry. It took one person's experience to see the whole thing fall apart.

"Garbage in, garbage out" is one of those phrases business analysis professionals have been using forever, but Russell put an uncomfortable finger on the assumption underneath: it assumes you can actually spot the garbage. And the ability to recognize a bad output almost always comes from experience with the real thing. 

In a world where AI can produce polished, plausible-sounding versions of almost anything, that experience is extremely valuable.

The barriers to speaking up

Russell was refreshingly honest about the hardest part. Knowing something is off is one thing, but saying so is a whole different can of tuna. He talked about times his instincts were screaming, but he stayed quiet because the room wasn't safe to speak up in. Most of us have been there before, right?

This is where the conversation really opened up for me. Psychological safety is a structural condition for the analysis process to produce good outcomes; in fact, it's built into the work itself! When instincts stay unspoken, decisions get made on incomplete information. And the pressure to quantify everything makes it worse, because most instincts don't show up with a number attached to them (if only).

Russell added an observation I hadn't quite put words to before: the more polished the output on the table, the harder it is for anyone to raise their hand and say, "I'm not sure this is right." Confidence (even the artificial kind) has a silencing effect.

How instinct shows up in the work

We ended up spending a good chunk of the conversation on the practical side. What does it truly look like to work with instinct as a business analysis professional?

Russell offered a framing that I'll be using more often from now on: instinct as a leading indicator. Instinct points; evidence confirms. You don't dismiss it, but you don't bet your house on it either. 

You go find out what triggered it and follow the thread until either the evidence supports what you were sensing or it doesn't. Either way, you've learned something valuable. It connects naturally to what IIBA has dubbed "being nimble": sensing and responding to change right then and there, in the actual work.

That's also where facilitation comes in. In every session I run, I watch the people who aren't speaking. A change in body language, a slight shift in breathing, or a look of disagreement that someone's trying not to show are the room's collective instincts picking up on things the conversation hasn't named yet. Our job, as much as anything else, is to make space for those signals to come out. 

Russell put it like this: business analysis professionals aren't just responsible for their own instincts. We're also responsible for creating the conditions that let other people's instincts into the room. And when the moment is right, he offered a practical move that I'm going to steal:

When you're in a data-heavy room, and something just feels off but you can't quite name it yet, ask: "What metric would make this false?"

That's a business analysis question. It doesn't require you to take a position you can't defend. It opens space for someone else's instinct to meet the data on its own terms.

A conversation worth having

Russell drew on philosophy, cognitive science, aviation history, his own experience in some of the most intense environments in finance, and yes, a little soccer—just as he promised at the top. What tied it all together was the same thing he thinks ties good analysis together: the ability to take complex ideas and make them easy to understand.

Your instincts are data. They aren't the whole picture, and they aren't a substitute for evidence. But they're worth taking seriously, especially now, when the outputs coming at us are becoming more polished, and the pressure to accept them at face value is increasing.

Russell's conversation is an invitation to reclaim something the profession has been quietly leaving on the table. And I think it's long overdue.

Listen to the full episode of Business Analysis Live! on Spotify to hear the rest, including the questions that came in live from the community.

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