Welcome back to Untangled. It’s written by me, Charley Johnson, and valued by members like you. This week I’m sharing my conversation with Nick Pyati, a former senior strategy leader at Microsoft and the founder of Belari AI. Nick spent nine years doing strategy for a company whose ground shifted every few months, and then all at once when ChatGPT landed. We disagreed about how much of the work AI will eventually do but on what strategy is and why most companies are getting it wrong, we landed in the same place. I hope you enjoy it — I certainly did! Untangled HQ Get on the waitlist for the second cohort of Stewarding AI: How to Build Responsible Principles, Workflows, and Practices. Here's what one participant had to say about the first cohort: Deep Dive What is an ‘AI strategy’? Nick and I talk about a lot of things: * What Nick learned about setting strategy at Microsoft in the months after ChatGPT, when planning horizons compressed from a year to a few months. * The three approaches to AI Nick sees right now, and why the companies that have done nothing about AI may be best positioned. * Facilitation as a strategic practice, and what the human conversations do that most analytical approaches to strategy can’t. * What AI is doing to knowledge work, and how companies are commodifying what once set them apart. But the question we kept circling back to was this: when a company’s context changes, how does it rebuild the link between that context and the way it works? Every team works inside a context — the people it serves, the competitors it faces, the technology available to it, and all of the interpersonal dynamics and ways of doing things that amount to culture. And every team has a set of habits, beliefs, and capabilities that fit that context. But then the context shifts. A new competitor arrives, customers start wanting something else, technology changes, etc. The habits, beliefs, and capabilities that made the team successful stop making sense. Yet they keep doing the same thing! Why? Because, as Nick argues, it served them well for years. Re-establishing the link means first recognizing that the old way of doing things is no longer serving you. High performing teams across companies, non-profits, and government will paradoxically have the hardest time because they have so many positive examples validating their current approach. And here we are. AI changed the context for everyone everywhere all at once. Your staff are working differently. The people you serve, and the board you answer to, expect something different from you. Somewhere a competitor you haven’t heard of is rebuilding the work from scratch. A context that felt durable now feels more like quicksand. As Nick notes, every company in every industry now has the problem Microsoft had in the months after ChatGPT, when planning horizons shrank from a year to a few months and the historical trend line stopped being a guide to, well, anything. Strategy, then, is whatever it takes to re-establish the link between how a team works and the context it now sits in. But strategy is also a big bet. Yes, Microsoft is data-rich and analytical, but Nick says almost none of that data decided the big questions. When your context is regularly changing, forecasting or trend analysis isn’t much use. Strategy stops being an analytical question because the future can’t be known. Instead, strategy becomes more about imagination, conviction, and sense-making; about what the team can believe in, and how the team adapts together without fragmenting. I’ve written recently about imagination and sense-making, so what makes for a good big bet? Nick thinks of it as a Venn diagram. The bet has to be plausible — supported by what the market is doing, with a real story for why this team has a comparative advantage. The team has to be able to get conviction around it. They have to believe in it. And the bet has to be big enough to be worth the risk, because of the fifteen good ideas on the table, maybe three will ever pay off at a scale that justifies the investment of time and resources. Companies and organizations aren’t making big bets mapped to the future they want to bring about. They’re focused on the same question every company has asked of every IT rollout before this one: where can we plug this in? What can we automate? And when you ask these questions of a technology that can reshape how the whole business works, you get a scattering of small bets with no idea underneath them. I wrote about the mechanism behind this in The Copycat Economy. When a technology is poorly understood, organizations copy each other rather than think for themselves — DiMaggio and Powell called it mimetic isomorphism. Every company is doing what its peers are doing, which is why their ‘AI strategies’ all look roughly the same. When your ‘AI strategy’ maps the technology to existing work, another differentiation problem pops up. The models are the same for everyone. Unless a company is using them in some thoughtful, tailored way, whatever it produces with them is essentially what everyone else can produce with them. That model is what a company turns itself into when it automates the work its people were doing. The idiosyncratic humans on staff, with their peculiarities, big unconventional ideas, and their weird accumulated knowledge of how things get done here, were the entire source of anything novel the company had to offer. As I argued in an essay on why data-driven organizations are the least prepared for AI, this knowledge is tacit and relational — it lives in bodies, in trusted relationships, in a team’s accumulated sense of how decisions get made versus how they’re supposed to get made. The knowledge isn’t in the text, so it isn’t in the model. Every time a CEO or Executive Director highlights the ‘efficiencies’ generated by cutting their workforce, Nick said you should interpret that as: you lacked the imagination to know what to do with those people now that you have the capability. I agree! It means that they started from the question “what is our AI strategy” and simply bolted the technology on to what they already do, as the context changed beneath their feet. The better question is this: what future are we trying to create, what do the people we already have make possible, and where — if anywhere — does AI help? Answer that and you have a bet. More soon, Charley Work With Me Here are 3 ways I can help: * Advising: I can help you navigate uncertainty, make sense of AI, and steward change in your system. * Organizational Training: Everything you and your team need to cut through the tech-hype and implement strategies that catalyze true systems change. (For either Stewarding AI or Systems Change for Tech & Society Leaders) * 1:1 Leadership Coaching: I can help you facilitate change — in yourself, your organization, and the system you work within. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit untangled.substack.com
