Recognising Value You Didn't Expect

Why Some Leadership Teams Unlock Stranded Capacity While Others Remain Prisoners of Annual Budget Cycles.

Last night, an executive coach posed a question to a forum of leaders: "Can you recognize value you didn't expect or never experienced before, where there is no historical performance data to cling to?"

The question matters because most companies are sitting on massive stranded capacity right now. You're paying for three to five times more productive capacity than you're actually utilizing. That capacity is parked in your existing salary line items, created by AI tools deployed twelve to eighteen months ago. The tools are working exactly as promised. Your teams are measurably faster. The constraint isn't the technology.

The constraint is whether leadership can recognize the value those tools created, even when that value threatens the foundations they built their careers on. An executive coach in the forum described what she sees in leaders who successfully unlock this capacity: they recognize rapidly emerging value in patterns they've never seen before, patterns that compress time, compress cost, multiply capability across existing teams humming at three to five times previous output, unleash talent in ways that weren't possible two years ago, and create entirely new competitive moats. These leaders see teams suddenly operating four times faster and immediately ask why that productivity spike isn't moving the corporate dial yet.

One leader in the forum runs weekly conversations with executives in unrelated industries. When she hears about emerging productivity spikes in banking or logistics, she goes first principles. "If they can do this, why can't we?" She drills down on why a particular team is suddenly four times faster while the rest of the organization operates at the old pace. She doesn't accept "that's just how we do things" as an answer.

We discussed JP Morgan and Norges Bank Investment Management during the forum. These are massive, complex organizations with decades of legacy systems and processes. Yet they're achieving productivity gains that smaller, supposedly more agile competitors haven't captured. The difference isn't their technology stack. It's their leadership's ability to recognize value that didn't exist in their experience base and reorganize around it rapidly. They can reallocate budgets and incentives in weeks, not through annual planning cycles. They avoid analysis paralysis by running controlled experiments rather than waiting for certainty.

The forum surfaced a pattern we've seen repeatedly: established corporations actually have leadership advantages over startups in many cases. Startups have structural advantages because they lack legacy baggage, but they often lack the deep operational expertise, customer relationships, regulatory navigation skills, and capital resources that established players possess. The question is whether established leadership teams can recognize when those structural advantages become liabilities.

Someone in the forum mentioned "the sunk cost of orthodoxy." They said it perfectly: "I spent twenty years learning how things work. Check my LinkedIn CV." That accumulated expertise becomes an anchor when the underlying rules change. Leaders who built careers on organizational structures, process mastery, relationship networks, and industry-specific knowledge face a real psychological challenge. They're not being asked to discover something new. They're being asked to believe that something genuinely unprecedented is unfolding, something that invalidates portions of their hard-won expertise.

The capacity we're discussing didn't exist two years ago. AI created it. This isn't about leaders missing obvious opportunities. It's about whether they can recognize value that has no historical performance data attached to it, value that manifests in patterns their experience doesn't account for. A sales rep who could handle forty accounts can now handle a hundred and twenty with the same relationship depth. An engineering team that could ship four features per quarter can now ship fifteen without cutting corners. A customer success team that maxed out at eighty accounts per person can now manage two hundred and fifty with better response times.

These aren't hypotheticals. These are actual productivity multipliers happening right now in companies that deployed AI tools eighteen months ago. The capacity is real. It's paid for. It's sitting there waiting to be unleashed. The question is whether leadership can see it.

Time was running out in the forum, so we summarized on a critical distinction. Some leadership teams look at these productivity spikes and ask: "How do we integrate this into our current process?" Others ask: "How do we reorganize around this?" That's the dividing line. The first question assumes the existing structure is optimal and the new capability should adapt to it. The second question recognizes that when fundamental economics change, the optimal structure changes with them.

The executive coach closed with an observation about foundation anxiety. Leaders built careers on specific foundations: org structures that made sense for pre-AI productivity, processes that optimized for constraints that no longer exist, relationship networks that were necessary when information moved slowly, industry knowledge that assumed stable competitive dynamics. Those foundations were genuinely valuable. Past tense. The anxiety isn't irrational. It's the recognition that those foundations are shifting under their feet.

But here's what we've learned working with leadership teams who successfully navigate this: the leaders who thrive aren't the ones who abandon all their expertise. They're the ones who can distinguish which parts of their expertise transfer to the new reality and which parts don't. They recognize that their deep customer knowledge still matters enormously, but the organizational structure built to deliver on that knowledge needs to evolve. They see that their process mastery was valuable for managing the old constraints, but AI removed several of those constraints entirely.

The leaders who unlock stranded capacity share a specific trait: they look at emerging productivity spikes in their organization and treat them as signals rather than anomalies. When a team is suddenly operating at four times previous speed, they don't dismiss it as unsustainable or attribute it to unusual circumstances. They investigate what changed, whether it can scale, and what organizational structures are preventing it from scaling. They ask first principles questions borrowed from adjacent industries experiencing similar shifts. They run experiments with real budget allocations rather than pilots with symbolic resources.

The opportunity right now is unprecedented. Most organizations have three to five times more capacity than they're utilizing, already paid for, sitting in existing salary line items. That capacity didn't exist in your historical performance data because it didn't exist at all until recently. The leaders who recognize it first and reorganize around it fastest will build moats their competitors can't cross. The leaders who wait for certainty, who integrate incrementally into existing processes, who remain prisoners of annual budget cycles, will watch those moats widen every quarter.

The forum question was: "Can you recognize value you didn't expect or never experienced before?" The answer determines whether your stranded capacity gets unleashed or whether it stays parked while competitors pull ahead.

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The Bottom Line:

You're already paying for three to five times more capacity than you're utilizing. AI tools deployed twelve to eighteen months ago created it. The capacity is real, measurable, and waiting. The only question is whether your leadership team can recognize value that has no precedent in their experience, even when that value threatens foundations they spent decades building. The teams that answer yes will capture that capacity. The teams that answer no will watch it stay stranded while their competitive position erodes quarter by quarter.