The insight-driven leader: How analytics unlocks business potential

Technology choices are often made under pressure - pressure to modernize, to respond to shifting client expectations, to demonstrate progress, or to keep pace with rapid advances in AI. In those moments, even experienced leadership teams can fall into familiar traps: over-estimating how differentiated a capability will remain, under-estimating the organizational cost of sustaining it, and committing earlier than the strategy or operating model can realistically support.

After decades of working with leaders through digital and technology-enabled transformations, I’ve seen these dynamics play out again and again. The issue is rarely the quality of the technology itself. It’s the timing of commitment, and how quickly an early decision hardens into something far harder to unwind than anyone intended.

What has changed in today’s AI-accelerated environment is not the nature of these traps, but the margin for error. It has narrowed dramatically.

For small and mid-sized organizations, the consequences are immediate. You don't have specialist teams running parallel experiments or long runways to course correct. A single bad platform decision can absorb scarce capital, distort operating models, and take years to unwind just as the market shifts again.

AI intensified this tension. It is wildly over-hyped as a silver bullet and quietly under-estimated as a structural disruptor. Both positions are dangerous. AI won’t magically fix broken processes or weak strategy, but it will change the economics of how work gets done and where value accrues.

When leaders ask how to approach digital platforms, AI adoption, or operating model design, four questions consistently matter more than the technology itself.

• What specific market problem does this solve, and what is it worth?
• Is this capability genuinely unique, or is it rapidly becoming commoditized?
• What is the true total cost - not just to build, but to run and evolve over time?
• What is the current pace of innovation for this niche?

For many leadership teams, answering these questions leads to the same strategic posture. Move quickly today while preserving options for tomorrow. Not as doctrine, but as a way of staying adaptive without mistaking early commitment for strategic clarity.

Why build versus buy is the wrong starting point

One of the most common traps organizations fall into is treating digital strategy as a series of isolated build-vs-buy decisions. That framing is too narrow, and it usually arrives too late.

A more powerful question is this. How do we preserve optionality as the landscape continues to evolve? Technology decisions often become a proxy for deeper organizational challenges. Following acquisitions or periods of rapid change, pressure frequently surfaces at the front line. Sales teams respond to client feedback. Delivery teams push for speed. Leaders look for visible progress.

In these moments, technology becomes the focal point for action. Not because it is the root problem, but because it is tangible.

The real risk emerges operationally. Poorly sequenced transitions, disruption to the core business, and value that proves smaller or shorter-lived than anticipated. Teams become locked into delivery paths that no longer make commercial sense, while underlying system assumptions remain unchanged.

The issue is rarely technical. It is temporal.

Optimizing for short-term optics, particularly client-facing signals of progress, often comes at the expense of longer-term adaptability. A cleaner interface over an ageing platform may buy temporary parity, but it can also delay the more important work of rethinking what is possible in the near and medium term.

Conservatism often shows up quietly here. Not as risk aversion, but as a preference for extending the familiar rather than exploring what could fundamentally change.

Licensing as a way to buy time and insight

In fast-moving areas such as AI orchestration, many organizations are choosing to license capability rather than build it internally. This is not because licensing is perfect. It rarely is. It introduces constraints and trade-offs. But it was fast. And more importantly, it acknowledged reality.

The pace of change in this space is such that what looks like a good architectural decision today may be actively unhelpful in twelve months. Licensing allowed us to operate right at the edge of what we actually understood at the time - without pretending we knew where the market would land six or twelve months later.

Licensing should not be seen as a lack of ambition. It is often a way of buying time, learning cheaply, and avoiding premature commitment. Building too early doesn’t make you visionary, often it just makes you rigid.

AI is neither a silver bullet nor a feature

Coaching is a useful microcosm of the broader AI debate.

Great AI coaching that is designed with intent and grounded in real coaching methodology can genuinely augment the experience and extend impact. The market is saturated with AI-enabled coaching tools and what is especially disappointing is that many are thin layers of prompts wrapped around a large language model. They are responsive, polite, and superficially impressive - and they largely miss the point.

Effective coaching isn’t about constant responsiveness. It’s about clarity. It’s about bringing experience, structure, credibility, and connection to moments where someone is stuck.

At the other extreme, coaches themselves are often deeply traditional. A heavy pen, a leather-bound notebook, and a Royal Copenhagen mug of coffee are far more likely to be sitting on the desk than the latest GPT or Gemini model.

That conservatism is understandable - coaching is built on trust, presence, and human connection - but it’s increasingly misaligned with how scale and impact are actually created.

The real opportunity for AI is not to replace human work with a chat interface. It is to codify what actually works. The decision points, frameworks, insights, and moments that drive behavior change. AI can then be used to augment and extend that value at scale.

A polished interface over generic capability is not enough. If AI does not strengthen the core value of the work, it is theatre, not transformation.

What this means for leaders

Across all of these examples, the same pattern shows up.

The hardest decisions are rarely about capability, they are about timing, alignment, and conviction.

Building from scratch only makes sense when you can clearly articulate:

• What you believe that the market does not
• Why that belief creates defensible value
• Why you’re willing to concentrate risk behind it

Clear vision scales extraordinarily well when it’s tightly held. The success of narrow, focused Silicon Valley start-ups is testament to that.

Larger organizations often carry a broader set of commitments. That complexity increases when depth of expertise is spread across functions, and even more so when sales teams have significant autonomy at the point of sale. Alignment becomes harder not because people are wrong, but because too many partial truths are competing at once.

In these environments, strategic clarity, not headcount or spend, creates advantage.

This is why many leadership teams choose to license early. Not because building is wrong, but because most organizations have not yet earned the right to build.

At BTS, we’re constantly challenging ourselves to innovate at speed. And right now, it feels like we’re standing at the edge of something massive. The energy? Electric. The velocity? Unprecedented. For many of us, the current pace feels a lot like the early days of the pandemic: disorienting, high-stakes, and somehow exhilarating. And honestly—it should feel that way. Our teams have been tinkering with AI, specifically LLMs, for the past 2.5 years and it has really been in the last eight months that I can see the profound impact it is going to have for our clients, for our services and our operating model.

The opportunity isn’t about the technology. The world has it and it’s getting better by the minute. The issue is people and people’s readiness to adopt it and be re-tooled and re-skilled. It’s about leadership. AI is deeply personal, it’s surgical. In fact, that’s its genius. So, getting full scale adoption of AI, re-tooling everyone in the company by workflow, so that they can invent new services, unlock new customer value, unlock new levels of productivity, even use it for a better life, is the current race. The central question I’ve been wrestling with, alongside our clients and our own teams, is this:

What does AI actually mean for leadership and culture?

And the answer is clearer by the day: AI isn’t just a new toolset. It’s a new mindset. It demands that we rethink how we lead, how we learn, and how we build thriving organizations that can compete, adapt, and grow.

The productivity paradox revisited

Let’s start with the elephant in the boardroom. There’s been a lot of buzz around AI and its promises. But many leaders have quietly wondered: Will any of this actually move the needle? A year ago, we were asking the same thing. We had licenses. We had curiosity. We had early experiments. But the results were modest, a 1% productivity gain here or there. But by April, we were seeing:

• 30–80% productivity gains in software engineering
• 9–12% gains in consulting teams
• 5%-20% improvements in client success and operations

Just as importantly, the innovation unlock and creativity across our platforms due to vibe coding along with new simulation layers, is leading to new value streams for our clients. This isn’t theoretical. It’s not hype. It’s real. The difference? Adoption, ownership, and a shift in how we lead in order to energize the AI innovation within our teams. The challenge now isn’t whether AI creates value. It’s how to unlock and scale that value across teams, geographies, and business units—and do it fast.

Two Superpowers of the Agentic AI Era

In working with leaders across industries, I’ve come to believe in two superpowers (there are more as well) that will unlock the potential of this AI era: Jazz Leadership and a Simulation Culture.

1. Jazz Leadership

Forget the orchestra (although personally I am a big fan.) The successful team cultures that are innovating with AI feel more like jazz. In jazz, there’s no conductor. There’s no fixed sheet music. There are core bars and then musicians make up music on the spot based on each other’s creativity, building off of each other’s trials, riffs and mistakes, build something extraordinary together. This is how experimenting with AI today, in the flow of work, feels like.

For each activity across a workflow, how can new AI prompts, agents, and GPTs make it better, codify high performance, drive speed and quality simultaneously? How can we try something totally different and still get the job done? How might we re-invent how we work? That’s how high-performing teams operate in the AI era. The world is moving too fast for command-and-control leadership, a perfect sheet of music with one leader who is interpreting the sheet music and directing. What we need instead is improvisation, trust, shared authorship, courage and a playful spirit because there are just as many fails as breakthroughs.Jazz leadership is about creating the conditions where:

• Ideas can come from anywhere
• People see tinkering and testing as key to survival and AI failures mean your team is at the edge of what’s possible for your services and ways of working
• Leaders say, “I don’t have all the answers, but I’ll go first, with you”
• People feel “I’m behind relative to my peers in the company” and the company sees this as a good sign because the pace of learning with AI means higher chance of success in the new era

At BTS, we recently promoted five new partners who embody this mindset. They weren’t the most traditional leaders. But they were the most generative. They coached others. They experimented and are constantly re-tooling themselves and others. They inspired movement. They are keeping us ahead, keeping our clients ahead and driving our re-invention. Jazz leaders make teams better, not by directing every note—but by setting the stage for breakthroughs. It is similar to the agile movement, similar to how it felt in Covid as companies had to reinvent themselves. It’s entrepreneurial, chaotic and fun.

2. Simulation Culture

The ability to simulate is a super-power in this next agentic, AI era. Simulation has always been part of creating organizational agility, high performance and leadership excellence. But AI and high-performance computing have transformed it into something bigger, faster, and infinitely more powerful. It means that building a simulation culture is within all of our grasp, if we tap its power.Today, companies simulate:

• Strategic alternatives - from market impact all they way to detailed frontline execution
• New business, new markets and operating models
• Major capital deployment e.g. build a digital twin of a factory before breaking ground
• Initiative implementation
• Workflows current and future
• Jobs to assess for talent and critical role readiness
• Customer conversations and sales enablement motions

With a simulation culture, where you regularly engage in scenario planning and expect preparation and practice as a way of working, billions in capital is saved, cross-functional teams are strengthened, high performance gets institutionalized, win rates increase, earnings and cash flow improves.

Where to get started

Below are a few examples of what leading organizations are doing. Consider testing these in your own organization:

• Conversational AI bot platforms used to scale performance expectations and the company’s unique culture.
• Agentic simulations built into tools so people can prepare and practice with 100% perfect context and not a wasted moment.
• Digital twins of the job created so that certifications and hiring decisions are valid.
• Micro-simulations spun up in hours to align 50,000 people to a shift in the market or a new operational practice.

Final Thoughts

• Lead like a jazz musician. Embrace improvisation, courage and shared creativity.
• Build a simulation culture. Because in a world that’s moving this fast, practice isn’t optional—it’s how we win.

This is a brave new world. Not five years from now. Right now.Let’s shape it—together.

When OpenAI launched GPT-5, the reaction was muted. No flashy new tricks or “wow” demo moment. If you stopped there, you might think nothing’s really changed. But the real story is bigger and far more important for leaders. OpenAI didn’t just release an updated model, they triggered a collapse in the cost of top-tier intelligence across the market. That cost shift will accelerate innovation in ways we’re only beginning to imagine, and it’s happening already. It’s important to note that there are two main ways people and companies use GPT-5.

1. Through the ChatGPT app, individuals and teams interact with the AI directly, writing prompts, asking questions, or creating content. It’s plug-and-play, no coding required, and now GPT-5 is the default model even for free users (with some usage caps).
2. Through the API, companies connect GPT-5 to their own systems or products so it can power customer support tools, automate large-scale analysis, or run AI features inside other apps.

The headline here is that OpenAI cut GPT-5’s API price to $1.25 per million input tokens and $10 per million output tokens numbers that would have seemed impossible not long ago. In simple terms, tokens are chunks of words. A million tokens of input is roughly 750,000 words, which is the equivalent of several full-length books. “Input tokens” are the text you feed into the model, and “output tokens” are the text it generates in response.

The new API pricing makes a big difference for large-scale, embedded use cases. Companies can now process massive amounts of data, run more experiments, and serve more customers for a fraction of the cost. Workloads that once felt budget-breaking are now affordable, opening the door to AI innovation at an entirely new scale. Combine this new cost structure with the decision to make GPT-5 the default in ChatGPT, and you have a dual shift: high-powered AI is dramatically cheaper for heavy users and instantly accessible to hundreds of millions of people, including your competitors. Intelligence that once required careful budgeting and scarce expertise is now abundant and that abundance changes the game entirely.

When intelligence gets cheap, the game changes

Just a couple of years ago, AI was expensive and resource-intensive, so leaders had to be selective about where and how they applied it:

• Licensing and compute costs were high: Running large models at scale through an API could cost thousands of dollars a month, even for modest use cases.
• Access was limited: The best models were behind higher subscription tiers or enterprise contracts.
• Specialized expertise was needed: Integrating AI often required dedicated data scientists or engineers, which added cost and slowed speed to value.
• Budget trade-offs were constant: Leaders had to choose a few high-priority projects for AI investment and delay or reject others.

In other words, leaders had to ration AI usage just like any other scarce, expensive resource. In a low-cost world, the constraint shifts from budget to imagination. The central question stops being “Can AI do this?” and becomes “How can we reimagine the way we work if this is possible everywhere?”

‍That’s when innovation accelerates. Experiments that once required hard trade-offs can now be run in parallel, testing ten ideas for the cost of one. AI copilots can quietly monitor, reconcile, and draft decisions in real time, expanding your team’s capacity without adding headcount. Entire archives or research libraries can be parsed in minutes. Intelligence can be embedded into the devices your people already carry, putting expertise within reach at any moment.

Two ways leaders commonly get this wrong

For some, the old assumption still holds: AI feels too expensive or too specialized to deploy widely. Their only exposure has been high-cost pilots, niche specialist teams, or consulting projects where each experiment felt like a big-ticket gamble. That may have been true last year it’s not true today.

For others, the issue isn’t what they say, it’s what their strategy reveals. They’ll tell you they know AI is now cheaper and more accessible but they still budget and resource it like a premium feature. It’s reserved for high-priority initiatives or “innovation” workstreams, rather than being built into core workflows and systems.In both cases, the result is the same: they’re underestimating how radically the playing field has changed. Intelligence is now abundant. The gate is no longer money it’s imagination and execution speed.

The organizations that win will be those that treat AI not as an experimental add-on, but as infrastructure integrated deeply enough that the question isn’t whether to use AI, but how to keep evolving it as the cost curve continues to drop.Strategies built without this shift in mind risk missing opportunities in a competitive landscape that’s already moving forward. The advantage now belongs to those who experiment, learn, and adapt faster than the cost curve drops.

‍We’d love to help you with your AI strategy: Contact us to get started.

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