Harness the Power of AI with BTS

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.

In the 1990s, Business Process Reengineering (BPR) was the Big Bet. Companies launched tightly controlled pilot programs with hand-picked teams, custom software, and executive backing. The results dazzled on paper.

But when it came time to scale? Reality hit. People weren’t ready. Systems didn’t connect. Budgets dried up. The pilot became a cautionary tale, not a blueprint.

We’ve seen this before with Lean, Agile, even digital transformations. Now it’s happening again with AI, only this time, the stakes are different. Because we’re not just implementing a new solution, we’re building into a future that’s unfolding. Technology is evolving faster than most organizations can learn, govern, or adapt right now. That uncertainty doesn’t make transformation impossible, but it does make it easier to get wrong.

And the dysfunction is already showing up, just in two very different forms.

Two roads to the same cliff

Today, we see organizations falling into two extremes. Most companies are either overdoing the control or letting AI run wild.

Road 1: The free-for-all

Everyone’s experimenting. Product teams are building bots, prompting, using copilots. Finance is trying automated reporting. HR has a feedback chatbot in the works. Some experiments are exciting. Most are disconnected. There's no shared vision, no scaling pathway, and no learning across the enterprise. It’s innovation by coincidence.

Road 2: The forced march

Leadership declares an AI strategy. Use cases are approved centrally. Governance is tight. Risk is managed. But the result? An impressive PowerPoint, a sanctioned use case, and very little broad adoption. Innovation is constrained before it ever reaches the front lines.

Two very different environments. Same outcome: localized wins, system-wide inertia.

The real problem: Building for optics, not for scale

Whether you’re over-governing or under-coordinating, the root issue is the same: designing efforts that look good but aren’t built to scale.

Here’s the common pattern:

• A team builds something clever.
• It works in their context.
• Others try to adopt it.
• It doesn’t stick.
• Momentum dies. Energy scatters. Or worse, compliance says no.

Sound familiar?

It’s not that the ideas are flawed. It’s that they’re built in isolation with no plan for others to adopt, adapt, or scale them. There’s no mechanism for transfer, no feedback loops for iteration, and no connection to how people actually work across the organization.

So, what starts as a promising AI breakthrough (a smart bot, a helpful copilot, a detailed series of prompts, a slick automation) quietly runs out of road. It works for one team or solves one problem, but without a handoff or playbook, there’s no way for others to plug in. The system stays the same, and the promise of momentum fades, lost in the gap between what’s possible and what’s repeatable.

We’ve seen this before

These aren’t new problems. From BPR to Agile, we’ve learned (and re-learned) that:

• Experiments are not strategies. Experiments show potential, not readiness for adoption. Without a plan to scale, they become isolated wins; interesting, but not transformative.
• Culture is the operating system. If the beliefs, behaviors, and incentives underneath aren’t aligned, the system breaks, no matter how advanced the tools.
• Managers matter. Without their ownership and support, change stalls.
• Behavior beats code. Tools don’t transform companies. People do.

Design thinking promised to bridge this gap with user-driven iteration and empathy. But in practice? Most efforts skip the hard parts. We tinker, test, and move on, without ever building the conditions for adoption.

AI and the new architecture of work

Many organizations treat AI like an add-on—as if it’s something to bolt onto existing systems to boost efficiency. But AI isn’t just a project or a tool; it changes the rules of how decisions are made, how value is created, and what roles even exist. It’s an inflection point that forces companies to rethink how work gets done.

Companies making real progress aren’t just chasing use cases. They’re rethinking how their organizations operate, end to end. They’re asking:

• Have we prepared people to reimagine how they work with AI, not just how to use it?
• Are we redesigning workflows, decision rights, and interactions—not just layering new tech onto old routines?
• Do we know what success looks like when it’s scaled and sustained, not just when it dazzles?

If the answer is no, whether you’re too loose or too locked down, you’re not ready.

The mindset shift AI demands

AI isn’t just a tech rollout. It’s a mindset shift that asks leaders to reimagine how value gets created, how teams operate, and how people grow. But that reimagination isn’t about the tools. The tools will change—rapidly. It starts with new assumptions, new stances, and a new internal leader compass.

Here are three essential mindset shifts every leader must make, not just to keep up with AI but to stay relevant in a world being reshaped by it:

1. From automation to amplification

Old mindset: AI automates tasks and cuts costs.

New mindset: AI expands and amplifies human potential, enhancing our ability to think strategically, learn rapidly, and act boldly. The question isn’t what AI can do instead of us, but what it can do through us—helping people make better decisions, move faster, and focus on higher-value work.

2. From efficiency to reimagination

Old mindset: How can we use AI to make current processes more efficient?

New mindset: What would this process look like if we started from zero with AI as our co-creator, not a bolt-on?

3. From implementation to opportunity building

Old mindset: Roll out the tool. Train everybody. Check the box.

New mindset: AI fluency is a core human capability that creates new realms of curiosity, sophistication in judgment, and opportunity thinking. Soon, AI won’t be a one-time training. It will be part of how we define leadership, collaboration, and value creation.

From sparkles to scale

In most organizations, the spark isn’t the problem. Good ideas are everywhere. What’s missing is the ability to translate those isolated wins into something durable, repeatable, and enterprise-wide.

Too many pilots are built to impress, not to endure. They dazzle in one corner of the business but aren’t designed for others to adopt, adapt, or sustain. The result? Innovation that stays stuck in the lab—or dies.

Designing for scale means thinking beyond the “what” to the “how”:

• How will this spread?
• What behaviors and systems need to change?
• Can this live in our whole world, not just my sandbox?

It’s not about chasing the next use case. It’s about setting up the conditions that allow innovation to take root, grow, and multiply, without starting from scratch every time.

Here’s how to make that shift:

1. Test in the wild, not just in the lab

Skip the polished demo. Put your solution in the hands of real users, in real conditions, with all the friction that comes with it. Use messy data. Invite resistance. That’s where the insights live, and where scale begins. If it only works in ideal settings, it doesn’t work.

2. Mobilize managers

Executives sponsor. Front lines experiment. But it’s team leaders who connect and spread. Equip them as translators and expediters, not blockers. Every leader is a change leader.

3. Hardwire behaviors, not just tools

The biggest unlock in AI is not the model—it’s the muscle. Invest in shared language, habits, and peer learning that support new ways of working. Focus on developing behaviors that scale, such as:

• Change readiness: the ability to spot opportunity, turn obstacles into possibilities, and help teams pivot.
• Coaching: getting the best out of your AI “co-workers” just like human ones.
• Critical thinking: applying human judgment where it matters most—context, nuance, and ethics.

4. Align to a future-state vision

To scale beyond one-off wins, people need a shared sense of where they’re headed. A clear future-state vision acts as an enduring focus, allowing everyone to innovate in concert. That alignment doesn’t stifle innovation. It multiplies it, turning a thousand disconnected pilots into a coherent transformation.

5. Track adoption, not just “wins”

Don’t mistake a shiny, clever prompt for progress. A great experiment means nothing if it can’t be repeated by many people. From day one, design with scale in mind: Can this be adopted elsewhere? What would need to change for it to work across teams, roles, or regions? Build for transfer, not just applause.

The real opportunity

AI will not fail because the tech wasn’t good enough. It will fail because we mistook experiments for solutions, or because we governed innovation into paralysis.

You don’t need more control. You don’t need more chaos. You need design for scale, not just scale in hindsight.

Let’s stop chasing sparkles. Let’s build systems that spread.

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