Why Transform When You Can Pioneer?
The fastest path to AI-Native isn't improving what you have.
Most companies are trying to transform faster instead of asking a harder question: whether transformation is still the right goal. Transformation assumes the way a company operates today is worth keeping, just run better. The companies pulling ahead are setting that assumption aside entirely, walking away from the old model instead of trying to fix it, and building what replaces it either alongside the business or in its place. The economics increasingly favor rebuilding, more than most executive teams assume. But none of it works if the people inside the old model get treated as a cost to cut rather than the reason the new one can move fast.
A management team spends a quarter building a three-year roadmap for AI-native competition. By the time the board approves it, a competitor a fraction of its size has already built what the team was planning for year two. That is becoming normal for any leadership team still planning in years while its sharpest rivals build in weeks.
The old rule held for decades: whatever a rival might eventually build would take years to reach the market, giving the bigger company time to see it coming and catch up. That rule depended on new capability costing the same time, money, and talent it always had. It no longer does. BCG's latest research on AI investment found the fastest-moving companies, roughly 15% of the field, are putting 75% of their AI budget to work right now, while the rest of the field commits about 25% and waits to see if it pays off.
Recognize the Real Spectrum
The real decision in front of most leadership teams is whether a company arrives at full AI-native operation on a timeline it sets for itself, or one the market sets for it later. Most are moving slowly toward that arrival because gradual change feels familiar. Familiar isn't a reason to believe slow is the right pace.
The choice rarely gets talked about as one choice. Most conversations about AI and transformation treat it as binary, doing something with AI or not. That hides where the real decisions happen: how much of the existing company survives the shift.
Most sit right where they've always sat, adding AI inside the business they already run, new tools, faster workflows, same org chart underneath. Real progress, and also the choice that changes least, since the assumptions and structures that built the current model stay in place.
Some go further, building something new beside what already exists rather than replacing it. A real step past adding tools, but still a hedge, since the new thing still answers to the old org chart, incentives, and sales motion.
A few go further still, deciding the new model and the old one can't share a roof, and splitting them apart completely so the new one starts without the old one's constraints. That only works when leadership knows exactly what the old business needs to leave behind.
And a small number go all the way, rebuilding the whole company as AI-native from the ground up rather than spinning off something separate. It's where the whole spectrum is heading no matter where a company starts. The stops in between buy time. None of them buy a lasting way to avoid ending up where the fastest movers already are.
Speed is the part of this most leadership teams underestimate. Every stop short of the far end still carries the old model's weight, systems, habits, and assumptions that have to be unwound before anything new can fully run. Pioneering skips that step. There's nothing to dismantle first, only something new to build, which is the real reason the companies furthest along this spectrum got there faster than the ones still working through the middle.
“Transformation assumes the old model is worth keeping, just run better. Pioneering sets that assumption aside entirely and walks away from it.”
Look at Who's Already Done It
Neither Sabre nor Capstone is a technology company dressing up a product announcement as a transformation story. One runs global travel infrastructure. The other distributes building materials. Both decided the stops in between would cost more time and money than committing outright, and both had leadership willing to decide the operating model itself needed to change.
Sabre spent years planning this in private before showing it in public. In March 2026, the travel technology company unveiled what it called a once-in-a-generation rebuild, not an upgrade, a full reconstruction of its technology and operations under a new platform named Mosaic. It even changed its brand identity, a step few companies attempting this will take, though a handful of recent examples have, signaling that the company underneath the logo wasn't the one that carried the old architecture for decades. That's what pioneering looks like when a large, established enterprise commits to it fully.
Capstone Holding, a much smaller building products distributor, made the same choice from a different starting point. In February 2026, its CEO announced the company was stepping away from the enterprise stack most mid-market organizations run without a second thought, building itself instead as a full-stack AI company. His reasoning was specific: the million-dollar, eighteen-month IT projects that used to be normal would now happen rapidly, at a fraction of the cost. That's the economics case made by someone living it.
One other example rounds out the picture without stretching it. Lemonade never had an old model to leave behind, running underwriting, pricing, and claims through AI-native systems built that way from its founding, proof the far end of the spectrum works as a starting point as well as a destination. Together with Sabre and Capstone, the pattern holds up across different industries and different starting points, all arriving at the same place.
Understand Why the Economics Favor Pioneering
The instinctive objection is cost. Rebuilding an operating model sounds like the expensive choice next to gradually improving what's already running, and for decades that was true. It no longer is, and a growing number of organizations are proving why.
Capstone's CEO put a number on it: the multi-million-dollar, eighteen-month IT projects that used to define modernization now happen rapidly, at a fraction of the cost, when a company builds AI-native instead of layering new capability onto systems built for a different era. That cost structure, years of rollout, integration work, legacy systems running alongside their replacements, doesn't apply to a company built AI-native from the ground up.
There's a second cost most executive teams overlook: what it costs to run AI at scale, the ongoing compute expense that shows up every month rather than once at implementation. Layered on top of a multi-year transformation budget, that combination is what makes AI adoption feel unaffordable to many CEOs weighing it. Pioneering removes the transformation half of that equation, leaving real room to manage what AI costs to run instead of carrying both bills at once.
The research backs the pattern. IBM found companies that redesigned all five core areas of the business together were four times more likely to hit their objectives. McKinsey found the same shape in one case: AI layered onto an unchanged workflow produced small gains, agents bolted on produced a bit more, and a workflow rebuilt around what agents can do produced returns several times larger than either. The value came from whether the process survived the change, which had little to do with which technology tier delivered it.
The expensive path has been years of capital spent modernizing a model destined for replacement anyway, while also absorbing AI's own running costs on top of it. The organizations recognizing that first are setting the economics the rest of the market will eventually have to match.
74% of AI's Value. 20% of Companies.
PwC surveyed 1,217 senior executives across 25 sectors. The companies capturing AI's value redesigned the workflow around it, rather than adding more AI to what they already had.
Redesign Around People, Not Instead of Them
Institutional knowledge is what makes pioneering faster, an asset to build on rather than a cost to eliminate. The people who understood the old operation's real constraints, what customers needed even when the process didn't ask for it, are the ones best positioned to help design what replaces it, given a role in doing so.
Most leadership teams reach for a different instinct first: cut the headcount tied to the old model, treating people as an expense retired along with the systems they ran. That instinct is what turns a sound technology decision into a bad outcome.
Klarna is worth looking at honestly, since it's both a caution and a proof point. Its AI system took over a large share of customer service work, and the company cut headcount sharply on the strength of it. Its CEO later walked part of that back, admitting cost had become too dominant a factor, and rehired some of the people let go. Pioneering aimed at cost reduction alone, with no plan for what displaced people should do inside the new model, produces a result even the company making the cuts doesn't want to live with.
Culture is the other piece most leadership teams miss. An organization that ran one way for a decade built habits and assumptions around that way of working, and those habits don't disappear just because the technology underneath changes. Pioneering is the chance to set a different set of defaults from day one, an AI-first way of operating built into how the place runs rather than layered onto how it already runs, with the same good people carrying it forward.
The organizations getting this right turn operational expertise into oversight of the new model instead of treating it as disposable. Their best people stop running the processes AI now runs and start knowing when the system is right, when it's missing something, and where the next capability needs to go, a more valuable use of talent already inside the company, and the difference between pioneering that strengthens people and pioneering that simply displaces them.
Decide Before the Choice Is Made for You
The real question most leadership teams haven't asked is whether the operating model is being protected because it's still the right one, or because it's the one everyone already knows how to run. None of this requires reaching the far end of the spectrum tomorrow, and the path isn't simple for every business or balance sheet. But that question deserves an honest answer before the model gets protected by default.
Sabre and Capstone didn't get ahead by moving faster through the same transformation playbook everyone else is using. They got there by deciding the playbook itself was the constraint, and building past it while the choice was still theirs to make. That's the real advantage right now: arriving at full AI-native operation on a timeline a company sets for itself, rather than one dictated later by competitors who got there first.
That window narrows every quarter the fastest-moving companies keep proving what full pioneering delivers, as more of the intermediate stops stop being places to rest and start being places every remaining organization eventually has to leave. The organizations reading that shift early aren't the ones with the most resources. They're the ones willing to conclude that the model that built them to where they are today isn't the model that gets them to where they need to go next.
Pioneering is the deliberate, well-evidenced version of a move the market is already making, with or without any single organization's participation. The advantage belongs to whoever takes it on their own timeline, built around their own people, rather than waiting to see how much of it is left once the choice gets made for them.
About John Fildes
I grow the top line by connecting marketing to business strategy. By leveraging powerful positioning, content marketing, and client insights, I help organizations drive qualitative and quantitative results at scale.
I've built an amazing network of incredibly talented people over the years. What I've appreciated most is those who have invested in me, mentored me, and helped me become the talented professional I am today. I pay it forward by doing the same for other high performing professionals and entrepreneurs.
Learn More: Growth Leader | Business Builder | Leadership Multiplier
All views are my own and not those of my current or prior employers.
