How often do we get a chance to rewrite the rules of an industry?
That's the question that drives me every single day. We're in one of those rare moments where the fundamental assumptions about how an entire sector operates are all in play at once. This is happening across enterprise operations everywhere.
I've watched organizations respond to disruption in two ways. Some work harder to preserve what they know, optimizing existing models. Others step back, examine assumptions, and rebuild from the ground up. The second group emerges stronger, not because their predictions were perfect, but because they understood when the rules had fundamentally changed.
We're in one of those periods now where leaders must redefine how value gets created by building systems that amplify human capability through intelligence.
The Economic Logic Has Changed
For most of this industry's history, revenue has been tracked by headcount. Investors saw people as the primary proxy for growth. That logic worked when scale meant adding capacity and when labor was the primary source of advantage. Today, intelligence and technology change the unit economics of work.
The shift is simple but profound. Value no longer is scaled with people alone. It is scaled with system intelligence, workflow design, and the ability to learn from every transaction.
I see this play out every quarter. There are quarters where our team size stays steady while revenue accelerates because intelligence, platforms, and redesigned workflows amplify capacity.
In other quarters, we intentionally invest in talent and capability ahead of revenue because we're building systems that create long-term leverage. It's about a workforce augmented by technology, where human capability and system capability compound together.
The Services-as-Software research we conducted with HFS Research surveyed over 500 enterprise leaders, and the data confirms where this is headed.
- Reliance on FTE-led delivery will drop from 42% to 28% over the next three years.
- Outcome-based models will nearly double from 20% to 39%.
- More than 40% of enterprises are preparing to shift customer experience delivery to AI-led models.
These shifts are already in motion because enterprises have reached a ceiling on speed and transparency with older operating models. They reflect a deeper change in leadership expectation. Enterprises want partners who can deliver outcomes, not capacity.
Most Leaders See It. Few Are Structured For It
What strikes me most isn't the direction of the shift; it's the execution gap. Many leadership teams understand the need for AI-enabled operating models, yet few are structurally prepared to deliver them.
For instance, one of the reputed publications recognized digital disruption early, invested heavily in digital subscriptions, and built one of the most successful media transformations. But that success required completely redesigning their newsroom workflows, retraining journalists for digital storytelling, and rebuilding revenue models around subscriptions rather than advertising. They had the insight in the early 2000s. The organizational transformation took over a decade.
Similarly, a retail player saw e-commerce rising, and invested in digital capabilities years before the pandemic. But their real breakthrough came only after they redesigned supply chain operations to enable same-day fulfillment, retrained store teams to handle online orders, and rebuilt inventory systems to work across channels. Technology wasn't the constraint. Redesigning the operating model was.
The pattern is consistent. Leaders understand the direction, but organizations are architected for a previous era. Insight isn't the differentiator. Operating models are.
The Debt We've Accumulated
Everyone talks about technical debt. But we've accumulated something broader. I call it PTSD debt: Process debt from workflows designed for a world that no longer exists. Talent debt from models that reward managing headcount instead of creating value. Skills debt from capabilities misaligned with modern requirements. Data debt from legacy systems that block what AI could unlock.
Add location debt to that list. In the labor arbitrage era, expanding delivery sites created an advantage. But, today, with real-time translation and AI-enabled workflows, the same dispersion often adds complexity rather than capability.
The HFS research identifies barriers like data readiness and integration complexity. These are symptoms of accumulated debt. They explain why AI doesn't scale in some environments even when technology works. The data is fragmented. The workflows were designed for another era. The talent model rewards the wrong behaviors. These shape organizational metabolism. A company with high structural debt moves slowly, even with excellent technology.
The real leadership challenge isn't adopting AI. It's unwinding the structural debt that prevents AI from creating compounding value. Most leadership teams overestimate their readiness and underestimate their debt. The gap between perception and reality is where companies stall.
Why Trust Matters Most in Transition
When elevators were first introduced, people were terrified that the machines would crash. So, elevators had operators whose only job was to stand there and push buttons. The operator wasn't mechanically necessary. The operator was there to create psychological safety, to build trust in the technology.
We're in the same point with AI. The human in the loop isn't there because technology falls short. Humans are there to build trust. Trust dictates speed, and speed determines advantage. Without trust, even good technology performs at the pace of the slowest approval loop.
I've watched organizations run pilots in 90 days, build commercial structures in parallel, and scale in 6 months. I've also watched organizations take 18 months to pilot, another year to align procurement, and then stall. Same technology, completely different metabolism. The differentiator is culture. Organizations that move create environments where people can learn fast, fail safely, and adapt quickly.
Three Anchors for Leadership
This is why the future belongs to leaders who can redesign assumptions, not just upgrade tools. The shift is organizational, not technical.
The HFS research doesn't just document the shift. It provides frameworks for understanding where your organization actually stands. What we've learned is that three principles separate companies making real progress from those running expensive experiments.
Build modular capabilities that scale intelligence
The world is moving too fast for large, monolithic bets. Modular architecture allows you to upgrade, replace, or reshape components as the landscape evolves. It protects you from overcommitment and keeps options alive.
Treat adaptable talent as your only durable advantage
Technology will keep changing. The constant is talent that can adapt with it. When I evaluate senior leaders, I look at what tools they use and how curious they are. Adaptability determines whether an organization can shift at the speed of the market.
Make experimentation part of the leadership operating rhythm
In a space defined by constant innovation, leaders need to place many small, thoughtful bets. Partnerships with startups. Token investments in promising technologies. Cross-functional experiments that test new models without disrupting the core. This creates a steady flow of insight into where the market is heading.
These anchors work because they create organizational capacity for change without requiring perfect prediction. The complete HFS research includes maturity assessments and implementation pathways that help leaders understand where to start based on their current debt load and market position.
The Leadership Imperative
Companies that move now are building advantages that compound. Better cost structures that create pricing flexibility. Operations that improve without adding resources. Access to markets that weren't economically viable before.
The market is grading operating models, not intentions. We have a rare opportunity to build enterprises where intelligence compounds, where outcomes matter more than effort, and where value creation becomes the true unit of measurement.
The organizations that pull ahead will treat intelligence as a core economic asset, not an add-on to legacy delivery. The next decade will reward leaders who rewire assumptions fast enough to meet a world where intelligence is the new scale.
Download the complete HFS-Firstsource Services-as-Software report for maturity assessments, implementation frameworks, and readiness tools.