From Pilots to Productivity — Getting Enterprise AI Adoption Right
- Dario V., Lead Editor Transformations
- Jan 13
- 4 min read
By: Dario V.
Lead Editor, Transformations and Advisory
There’s a moment in every transformation where optimism meets operational reality.
For many enterprise leaders, that moment came and went in 2025. The excitement over AI’s potential quickly turned into frustration, confusion, or stalled progress. What began as bold initiatives to increase productivity often became bogged down by poor implementation, underused tools, and team resistance.
The Gap Between Deployment and Impact
As my colleague Liora highlighted in her piece on AI and cybersecurity, the biggest vulnerabilities didn’t stem from technology failures — they came from human gaps in understanding, preparation, and behavioural readiness. That same dynamic has shown up across transformation efforts. Too often, organizations introduced AI assistants or copilots without preparing the business ecosystem around them.
The assumption was simple: “We’ll roll out the tool, and productivity will improve.”
But AI doesn’t work like that.
Without the right strategy, support, and structure, what’s intended as an enabler quickly becomes just another layer of noise. In fact, one of the most common missteps we saw last year was companies launching AI tools without providing proper onboarding, clarity of use cases, or internal governance frameworks. The tools may have been powerful—but teams weren’t prepared to use them, didn’t know when to trust them, or feared they might be replaced by them.
The result?
Tools went under-utilized. Sensitive information was occasionally pasted into unsecured platforms. Cross-functional confusion slowed down workflows. And in many cases, transformation fatigue set in—especially among middle managers who were expected to “make it work” with little support.
What 2025 Taught Us About AI Integration
Yet despite these challenges, 2025 wasn’t a complete failure, it was a learning year. The companies that did see results from AI adoption approached it not as a tool rollout, but as a strategic digital transformation.
They started by aligning AI to clear business outcomes. They invested in internal assessments to map which teams, roles, and processes were actually suited for augmentation. They focused on one or two high-value use cases, rather than trying to deploy across the entire enterprise at once. And perhaps most importantly, they included operations, legal, IT, and people teams in designing both the guardrails and the workflows from the outset.
Where Enterprise Leaders Should Focus in 2026
As we’re firmly set into 2026, this shift from experimentation to execution will define the difference between success and stagnation.
So, where should enterprise leaders focus now?
1. Start by treating AI assistants like team members.
You wouldn’t drop a new hire into your organization without onboarding, role clarity, and mentorship — and the same applies to AI. Teams need to understand not just how a tool works, but why it exists, what it can and cannot do, and how it will evolve. Next, resist the temptation to chase popular features.
Begin by asking:
What problem are we trying to solve?
What would success look like?
Who needs the most support—and where are they currently losing time or momentum?
** NOTE ** Let these answers shape the tool you choose, not the other way around.
2. Build a cross-functional AI-enablement team.
This group should be composed of representatives from IT, operations, legal, and key business units, which can help oversee everything from use case prioritization and risk reviews to training and internal comms. This isn’t about adding bureaucracy. It’s about giving AI a proper foundation to scale without introducing chaos.
3. Design for change.
Remember, your business needs will evolve. Your teams will grow. And the tools themselves will continue to iterate. Whether you're introducing new AI capabilities or replacing early-stage tools with enterprise-grade platforms, your processes should be flexible enough to adapt without forcing reinvention each time.
The lesson from last year is clear:
AI assistants won’t magically transform your business.
But if you build the right structure around them—clear workflows, strong governance, empowered teams—transformation becomes not only possible, but scalable.
We don’t need to move faster in 2026.
We need to move smarter.
Up Next in the Series…
Now that we’ve explored how AI can amplify productivity across large organizations, what about the businesses that don’t have transformation offices, large-scale budgets, or full-time technical teams?
That’s where my colleague Kai, Lead Editor for our Executive Insights Exchange (EIE) Networking and Masterminds, offers a valuable lens. Kai works closely with small business owners, entrepreneurs, and solo operators who are using AI not as a replacement, but as a force multiplier.
In Part 3, he’ll unpack how lean teams are using AI to compete with bigger players, extend their reach, and scale their output—without compromising security, brand integrity, or customer trust. His insights show that you don’t need a massive budget to unlock big wins—just a thoughtful approach to the right tools, at the right time, in the right places.
About the Editor
Dario brings deep expertise in strategic advisory, enterprise transformation, and organizational alignment. With a track record of guiding large-scale initiatives across private and public sectors, Dario’s insights are rooted in evidence-based leadership, scalable frameworks, and real-world execution. He specializes in turning complex goals into clear, actionable strategies that accelerate impact without compromising integrity.
“Transformation is not about forcing change — it’s about aligning what matters most, and executing with relentless clarity."
Comments