Enterprises are entering a more decisive phase in their AI journey, where success can no longer defined by the number of pilots launched but by the tangible value delivered. Without a value-first approach, even the most advanced AI deployments risk stalling, making impact measurement, governance, and strategic alignment critical to scaling AI effectively.
In a conversation with iTNews Asia, Steven Yurisich, Regional Managing Director for APAC at Thoughtworks, shared insights on how enterprises are now transitioning from AI ambition to execution and why this year will mark a turning point for measurable outcomes.
According to Yurisich, after several years of experimentation, many organisations are now confronting a critical reality, where AI initiatives must move beyond pilots and begin delivering tangible business value. While AI models themselves are powerful, he pointed out that the real barriers lie in foundational gaps, particularly in data, operating models, and governance.
Fragmented and inconsistent data continues to undermine AI effectiveness, while poorly defined operating models limit scalability. “It’s not the technology that’s the problem. It’s the scaffolding around it that isn’t in place,” he noted.
Legacy systems further complicate progress, with enterprises still spending a majority of their resources maintaining existing infrastructure instead of enabling innovation.
The illusion of progress in AI scaling
A recurring theme in enterprise AI journeys is the illusion of progress. Many companies chose use cases that were easy to implement but failed to create meaningful business impact.
One of the biggest misconceptions leaders face is equating quick AI wins with meaningful business impact. The easiest places to start may not be the highest impact.
- Steven Yurisich, Regional Managing Director for APAC at Thoughtworks
He described 2024 - 2025 as a phase where companies appeared to move faster, but didn’t necessarily produce more value. Even when employees gained time efficiencies, organisations frequently struggled to channel those gains into business results that matter to leadership.
What enterprises still lack?
As enterprises head into 2026, the gap is no longer about understanding AI models. Most organisations now have a reasonable grasp of the technology.
Yurisich said the real challenge lies in operationalising AI effectively and it includes thinking through the entire lifecycle, from defining strategic intent and governance to ensuring security and measuring outcomes. The non-deterministic nature of AI models adds another layer of complexity, requiring thoughtful guardrails and clear decisions about where AI should and should not be used.
Interestingly, some organisations overcorrect by imposing excessive controls, which ultimately stifles innovation and prevents scaling altogether. The balance between governance and agility remains delicate, he explained.
The new AI playbook
Perhaps the most important shift underway is how organisations perceive the value of AI. Over the past two years, much of the focus has been on efficiency, reducing costs or automating tasks. However, Yurisich believes this approach is too narrow.
A key shift for 2026 is moving beyond cost savings toward business model transformation. “AI has the power to drive growth and differentiation – not just efficiency,” he added.
The real opportunity lies in using AI to drive growth, deepen customer relationships, and create differentiated experiences. Organisations that succeed will be those that integrate AI into the core of their business strategy, rather than treating it as a peripheral tool. This shift is already beginning to take shape among forward-looking enterprises, though it remains in its early stages.
To truly embed AI into core operations, enterprises must rethink how they measure success. Yurisich outlined a “balanced scorecard” approach, but stressed that impact should be the primary lens:
● Business impact metrics: revenue growth, customer acquisition, lifetime value
● Adoption metrics: usage across teams and functions
● Technology integration: extent of AI embedded in core systems
“The first lens should be understanding the value it’s driving, whether that’s growth, experience, or business impact,” he said.
A defining year for AI monetisation
For leadership, the era of experimentation is over and 2026 represents a moment of accountability. Yurisich underscored that CEOs must now focus on how AI is monetised and how it contributes to competitive differentiation. Superficial initiatives and symbolic investments will no longer suffice.
The shift is stark: organisations must demonstrate real impact or risk falling behind. As with previous waves of digital transformation, those that fail to adapt quickly may struggle to remain competitive. “The organisations that take a growth and value lens will be more successful,” he said.
