Many organisations across the globe are racing to deploy generative tools and automation across their operations. But beneath the surge in pilots and proof-of-concepts, a deeper challenge is emerging where companies are struggling to turn AI experimentation into meaningful, business-wide transformation.
Ryan Meyer, Managing Director (APAC) at workforce skilling firm General Assembly, told iTNews Asia that the gap lies not in technology adoption, but in leadership strategy, workforce readiness and clear definitions of AI literacy.
Meyer said companies often mistake AI adoption for tool deployment, instead of embedding it into everyday decision-making and workflows.
“Many companies still see AI as a technical implementation rather than a key part of their overall business strategy. They focus on basic training or piloting tools without fully integrating AI into daily operations. It becomes a box-ticking exercise,” he explained.
He added that unclear leadership direction, siloed initiatives and fragmented training programmes frequently prevent meaningful progress.
Leadership gaps stall strategy
According to Meyer, a lack of executive ownership is one of the biggest barriers to deeper adoption. Without a top-down strategy, organisations tend to run isolated experiments that fail to scale.
When AI is seen as just another IT rollout, it lacks strategic direction and executive sponsorship. It leads to fragmented pilots, limited adoption and poor returns.
- Ryan Meyer, Managing Director (APAC), General Assembly
He recommends setting AI-specific business objectives tied to company outcomes, encouraging cross-functional collaboration and ensuring teams are empowered to experiment with tools inside their daily workflows.
Without this, he warned that AI tools risk being “bolted onto” existing processes instead of reshaping how work gets done.
He also warned that many organisations are deploying tools faster than they are building controls. Leaders do not need deep technical expertise, but they must understand the technology’s limitations and risks well enough to guide teams responsibly.
“Ground optimism in clarity, not complexity. Leaders should put simple, repeatable governance in place, such as clear accountability, transparency standards and ethical checkpoints. This ensures that innovation moves quickly and safely.”
Defining AI literacy by role, not theory
A further complication Meyer points is the absence of clear standards around AI literacy.
He noted that expectations often differ widely between HR, executives and technical teams, making hiring, training and performance measurement inconsistent.
Instead of generic courses or certifications, he said literacy should be tied directly to job responsibilities. Closing the skills gap requires continuous, role-specific upskilling and giving employees opportunities to experiment with AI in real workflows.
“Learning has to become an ongoing habit. Executives need governance and ROI understanding, practitioners need hands-on skills like prompt engineering and data handling, and frontline teams need practical guidance for safe everyday use,” he added.
Meyer pointed to initiatives such as UOB’s bank-wide AI reskilling programme as examples of practical workforce development.
Successful AI transformation
For Meyer, true digital transformation is visible in measurable operational gains rather than isolated pilots. If upskilling fails to keep pace with adoption, he warned organisations could struggle to extract value from their AI investments.
AI should be treated as an ongoing capability rather than a discrete project. “Organisations that win will treat AI as a continuous driver of competitive advantage,” he said. “That means sustained investment in people, governance and experimentation - not one-off deployments.”
For enterprises hoping to unlock tangible returns, Meyer’s message is clear: AI transformation starts not with tools, but with strategy, skills and leadership accountability.
