JustUpdateOnline.com – As enterprises globally accelerate the integration of automated technologies and generative AI into their daily activities, a significant hurdle is becoming apparent. While many businesses are currently launching various pilot programs and initial prototypes, they are finding it increasingly difficult to transition these small-scale tests into comprehensive, company-wide transformations.
Ryan Meyer, the Managing Director for the Asia-Pacific region at the professional training firm General Assembly, suggests that the primary obstacle is not the technology itself. Instead, the disconnect stems from a lack of executive vision, insufficient employee preparation, and a vague understanding of what it means to be proficient in AI.
Meyer observes that businesses frequently confuse the simple installation of software with true operational adoption. He argues that rather than being woven into the fabric of daily decision-making and standard workflows, AI is often treated as a peripheral technical task. This approach results in a superficial "check-the-box" mentality that fails to produce lasting organizational change. He further noted that progress is often hindered by fragmented training efforts and a lack of cohesion between different departments.
Executive Guidance Necessary for Success
A major sticking point identified by Meyer is the absence of accountability at the highest levels of management. When leadership fails to provide a cohesive, top-down roadmap, AI initiatives often remain isolated within specific silos. This fragmentation typically leads to limited usage and a poor return on investment.
To combat this, it is recommended that companies establish specific business goals for AI that align with their overall corporate objectives. Meyer advocates for an environment where cross-functional teams are encouraged to collaborate and experiment with these tools within their actual daily responsibilities. Without this strategic integration, there is a risk that AI will simply be "bolted on" to outdated processes rather than serving as a catalyst for redesigning how work is performed.
Furthermore, there is a growing concern that the speed of technology rollout is outpacing the establishment of necessary safety protocols. While executives do not need to be technical experts, they must possess enough knowledge of the technology’s risks to implement transparent oversight, ethical standards, and clear lines of accountability.
Customizing AI Proficiency by Job Function
Another challenge is the lack of standardized benchmarks for AI literacy. Meyer pointed out that human resources, executive boards, and technical departments often have conflicting expectations regarding what employees should know, leading to inconsistent hiring and training results.
Rather than relying on generic certifications or theoretical courses, Meyer suggests that AI education must be tied directly to an individual’s specific job duties. Closing the talent gap requires a commitment to ongoing, role-specific upskilling. For instance, while executives need to focus on governance and ROI, frontline staff require practical training for safe, everyday tool usage. He cited the bank-wide retraining initiatives at UOB as a prime example of a practical approach to workforce evolution.
The Path to Meaningful Transformation
Ultimately, Meyer believes that the success of a digital transformation is found in measurable operational improvements rather than the number of pilot projects launched. If a company’s investment in its people does not keep pace with its investment in technology, the business will struggle to see any real value.
The organizations that will thrive in the future are those that view AI as a permanent, evolving capability rather than a one-off IT project. Achieving tangible returns requires a sustained commitment to leadership accountability, robust governance, and continuous human development. In the end, a successful AI strategy is less about the software and more about the people and the vision guiding its use.