Most boardrooms still frame the AI conversation around efficiency and cost. According to Karalee Close, Global Lead, Talent at Accenture, that framing is precisely the problem.
Her assessment draws on Accenture’s Talent Reinventors report, based on a survey of 1,320 C-suite executives and 4,560 employees across 20 industries and 12 countries. The study identifies an elite group—just 18% of organisations, the ones Accenture calls “Talent Reinventors”—that is delivering measurable value from AI. The other 82%, in Close’s account, have “confused deploying AI with transforming with AI.” The gap between the two is where what she calls “human debt” accumulates.
The symptom is a familiar one: speed rises, but meaningful outcomes do not. Close describes this as a “performance illusion,” in which organisations move faster without producing better results. The cause, in her account, is structural. “Most organisations are still designed around old jobs and old structures,” she tells HRM Asia, “when the real opportunity from AI comes only with redesign of how the work itself gets done.”
The infrastructure for that redesign is largely absent. In Accenture’s survey, 54% of C-suite respondents cited fragmented systems and outdated roles as their biggest obstacle, 47% pointed to limited visibility into current and future workforce skills, and 36% cited inconsistent sponsorship at the top.
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The human cost of AI and work
The toll of operating in that gap shows up in the employee data. More than a third of employees surveyed said they spend most of their energy simply adapting to constant change. Globally, 20% reported feeling undervalued, and only about a third strongly believe their employer is committed to helping them stay relevant. Among leaders, 55% reported cognitive overload across their workforce – employees managing rising complexity, in Close’s framing, “without clear guidance or redesigned roles to support them.”
For Close, the conclusion is that work redesign is not an operational detail to be delegated. It is, she says, “a CEO-level growth strategy, directly shaping productivity, innovation and long-term competitiveness” – one that demands the same urgency and personal ownership leaders already bring to the technology decision itself.
Accountability as a design principle
As AI systems take on more cognitive work, Close argues, responsibility does not automatically scale with intelligence—and the organisations pulling ahead have designed for that gap rather than discovering it after deployment.
The behavioural difference is measurable in the survey. Talent Reinventors clarify the decision boundaries between human judgment and AI recommendations and formalise override protocols to reinforce accountability. According to the report, they are 2.8 times more likely than their peers to say AI enhances collective judgment and 1.7 times more likely to use AI-generated matching to flag risks such as burnout or exclusion, while 76% use AI-enabled systems to adjust goals in real time.
Building that architecture, she argues, requires a partnership that most organizations have not yet formed. Close pointed to a perception gap among leaders: While over 60% are focused on investing in a strong data and technology foundation, fewer than 30% treat helping their workforce adapt as a top priority. She attributes the gap to the traditional siloed model, in which the CIO managed the technology and the CHRO managed the people—an approach she calls obsolete “because you can no longer separate the technology from the employee.”
In the model she describes, the CIO’s remit expands from managing systems to governing a secure and accountable agentic workforce, while the CHRO’s expands from managing human resources to redesigning the jobs and career paths that will use the new AI capacity. The report quantifies the divide: 96% of Talent Reinventors have a talent strategy fully integrated with technology and AI, compared with 16% of other organisations.
What it means for the individual
For the Gen Z and millennial professionals reading this, the survey describes a landscape shifting in real time. Some 76% of employees reported that their career pathways are unclear, and 45% said it is difficult to find internal roles, projects or learning opportunities. Asked what would most accelerate their careers, employees prioritised targeted training to build future-relevant skills (56%), clear internal pathways (34%), and greater autonomy and flexibility (30%).
Close’s single recommendation for the next 12 months is to adopt a skills-first mindset and act on it through what she calls “co-learning”—treating a career as a portfolio of skills built continuously rather than a fixed title. The mechanism is the work itself: seeking out projects that require working alongside AI, and volunteering for cross-functional assignments where AI tools are embedded, so that each project becomes a capability-building moment.
The early evidence in the report favours those who start now. Among employees already working this way, 68% said AI saves time on routine tasks and 59% said it improves their work quality. The skills built over the coming year, in Close’s view, will determine “whether you lead the redesign of work or get redesigned around.”
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