This is article three in a six-part series exploring new research findings from HCLTech and Raconteur. You can read the rest of the article series in ‘The Blueprint for AI Leadership’ here or download the full report here.
Findings from Raconteur and HCLTech’s new research suggests that confidence in workforce preparedness for AI-driven roles is high among the majority of organisations, with 79% very or somewhat prepared to train and upskill employees for operational changes driven by Agentic AI.
Yet at the same time, only 33% currently have a comprehensive organisation-wide retraining strategy or plan for upskilling employees; nearly two-thirds say it is defined but limited in scope. In other words, while workforce AI enablement is widely recognised as important, this is not consistently translating into action at scale.
Who are AI Leaders & Followers?
AI Leaders, who represent 18% of the sample, are maximising ROI not simply by using AI to reduce costs, but by applying it to drive growth, innovation and improved customer experience.
AI Followers, by contrast, represent 60% of the sample. While they are realising returns from AI investments across business functions, they significantly trail Leaders in the higher-value use cases that are reshaping operating models and competitive advantage.
Organisations are equally confident in their ability to adapt recruitment and onboarding processes (79% very or somewhat prepared) and to orchestrate work through a blend of humans and agents (80% very or somewhat prepared). However, confidence drops when it comes to managing role redesign, redeployment and consistent adoption at scale. This points to a persistent gap between belief and capability.
“Confidence without strategy is a form of organisational wishful thinking. It’s comfortable in the short term but creates significant risk as AI capabilities mature and workforce requirements evolve,” says Lester Lam, executive vice president and global head, advisory and consulting, digital business services at HCLTech.
Confidence in managing AI-driven change remains limited. Only 27% of organisations say they are very confident today, with a stark divide between Leaders and Followers reinforcing how uneven preparedness remains. While Leaders are significantly more confident than Followers, many organisations expect this gap to narrow within three years. Such optimism raises a critical question: are organisations assessing AI returns based on their ability to execute today, or on expectations of future readiness?
The disconnect between confidence and workforce capability is further emphasised by the gap between AI Leaders and AI Followers. For example, 93% of Leaders have a comprehensive strategy for retraining or upskilling employees, compared with just 20% of Followers.
“AI Leaders are treating upskilling as a strategic priority with commensurate investment,” says Lam. “They’ve made substantial commitments of time, money and executive attention to ensure dedicated learning time is built into work schedules, not just optional evening courses.”
AI Followers, by contrast, tend to treat workforce development as a periodic HR initiative rather than embedding it within organisational structures, with only 24% prepared to manage continuous organisational changes driven by AI, compared to 52% of AI Leaders.
“Upskilling efforts that are disconnected from how performance is evaluated or how work is organised will always underperform, because employees correctly read that the capability being developed is not actually required to succeed in their current role,” says Sebastian Reiche, professor of managing people in organisations at IESE Business School.
AI Leaders are treating upskilling as a strategic priority with commensurate investment
The problem is compounded by the fact that Followers are three times less likely than Leaders to encourage employees to experiment with AI tools and new ways of working.
Reiche’s own work points to a set of structural features that distinguish those organisations that successfully embed capability development. These organisations organise work in ways that generate learning as a natural byproduct, for example, through role rotation, peer feedback and giving employees genuine agency over how their work is configured.
They also treat alignment between work design, performance management and incentives as a coherent system. Crucially, they approach AI literacy not simply as a technical training challenge, but as a work design challenge. organisations should define what each role looks like when augmented by AI and then build development programs around that reality rather than focusing on generic tool adoption.
Closing the capability gap
For AI Followers, the window to close this capability gap is beginning to narrow. The research shows that Leaders are already as far ahead on skills as they are on technology. In many cases, this is because they have already built the workforce infrastructure – including development systems, experimentation cultures and role redesign capabilities – necessary to convert AI investment into a sustained, compounding advantage.
Followers who are struggling to build workforce capabilities may also be focusing on weaker or misdiagnosed barriers to adoption. Conventional narratives often frame AI workforce challenges as a resistance problem, portraying employees as reluctant to adopt new tools or unwilling to change. The research, however, does not support this view.
In reality, the dominant barrier is a skills shortage, cited by 47% of organisations. At first glance, this suggests a workforce struggling to keep pace with AI – but the evidence points elsewhere. Employee resistance is cited by just 20% of organisations, with leadership misalignment ranking higher at 29%.
Attracting and retaining AI-proficient talent ranks as a top challenge for 38% of Leaders. Logic suggests we’d see a much higher percentage of Followers who rank finding and keeping top talent as a major challenge, yet significantly fewer (just 26%) actually do. This reveals an important shift: as organisations approach AI maturity, the primary workforce challenge is no longer building baseline capabilities but rather competing for specialised talent – and Followers just aren’t there yet.
Insufficient training programs are cited by 30% of Followers as a top barrier, compared with just 15% of Leaders. This further reinforces that Followers are not being held back by resistant employees, but by the absence of the development infrastructure that Leaders have already established.
Employee resistance as a narrative is indeed convenient, but it is incomplete
“AI’s transformational potential isn’t realised when you implement AI systems. It’s realised when people throughout your organisations know how to leverage those systems effectively, can identify new opportunities for AI application and are comfortable working in AI-augmented ways,” says Lam. “The technology is necessary but not sufficient.”
Reiche reinforces this point: “employee resistance as a narrative is indeed convenient, but it is incomplete.” Workers are often more willing to adapt than assumed, particularly when they are empowered to shape how tools are integrated into their roles and can see a clear vision of how their work will evolve.
In many cases, deeper structural issues are at the root of this gap. The dominant model of work design, in which tasks are centrally defined and assigned, is ill-suited to the dynamic reconfiguration that AI integration demands. “That model produces clarity and control, but it is very poorly suited,” Reiche explains.
When AI is introduced, organisations must rethink what roles are for, how work flows between people and systems and where human judgment adds value. These are fundamentally questions of work design, yet many organisations lack both the frameworks and the operational habits to address them effectively.
Upskilling for the autonomous era
Addressing this challenge is becoming increasingly urgent. While less than 10% of today’s enterprise AI capability is autonomous, that percentage is expected to rise sharply over the next three years across several operational functions, including technology, infrastructure, data and analytics.
A clear gap remains between Leaders and Followers in preparing for this shift to autonomous systems, particularly in managing workforce displacement (38% of Leaders say they are very prepared vs. just 19% of Followers) and redesigning existing roles (51% vs. 29%).
Governance, oversight and the ability to challenge autonomous systems will become critical capabilities for employees. These skills will not emerge organically; they will need to be deliberately developed. Even today, readiness for AI governance sits at just 43% across surveyed organisations, well behind readiness to deploy or maintain AI systems.
“Organisations cannot govern what they do not understand,” says Reiche. “Building a workforce capable of meaningful AI oversight requires, first, that people working alongside autonomous systems have enough understanding of how those systems operate to know when something is wrong.”
If AI systems are treated as black boxes and employees are expected simply to execute outputs, governance capabilities will remain limited. By contrast, when work is designed so that employees actively interrogate outputs, flag anomalies and contribute to system refinement, oversight becomes embedded in day-to-day operations.
Beyond individual capability, organisations will also need to invest in roles that operate at the interface between AI systems and human context. As Reiche notes, these roles require contextual judgment, ethical reasoning and the ability to challenge system outputs. Each is a capability that must be explicitly designed and developed.
Without the right workforce skills and a culture of continuous learning, AI initiatives are likely to remain promising but limited. Where workforce systems are deliberately redesigned to integrate learning, oversight and adaptation, organisations are far more likely to translate AI investment into sustained, enterprise-wide impact.
This is article three in a six-part series exploring new research findings from HCLTech and Raconteur. You can read the rest of the article series in ‘The Blueprint for AI Leadership’ here or download the full report here.
This is article three in a six-part series exploring new research findings from HCLTech and Raconteur. You can read the rest of the article series in 'The Blueprint for AI Leadership' here or download the full report here.
Findings from Raconteur and HCLTech's new research suggests that confidence in workforce preparedness for AI-driven roles is high among the majority of organisations, with 79% very or somewhat prepared to train and upskill employees for operational changes driven by Agentic AI.
Yet at the same time, only 33% currently have a comprehensive organisation-wide retraining strategy or plan for upskilling employees; nearly two-thirds say it is defined but limited in scope. In other words, while workforce AI enablement is widely recognised as important, this is not consistently translating into action at scale.
