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It’s a sobering statistic: 95% of generative AI pilots fail, according to MIT Sloan research. But the problem isn’t the technology – it’s that organisations approach AI as a technology initiative rather than a human transformation challenge.
As enterprises race to deploy AI across their operations, the fundamental question isn’t whether machines can perform tasks, but whether leaders can reimagine work itself around human experience, empathy and trust.
Human-centric AI leadership: Technology follows people, not the other way around
AI’s promise will remain unfulfilled unless leaders fundamentally shift their approach from technology deployment to human empowerment.
Success hinges on designing AI around human experience – it requires leaders to double down on what machines cannot replicate: empathy, vision and trust. Research shows that AI can, paradoxically, make leaders more human by freeing them from tactical work, creating space to invest in awareness, wisdom and compassion – qualities that elevate leadership impact.
While technical pillars like data, intelligence and guardrails are critical, these frameworks mean nothing without the sociological infrastructure to support them.
Our 2024 Global AI Study found that 65% of executives believe AI will augment human capabilities, enabling people to focus on higher-value activities requiring creativity and strategic thinking – humanity’s competitive advantage. Leaders need to manage hybrid workforces of humans and AI agents in a way that promotes clarity and limits confusion and duplication. When 45% of executives expect half their employees to use generative AI capabilities within three years, the leadership imperative is not technological fluency but the ability to orchestrate human-agent ecosystems where both thrive.
Evolving the employee journey: From skills to identity
AI transformation alters the employee experience, requiring people to adapt, reskill and redefine their professional identity. While the World Economic Forum’s Future of Jobs Report 2025 offers grounds for optimism – projecting a net increase of 78 million jobs globally by 2030 – the view from the workforce is often more complex. I saw this disconnect highlighted in a recent study of UK professionals, which found that just over half (56%) shared this optimism – and almost two-thirds (61%) were overwhelmed at the speed of technology development.
Effective change management must articulate how AI frees humans from routine tasks, elevates them to higher-value work, and establishes clear collaboration models between people and agents. The alternative – presenting AI as automation that displaces workers – breeds resistance and undermines adoption.
We are seeing a shift where AI empowers people to become creative generalists, broadening their ability to support business needs beyond narrow specialisation. But organisational architecture and success metrics must evolve simultaneously to enable this.
With only a fraction of organisations having truly transformed their business despite widespread deployment, as the MIT Sloan research shows, the gap reveals that deployment is merely the starting point. True transformation requires metrics of adoption, workflow redesign, governance of agent behaviour, continuous training and cultural shift – with leadership owning this end-to-end, not simply delegating it to IT.
The redefinition of work: From system-based to conversation-based
Work is fundamentally shifting. AI adoption must focus on empowering people, not replacing them. Leaders must redesign roles, evaluation systems, rewards, teaming practices and human-agent trust mechanisms while safeguarding wellbeing. The human element – how people feel, engage and collaborate with digital co-workers – cannot be an afterthought.
We need to think beyond isolated projects, towards learning systems where humans, data, models and agents work together to improve decision-making. This requires leaders to conceptualise their teams as ‘people plus agents’, setting goals, measuring performance, allocating tasks between human and machine, and monitoring unintended consequences.
Machines are entering workflows that humans once dominated. This introduces synchronisation and trust challenges: Do humans trust machine output? Does the machine hand off appropriately? Who remains accountable? Leadership must navigate these social and governance issues, not merely the technology.
The regulatory environment reflects this urgency, with 81% of executives in our global study highlighting the need for AI standards and regulation. Managers must build frameworks around explainability, bias mitigation, human override capabilities, monitoring and accountability. These competencies define leadership in the human+AI workforce.
Designing workplaces that combine efficiency with empathy
Leadership now spans both humans and digital agents. The integration of large language models into enterprise systems demonstrates how agentic workflows are becoming an operational reality.
But efficiency alone creates brittle systems. Success demands future-ready digital skills combined with hands-on learning through AI experience zones at all organisational levels. This preparation is vital because we are only in the first phase of AI’s evolution; far more advanced technologies are on the horizon. Quantum computing, neuromorphic architectures and others yet to emerge will redefine what intelligent systems can do – making perpetual adaptability the ultimate competitive advantage.
Leaders must avoid the ‘technology pull’ that deploys AI because it’s possible rather than because it serves human and business-value imperatives.
The question should never be “let’s deploy agents everywhere” but rather “what human-agent combination delivers higher value, and what capabilities must each bring?” Leadership must keep human purpose front and centre, designing workplaces where efficiency serves empathy and creativity rather than replacing them.
The conclusion is clear: “the future is AI, the future is human”. This isn’t a contradiction – it’s the only sustainable path forward. The human+AI workforce demands leaders who recognise that AI’s success depends as much on human transformation as on algorithms, who design technology around human experience, and who understand that augmentation, not replacement, unlocks the true potential of human-machine collaboration.
About the author:
Sapthagiri Chapalapalli, Head of Tata Consultancy Services – Europe (TCS)