Invest in the workforce for the AI age: A blueprint for scale, skills and responsible growth

Artificial intelligence is reshaping how work is done across every function and role. For CEOs, the question is no longer how rapidly AI will scale, but how quickly organizations can align their workforce, operating models and governance to translate that scale into sustained business value.

The evidence is clear. Through its Reskilling Revolution initiative, the World Economic Forum estimates that around 1.1 billion jobs could be transformed by technology over the next decade. Its Future of Jobs Report 2025 suggests that AI and information processing will affect 86% of businesses by 2030. Other analysis suggests AI will create more jobs than it displaces, but only if companies invest deliberately in people and redesign work, rather than simply layering technology onto old structures.

In an AI-intensive enterprise, transformation must begin with a clear view of how an organization can evolve, not just what tools to deploy. Leaders must understand what capabilities drive differentiation, how roles will change as AI becomes embedded in everyday work, and how new learning pathways can help employees move from service execution towards higher-value problem-solving and intellectual property creation.

Employees increasingly recognize that continuous learning is part of the job. In return, they expect clarity on which skills matter, access to relevant learning and real opportunities to apply those skills. From training for roles to building capabilities that can be recombined into new solutions, this shift is where AI begins to change the economics of work.

At the enterprise level, three elements are proving practical:

At HCLTech, workforce strategy and AI strategy are increasingly managed together. Over the past year, almost 80% of employees have been trained in core skills, with more than 115,000 building digital capabilities and over 116,000 trained in generative AI. This momentum continued in the most recent quarter, with more than 38,000 employees trained on GenAI and over 600 on responsible AI, while we have the highest number of OpenAI-badged experts among all OpenAI partners. The intent is not training for its own sake, but a structural shift towards solution and IP-led value creation rather than purely services-based skills.

As artificial intelligence becomes embedded across service transformation, advanced AI and automation, most roles can now be broken down into tasks that machines can perform and those that require human judgment. Making this distinction explicit is essential to ensuring that human work becomes more valuable, not less.

Demand is rising for AI engineers, data specialists and domain-led solution architects, alongside enduring needs for leadership, analytical thinking and socio-emotional skills. The result is a move toward human-led, AI-enabled teams, where productivity gains come from orchestration rather than substitution.

Across operations, customer experience, risk and R&D, effective redesign typically follows three principles:

Technology ecosystems are central to making this real, and many organizations are now scaling AI in production by aligning engineering depth with industry context.

One European example is Cynergy Bank, which worked with HCLTech to modernize its customer service ecosystem. By digitizing repeatable contact centre and back-office workflows and integrating case management, voice analytics and GenAI-based agent assistance, the bank automated routine work while freeing employees to focus on higher-value customer interactions. The result was a more effective operating model, with complaints reduced by over 50%, productivity up 8%, and customer experience scores up 25%.

The challenge for leaders now is to increase the pace of AI adoption without overwhelming the organization or eroding accountability. The most effective organizations are converging on a disciplined approach: focusing on a small number of AI missions tied directly to business outcomes, supported by strong engineering foundations and ecosystem partnerships.

These missions – whether reducing cycle times, improving customer satisfaction or accelerating product engineering – are increasingly AI-led by design. Each has a senior business sponsor, with technology, risk and people leaders jointly accountable for delivery, safeguards and skills. Crucially, AI is deployed into production on a regular cadence, with benefits made visible to the teams affected.

This shift is also changing how organizations work with partners. AI today is less about isolated tools and more about coordinated platforms, spanning cloud, data, hardware, software and industry solutions, which can be repeated and scaled. Ecosystem-aligned operating models allow teams to collaborate across boundaries and move faster from idea to impact.

Trust will determine how far and how fast AI can scale. Employees, customers, regulators and society will judge leaders not only on what AI delivers, but on how responsibly it is deployed.

The AI era also offers a chance to correct historical under-representation embedded in today’s workplace. As organizations move toward an AI-driven workforce, inclusive representation in designing, testing and governing AI systems is essential to ensure technology reflects the full spectrum of human experience.

Three elements stand out:

This responsibility extends beyond individual organizations. Companies, governments and educators will need to align on shared skill frameworks, learning credentials and principles for trustworthy AI, so talent and innovation can move across sectors and regions.

AI will continue to advance. The differentiator will be leadership choices: connecting strategy to skills, shifting from services to IP-led value creation, redesigning work so people and AI perform together, scaling through ecosystems and embedding trust from the outset. CEOs who take this approach are best positioned to build AI that delivers ROI, including sustained productivity, growth and a more inclusive future of work.