By Neeraj Deginal, CTO, SHRM India, MENA, APAC
For a long time, organisations approached skills as static assets. We documented them in job descriptions, validated them through certifications, and refreshed them through periodic training. The assumption was straightforward: once a workforce was trained, capability would remain stable for a reasonable period.
That assumption is no longer valid.
Technology cycles have shortened to the point where knowledge depreciates faster than organisational processes can adapt. Cloud platforms evolve quarterly, cybersecurity threats evolve daily, and AI capabilities evolve almost continuously. The challenge for IT leaders is therefore not simply acquiring skills but maintaining relevance.
The real question for 2026 is not whether organisations can hire skilled people. It is whether organisations can continuously renew capabilities.
Power skills: Enabling technology to deliver value
Within IT, we historically prioritised technical expertise because it was measurable. Architecture frameworks, programming languages, platform certifications, and security standards were used as proxies for competence. Yet when digital initiatives fail, the cause is rarely technical inadequacy.
Most failures occur at the interface between technology and business.
Projects suffer from unclear requirements, conflicting stakeholder expectations, and delayed decisions. Teams may build systems correctly but not necessarily build the right systems. The gap is not technical; it is cognitive and collaborative.
These capabilities are often labelled “soft skills”, but in modern IT environments they function as operational enablers. They are better described as power skills: problem framing, structured communication, stakeholder alignment, and decision-making under uncertainty.
AI has amplified this reality. Generative tools can now produce code, documentation, and even architectural suggestions. However, they cannot define organisational priorities, interpret business risk, or exercise contextual judgement. As automation accelerates execution, human contribution shifts toward interpretation and direction.
The most effective technology professionals in the coming years will therefore not be those who only implement solutions but those who can translate business ambiguity into executable technology decisions.
Adaptive learning: From training to capability systems
Traditional learning models assumed periodic upskilling. Employees attended training programmes, obtained certifications, and applied the knowledge afterwards. This model worked when technology evolution was predictable.
It no longer works in an AI-driven environment.
Learning must now occur inside the workflow. Teams routinely encounter unfamiliar tools, frameworks, and architectural patterns. Waiting for formal training delays delivery and reduces competitiveness.
Adaptive learning addresses this by embedding learning mechanisms directly into operations: peer design reviews, cross-functional squads, shadow assignments, short-cycle projects, and continuous feedback loops. Engineers learn by solving real production problems rather than simulated exercises.
This requires a leadership shift. Managers must operate less as supervisors of tasks and more as developers of capability. Providing context, encouraging questioning, and allowing controlled failure to become essential leadership behaviours. Without psychological safety, teams optimise for performance metrics rather than learning.
Organisations that adopt this model reduce ramp-up time for new technologies and improve delivery resilience.
Internal talent: The underused capacity
The industry frequently describes the skills gap as a shortage of talent. In many enterprises, however, the issue is not shortage but utilisation.
Capabilities often remain locked within organisational silos. Employees accumulate adjacent skills through projects, but those skills are not visible outside their immediate teams. Consequently, companies hire externally for expertise that already exists internally.
Internal talent marketplaces are emerging as a response. By enabling employees to participate in short-term initiatives across departments, organisations can deploy capability dynamically. This approach improves project staffing speed, increases engagement, and reduces hiring dependency.
The larger challenge is managerial mindset. Traditional structures emphasised team ownership. Modern digital organisations require talent mobility. Leaders must be evaluated not only on project delivery but also on how effectively they develop and share talent across the enterprise.
This is particularly critical for IT. Specialised expertise now has a short lifecycle. Hiring a new specialist for every emerging platform or toolset is neither scalable nor economically viable. What organisations need instead is learning agility — professionals capable of transitioning between technologies with minimal friction.
Redefining workforce readiness
For CTOs/CIOs, workforce readiness must now be defined differently. The relevant metric is not the number of certified employees but the speed at which teams can adapt to unfamiliar technology and new business requirements.
Three elements determine this readiness: power skills that enable sound decisions, adaptive learning that enables continuous development, and internal mobility that unlocks existing capability.
Technology will continue to evolve unpredictably, particularly with AI becoming embedded across enterprise systems. Organisations that depend primarily on external hiring will experience persistent skills shortages. Those that invest in capability building and talent mobility will develop a workforce that evolves with the business.
Competitive advantage will therefore not come from hiring more talent alone but from designing organisations where talent can continually grow. In the coming years, the defining strength of an IT function will not be the tools it deploys but how quickly its people can learn, adapt, and apply judgement.