A manager recently approached me about an employee who was struggling with performance. From our discussion, I believed the underlying issue called for structured coaching rather than immediately initiating a formal performance improvement plan. My next challenge was to translate that assessment into a practical intervention.
Instead of spending hours developing a coaching framework from scratch, I used artificial intelligence to challenge my initial thinking and draft a 30-day coaching plan. Within 10 minutes, I had a structured starting point to review and refine.
The time saved was useful. But it raised a more important question: What if AI could help every manager become a more effective coach, rather than simply helping HR complete tasks faster?
Beyond the AI hype: Are we asking the right questions?
From generative AI assistants to autonomous agents, organizations are investing in technologies promising greater productivity, efficiency and innovation. Yet, despite the enthusiasm, a fundamental question remains: How much of this investment translates into better employee performance, stronger capabilities and sustainable business growth?
For many HR teams, AI adoption still revolves around familiar applications: drafting job descriptions, summarizing documents, creating learning and development content or answering employee queries. These applications deliver efficiencies, but they barely scratch the surface of AI’s potential to transform how organizations develop and deploy talent.
Having spent more than two decades working across multinational corporations, diverse business environments and international markets, I have witnessed successive waves of HR transformation. From traditional personnel administration to strategic business partnering, digital HR platforms and people analytics, each evolution has promised to make HR more strategic. AI presents another opportunity, but this time the implications extend beyond HR processes.
The real transformation is not simply about making HR more efficient. It is about fundamentally rethinking how people learn, perform, collaborate and grow alongside intelligent technologies.
This requires HR leaders—particularly those at the helm of talent strategy—to shift their attention from adopting AI tools to designing an AI-enabled talent ecosystem.
While using AI helped me to develop the coaching plan faster, there was greater opportunity to refine and scale this knowledge with people managers. It also got me thinking about how to put humans in the loop.
From HR automation to workforce augmentation
Historically, HR technology investments have focused on automating transactions, standardizing processes and improving employee access to information. Enterprise platforms have brought considerable improvements in recruitment, payroll, learning administration, performance management and workforce reporting. Nevertheless, many organizations continue to struggle with fragmented employee information, inconsistent managerial practices and limited visibility into workforce capabilities.
Generative AI and increasingly capable AI agents offer opportunities to bridge some of these gaps although significant challenges remain in data quality, integration and adoption. I have identified three distinct levels of AI-enablement:
Level One: AI-assisted productivity. AI supports individuals in completing existing tasks faster. Examples include drafting performance feedback, generating learning materials, preparing development plans and summarizing employee surveys.
These applications are relatively easy to introduce and can generate immediate productivity improvements. However, their impact is largely confined to individual tasks. This is where my coaching plan would be if I had asked AI to develop a coaching plan based on my context or description, and stopped there.
Level Two: AI-enabled workflows. AI connects information and activities across HR systems, learning platforms and business processes. For example, an AI-enabled development workflow could identify competency gaps, recommend learning interventions, prepare coaching questions for managers and monitor agreed development milestones. At this level, AI begins improving the consistency and effectiveness of talent processes, becoming a sounding board for managers rather than merely accelerating administrative work.
Level Three: AI-augmented workforce performance. The greatest opportunity emerges when AI connects workforce capabilities with business outcomes. Imagine an organization identifying emerging technical skills required for its future product portfolio, mapping existing employee capabilities, recommending targeted development experiences and deploying talent into strategic projects. AI becomes an enabler of workforce adaptability and organizational performance.
The distinction is important. While automating HR activities creates efficiency, augmenting workforce capabilities creates true strategic value.
One technology revolution, different organizational realities
AI-enabled HR does not follow a universal implementation model. The challenges facing a small or medium-sized enterprise are fundamentally different from those confronting a multinational corporation.
For SMEs, particularly those operating across diverse Asian markets, HR responsibilities frequently fall upon business owners, functional managers or small HR teams. These organizations may lack dedicated specialists in talent management, L&D, organizational development or people analytics. AI can become a capability multiplier.
A business owner managing 50 employees could use an AI assistant to develop structured onboarding programs, prepare competency-based performance discussions, create personalized learning plans and guide managers through employee development conversations. These capabilities previously required considerable HR expertise or external consulting support.
For MNCs, the challenge is often the opposite. Large organizations possess sophisticated HR systems; specialized centers of expertise and extensive workforce information. Yet their scale introduces complexity through disconnected systems, inconsistent skills definitions, multiple reporting structures and diverse regulatory environments. Here is where the potential of AI lies in connecting organizational intelligence. A multinational organization could use AI to consolidate skills information across business units, identify capability shortages, support internal mobility and model alternative workforce development strategies.
However, scale does not automatically translate into better AI outcomes. Fragmented data, weak governance and inconsistent managerial adoption can undermine even the most sophisticated technology investments. SMEs may benefit from agility and simplicity, while MNCs can leverage greater data depth and organizational resources. The common success factor is not organizational size or technology expenditure. It is the clarity of the business problem being solved.
Rethinking talent development: From learning activities to capability growth
One of the most promising applications of AI lies in transforming how organizations develop employee capabilities. Traditional L&D approaches frequently begin with identifying training needs, creating courses and measuring participation. While necessary, these activities do not always demonstrate whether employees have become more capable or whether their improved capabilities translate into business results. AI creates opportunities to move toward a more dynamic development model.
Consider a technology company introducing a new software solution across several markets. Traditionally, employees might attend product training, complete assessments and receive certifications.
An AI-enabled approach could take it further: The organization could identify the technical and commercial capabilities required for successful customer engagement, assess existing competencies, recommend personalized development pathways and provide AI-powered simulations of customer conversations. Employees could practice handling objections, explaining complex solutions or responding to unfamiliar customer scenarios before engaging real customers. Managers could then review observed capability gaps and provide targeted coaching with support from AI.
In a pilot within my organization, I observed encouraging improvements among participants using AI-assisted sales development, particularly in selling confidence, self-directed learning and their ability to practice customer conversations. The experience also reinforced the importance of connecting these capability improvements with commercial indicators such as conversion rates and time to productivity.
Importantly, the organization could connect these development interventions to commercial indicators such as sales readiness, customer engagement quality, opportunity progression and time to productivity.
This changes the conversation from, “How many employees completed the training?” to, “How much more capable are our employees, and how has that capability improved business performance?” The opportunity extends beyond technology and sales.
Performance management: Moving beyond the annual review
Performance management represents another significant opportunity for AI augmentation.
Despite years of redesign, many organizations continue to struggle with inconsistent goal setting, subjective evaluations, insufficient coaching and performance conversations that occur too infrequently. AI can help address some of these challenges, provided it supports rather than replaces managerial judgment.
For example, an AI-enabled performance assistant could help managers translate business priorities into measurable objectives, consolidate documented progress against agreed goals and prepare evidence-based coaching questions. It could also identify where employees may require additional support, development opportunities or clarification of expectations. It can become a 24/7 coach for people managers and employees to better utilize performance management as a tool rather than as an administrative exercise.
However, performance management should never become an exercise in allowing algorithms to determine employee value. AI generated insights can be incomplete, biased or misleading, particularly when underlying information lacks context. Managers must remain accountable for interpreting performance, considering circumstances and making consequential decisions. AI can serve as a decision-support and coaching assistant for managers to coach and guide their employees better.
The more compelling opportunity is to strengthen the quality and frequency of performance conversations. Instead of relying primarily on retrospective annual evaluations, organizations can establish more continuous cycles of goal alignment, feedback, coaching and development.
AI should make managers better coaches, not turn performance management into automated surveillance.
The next frontier: Skills, not just jobs
Perhaps the most consequential shift for talent leaders is the growing importance of skills-based workforce management. Traditional organizations have largely been structured around jobs, reporting relationships and relatively fixed role descriptions. Yet as AI absorbs certain tasks and changes how others are performed, job titles alone become increasingly inadequate indicators of organizational capability.
A sales professional, for example, may require capabilities in consultative selling, industry knowledge, data interpretation, AI-assisted customer research and cross-functional collaboration. Some of these activities can be augmented by AI. Others depend heavily on human judgment, trust and relationship-building.
The challenge for HR, and in particular talent leaders, is not simply to determine which jobs AI might replace. It is to understand which tasks will change, which capabilities will become more valuable and how employees can be redeployed or developed accordingly.
AI-supported skills intelligence can help organizations map existing competencies, identify emerging requirements and connect employees with learning opportunities, mentors, projects and potential career pathways. Nevertheless, skills intelligence is only as reliable as the information supporting it. Self-reported skills, incomplete work histories and algorithmically inferred capabilities should not be treated as definitive evidence of proficiency. Skills frameworks must be validated through demonstrated performance, relevant assessments and managerial input.
For talent leaders, this creates an opportunity to evolve from managing positions and headcount toward architecting organizational capabilities. Job redesign must therefore begin with identifying evolving skill requirements and developing targeted reskilling pathways.
