In the middle of this year, my organization decided to embark on a mission to make us an artificial intelligence-native company. This meant that everything we designed or developed ground up for internal or external customers had AI as a core component and not bolted on as an afterthought. All functions including HR were involved in this transformation and selected individuals were given access to large language models to develop use cases and practical solutions for our respective job scopes.
I was one of the “lucky” recipients of this initiative and was allocated access to one of the closed model LLMs. My fellow HR colleagues who were part of this transformation started coming up with a flurry of use cases, from optimizing international mobility and sourcing processes to compliance and benchmarking for total rewards and performance management. While the business objective of the transformation was clear, I wonder if creating so many HR use cases was truly fulfilling a business need, or merely fulfilling a KPI that drove activity more than impact.
For example, one of the HR use cases was related to content design and development where the identified problem was the manual rebuild of every slide and rework when content changes for SCORM compliance. There was also dependency on tool licenses and employees equipped with the right skills to use the authoring tools.
The proposed solution was to use Claude to convert each deck into SCORM compliant content using a fixed template and acts as an instructional designer and e-learning developer. Looking at this with a business lens, the first question I had was, “What business problem was this use case trying to solve?”
Does it solve the issue of learner participation rates, learning retention and knowledge utilization? This prompted me to think more deeply about implementing AI in the HR function. Before we start collecting use cases from various HR pillars and investing resources to experiment, what are some key factors to consider?
This is part 2 of a two-part series on talent growth and artificial intelligence. Also read: “Unlocking talent growth and performance in an AI-enabled organization.”
Making AI-enabled HR practical: 3 starting points
For organizations beginning this journey, the temptation is to invest immediately in sophisticated AI platforms, I would suggest a more disciplined approach.
Start with a business problem, not an AI solution. Identify where workforce capability or performance is constraining business growth. I have observed in my own organization and others where some leaders become too preoccupied by the latest trends in AI that they forget about defining meaningful, business-relevant use cases. Examples might include lengthy onboarding periods, inconsistent sales productivity, weak managerial coaching or shortages of critical technical skills.
However, not every organizational challenge requires an AI solution. Sometimes, clearer performance expectations, better managerial accountability or simpler processes will deliver greater value than additional technology. The discipline lies in knowing when AI can genuinely augment human capability and when it merely adds another layer of complexity.
Establish a trusted knowledge foundation. AI requires reliable organizational information, including competency frameworks, performance expectations, policies, learning resources and relevant business data. Organizations should establish clear data ownership, access permissions and appropriate privacy safeguards before connecting sensitive employee information to AI systems. There needs to be a single source of truth for business leaders and relevant stakeholders to draw data from to make timely practical decisions.
Introduce AI through manageable use cases. Start with applications where value can be demonstrated without introducing disproportionate risk. AI-assisted onboarding, learning recommendations, manager coaching preparation and internal knowledge assistance are practical starting points.
Governance: The foundation of sustainable adoption
AI introduces important questions concerning privacy, fairness, transparency, employee trust and accountability. These concerns become particularly significant when AI influences recruitment, performance evaluation, promotion, compensation or career opportunities. Organizations operating across multiple countries must also consider differences in employment regulations, data protection requirements and employee expectations.
Consequently, AI governance cannot be treated as an afterthought. HR leaders should work with technology, legal, cybersecurity and business stakeholders to establish appropriate controls, including role-based data access, testing for inaccurate or biased outputs, auditability and meaningful human oversight. The degree of autonomy granted to AI should reflect the potential consequences of the decision.
An AI assistant drafting a development plan presents a different risk profile from an AI system recommending which employees should be promoted or made redundant. The guiding principle should be straightforward: AI can inform and augment human decisions, but accountability for consequential people decisions must remain with people. When I was sharing the manager’s description of the employee into the AI chat, I omitted any personal information that could identify manager or employee. While AI can be useful, it takes human judgement to ensure that confidentiality and security is maintained.
The future talent leader: From process owner to workforce architect
As AI becomes increasingly integrated into business operations, talent leaders must evolve from managing processes to architecting workforce capabilities. Managers will increasingly orchestrate work between employees and AI-enabled systems, while organizations rethink job design, performance expectations and career development.
The critical question is no longer whether employees can use AI tools, but whether they are equipped to thrive in an AI-enabled workplace. This requires more than digital literacy. HR must deliberately cultivate adaptability, critical thinking, problem-solving, collaboration and sound judgment—human capabilities that complement AI rather than compete with it.
However, technology adoption alone does not guarantee business value. A small or medium-sized enterprise leveraging AI to strengthen managerial coaching and accelerate employee development may achieve greater impact than a multinational corporation investing heavily in sophisticated platforms without clear business objectives. Similarly, automating administrative processes without developing workforce capabilities risks creating a more efficient organization, but not necessarily a more competitive one.
The future of HR transformation must therefore focus on three interconnected outcomes: stronger workforce capabilities, improved individual and team performance, and greater organizational adaptability. Achieving these outcomes requires more than technological investment. Leadership commitment, organizational trust, effective governance and purposeful talent development remain fundamental.
Ultimately, HR and talent leaders must move beyond asking, “How can we use AI in HR?” to a more strategic question: “How can we leverage AI to unlock human potential and translate workforce capabilities into sustainable business performance?”
This is the moment AI-enabled HR moves beyond process efficiency and becomes a strategic catalyst for talent performance, long-term competitiveness and organizational growth.
