Krupa NS of Xoriant on unlocking HR tech's value realisation

In India, the report found that only 23% are AI ready. This puts value realisation of over 64% companies with mature HR tech architectures under threat. 

Our conversation with Krupa NS, CHRO at Xoriant explored the barriers HR leaders face in unlocking tech value and what can be done in 2026 to improve HR's ability to create business and talent impact through their AI first solutions. 

Below are a few excerpts for our conversation. 

Most organisations today are not short on HR technology or AI tools. What’s missing is strategic intent. AI is still being used to automate efficiency, not to fundamentally change how HR shapes business outcomes. 

The first shift required is at the leadership mindset level. HR must stop viewing AI as a technology layer and start treating it as a decision infrastructure. The real value of AI lies in helping leaders anticipate future skills, workforce risks, leadership gaps rather than reacting through lag indicators like attrition or engagement scores. 

The second shift is in capability and governance. HR leaders need teams that can interpret data with business context and ethical judgement. AI outputs are only as valuable as the questions being asked and the confidence to challenge what the data suggests. This is where senior HR leadership plays a critical role setting guardrails, ensuring fairness, and keeping human accountability at the centre. 

The third shift is operating model redesign. Mature HR tech stacks creates headroom. The opportunity is to reinvest that capacity into higher-order work—workforce strategy, skills adjacency planning, and leadership enablement. This requires moving away from static policies to adaptive, insight-led interventions. 

This philosophy is also reflected in Xoriant’s own HR technology that we have built in house. We designed it with the belief that HR systems should not just record what has happened. They should help leaders decide what to do next. The focus has been on connecting people data, skills signals, and business priorities so that leaders can act faster and with more confidence. 

The real challenge for HR is not keeping up with technology. It is making sure technology actually changes behavior and drives business outcomes. Also, deployment alone does not create value. Adoption does. And adoption only happens when technology feels natural to use and clearly improves how people work every day. If employees have to “learn” the system, adoption drops. So, we designed experiences that feel familiar, simple, and embedded in the flow of work rather than sitting as a separate HR destination people visit occasionally. 

From an HR leadership perspective, outcome focused adoption comes down to three things: 

First, anchor technology to a clear business problem. Every AI or digital intervention should be linked to outcomes such as faster productivity ramp-up, reduced critical-skill gaps, or improved leadership bench strength. If the outcome is unclear, adoption will remain superficial.

 Second, shift ownership to the business. HR should enable, not own, adoption. Managers are the real users of HR intelligence. When leaders see technology improving the quality of their decisions—around hiring, performance, or internal mobility—adoption follows naturally. 

Third, institutionalise feedback and course correction. Technologies evolve fast, but organisations often don’t. HR must create continuous feedback loops to track usage, quality of insights, and real-world impact and be willing to refine or even retire tools that do not deliver value. Ultimately, ROI in HR technology is realised when tools become invisible enablers of better decisions. HR’s role is to ensure technology moves beyond dashboards and into daily leadership practice. 

Yes, there is a clear need to rethink how ROI is defined and measured in the context of AI-led HR investments. Traditional HR ROI models were designed for static systems and linear outcomes. AI, by contrast, delivers value in more dynamic and distributed ways often through better decisions rather than immediate cost reduction. 

HR leaders must move beyond narrow efficiency metrics and adopt a business-linked ROI lens. The most meaningful measures today sit at the intersection of people and performance speed to productivity, quality of hiring, internal talent movement, leadership readiness, and risk mitigation. These are harder to quantify, but far more material to the business. At the same time, efficiency still matters. 

AI should demonstrably reduce time spent on low-value work, improve cycle times, and enhance accuracy. However, these gains should be treated as baseline benefits, not the end goal. From an execution standpoint, the focus has to be on measurement discipline. This means defining success metrics upfront, tracking adoption and decision usage not just system usage and regularly reviewing whether insights are actually influencing business actions.

This discrepancy exists because HR technology decisions are still approached largely as procurement and cost-efficiency exercises rather than long-term capability investments. Cost optimisation is immediate and measurable, while the value of analytics is realised over time and often requires organisational maturity to unlock. 

Another contributing factor is the way analytics is positioned by many vendors and perceived by HR teams. It is frequently reduced to reporting and dashboards rather than being seen as a decision-making engine. When analytics does not consistently influence leadership decisions, it naturally receives less priority during selection. 

What HR leaders need to do better is anchor technology choices to future business value rather than current operational efficiency. Strong HR platforms should enable deeper insight into skills, productivity, talent risk, and workforce readiness, and support scenario-based planning as the organisation evolves. 

Equally important is assessing the quality and applicability of insights, not the volume of data. Analytics should provide context, explain patterns, and enable informed action. Without this, it remains a passive layer that adds little strategic value. 

Finally, HR leaders must involve business and technology stakeholders early in the evaluation process. When analytics is aligned to leadership use cases such as workforce planning, talent mobility, and capability building, its importance becomes self-evident and investment decisions become more balanced. 

These advancements mark a shift in HR technology from supporting processes to shaping organisational capability. 

AI-driven skill management allows us to move beyond static role definitions and toward a more dynamic understanding of skills, adjacencies, and future readiness. One of the ways we are applying this is through our role-based skill development architecture. We have mapped training journeys across nearly 3400 roles, aligned to our top 15 skill clusters spanning engineering, data, AI, cloud, and platform capabilities. This allows us to move away from generic learning programs and instead focus on what a role truly needs to stay relevant and future ready. 

Comprehensive workforce intelligence brings together data across skills, performance, learning, and delivery outcomes. When these signals are viewed in combination, HR can provide leaders with a clearer picture of capability strengths, emerging gaps, and talent risk. This elevates HR’s contribution from reporting to strategic insight. GenAI-enabled learning further amplifies impact by making development more personalised and contextual. Learning shifts from being programme-led to need-led, supporting employees in the flow of work and accelerating skill application rather than just course completion. 

Together, these innovations help talent management become more forward-looking and outcome-driven. The focus moves from managing roles to building capabilities at scale, enabling the organisation to respond faster to change while creating meaningful growth opportunities for employees.