Work is evolving with AI transformation. Are organisations able to keep up with the change? Indian organisations are managing the AI-driven disruption through careful experimentation with AI augmentation of workflows but are still hesitant to take these experiments to scale.
The AI maturity of Indian enterprises is marked by a 46% share of laggards who are launching pilot programmes and haven't deployed AI at scale. Only 22% of organisations are leading AI transformations at enterprise AI maturity levels. The gap is wide for HR leaders to consider and puts the focus on getting the right foundations in place.
The research also suggests that HR leaders in India might be looking at AI adoption from a short-term measurability focus instead of a long-term value creation lens. This could be due to the demands for return on AI investments from HR leaders and their preference to stick to near-term success that can be explained objectively over long-term impact.
In a recent webinar hosted by People Matters, industry leaders Bhavna Batra, VP People at S&P Global and Harjeet Khanduja, SVP HR at Reliance Jio, discussed the topic, "From AI in HR to Enterprise AI transformation", moderated by Cheshta Dora, Head of Research at People Matters. The leaders shared their perspectives on the findings of the report and how they are trying to build AI maturity in their organisations.
The leaders discussed the importance of context, alignment to business strategy and an ecosystem-based approach towards AI-enabled capabilities in HR.
Key insights from the conversation
Defining the North Star for AI transformation: Any transformation or intervention has to be guided by a business need, which becomes the guiding North Star. The business need ties the transformation to an objective, which ensures leadership and workforce participation in the transformation and the efforts required to make it a success.
Focusing on the broader view for AI solutions: Organisations are fixing part of the problems with pointed solutions but are not able to tackle the whole problem. There is also an assumption that AI can fix everything, which is not the case in reality. HR tech providers should provide ecosystem-based solutions that have integration across platforms so that HR can take full advantage of it.
Building the right foundations for data: In order to strengthen their data capabilities, organisations should ensure that their data is clean, error-free, and interoperable across systems and processes. Additionally, they should have data protection and security guidelines that are not just defined but actually practised. Lastly, strong process design helps capture better data for decision-making.
Developing workforce AI readiness around usage:Organisations should be training AI users based on the AI skills they need on the job instead of blanket training programmes. Building the right guidelines on AI usage creates clarity for the workforce to use the AI tools with more confidence. While the workforce has to be trained, it is equally important to train the AI engines with the correct context and data.
Driving collective ownership of AI transformation: In order to build sustainable transformations, all stakeholders have to be in sync with each other and work in tandem. Ensuring that the objective of the AI transformation is relevant to the context of the workforce helps in building collective ownership from the workforce.