As leaders outline the possibilities brought in by AI tools, the implications of the new capability are still under purview. To clear the hype and break those complexities are Brian Sommer, founder and president of TechVentive and Mukesh Jain, CTO, VP and Global Head of People Analytics, Capgemini, who will be at People Matters TechHR Pulse Mumbai on March 14. In a precursor LinkedIn Live with Ester Martinez, CEO and Editor-In-Chief, People Matters, the two technologists revisit the initial euphoria surrounding AI, skills and capabilities to drive the technology and why it is best to cautiously begin with the experimentation. 

Gen AI Tools: Moving Past the Initial Euphoria 

While organisations are optimistic about Gen AI system capabilities, they are cautiously refraining from going all in. Brian highlights the reason for this hesitation, with the obvious being information risk and privacy. But from an HR perspective, organisations are apprehensive to train a language model using confidential personnel data. But at the heart of this lies a gap that majority of the leaders are unable to grasp: how do AI algorithms work?

Looking towards 2024, Brian predicts that over-the-top hysteria around AI will slow down as real-world consequences come to light. His advice to organisations looking towards AI is: Choose how you wish to industrialise and productise AI capabilities as you reimagine work processes. Adding to it, Mukesh urges organisations to choose wisely how they wish to harness the value that accompanies AI and analytical tools for the greater good. 

If you are redesigning your tech, Mukesh encourages you to have an approach where you look at the business problem first rather than tech. And one of the essential key here is data literacy. “Only when you understand the nuances, possibilities, challenges, opportunities and risks of data-driven processes, will you be able to unlock and leverage AI tools effectively.”

Touching upon the need for responsible AI as we move ahead, he recounts the critical factors that will enhance transparency in AI decision-making. Before jumping on the bandwagon, Mukesh shares the same perspective as Brian, of leaders understanding AI before integrating it into existing processes. And it is best for organisations to have a CDO (Chief Data Officer), who can guide them towards the right AI strategy so each and every stakeholder understands the technology and its impact. 

Developing AI centric Skills and Capabilities

Mukesh believes that acquiring new skills in AI will begin with asking the right questions and having the right resources. At Capgemini, Millennial Garage, an initiative that focuses on solving specific problems using new tech within a timeframe is highly successful. 

Rather than theoretical learning, experiential learning will take precedence when AI applications are in contest to understand and use the knowledge across several aspects of implementation, including research, development, deployment and operations. And as the world starts to understand the possibilities, organisations will have to enable its workforce to imagine real-world applications so each one of the systems architect is able to implement innovative uses of AI technologies. 

Brian’s simple message for leaders in this regard is to have a clear vision along with a realistic assessment of how severe the organisational challenge is so you understand the AI skills necessary to build and henceforth, plan the learning process. But he cautions organisations not to dive into purchasing the first piece of impressive tech you come across. “It’s essential to have process designers understand what AI can do, what it can fix and what it can’t solve.”

The benefits offered by AI, ML and automation have been in existence for over a decade now but organisations haven’t fully realised it. Now to jump on the Gen AI bandwagon without making sense of the fundamental aspects of the existing processes can have grave consequences. 

For all the leaders, who are trying to solve way too many problems with a single piece of technology, Brian offers a different approach. Try and implement skilling and experimentation as a continuous process, where the workforce learns from both. Change never occurs linearly and hence, it is better to approach learning with flexibility and resilience to build on innovation. AI is challenging and opportunistic in equal parts, hence AI skilling needs to be constantly changing and evolving. 

The Use-Cases of AI and Lessons Learnt

With the diverse usage of AI, what experts are seeing today is majority of the CTOs lack maturity, knowledge and clarity, which is why many choose a popular vendor in the market. But to yield better results, leaders should research, experiment and test model. And before anything else, look at how their own competitors are using the same tools. 

Sharing an example to drive the point, Brian highlights that if a popular job description generator AI tool is helping all players in your industry to improve job postings, will it really help you to win the war for talent? If you think about it, job descriptions will get better but everyone will have similar results so you won’t have that competitive advantage. That’s why rethinking how you wish to remain competitive is a great starting point for leaders to reach actionable results. 

Mukesh sums up the discussion as he suggest leaders and organisations to focus on learning the dynamic aspects of AI and consciously make all decisions based on data. When you gather more data before arriving at any conclusion, you are one step closer to realising how to use AI to improve existing processes.  

If you wish to extract insights and gain clarity on new developments in AI, you can meet Mukesh Jain and Brian Sommer in-person at People Matters TechHR Pulse Mumbai on March 14 at Grand Hyatt. Register now for the exclusive Future-Ready Workshop with Brian Sommer.