CXOs are still working out what they can do with generative AI and data, as well as its potential for the future. But as enterprise tech changes at a rapid pace, how do these two factors change the way that businesses operate?
When business users have the ability to interrogate and interact with data, and apply it against their specialized area of expertise, data insights can be produced much more effectively. While many CEOs see the importance of a single source of data that can be harmonized across departments and regions, the nuances of interpreting that data, especially with generative AI and decision science, add another layer of complexity to the mix.
I spoke with Sanjay Srivastava, Genpact’s chief digital strategist, to discuss his experiences of extracting value from data insights, and what this means for recruiting and retaining the right staff to use emerging technologies.
Srivastava is a seasoned technology professional, with a wealth of experience that began with building computer servers for Hewlett Packard enterprises. His career progressed from entrepreneurship to leadership roles in product management, global sales, engineering, and services businesses, before blending business with technology at Genpact. So, how has his view of technology changed over time?
I now have the firm opinion that technology is no longer the long pole in the tent. And it's how you orchestrate technology with people and process and data and design and experience that really makes a difference.
Srivastava chairs a quarterly executive technology board of over 50 CIOs, CTOs and CEOs from across multiple industries to discuss topics and challenges that are top of mind. High on the agenda lately has been the operating model for where AI, data analytics and machine learning sits within an organization. This question has become quite the debate among CXOs in the peer group, especially for larger corporations with data in slices across a number of regional business units. As Srivastava notes:
There are no operating manuals for what many of us do, particularly with emerging tech.
The need for speed
As diginomica has seen from countless use cases, people are one of the biggest factors in digital transformation. Given the rapid pace of technological advances, what challenges does this pose for C-Suite executives? Srivastava didn’t minimize the situation:
The pace of change we see now is the slowest it will ever be.
We turned to what this means for hiring, and talent management in particular:
The talent that you hire today doesn't really have the skills of the future. Because those skills were dependent on the technologies we then used and we know now that those technologies will change fast – and faster than they have in the past. It's a losing proposition in my mind to hire for skills, and yet most of us will go and look at resumes, educational backgrounds and work experience. And at the end of that, what are we looking for? We're looking for the skills that an individual employee might potentially bring. It's super important for us as corporations to change our recruiting methodologies.
Using AI for continuous learning
Professional services firm Genpact redesigns and runs thousands of processes for hundreds of global companies, and applies data, technology and AI to design, build, and transform their businesses. With around 110,000 employees across 30+ countries, its staffing base stretches from process engineers to accounting specialists to machine learning engineers and everything in-between. When it comes to staff development and continuous learning, Genpact has made some big changes over time. Srivastava explained the thinking behind the organization’s approach to learning opportunities for its employees:
It's about having a working knowledge of technologies and the curiosity, humility and desire to learn new technologies and then applying that in new and creative ways. For us, I think there are only two big truths. The first one is the value at the intersection of disciplines. What the world needs is people that have intersections, someone who understands manufacturing operations and computer vision; someone who understands sales forecasting and machine learning; someone who understands finance and accounting and also process automation. We've been on a tremendous journey of taking people inside the organization and cross-training them in different disciplines.
50,000 data scientists can’t be wrong
This cross-training is being done through a platform called Genome, which captures content from practitioners as they problem-solve, and then uses AI to recommend possible areas of further development by interest and competency level:
It's very specifically providing a guided curriculum that allows you to take advantage of growing to learn multiple disciplines and that's been a big thing for us. We've trained, as a result of that, almost 50,000 data scientists — and these are people that in a normal walk of life would be a chartered accountant or a CPA, or a professional working on procurement. But then at the same time, they're very literate on data and this takes me back to the point I made about interrogating data and putting that into play. They're really becoming experts, so that it links back to why those qualities about curiosity and aptitude are so important. Because those are the qualities that will fuel innovation and development and really start to look at what the future needs are going to be and that's how you'll get there.
What advice would he give to others wondering where to begin? The desire for change is key, advises Srivastava:
The number one thing that comes to my mind is, the technologies that are coming through now are so amazingly interesting and invigorating. And the desire and propensity is for many of us to say ‘Oh my god, this is amazing. Let's jump in. This is going to change the way we operate. Let's just go get started with innovation.’ But the problem is, that’s starting with an answer. And that almost always never works. The most important thing is to start with the question first, which is, ‘This is the end result I want, how do I go about it?’
This resonates with the questions being debated by executives in the peer group think tank - generative AI may be top of mind for everyone, but where does the intersection of their organization and generative AI land - and is it relevant for them to utilize it right now? Just because you can, amid announcements that everyone else is doing it, doesn’t mean you should.
During our conversation, I was struck by Srivastava’s commitment to digital ethics. In his previous role as Chief Digital Officer at Genpact, he built out the company’s offerings in AI, data and analytics, automation, and digital technology services. With that came a keen focus on governance, and that still keeps him up at night. Before concluding the chat, he emphasized how aware he is of the responsibilities that come with embracing digital transformation at a rapid pace to keep up with technology – finance, ethics and security.
By developing a culture of continuous learning and curiosity to understand different intersections of an organization, any transformation that stems from emerging technology can then be fostered by thinking through the data strategy, operating models and the processes and integration for getting it right. In tandem with a strong people development strategy, these are the foundations that make change successful.