Founded 24 years ago, Genpact is a global professional services firm delivering digital transformation by putting digital and data to work to create a competitive advantage. In August 2007, the company was listed on the NYSE under the symbol ‘G’. Since then the company has grown from 32,000 employees and revenue of US$823 million to 96,000+ employees and revenues of US$3.7 billion (2020). Here are some insights of Sanjay Srivastava, Chief Digital Officer, Genpact, into why ‘digital’ is easy whereas ‘transformation’ is not. That’s because what makes transformation harder is that it’s about simultaneously orchestrating change across four different dimensions – technology, data, people, and process across the corporation and that’ needs to be a rigorous, well-planned process. Let’s learn more in the following info.
“We have seen tremendous amounts of innovation – elastic computing is now available on tap, mature hyper-automation software as a service are digitizing virtually any processes, and composable services are rapidly being assembled into a composite capability to address new business requirements. Data for the first time is a first-class citizen of enterprise architectures and deep learning engines have come to be very inexpensive to train, accelerating productive deployments that deliver real value. The timing couldn’t be better – resilience and response to the pandemic have driven a sharp focus on new capabilities needed in the new normal. And all this innovation in technology is providing the much-needed and necessary platform for it to be delivered.
We are on a journey from automation to autonomy. In the first wave of automation, enterprises had already automated what they thought was automatable, but cloud and AI have opened new possibilities and changed the definition of what is automatable. We have seen cars go from manual to automated with lane change warning or cruise control, and we are now looking at an auto park or auto steer with autonomous driving. Much in the same way, enterprise processes are moving from automated to autonomous – and the difference is in automating some components of a process to automating the entire process end to end.
We are seeing autonomous systems in so many areas now, from data center operations to online commerce, from IoT-enabled edge applications, to fully autonomous enterprise processes like finance and accounting. But to get to autonomous, we need to automate more of the complex decisions and edge use cases – and this requires more AI, cloud, data, and intelligent automation. Cloud is giving us the ability to deal with lots of data and computing power – which opens use cases for AI that we didn’t think existed before. As a result, we foresee that a lot of processes will move to no-touch, low-touch, or fully autonomous processing with the help of these technologies.
Today, companies understand that they must accelerate their digital transformations or be left behind. However, digital transformation requires the intersection of three vectors. The challenge lies when all three do not come together, that is when initiatives fail. These vectors comprise a deep understanding of domain, digital tools, and capabilities to architect the new business framework, and large-scale program management to orchestrate people, processes, technology, and data. In that way, it is possible to drive change and deliver business outcomes in a predictable managed fashion.”
Besides, here are some areas of focus when it comes to digital transformation efforts:
- Prediction capability in Augmented Intelligence: AI is not artificial intelligence for us, it is augmented intelligence. AI and data analytics combined can bring possible solutions for us at amazing speed, but the actual decision still belongs to humans. The prediction capability in AI requires judgment and action in responding to that prediction, thus introducing the concept of “humans in the loop”.
- Need for modularization: The top barriers to AI adoption are the lack of skills to design, implement, and maintain AI solutions, and no clarity on where to use AI effectively. There is a need to think about AI in modules – in small components that can re-aggregate into larger business processes.
- Ethical governance: AI is intrinsic and influential. There have been concerns regarding how organizations use it to make decisions, including bias in AI systems and a lack of skills to design AI solutions. are not new, but there seems to be an increase in board-level recognition of the pitfalls of this and the need for ethical frameworks.
With these frameworks, businesses can build trust with consumers, which supports AI adoption and brand reputation.”
