The pandemic has seen accelerated interest in process automation as organizations have scrambled to overhaul business processes and double down on digital transformations in response to disruptions brought about by COVID-19. And for IT leaders stepping into or already steeped in such modernization efforts, artificial intelligence -- mainly in the form of machine learning -- holds the promise to revolutionize automation, pushing them closer to their end-to-end process automation dreams. But for now, AI-powered process automation remains a piecemeal approach, in which AI is involved in individual tasks but not across the entire process chain. Regardless of how vendor's spin it, fully intelligent automation has not yet arrived -- but organizations working to fill the gaps are finding innovative ways to this promising concept closer into being. A typical use case for AI in automation includes the following: instead of requiring someone to manually re-key information from a PDF into a form, an AI is trained to do it for them.

AI Coach enables Genpact to develop confident top-performing contact center agents through voice and chat simulations that provide highly realistic immersive learning experiences. Integrating the AI technology with Genpact's Cora Banking ecosystem allows Genpact to elevate its performance for more than 700 global clients. AI Coach is transforming how contact centers develop confident, prepared new hires before their first call and close skill gaps for experienced agents. The simulation training platform creates hyper-realistic simulations of any voice or chat scenario, allowing agents to learn through practicing, solving problems and navigating errors. By providing a platform to build proficiencies, risks and costs are minimized in the short and long term for clients. "Genpact is helping freshly hired agents improve their confidence before their first call.

IT leaders seeking to derive business value from the data their companies collect face myriad challenges. Perhaps the least understood is the lost opportunity of not making good on data that is created, and often stored, but seldom otherwise interacted with. This so-called "dark data," named after the dark matter of physics, is information routinely collected in the course of doing business: It's generated by employees, customers, and business processes. It's generated as log files by machines, applications, and security systems. It's documents that must be saved for compliance purposes, and sensitive data that should never be saved, but still is.

A new study about employee morale and motivation post-pandemic found that while 97% of business leaders think hybrid work environments will not damage corporate culture long-term, there are negative consequences to having so many people working remotely. Genpact's Tech for Progress 360: Engage employees, strengthen company culture study found that 76% of respondents from organizations who adopted new technologies such as artificial intelligence (AI) and advanced analytics during the pandemic strongly agreed that their company can maintain its culture in a hybrid working environment. Not surprisingly, business leaders who saw improved business performance during the pandemic were more likely to view new technologies in a favorable light. "It's proven work can be done remotely at scale," said Tiger Tyagarajan, Genpact's CEO. "To lead through this seismic shift, businesses must combine digital technologies and smart data to drive actionable insights that integrate employees into company culture, enable collaboration and maintain learning and wellbeing--whether people work remotely, in the office or blend both."

Digital transformation has become a business imperative in the wake of the pandemic. Companies that have not modernized their processes and integrated cloud and analytics capabilities are severely disadvantaged. Genpact's latest study, "Data-Driven Business Transformation," reveals that only 8% of enterprises have fully modernized. This digital divide will result in those companies who can move quickly and take advantage of new opportunities, leaving behind those who are not. In addition to Genpact, Peak's latest report, "State of AI 2022," revealed that 55% of enterprises will be AI-First by 2025.

"Starting in the 1950s, AI has a long history of being the next big thing," said Megha Sinha, vice president of Digital – Data Science, AI, ML at Genpact in her talk titled "MLOps – The Strategic move to actuate Data Insights". She discussed MLOps at length during her session at The Rising. Today, every enterprise aspires to be data-driven, she said. While it has become imperative for business leaders to actuate data insights for business growth, studies show only half of AI proof of concepts are scaled to production. MLOps holds the key to create production-ready, scalable AI solutions.

Employees are quitting jobs at record rates and companies are having a hard time luring them back. Exacerbating the problem is the fact that employees are now frequently working from home, making it harder for managers to identify employees who are unhappy. Plus, getting new hires up to speed is more challenging when they can't attend in-person training sessions or shadow experienced employees. To solve all these issues, companies are increasingly turning to artificial intelligence. But there's a limit to how much AI can do.

Responsible AI, data and innovation will be some of the leading trends in digital transformation in 2022, according to Genpact. Sanjay Srivastava, chief digital officer for Genpact, has predicted hot industry trends for the coming year. As the role and skills of CIO evolve, it requires a balance of being an outsider and an insider. "As digital becomes the driving force behind business transformation, CIOs are increasingly co-creating new business models and future-ready companies," Srivastava says. "This shift requires innovating at the intersection of the people, process, data, and technology spectrum."

More enterprises have adopted RPA functions to automate rote, repetitive tasks, but sometimes they need more capabilities. Enter machine learning functions and the result is "intelligent automation" which, unlike RPA, can learn and adapt. The choice between the two should depend on the use case, but in today's AI-crazed world, there's a misconception that intelligent automation must be better when, in fact, robotic process automation (RPA) may be a more elegant solution. "We view AI/ML as knowing what to do, RPA is knowing how to do it," said Muthu Alagappan, chief medical officer at intelligent automation platform provider Notable Health. "For example, OCR can be used to extract information from insurance cards, photo IDs and clinical documents. From there, RPA [enters] the extracted data into existing systems of record." RPA simply executes its programming, so if requirements change, it needs to be reprogrammed.

With a surge in AI and ML applications to improve business process efficiencies, there has been an increase in data science hiring across companies. It is one of the fastest-growing areas and often requires a robust hiring process to get the best candidates in the team. We got in touch with Sreekant Menon, VP, Data Science at Genpact to understand their data science hiring process. Menon shares that not only their hiring mechanism looks for the best available candidate but also retains them by providing opportunities to improve their skills over time. Menon shared that with years of experience in leveraging data analytics for solving challenges across industries and functions, Genpact has identified the most vital and useful attributes that a potential data scientist needs to have.