The COVID-19 outbreak compelled the life sciences industry to innovate and transform digitally. However, the current global macroeconomic and socio-political uncertainty, coupled with increasing research and development IT expenditure, have created immense pressure on the pharmaceutical industry to expedite the drug discovery and development process while reducing resource consumption. As a result, pharmaceutical companies are prioritizing investments with the potential for quicker return on investment over moonshot investments. Legacy technologies lack the real-time or data integration capabilities that AI and analytics solutions offer, leading to a lack of visibility for pharmaceutical enterprises into the interplay among stakeholders in the life sciences technology landscape. This has caused pharmaceutical companies to partner with product and IT service providers to improve their in-house talent, reduce time-to-insights, replace obsolete models, and understand fast-evolving customer behavior. Although AI offers significant benefits, it presents unique challenges such as the availability and quality of training datasets from multiple sources, a demand-supply mismatch for talent with industry and technology expertise, and infrastructure and complex integration issues. Therefore, pharmaceutical companies need to prioritize use cases based on specific market requirements and business needs. In this report, we discuss how a blueprint for success can enable enterprises to maximize the value of their AI-empowered initiatives and investments. Scope Industry: life sciences Geography: global Contents In this report, we examine: The current state of AI in the pharmaceutical industry Trends driving the adoption of AI across the pharmaceutical value chain Roadblocks and controversies around AI in the pharmaceutical industry Prominent AI use cases across the pharmaceutical value chain Sourcing considerations for AI solutions and suppliers Membership(s) Life Sciences Information Technology Sourcing and Vendor Management

Though Artificial Intelligence (AI) has been around for a long time, it is only in the past decade that we have seen its explosive adoption across industries, functions, and geographies. Algorithmic advancements, lower investment costs, and the advent of big data have together made it easier for enterprises of all sizes and industries to invest in building AI use cases. Having said that, AI adoption levels vary among industries. Customer-sensitive and technology-intensive verticals such as BFSI and technology & communications experience far higher AI adoption than others. This is in part because different industries have different goals with respect to AI. Whatever be the goal, one thing is clear: enterprises are increasingly deploying AI to achieve different business objectives. Everest Group categorizes the use of AI to drive business impact into four broad categories, depending on the kind of value it delivers: AI for efficiency AI for effectiveness AI for experience AI for evolution AI mature verticals such as BFSI and technology and communication are leveraging AI for not just a better stakeholder experience but also for their overall evolution as well. In this report, we analyze 230 large enterprise-class AI adoption use cases, identify 38 unique use cases across different industries, and map them on our AI for business impact framework to recognize how enterprises are leveraging AI and the impact they are deriving from it and to understand the driving factors for AI adoption. Membership(s) Digital Services

The advent of Robotic Process Automation (RPA) helped enterprises automate documents with structured data sources; however, content-centric documents – accounting for almost 80-85% of enterprise document load – cannot be automated using conventional rules-based solutions. Consequently, manual processing of these documents often leads to issues such as long turn around time, high cost of operations, high incidence of errors, and difficulty in automating heterogenous data. Driven by these factors, enterprises, today, are looking for solutions that incorporate elements of Artificial Intelligence (AI), so as to process documents with unstructured data sources. The AI-based solutions, often called Intelligent Automation (IA) solutions, possess capabilities such as computer vision, machine learning, and NLP that can be integrated with RPA and BPM workflow to provide an end-to-end automation experience. Technological advancements, such as transfer learning, are further easing the barriers or inhibitions that enterprises have while adopting any intelligent automation solution. This viewpoint covers the entire document processing conundrum in detail, with details on applicability of AI to provide an end-to-end automation experience. The sections covered in the viewpoint include: Limitations of RPA and OCR / template-based solutions in processing content-centric data The role of AI in document processing How AI augments RPA and BPM to provide an end-to-end process automation experience Key technologies powering AI capabilities, including transfer learning A case study explaining how an enterprise utilized an intelligent automation solution to automate document processing Membership(s) Service Optimization Technologies (SOT)

Although Artificial Intelligence (AI) has been around for decades, both its hype and adoption have grown exponentially in the past few years. Enterprises are recognizing the value of AI as a competitive differentiator and value creator, and many are already investing in AI. In our recent survey of IT heads of 200 global enterprises, 65% have already invested in some AI project. However, while interest and investment are high, AI is far from pervasive in most enterprises’ business strategies. In fact, more than 80% of the enterprise executives told us that they were unable to adopt AI at scale or achieve any significant business outcomes. Lack of talent, poor data management, and misalignment with the business context are some factors that act as barriers to enterprise wide AI adoption. Other issues such as change management and failure in early projects are some other factors that are forcing enterprises to take a highly risk averse approach towards their AI investments. Drawn from our experience of working with the leading technology majors such as Google, Microsoft, AWS, and some other non-technology firms that have been able to successfully adopt AI, we have compiled this playbook to help enterprises build a roadmap to achieve business outcomes from their AI investments. In this report, we will you through five key commandments that any enterprise needs to follow to achieve success with AI, whether they are making early inroads in AI investment or scaling up their existing AI initiatives. Membership(s) Digital Services

Research on advanced intelligent systems is underway to solve critical business problems and world issues. Intelligent loan approval, automatic medical diagnosis, autonomous cars, and predictive criminal defender identification are some of the revolutionary applications of AI that promise to deliver immense potential in terms of cost savings and performance improvement. While the high potential is appreciated by enterprises, our research suggests that only one in five enterprises have adopted AI at scale to deliver meaningful business results. Is the training data bias free? How did the system arrive at these results? How do I validate the results for accuracy and ensure compliance? Is there an exceptional situation where the system may fail? Such decisive questions have been left unanswered by AI systems leading to muted trust and limited large-scale adoption. This key roadblock is the black box nature of AI systems that limits explainability. Enterprises increasingly demand trustable, ethical, and repeatable behavior from AI algorithms that can ensure fairness and accuracy of results. In this paper we detail out the meaning and need of explainable AI systems. In addition, we: Identify enterprise expectations from an explainable AI system Establish a framework for enterprises to evaluate the extent of explainability required Explore the existing approaches to enable explainability at training data stage, functionality stage, and interpretation stage Comprehend the limitations and challenges with existing AI systems and the road ahead Membership(s) Digital Services

Artificial Intelligence (AI) is now at the cusp of mainstream enterprise adoption, given a significant number of successful initiatives undertaken by proponents of AI including enterprises, service providers, and technology players. The AI adoption journey is set to fundamentally redefine the role of IT. The IT function will need to work closely with business to establish itself as an enabler, a governor, and the eventual flag bearer for enterprise AI initiatives. Cross-pollination of skills and responsibilities will lead to extensive blurring of lines between business and IT in the long run – an impending change that IT function needs to brace for. It is imperative for enterprises and their IT functions to evaluate where they stand in the AI adoption journey roadmap, understand prevalent challenges, and formulate a long-term strategy for programmatic, practical, and risk-free adoption. Everest Group is demystifying this critical role of IT in the enterprise AI strategy through a series of three viewpoints (meant as a practical guidebook): Volume I: Delineating the role of IT in the age of AI Volume II: Establishing the IT blueprints for AI adoption Volume III: Exploring the third-party AI vendor ecosystem The Volume I report of the series – “Delineating the role of IT in the age of AI” – establishes the current state and outlook for AI adoption within enterprises, outlines current enterprise challenges/apprehensions around AI adoption, and highlights valuable lessons for IT to learn from enterprise cloud adoption journeys. In addition, it also explores the expected roles/responsibilities of the IT function in the AI journey and provides an IT checklist to help enterprises gauge their AI readiness and journey maturity. Membership(s) Application Services Cloud & Infrastructure Services Digital Services