Rewiring Business and the Future with AI
Just like the discovery of fire sparked previously unfathomable progress for humans, AI – and now generative AI – offers fundamentally new ways for businesses and societies to tackle major challenges and unexplored potential. But this new technology also presents uncertainty and challenges, especially for those enterprise leaders trying to force generative AI into old business models without a strategy. Amid these changes, analytics leaders are central to seizing this moment if they act swiftly to avoid getting left on the sidelines. From organic experimentation to scaling enterprise adoption Businesses have been trying to bring AI into their organizations for some time, but with the arrival of gen AI, those aspirations have increased. A recent study by McKinsey Digital estimates that generative AI could bring in an additional $4 trillion in economic value annually[1]. In addition, software development cycles could double[2], and our own experience shows a 30–40% improvement in collaboration and decision-making. That said, organic adoption is mostly among developers and lacks structure or measurement. Few companies have moved beyond this phase, which is why data and analytics leaders must take centre stage. Their data management skills are key. But coupled with the ability to bring all the right roles to the table – business leaders, IT, risk and compliance, legal, and people functions – analytics leaders are crucial. The building blocks to maximize AI-powered outcomes The shift to becoming an AI-first enterprise isn’t easy. But it must be done quickly: 57% of Fortune 1000 companies report that their boards expect a double-digit increase in revenue from AI/ML investments in the coming fiscal year[3]. Based on our experience helping enterprises scale AI, we’ve identified several building blocks for success. They include embedding analytics in processes and workflows, establishing a cloud-based technical architecture, and creating a scalable operating model. But here are three steps to focus on in particular: 1. Prioritize the right use cases With so many places to start, deciding where your investments will deliver the greatest value can be difficult. I recommend using generative AI where you don’t need high levels of accuracy or deterministic results, focusing on the opportunities with the greatest potential. For instance, in healthcare, enhancing the patient experience is a core goal. Generative AI empowers healthcare practitioners to make better decisions by creating personalized patient health summaries that they can review based on encounter and claims data. As a result, healthcare professionals can speed up patient response times and improve patient outcomes. As you weigh up opportunities, be mindful not to fall into the productivity trap. Instead, prioritize creating end-to-end value and reimaging outcomes – not just doing the same things faster. 2. Prepare employees Becoming an AI-first enterprise requires significant change management and upskilling, especially as many employees are wondering, “What will happen to my job?” AI will empower employees, open new career opportunities, and allow them to tap into the full value of their unique knowledge, augmenting their impact on the business. But only if you invest in upskilling them. And again, velocity is key. We’re on this journey ourselves, deepening data literacy skills across the business. We’ve given 70,000 employees new data skills since 2021, and over the past two months alone, we’ve trained 20% of our colleagues in generative AI – we will reach 40,000 by year-end. Generative AI is at play here too, giving learners instant access to Genpact’s collective intelligence through our learning platform, Genome. 3. Make AI-driven decisions responsibly Demonstrating that your AI practices are responsible and explainable has always been important, but even more so now that employees in any part of your business can now access generative AI tools independently. With generative AI, we’ve entered a maze of ethics, copyright, and intellectual property complications. Developing a strategy for these evolving concerns is daunting, and many companies feel ill-equipped to do so. That’s why we’ve created a responsible generative AI framework that you can use as a launching pad or a plug-and-play solution that maintains your reputation by: Protecting your IP, data security, and models Taking into account how responsible AI differs by region and industry with experts who understand the regulations Enabling responsible decision-making Moving beyond point solutions to AI-driven business models Put together, these building blocks lead to a major shift for enterprises. As our board member Ajay Agrawal points out in his book, Power and Prediction, enterprise leaders have limited most AI projects to date to point solutions – quick fixes to existing problems without addressing the underlying systems. While these initiatives may increase productivity or provide incremental improvement, they fall short of AI’s full potential. But with systems-level AI implementation, businesses can realize exponential gains. In the past, companies might have used AI to automate customer service chats. Now, systems-level implementation could turn data from customer service chats into insights that inform decision-making at speed, from product innovation to pricing to how to improve the customer experience. Achieving this objective requires the right tech stack and technology partners and a scalable operating model that allows chief data and analytics officers to adapt their roles and lead this shift toward AI-first businesses. The enterprises that remain agile enough to shift quickly and realign their business models with an AI-first strategy stand to gain the most from the AI revolution. What’s next? As we look to the future, centuries-old stories still hold lessons for us in the age of gen AI. Greek mythology introduces us to Prometheus, one of the Titans, who stole fire and gave it to humanity. It unlocked knowledge, technology, and advanced civilizations. As analytics leaders, you’re our Prometheus. You can take the fire started by AI and generative AI and turn it into an unimaginable impact that will advance not only your business but the future of our societies and the planet. _____________________________________________________ The insights in this blog were part of Genpact’s keynote at MachineCon in New York. Watch the recording here. [1.] Aamer Baig et al., “Technology’s generational moment with generative AI: A CIO and CTO guide,” McKinsey Digital, July 11, 2023. [2.] Begum Karaci Deniz et al., “Unleashing developer productivity with generative AI,” McKinsey Digital, June 27, 2023. [3.] ClearML, “Transforming Generative AI Investments into Business Value: Fortune 1000 Survey Reveals Top Challenges and Economic Impact,” ACCESSWIRE, July 19, 2023.
Process innovation in the age of algorithms.
Process innovation in the age of algorithms.
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["Go", "AI", "ML", "Responsible AI", "Scala", "Git", "Aim", "generative ai companies", "analytics", "generative AI", "generative ai applications", "Generative AI", "R", "ClearML"]
["AI", "ML", "analytics", "generative AI", "ClearML", "Aim", "R", "Go", "Scala", "Git"]
Chief Strategy Officer and Global Business Leader, Enterprise Services and Analytics at Genpact