| 12 | | 13 | EXPERT OPINION Nothing demonstrates Artificial Intelligence’s impact on our public consciousness better than leading dictionary publishers Collins, naming the abbreviation of the term - “AI” as their words of the year. The need to regulate AI been as hotly debated as its benefits and its impact on the economy and on jobs. The World Economic Forum’s Future of Jobs Report 2023 found that nearly three-quarters of companies surveyed plan to adopt AI, with 50% expecting it to lead to job growth. In the book Bridgital Nation, featured by WEF here, our Chairman Mr. N. Chandrasekaran had predicted that AI could add 30 million jobs by 2025, and provide a major impetus to economic growth. Not surprisingly, the impact of AI on the economy will be a major topic at the forthcoming Annual Meeting of the WEF in Davos. And it is easy to see why. We’re at the start of a transformative decade in which generative AI is already reshaping work, life, and business. It will unlock benefits in healthcare, education and climate change, as well as having a substantial economic impact, with some projections suggesting the addition of an economy equivalent to India and China by 2030. Yet, despite the intense focus on AI, we are missing something fundamental. Public attention has largely been on how productivity can be increased with accelerating outputs. But the real potential lies in the decision-making processes that precede that. Here, AI can augment what humans are capable of, taking them from average to great rather than rendering them obsolete. Novices can be made as proficient as experts, by harnessing the tacit knowledge present in organizations, using AI. Boosting productivity is just the side show. Transforming good work into great work will be real value of Artificial Intelligence AI’s evolution Before we look into this, let’s define what we mean by AI. Its evolution has been rapid and can be delineated by its expanding capabilities. Initially, AI focused on recognition tasks, like identifying objects in images. Its next iteration involved reasoning, analysing what is happening, why it is happening, what the likely outcomes are, what we should do about it, and decisionmaking based on that understanding. The most transformative shift happened with the advent of generative or operative capabilities, exemplified by Large Language Models (LLMs) like GPT, LaMBDA and LLaMA. These models leverage predictions made during the reasoning stage and can make decisions and propose actions. LLMs go beyond the information fed to them and extrapolate, resulting in non-deterministic responses to the same input. These LLMs have the power to extract insights from unstructured content and when combined with enterprise-specific models, they can create a knowledge superstructure, enhancing decision speed, effectiveness, and customer experiences. Augmenting, not replacing humans It’s clear that the most effective use of generative AI will be in augmenting human creativity, rather than in replacing humans. AI won’t replace jobs, but make people better at them, in part by the democratisation of knowledge. This will allow for jobs to be elevated by AI, levelling existing knowledge gaps and increasing global access to information. Indeed, the World Economic Forum predicts that AI will be a net job creator between now and 2027. While there is an onus on businesses to support their staff in re-and-upskilling in line with these developments, the benefits are clear for all to see and increased efficiency and thus often higher output is a welcome side effect. The need to reskill people needs to be our first priority towards realizing this opportunity. At TCS we have trained over 100,000 people on Generative AI in the past year, and I am heartened to see similar efforts across the industry, which are essential in building up an AI-ready workforce. A new, AI-first Architecture for enterprises In today’s knowledge economy, enterprises generate value through knowledge work, which involves decisions and actions taken by individuals or groups. Traditional techniques like machine learning and data analysis have been used to extract information from structured data, but unstructured content and data repositories have led to a significant amount of tacit knowledge. This reliance on tacit knowledge makes decisions difficult to explain and causes variability in decision outputs, outcomes, and customer experiences. GenAI or large language models have the potential to extract insights from this unstructured content. Foundational models, such as GPT, LIama, and open-source models, are world-wise, understanding common knowledge. However, when combined with enterprise-wise models and traditional AI/ML techniques, a knowledge superstructure can be created within an enterprise, increasing the speed and effectiveness of decisions and actions. This can improve customer experiences, productivity, and talent utilization. The greater benefit of these technologies is in digitizing an enterprise’s knowledge and reducing reliance on tacit knowledge in decisions and actions. This requires creating large numbers of enterprise fine-tuned purposive models or agents for each activity, which will be further augmented by these knowledge superstructures. AI should be viewed as a business redefinition exercise, involving various parts of the business, such as legal, security, data, compliance, and tech teams, to identify the highest value activities that can be transformed through a knowledge superstructure. Enterprises will need to invest in a four-tier architecture, powered by hundreds or thousands of purposebuilt models optimized for cost, quality, security, and privacy. This complex undertaking offers significant opportunities in reducing reliance on tacit knowledge, delivering elite quality value, and reducing variance in output quality. However, a sophisticated and well-thought-out strategy is required to drive this transformation within an enterprise. Taking decision-making from average to great Ultimately, generative AI offers an enterprise-wide opportunity to improve decision-making processes and overall efficiency. This integration digitises enterprise knowledge, reducing reliance on tacit knowledge and boosting productivity and talent utilisation. And it’s here where the current focus of public debate is misplaced, focusing almost exclusively on “action”, rather than on the potential to digitise an enterprise’s knowledge and reduce reliance on tacit knowledge in decisions and actions. Simply put, the real value of AI and Machine Learning lies in the step before output, in improving decision-making. Why? Because it can help us overcome the gap between average and great outputs and unlock higher-quality outputs more consistently. It allows us to democratise knowledge. This could help improve outcomes in many areas – just think of enabling doctors to diagnose and treat patients more consistently despite differences in experience and time constraints. If we harness generative AI in the right way – and focus on how it can augment our decision-making and creativity – we can unlock a powerful knowledge superstructure, enhance decision speed and effectiveness, and supercharge customer experiences. Turning average or even good work to great. That is the true opportunity ahead. • The Four-Tier Architecture for AI, conceived by Tata Consultancy Services 2023 will be remembered as the year generative artificial intelligence became mainstream. By K. Krithivasan CEO and MD, Tata Consultancy Services
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