<h2>Executive Summary</h2><ul><li><p>Just like reading and writing, knowing how to work with AI will soon be a basic requirement across all roles and industries.</p></li><li><p>AI’s biggest impact in terms of everyday work will be felt through its ability to <strong>automate routine tasks</strong> and <strong>free up time</strong> for creativity and critical thinking.</p></li><li><p>While some roles may disappear, new ones are emerging. The real threat for the average worker is not AI, but professionals who know <strong>how to use it better</strong>.</p></li><li><p>Don’t start with an enterprise-wide transformation. Take a layered approach and begin with personal productivity tools, move to small team use cases, then scale to enterprise automation and customer-facing AI.</p></li><li><p>AI boosts creativity by empowering users to create more, not less, and makes them <strong>powerful allies</strong> in creative workflows.</p></li></ul>.<p>AI has moved from research labs to real-world enterprise settings, transforming both productivity and strategic decision-making. At a recent session of the IMA India Forum in Chennai, Jaspreet Bindra, Founder of Tech Whisperer and AI and Beyond, unpacked the evolution of AI technologies, explained the growing influence of generative models and offered clear guidance on how businesses can build internal capability while navigating rapid change. With a mix of global case studies, emerging trends and practical advice, the discussion provided a comprehensive overview of AI's business relevance today. </p>.<h2>From Prediction to Generation: The New AI Paradigm </h2><p>AI can be broadly understood through two core capabilities. Predictive AI analyses historical data to estimate future outcomes, such as forecasting demand or identifying fraud. Gen AI, by contrast, creates new content including text, code, images, video and synthetic proteins using natural language inputs. This shift is significant because it enables non-technical users to engage directly with AI tools. The underlying architecture, known as transformers, allows AI to process prompts in plain language and generate useful context-specific outputs at scale. This accessibility broadens the reach of AI far beyond data science and engineering teams. </p>.<h2>Redefining Work and Unlocking Use Cases </h2><p>One of the most immediate opportunities offered by Gen AI is in terms of improving day-to-day productivity. Studies show that only 40% of white-collar work involves core value creation while 60% is consumed by formatting, collaboration and administrative tasks. Gen AI can help reduce this burden, freeing up time for more strategic work. By breaking work down into three components — acting in a role, creating outputs and presenting them — it becomes clear that AI can support or automate a significant share of routine processes. Beyond internal efficiency, a range of practical use cases are already demonstrating value across sectors: </p><ul><li><p><strong>Customer support</strong>: Fintech company <strong>Klarna</strong> replaced the equivalent of 700 agents using generative AI, reducing resolution time and improving customer experience with a measurable impact on the bottom line. </p></li><li><p><strong>Retail personalisation:</strong> <strong>Walmart’s</strong> AI assistant Ask Sam enables customers to plan themed events and automatically generates shopping lists with products, pricing and availability </p></li><li><p><strong>Scientific innovation:</strong> Gen AI is being used to develop new proteins and molecules through textto-protein prompts, accelerating drug discovery and materials science </p></li><li><p><strong>Sectoral applications:</strong> In industries such as banking, manufacturing and healthcare, AI is being used for legal drafting, compliance checks, marketing automation and workforce planning </p></li></ul>.<h2>Where and How to Start: A Practical Approach </h2><p>While interest in AI is high, many companies struggle with where to begin. A phased approach is recommended over attempting immediate enterprise-wide transformation. Businesses can unlock early wins and build internal capability through the following progression: </p><ul><li><p><strong>Start with individuals:</strong> Equip employees with AI tools to automate tasks such as summarising reports, writing content and structuring documents </p></li><li><p><strong>Extend to teams:</strong> Integrate AI into collaborative workflows particularly in marketing, legal, engineering and customer service </p></li><li><p><strong>Scale to the enterprise:</strong> Once comfort and fluency grow, move into broader applications such as operations automation and AI-enabled customer engagement </p> </li></ul><p>This practical path helps reduce risk, demonstrate value and build confidence across functions. Global examples from Klarna and Walmart show that early adopters who focus on targeted iterative use cases can capture measurable benefits. </p>.<h2>A New Operating Model: Humans Plus Agents</h2><p>AI agents are emerging as a natural next step in enterprise automation. These systems can take the initiative, performing tasks and delivering outcomes without the need for ongoing human supervision. They will reshape traditional software workflows by turning software as a service (SaaS) into ‘service as software’, where businesses pay for <em>results</em> and not just <em>tool access</em>. In this new model, teams will increasingly include both human employees and digital agents. This will require a shift in how companies manage talent, structure operations and deliver services. </p>.<h2>AI Literacy as a Strategic Priority </h2><p>The growing integration of AI into everyday tools and workflows means that AI literacy is becoming essential for workforce effectiveness. Just as reading, writing and numeracy once defined employability, the ability to use AI tools productively will become a foundational skill. Organisations must support this shift by making AI tools widely available and encouraging experimentation across departments. Rather than centralising expertise in innovation labs, companies should focus on democratising such capabilities across the business. Many employees are already bringing their own AI tools to work, a trend that is only expected to accelerate. </p>
<h2>Executive Summary</h2><ul><li><p>Just like reading and writing, knowing how to work with AI will soon be a basic requirement across all roles and industries.</p></li><li><p>AI’s biggest impact in terms of everyday work will be felt through its ability to <strong>automate routine tasks</strong> and <strong>free up time</strong> for creativity and critical thinking.</p></li><li><p>While some roles may disappear, new ones are emerging. The real threat for the average worker is not AI, but professionals who know <strong>how to use it better</strong>.</p></li><li><p>Don’t start with an enterprise-wide transformation. Take a layered approach and begin with personal productivity tools, move to small team use cases, then scale to enterprise automation and customer-facing AI.</p></li><li><p>AI boosts creativity by empowering users to create more, not less, and makes them <strong>powerful allies</strong> in creative workflows.</p></li></ul>.<p>AI has moved from research labs to real-world enterprise settings, transforming both productivity and strategic decision-making. At a recent session of the IMA India Forum in Chennai, Jaspreet Bindra, Founder of Tech Whisperer and AI and Beyond, unpacked the evolution of AI technologies, explained the growing influence of generative models and offered clear guidance on how businesses can build internal capability while navigating rapid change. With a mix of global case studies, emerging trends and practical advice, the discussion provided a comprehensive overview of AI's business relevance today. </p>.<h2>From Prediction to Generation: The New AI Paradigm </h2><p>AI can be broadly understood through two core capabilities. Predictive AI analyses historical data to estimate future outcomes, such as forecasting demand or identifying fraud. Gen AI, by contrast, creates new content including text, code, images, video and synthetic proteins using natural language inputs. This shift is significant because it enables non-technical users to engage directly with AI tools. The underlying architecture, known as transformers, allows AI to process prompts in plain language and generate useful context-specific outputs at scale. This accessibility broadens the reach of AI far beyond data science and engineering teams. </p>.<h2>Redefining Work and Unlocking Use Cases </h2><p>One of the most immediate opportunities offered by Gen AI is in terms of improving day-to-day productivity. Studies show that only 40% of white-collar work involves core value creation while 60% is consumed by formatting, collaboration and administrative tasks. Gen AI can help reduce this burden, freeing up time for more strategic work. By breaking work down into three components — acting in a role, creating outputs and presenting them — it becomes clear that AI can support or automate a significant share of routine processes. Beyond internal efficiency, a range of practical use cases are already demonstrating value across sectors: </p><ul><li><p><strong>Customer support</strong>: Fintech company <strong>Klarna</strong> replaced the equivalent of 700 agents using generative AI, reducing resolution time and improving customer experience with a measurable impact on the bottom line. </p></li><li><p><strong>Retail personalisation:</strong> <strong>Walmart’s</strong> AI assistant Ask Sam enables customers to plan themed events and automatically generates shopping lists with products, pricing and availability </p></li><li><p><strong>Scientific innovation:</strong> Gen AI is being used to develop new proteins and molecules through textto-protein prompts, accelerating drug discovery and materials science </p></li><li><p><strong>Sectoral applications:</strong> In industries such as banking, manufacturing and healthcare, AI is being used for legal drafting, compliance checks, marketing automation and workforce planning </p></li></ul>.<h2>Where and How to Start: A Practical Approach </h2><p>While interest in AI is high, many companies struggle with where to begin. A phased approach is recommended over attempting immediate enterprise-wide transformation. Businesses can unlock early wins and build internal capability through the following progression: </p><ul><li><p><strong>Start with individuals:</strong> Equip employees with AI tools to automate tasks such as summarising reports, writing content and structuring documents </p></li><li><p><strong>Extend to teams:</strong> Integrate AI into collaborative workflows particularly in marketing, legal, engineering and customer service </p></li><li><p><strong>Scale to the enterprise:</strong> Once comfort and fluency grow, move into broader applications such as operations automation and AI-enabled customer engagement </p> </li></ul><p>This practical path helps reduce risk, demonstrate value and build confidence across functions. Global examples from Klarna and Walmart show that early adopters who focus on targeted iterative use cases can capture measurable benefits. </p>.<h2>A New Operating Model: Humans Plus Agents</h2><p>AI agents are emerging as a natural next step in enterprise automation. These systems can take the initiative, performing tasks and delivering outcomes without the need for ongoing human supervision. They will reshape traditional software workflows by turning software as a service (SaaS) into ‘service as software’, where businesses pay for <em>results</em> and not just <em>tool access</em>. In this new model, teams will increasingly include both human employees and digital agents. This will require a shift in how companies manage talent, structure operations and deliver services. </p>.<h2>AI Literacy as a Strategic Priority </h2><p>The growing integration of AI into everyday tools and workflows means that AI literacy is becoming essential for workforce effectiveness. Just as reading, writing and numeracy once defined employability, the ability to use AI tools productively will become a foundational skill. Organisations must support this shift by making AI tools widely available and encouraging experimentation across departments. Rather than centralising expertise in innovation labs, companies should focus on democratising such capabilities across the business. Many employees are already bringing their own AI tools to work, a trend that is only expected to accelerate. </p>
