AI one of the biggest opportunities ahead
Empower your mind, elevate your skills
AI Geopolitics and artificial intelligence are the two biggest themes among clients of consulting firms , according to Asutosh Padhi , global leader of firm strategy at McKinsey & Company But despite the hype around AI, what remains to be seen in early adopters is tangible on-ground impact, which 'is still a work in progress', Padhi told ET in an exclusive interview. 'With AI, companies are still in the very early stages of adoption. Maybe 10% of companies are starting to show signs of AI impacting the P&L. I've heard CEOs say, 'We see AI everywhere—except in the bottom line'. That sums up where most businesses are today,' he said.The consultant said one of the CEOs he spoke with recently told him about the real impact of AI: 'Analysis will become easier, but strategy will become harder'.Currently, there's a race among consulting firms to embed AI across their workflows and make it an integral part of service delivery. McKinsey & Company has weathered many waves of change in its century-long journey, but none as disruptive or thrilling as artificial intelligence.'We see AI as one of the biggest opportunities ahead for McKinsey,' said Padhi, who is also a member of McKinsey's Shareholders Council.At a time when companies are racing to implement AI across their operations, consulting firms like McKinsey have been among the earliest and most aggressive adopters, rivalling even tech giants.Padhi said the firm is focused on getting the right mix of AI capabilities and human judgment to create real-world impact.'Today at McKinsey, we have around 11,000 AI agents working alongside our 40,000 people. These tools are shortening learning curves, making knowledge more accessible, and speeding up certain types of analysis,' he said. 'But it's a bit like a plane taxiing on a runway. The AI can help you gather momentum, get moving. But to lift off and actually reach your destination, you still need pilots. You still need human judgment, creativity, and leadership.'On the client side too, it's clear that AI tops the boardroom agenda.McKinsey believes companies must be clear on their business objectives—what Padhi calls a 'value creation thesis'—to be AI-ready. 'A CEO should ask: 'Where are we headed over the next five years?' Then, determine how AI can accelerate that journey. This could involve AI-powered product development, customer acquisition, cross-selling, or pricing optimisation,' he said.'But true readiness isn't about scattered experimentation. It's about choosing one high-impact, business-critical problem and solving it end-to-end with AI. That's where meaningful learning, momentum, and scale happen.'Talking about how consulting will evolve over the next decade, Padhi said the industry will undergo three major shifts: deeper tech integration across all functions, a shakeout where only firms with truly differentiated strengths survive, and a rise in strategic partnerships between consulting and tech players to deliver deep impact that no single firm can achieve alone.McKinsey itself is sharpening its AI edge, said Padhi.'First, we've built our own AI platform called Lilli, trained on McKinsey knowledge, which now sees over 95% usage. Whether it's prepping for a C-suite conversation, ramping up on an industry, or exploring a new function, Lilli helps people do it faster and better. Second, AI is baked into training from day one, regardless of role—even senior partners go through capability-building. Third, we've identified about 25 global 'client lighthouses' where our best people use accelerators—repeatable software assets—to deliver more impact, faster,' he said.
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Going Beyond Labels: Functional Icons for the Entire Process The HMC system goes beyond top-level labels by introducing nine functional icons that show where, in the content creation process, human–machine collaboration took place. These icons are especially relevant to common research workflows and publication tasks, helping identify exactly how machines were involved. As a foundation, the classification system reflects key stages that typically involve machine assistance in research and content production. These include: Ideation Generating and developing ideas, brainstorming, framing problems, and designing research approaches to create new insights or solutions. Literature Review Searching academic and non-academic sources to gather background knowledge that helps frame research questions and objectives. Data Collection Using various methods to gather information through primary (surveys, experiments) or secondary (existing datasets, archives) research. 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This enables a more nuanced and transparent evaluation, providing valuable insight not only into the what but the how of content creation. Importantly, the system does not attempt to assign numeric percentages or weights to the machine's contribution, acknowledging that these judgments can often be subjective. Instead, it empowers creators to be honest and transparent about the involvement of machines, providing audiences with the information they need to evaluate authenticity and integrity. A Call for Global Adoption Sheikh Hamdan's announcement is more than a local policy, it's a global invitation. 'We invite researchers, writers, publishers, designers, and content creators around the world to adopt this new global classification system and use it responsibly and in ways that benefit people,' he stated. This marks more than just a response to rapid technological change, it's a clear signal that Dubai wants to shape how the world works with AI, not just adapt to it. As intelligent machines become more deeply involved in how we create and communicate, the line between human input and machine output continue to blur. The HMC Icons are meant to bring that line back into focus, not to hold innovation back, but to make sure people know what they're looking at, and can trust what they're reading or watching. By launching this system, Dubai is taking a leadership role in shaping ethical AI use and setting new standards for content transparency. The classification model is built to be adaptable across multiple sectors, such as academia, design, video production, and software development, where AI is increasingly integrated into everyday workflows. At its core, the system promotes honest disclosure of machine involvement, encouraging creators and institutions to uphold integrity by clearly showing how content is produced. By normalizing transparency, it may also reshape how AI involvement is perceived, less as a hidden shortcut and more as a declared, deliberate part of the creative and research process.
