McKinsey: 'Every Business Leader Must Become a Tech Leader’

Business leaders face pressure to integrate AI into their organisations while maintaining growth and managing costs. As companies navigate this transition, McKinsey & Company, the global management consultancy, has undertaken its own transformation through AI adoption.

The integration of generative AI (Gen AI) - machine learning models that can create text, code and other content - marks a significant change in how businesses operate. While many organisations run pilot projects, fewer have achieved broad implementation of AI across their operations.

In this interview, Rodney Zemmel, Global Leader of McKinsey Digital, discusses how organisations can move beyond pilot projects to achieve measurable returns from AI investment.

Could you tell us about your role at McKinsey Digital?

I have two main roles. My day job is leading McKinsey Digital, which focuses on three key areas: transforming client organisations through AI, modernising core technology, and building new digital businesses. My night job, as I like to call it, is rewiring McKinsey itself – essentially applying that same AI transformation expertise to our own organisation.

Tell us about that journey of rewiring McKinsey?

While we’ve always been fairly tech-forward, when Gen AI emerged, we recognised it as a once-in-a-generation shift that required immediate action. Initially, we approached it like typical consultants – creating extensive lists of use cases and prioritisation matrices. But then we realised we needed to follow our own "rewired" playbook.

We identified key domains where we wanted to drive change, with knowledge management being our top priority for competitive advantage. While we were happy to use off-the-shelf solutions in other areas, for knowledge management we built our own AI agent called Lilli. It's been incredibly successful – more than 85% of the firm uses it, with the average user engaging 17 times per week, and we've reached over 8.5 million queries.

Beyond that, we're focusing on three major shifts. First, we’re putting technology at the center of all our client service work. Instead of using tools like Excel for pricing analysis, we’re building sophisticated pricing tools. Second, we’re transforming our talent model. We believe every business leader needs to be a technology leader – McKinsey needs to be not just a business leadership factory, but a business and technology leadership factory. Third, we’re implementing AI across all our internal operations, from knowledge management to basic tasks like booking flights.

How have conversations with business leaders evolved since the emergence of Gen AI?

We've seen some interesting patterns. In our research for the "Rewired" book, we studied 50 banks to understand digital success. While many had similar-looking apps, only about a quarter were really making money through digital channels. The key differentiator wasn't the technology itself, but how well business and technology teams worked together.

We’re seeing this become increasingly important in leadership selection. While it’s still relatively rare to see CIOs or CDIOs become CEOs, technological capability is becoming a key differentiator in CEO searches. When boards are looking at candidates with similar operational experience and strategic vision, the tiebreaker is often their track record of delivering impact through technology.

Could you tell us about the opportunities in Gen AI and how they fit into the bigger digital transformation picture?

It’s important to understand that Gen AI is just a subset of the overall opportunity. In digital transformation, roughly 30% of the value still comes from basic process digitisation. Of the remaining 70%, it’s split evenly between analytical AI and Gen AI.

Right now, we're seeing five hot areas. First is customer journeys, which is increasingly focused on growth rather than just cost reduction. Second is coding and software productivity. Third is precision engines for knowledge management. Fourth is creative content generation. And fifth, which is emerging strongly, is agented approaches to corporate automation - this is likely to be next year's major focus.

Could you elaborate on the coding aspect, particularly regarding the balance between human and machine capabilities?

The biggest beneficiaries of Gen AI have been software developers, but the impact goes beyond just saving 20 minutes a day with tools like GitHub Copilot. When companies think strategically about the entire development cycle and build proper training routines, that’s where we see massive productivity improvements. We’ve seen improvements ranging from 15% at major software companies to over 70% at banks that have adopted these approaches. The goal isn’t downsizing teams – it’s getting more leverage from existing talent.

What are some of the main challenges organisations face in implementing these technologies?

One of the biggest challenges is incentive models. The current environment encourages piloting – it’s easy to show something cool and get quick results. But few organisations have incentives for broad-scale adoption, which requires harder changes like transforming job roles. There’s also often a misalignment in ownership – when discussing AI-powered initiatives, people often look to the CDIO, when really it should be owned by business leaders.

Looking ahead, what advice do you have for businesses trying to capitalise on these opportunities?

The key is to avoid taking a technology-first approach. We often get calls asking for VR strategies or metaverse plans – these are the wrong questions. Start with the business problem: How do you improve customer satisfaction by 20%? How do you drive revenue growth through marketing? Then identify the technologies that can help solve these problems, rather than the other way around.

Regarding agents, we’re really just at the beginning. A lot of what people call agents today is simply linked transformer models with basic actions. For agents to be truly powerful, we need three things: sophisticated exception handling, reinforcement learning so they can learn as they go, and what we call ‘agent squads’ – how agents work together in teams. That's where we’ll see the real breakthrough potential.

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