The Role of AI in Modern Organizations

Here’s a paradox: AI has never been more talked about than in the last year. Yet, according to McKinsey Senior Partner Alex Singla, who serves as Global Leader of QuantumBlack, AI by McKinsey, most organizations are still trying to wrap their heads around what AI actually is.

Just a few years ago, only a fraction of McKinsey’s clients wanted to have a conversation about AI. Today, Singla says, every client is asking about it. But being curious and being prepared are two different things. Many leaders assume AI alone will close performance gaps, spark growth, or modernize their business overnight. In reality, adopting AI takes form as a slow, structural rebuild. It means reworking old processes, updating creaky systems, and taking an honest look at how work really happens.

So what does it take to pull AI out of the conference room and into the places where real work happens? Singla sat down with CAAI Faculty Director, Sanjog Misra, also Charles H. Kellstadt Distinguished Service Professor of Marketing and Applied AI at Chicago Booth, during a fireside conversation hosted by the Specialized Masters Student Program and the Center for Applied Artificial Intelligence. Together, they unpacked where AI is genuinely moving the needle, where leaders still stumble, and what students should be thinking about as they prepare for careers shaped by these technologies.

The Reality Behind AI Adoption

Imagine trying to remodel a house while you’re still living in it. That’s often what AI adoption feels like inside large companies. Outdated code, fragmented data, and long-standing habits collide with the ambition to bring AI into everything.

According to Singla, early on domain transformations centered around efficiency where AI has delivered the clearest wins so far. Automating routine technical work, improving claims-handling workflows, or tightening customer service processes are all areas where AI is already producing measurable improvements.

The more ambitious, growth-oriented uses of AI—especially in complex or highly regulated industries—are taking shape and showing signs of impact. Singla pointed to a project in the pharmaceutical space where McKinsey helped create an LLM-based tool that acts almost like a mentor for sales reps. It pulls together relevant customer information and helps newer reps get up to speed faster. It’s early-stage, but it reflects what many frontline jobs may look like soon: humans supported by AI systems that make them sharper, faster, and better informed.

Inside the Firm: Reimagining Access to Expertise

Singla also spoke about the broader shift toward using AI to help people find and use institutional knowledge more easily. Instead of digging through old systems or relying on what coworkers happen to remember, organizations are beginning to experiment with AI-powered platforms that surface relevant insights quickly and clearly.

At McKinsey, this idea shows up in tools like Lilli, which helps consultants navigate the firm’s broad base of past work and expertise. Singla sees these tools not as replacements for human judgment, but as facilitators of ability and output. The end goal is to give teams a stronger starting point so they can spend more time analyzing, interpreting, and problem-solving, thereby redesigning what management means.

AI, Labor, and Leadership: A New Equation

AI naturally raises questions about the future of work. Singla didn’t shy away from this. Conversations about role redesign and efficiency gains—topics that leaders used to tiptoe around—are now consistently discussed. Companies have to decide which improvements they will actually pursue, not just admire in theory.

Still, Singla pushed back on the idea that AI will wipe out wide swaths of jobs. Instead, AI is shifting how work gets done and putting more emphasis on the human skills that are hardest to automate. Things like navigating ambiguity, communicating clearly, breaking down problems, and leading teams through change now matter even more.

And for students worried that they need deep technical skills to stay relevant, Singla offered reassurance: curiosity, structured thinking, and understanding how to use AI tools well will take you further than learning any single programming language.

Three Predictions for the AI Frontier

Looking ahead, Singla shared a few shifts he expects to see:

1. Broad, end-to-end transformations will accelerate as companies rethink entire processes with AI in mind. But tech alone won’t fix broken systems.

2. Organizations will need to redesign workflows and behaviors for AI to deliver meaningful results.

3. Spending on AI and tech partners will keep climbing, but financial benefits will depend on careful implementation rather than chasing shiny, new tools.

These trends highlight both the promise and the complexity of AI. Like human reasoning, AI systems work best when the context is right and falter when it’s not.

Leaning Into AI: Guidance for Emerging Leaders

Singla’s advice to students was refreshingly straightforward: use the tools. Try things. Experiment. Learn how different systems respond. Good prompting, he said, is becoming a modern-day literacy. And while AI can help you move faster, it can’t replace the kind of critical thinking and structured problem solving that Booth teaches so intentionally.

If anything, the rise of AI raises the bar for leadership. As Singla and Misra noted in their discussion, understanding how to question, guide, and shape these systems may become one of the defining skills of the next generation of leaders.