Four disruptive trends reshaping the enterprise

Cliff Justice is the KPMG U.S. Leader of Enterprise Innovation. He and his group focus on identifying, developing, and deploying the next generation of technologies and solutions for KPMG and its clients. Here he reveals four trends that emerged from KPMG’s leading tech and innovation event.

In an earlier post, I explored how generative artificial intelligence (AI) has been compared to the lightbulb because of its potentially far-reaching impacts on how we live and work. There were plenty of micro “lightbulb moments” during the KPMG 2023 Tech and Innovation Symposium in Park City, Utah. Here are the top takeaways that stood out to me:

1) Disruption may be unprecedented, but so is our ability to navigate it

We’ve been living—and doing business—in relative harmony since World War II ended. But demographic, geopolitical, economic, and technological forces are quickly reshaping the world. Trading partners and supply chain assumptions have changed, are changing, or will change in the not-so-distant future.

It’s not the time to finetune the status quo; it’s time to forge entirely new ways of doing business. Overwhelming? Yes. Impossible? No—especially given the rapid pace of advancements we’re witnessing in technologies like generative AI, quantum computing, blockchain, and spatial computing.

So, as we encounter challenges we’ve never faced, we also have truly breakthrough technology to help manage them.

2) Corporate innovation is no longer a side gig; it’s an imperative

Given the pace and scale of change, companies need to double down on technology-driven innovation. That includes working with big technology partners and startups to accelerate innovation and drive real change. For successful collaboration with startups, corporate innovation leaders need to blend a blue-sky mindset with strong connections within the organization. They also need a structure for implementing innovation and bringing it to life within the organization—the very focus of what we at KPMG call “innovation that works.”

At the symposium, Gamiel Gran, Chief Commercial Officer from venture capital firm Mayfield, shared his thesis on the “three Is” of corporate innovation: ideation, incubation, and investment. I agree with his advice to nurture a culture of innovation that allows for new ideas to be tested and even to fail. It’s never been more important to take risks, make bold bets, and do it all rapidly.

3) It’s time to infuse tech innovation into the enterprise

The symposium affirmed that technology innovations are moving at a breathtaking pace. I was reminded how modern and abundant satellite networks have enabled global connectivity and opened the door to edge computing, especially for manufacturing.

Speakers and panelists provided compelling examples of how AI is making the leap from curation to creation. Blockchain and new database architectures are creating blueprints to ledger-based computing. The metaverse—or, more broadly, spatial computing—is continuing to advance below the hype curve. And quantum computing is powering up to take on some of the world’s highest-stakes challenges—from accelerating drug discovery to solving for climate change.

These aren’t long-term innovations—they’re happening as you read this. Our speakers were bullish on the need to jump on board or be left behind.

Of course, unlocking the value of these innovations within the enterprise takes more than a technology implementation. When it comes to generative AI, for example, some of the greatest obstacles will be related to process and workflow. What happens when you compress tasks so that they take minutes, not weeks? How do you reorganize and reskill your resources when the nature of work fundamentally changes? Weaving generative AI into the fabric of an organization represents a huge transformational effort—from overhauling business processes to managing change within the workforce.

Another message I heard clearly: Data strategy is a prerequisite for AI strategy. Organizations need to build and tune their proprietary models on data lakes that are organized, optimized, and trusted. New vector databases are becoming a critical component for making AI work. At the symposium, one of the speakers referred to many existing data lakes as “landfills.” AI puts that old saying about “garbage in, garbage out” on steroids.

4) Focus on the human side of innovation

It’s not unusual for someone to voice concern that AI will take their job. What I heard at the symposium is that it’s unlikely AI will replace you—but you could be vulnerable to another human who’s a more effective AI coworker.

In the quest for responsible and ethical AI, we must take a hard look at who’s around the table making decisions. Our tech talent panelists offered a reminder that there’s still a lot of work to be done to ensure that we bring together diverse perspectives and experiences. But the speakers made me optimistic about how we can fundamentally innovate talent pipelines by developing new and identifying alternate sources of tech talent.

Above all, human values should shape technology, and technology should deliver value to people.

What’s your take?

How is your enterprise navigating disruption, exploring emerging technologies, and investing in the human side of innovation? Where are you experiencing momentum? Encountering resistance? I’d like to learn more about how you’re tackling these once-in-a-generation opportunities.

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