Timothy Persons of PricewaterhouseCoopers (PwC) talks with Ben Lorica about adoption of AI in the enterprise. They discuss the challenges enterprises experience, including the need to change corporate culture. To succeed, it’s important to focus on solving well-defined problems rather than just doing something cool with AI. Good data strategies and data governance are essential. Persons also highlights the importance of training and education for everyone in the organization and the need to create safe environments where people can experiment.

Points of Interest

• 0:00: Introduction.
• 1:00: We are seeing an uptick in adoption of AI in the enterprise. CEOs are planning to adopt AI and pursue business reinvention. Many companies are still kicking the tires. There is more adoption in the backend where risks are lower.
• 3:36: AI budgets are on an upward trend. It is not a small spend and there’s a tendency to underestimate cost.
• 4:54: What are some of the key challenges that enterprises face when they go to deployment?
• 5:10: It’s all about trust and culture: getting employees and executives comfortable with the technology. That implies upskilling and internal conversations.
• 7:09: What is a data strategy for generative AI?
• 7:37: Companies need data governance, which must be more than a well-written policy document.
• Governance means operationalizing the policy. Once you focus on quality data and abide by governance, you have the foundation for a good future.
• 9:26: How do you measure that you’re delivering ROI? How do you evaluate so that you know your LLM-backed application is ready to go?
• 10:50: ROI—We need to separate R&D. For R, ROI doesn’t work well. But when you cross from R to D and investments scale, you need to think about ROI.
• 12:15: Evaluation—We can measure LLMs today. But what does that mean in the context of the problem you’re solving? AI in autonomous vehicles is different from AI in medical systems.
• 13:58: Companies need to invest in educating the workforce. Upskilling is not just for expertise; it is also for interdisciplinarity. Changing organizational culture means changing the way organizations communicate and partner.
• 15:38: People underestimate the importance of creating a good user experience. Design thinking is needed. Focus on end-user experience and work back from that.
• 16:59: What are some of the most common use cases for AI?
• 17:17: In the back office, you often have a corpus of information customized to your situation. You can build fit-for-purpose chatbots for key support functions. The best lawyers can’t read everything possible in the corpus or keep up with all the regulatory changes coming in.
• 21:11: AI will increase the value of labor investments. It will expedite the L&D curve for new employees. It will improve users’ lives. And AI is getting much better. We’ve only seen the floor, not the ceiling.
• 24:38: Do you have a checklist or a playbook to help companies prioritize use cases?
• 24:57: Companies need to think “What problems do I need to solve?” Think from a problem-centric approach.
• 27:32 Are there best practices for sharing learning across different groups?
• 28:17: We’ve seen centers of excellences rise. Sharing what didn’t work is important. GenAI is very democratizing—not everyone needs a PhD. When companies reward sharing, including what didn’t work, it really engenders collective learning and great ideas.
• 30:15: What have leading companies done to prepare their workforces?
• 30:31: PwC made a major investment in MyAI, which was focused on the ability to get AI into the hands of users, down to entry-level interns. It was an intentional L&D process that was focused on AI. We gave people the tools and a safe space to use them.
• 32:43: It’s learning by doing, and it’s fun. And it can be customized to a company or a firm.
• 33:03: If we didn’t provide a controlled environment, our people would go out into an uncontrolled environment.