With the right approach, artificial intelligence isn’t “just a tool.” It can be “a real-time decision-making partner”—one that “empowers the workforce, making knowledge more accessible while ensuring that organizations have faster and smarter operations.” So says Ron Norris, retired director of innovation at Georgia-Pacific.

At the Baldrige Performance Excellence Program’s 36th Quest for Excellence Conference on March 30–April 2, 2025, Norris will share insights for organizational leaders about how to adopt artificial intelligence (AI) to overcome some of their key challenges.

Following is the full exchange I had recently with Norris about his planned keynote presentation for Tuesday, April 1.

Christine Schaefer: Would you please highlight the issues you plan to address in your keynote presentation, “Beyond Automation—How AI Is Reshaping Knowledge Retention and Workforce Excellence”?

Ron Norris: Organizations today face challenges bigger than automation, particularly challenges related to the need for knowledge retention and rapid decision making. Experienced employees leave, taking years of expertise with them. New hires take too long to get up to speed. Decision-making often drags because leaders are stuck gathering and analyzing data rather than acting on those data.

Traditional AI, which relies solely on data, makes recommendations based on statistical patterns. Stand-alone automation software can streamline tasks, but it doesn’t help organizations retain expertise or understand why decisions should be made in a particular way.

That’s where causal AI comes in. Instead of just analyzing past trends, causal AI learns from both data and human expertise, helping employees think and decide faster. Unlike traditional AI, which provides generic answers, causal AI integrates two key elements:
• Machine learning (data AI): Continuously refines itself by analyzing real-world data.
• Machine teaching (knowledge AI): Captures the wisdom of experts and embeds institutional knowledge in the system.

With this approach, AI isn’t just a tool—it becomes a real-time decision-making partner. This shift empowers the workforce, making knowledge more accessible while ensuring that organizations have faster and smarter operations.

CS: In your work with large U.S. corporations, what are some of the key lessons you learned about change management and innovation?

RN: One of the biggest lessons I’ve learned throughout my career is that people don’t resist technology—they resist uncertainty. At Georgia-Pacific, and in my work with other large organizations, I’ve seen how adopting new technology happens faster when employees feel like they’re part of the process.

The key? Make employees active participants, not passive users. When AI is positioned as a learning tool rather than just a system to adopt, people engage differently. Instead of replacing experts, AI learns from them. When employees see the correct knowledge reflected in the system, their trust in AI skyrockets.

Another major lesson is that organizations need a safe way to experiment. Fear of failure is one of the biggest barriers to innovation, but AI-powered scenario modeling eliminates that fear. Employees can test ideas, tweak strategies, and see potential outcomes before taking real-world action. This encourages a culture of calculated risk-taking and continuous learning, which are both essential for long-term excellence.

When AI preserves institutional knowledge and enables experimentation, it doesn’t just automate tasks—it creates a smarter, more adaptive workforce.

CS: What do you consider to be the greatest challenges and benefits for organizations in relation to AI?

RN: One of the biggest challenges for organizations today is that not all AI is created equal. For AI to be truly valuable, it must do more than just provide answers—it has to act, learn, and improve over time. This is where “AI agents” come in. Unlike traditional AI, an AI agent learns from expert knowledge and real-world data, predicts possible outcomes, and reasons through decisions—just like a high-performing employee would.

That’s what makes causal AI-powered agents so transformational. Imagine a workforce mentor who never leaves—one that helps employees make better decisions, explains the reasoning behind those decisions, and continuously adapts based on new insights. This kind of AI not only assists employees but also makes them better at their jobs.

AI agents also personalize learning and development at scale. Think of how a great manager tracks an employee’s growth, identifies skill gaps, and offers the right opportunities for advancement. Now imagine that same kind of personalized guidance available on a 24/7 basis to every worker. AI thus becomes a performance companion, recommending development opportunities, tracking progress, and providing real-time feedback.

In short, AI agents don’t replace employees—they elevate them. They give organizations a faster, smarter, more resilient workforce that continuously improves over time.

CS: Would you share one or more tips for organizational leaders on how to help their organizations use AI to support workforce excellence and achieve long-term success?

RN: If there’s one thing leaders should know about AI, it’s this: AI is only as valuable as the people who use it and the knowledge it absorbs. The more AI learns from your people, the better it becomes at guiding decisions, supporting employees, and preserving institutional expertise. Based on that understanding, I offer the following four tips:

1. Treat AI as an active learner. Traditional AI pulls from data, but causal AI learns not only from data but also directly from your best employees, embedding expertise in the system. This ensures that institutional knowledge doesn’t disappear when employees leave, and that new hires can ramp up more quickly than ever before.

2. Give employees room to experiment. AI-powered simulations allow teams to test strategies and see the impact of their choices before taking action. This approach builds confidence, reduces fear of failure, and creates a culture where employees feel empowered to learn and innovate.

3. Transparency is everything. AI shouldn’t feel like a black box. Encourage employees to engage with the system, ask questions, and see why recommendations are made. This builds trust and ensures that AI isn’t just providing answers—it’s teaching people how to think more critically.

4. Treat AI as a long-term partner, not a one-time investment. The best AI systems evolve with your business, continuously learning from new data and adapting to new challenges. Leaders who commit to this mindset will be the ones who future-proof their organizations and set the standard for operational excellence.

Published March 4, 2025, in NIST’s Blogrige: The Official Baldrige Blog.