In my previous post, I shared my personal journey with large language models like ChatGPT and Claude and how they’ve transformed my productivity. Now, I want to take you deeper into what I’ve learned from both my experience and from discussions at our recent Distribution Strategy Group events. 

The Power of Prompt Engineering 

One of the most eye-opening discoveries from our recent workshops is how dramatically the quality of LLM output improves with effective prompting. As I mentioned in a recent Distribution Strategy Group AI Workshop: “The best AI results don’t just come from asking questions: They come from asking the right ones.” 

I’ve found that directing an AI with specificity makes all the difference. Rather than asking “Help me with a sales email,” I now use structured prompts like “Create a sales email for a VP of Sales at an electrical distribution company about our new vendor managed inventory system, emphasizing fill rates and quick implementation.”  

The difference in output quality is remarkable. 

From our workshop exercises, we identified four key elements of effective prompts: 

• Define the role: Tell the AI who you want it to be for (e.g., Sales Professional, Marketing Leader) 

• Specify the goal: Use action words like “Summarize,” “Explain” or “List” 

• Provide context: Share relevant background information 

• Set the output format: Specify whether you want bullet points, paragraphs or a table 

Moving from Personal to Organizational Transformation 

While my personal productivity gains of 35% are significant, what’s even more exciting is seeing how these tools can transform entire departments. In our recent State of AI in Distribution webinar, we discussed real-world examples of companies using AI to automate complex processes. 

One mid-sized construction products distributor implemented AI agents that can listen in on customer calls in real-time. As the sales rep talks with the customer, the AI captures the order details, and by the time the call ends, the order is already in their ERP system. This eliminates the manual data entry that used to happen after calls, freeing reps to focus on relationship building and sales strategy. 

Creating a Systematic Approach to Implementation 

Our research has identified that 16% of distribution organizations have moved beyond exploration to identify specific AI projects for implementation, with an additional 50% applying some effort in this direction. The companies seeing the greatest success are taking a methodical approach: 

• Education and knowledge building: 14% of companies participating in AI events, with another 53% applying some effort to doing so 

• Technology assessment: 13% conducting formal reviews of available AI technologies, plus 52% making some effort in this direction 

• Security considerations: 13% educating themselves on new IT security needs due to AI, with 47% applying some effort on this 

• Strategic planning: 9% formulating detailed AI strategies, alongside 42% making some progress in this direction 

The “Notebook” Approach to Business Process Documentation 

One of the most practical applications I’ve found is using LLMs to create comprehensive process documentation. In our workshops, we demonstrated how these tools can transform business processes into structured, easy-to-follow materials that serve multiple purposes: 

• Converting complex business processes into clear, step-by-step guides 

• Organizing FAQs and reference materials in a structured format 

• Developing comprehensive training materials for new hires 

• Adapting training content for diverse teams 

The impact on training efficiency has been particularly impressive. Where it once took weeks or months to get new team members up to speed, distributors are now seeing dramatic reductions in onboarding time while improving knowledge retention. 

From AI Experimentation to Strategic Implementation 

What I’ve observed at our recent events is a clear shift from “What is AI?” to “How do we implement AI strategically?” Distribution executives are increasingly focused on practical applications that deliver measurable results. 

Our research shows that by 2027, AI adoption will be widespread across all major business functions, with even traditionally conservative areas embracing AI technologies: 

• Supply chain management: 32% plan deployment within 1-2 years 

• Warehousing: 28% plan deployment within 1-2 years 

• Purchasing: 38% plan deployment within 1-2 years 

• Finance: 21% plan deployment within 1-2 years 

The pattern of adoption suggests a thoughtful, strategic approach where organizations are learning from early implementations and applying those lessons to subsequent deployments. 

The Competitive Imperative 

As I reflect on all I’ve learned on this journey, one thing is abundantly clear: This isn’t optional. The research we’ve presented at our events shows that early adopters are positioned to gain significant competitive advantages, while late adopters risk falling behind. 

McKinsey’s projection that early AI adopters could increase cash flow by 122% while late adopters may lose 23% should be a wake-up call for every distribution executive. These aren’t just numbers — they represent real market share, real revenue and real business opportunity. 

Looking Ahead 

I’ll be continuing to share my journey and insights as we approach our Applied AI for Distributors conference in June. The conference will feature interactive demonstrations, hands-on workshops and real-world case studies to help you develop a practical AI implementation roadmap. 

As I told the audience recently at Convenience Distribution Association, the question isn’t whether to adopt these tools, but how to implement them effectively to stay competitive. I’ve seen firsthand how transformative they can be, and I’m convinced that they represent the future of our industry. 

Join me and other distribution leaders to learn what’s possible and develop your AI roadmap for 2025 and beyond at the Applied AI for Distributors conference, June 24-26in Chicago. Learn more about the conference.