Everyone is talking about AI. At every conference I attend, every other presentation seems to be about artificial intelligence. But when I asked Paul Zikopoulos, Vice President of IBM Technology Group, to join me on the Conquer Local podcast, I wanted to cut through the noise. Paul has written 21 books on AI and big data, been consulted by 60 Minutes on the topic, and spent 28 years at IBM learning and teaching technology. If anyone can explain what AI really is in plain language, it is Paul.
This conversation was exactly what our audience of sales professionals and business leaders needed to hear.
Learning Never Ends
Paul never took a computer course. He applied for a job in document writing at IBM, got hired for $32,000 a year, and thought he was rich because he had been broke in university. From there, he just started learning how to communicate technology, and at the same time, learning the technology itself.
His story is a reminder that no one has to stay stuck. As Paul put it: “No time in history have you ever been able to pivot or learn something about anything at any time. There is really an opportunity for everyone to get involved in whatever they want to do.”
That resonated with me. I work in a tech company, surrounded by smart young people who push me to keep my skills sharp every single day. Paul gave a great analogy about this: if a personal trainer told you that after two months of working out, you would never have to exercise again, you would think they were crazy. So why do we think our professional skills are any different?
He also pointed to Bradley Cooper preparing for A Star Is Born. Cooper spent three and a half years taking piano, singing, and guitar lessons for a film that was shot in 42 days. That level of commitment to continually developing your craft is something every sales professional can learn from.
What AI Actually Is (and Is Not)
There is so much confusion about what AI means. Paul broke it down in the clearest way I have ever heard.
Traditional software works by writing rules. A developer writes code, defines conditions, and the application follows those instructions. AI works differently. Instead of writing rules, you feed the system examples of data, and the AI builds its own logic from those examples.
Paul used a simple illustration: if you wanted to teach AI what the letter “A” looks like, you would give it thousands of examples of the letter A in different fonts. Eventually the AI identifies patterns on its own. It notices a triangle shape, an apex, a bridge between two lines, and two feet at the base. That same approach applies to detecting cancerous moles, assessing insurance claims, or evaluating credit risk. You change the data, and the AI adapts.
The critical point he made: AI is not magic. It is pattern recognition at scale, powered by data.
The Price of Not Knowing
Paul described something he calls “the price of not knowing.” We are collecting more data than ever, but our ability to understand that data has not kept pace. If you plotted a data collection curve, it would be steep. A data understanding curve would be nearly flat. The gap between those two curves is where businesses lose money, miss opportunities, and fall behind.
This is where AI becomes essential. It helps us see patterns, process language, and make sense of information that no human team could handle alone. But only if we approach it with a clear business purpose.
How to Get Started with AI
Paul gave practical advice for any business thinking about AI. The first step is to forget about running AI projects for the sake of having AI. Instead, start with real business problems.
He suggested organizing investments into two categories:
- Spend money to save money: This means renovating your infrastructure, reducing friction, and improving efficiency.
- Spend money to make money: This means innovating, building new capabilities, and transforming how you serve customers.
From there, he recommends sorting your business initiatives into three AI pillars:
- Optimize: Make existing processes better and faster.
- Automate: Remove manual steps from repeatable workflows.
- Predict: Use data to forecast outcomes before they happen.
This framework gives you a blueprint for where AI delivers real value. You start with the business need, not with a data science team running algorithms with no clear goal. I have seen too many businesses invest in technology without a plan. This approach keeps the focus on outcomes that actually move the needle, which is something I talk about when discussing why sales is a science.
The Data Is Everywhere
One of the most interesting parts of our conversation was Paul’s point about where data comes from. He shared a story about WhatsApp being used as evidence in Italian divorce cases. The head of the matrimonial lawyers association in Italy noted that WhatsApp messages were being used to catch adulterers maintaining multiple relationships. No one would have predicted that a messaging app would become a tool in family law. But that is how wide the data aperture has become.
For business owners and salespeople, this means the data that drives your decisions might come from places you have never considered. Customer reviews, chat logs, social media behavior, even location data. The opportunities are massive if you know where to look and have a framework for making sense of it.
AI Will Not Replace You (But You Need to Understand It)
Paul was clear that the Terminator scenario is best left for the movies. The real danger with AI is not robots with autonomy. It is the quality of data we use to train these systems. As he put it, AI is going to help decide “whether you live, buy, die, or try.” That means we need accountable AI, not just accurate AI.
For sales professionals, the takeaway is this: AI will not replace your job, but it will change how you do it. The ones who take time to understand what AI can do, and where it fits into their business, will have a significant advantage. The ones who ignore it will get left behind.
The Bottom Line
Paul Zikopoulos reminded me that learning never ends. Whether you are trying to understand AI, improve your sales process, or build deeper trust with customers, the commitment to growth is what separates good professionals from great ones.
AI is not magic. It is a tool. And like any tool, its value depends on how you use it. Start with a business problem. Organize your approach around optimization, automation, and prediction. And never stop learning.
Watch the full Conquer Local podcast conversation above for more from Paul on AI, data, and what every business leader needs to know.