AI Starts with a Business Problem
It has become one of the most common conversations in business. "We need an AI strategy." "We should build an AI assistant." "Our competitors have AI." The problem is that these statements rarely begin with a problem. They begin with a technology. That's usually backwards.
AI is not the goal
Artificial intelligence is incredibly powerful. It can summarize documents, enrich product data, automate repetitive work, assist customer service, generate content, analyze trends, and much more. But none of those things automatically create value. The real question isn't: "How can we use AI?" It's: "What is slowing our business down today?" Only then does AI become interesting.
Start with friction
Every company has it. Employees copy information between systems. Customer service answers the same questions every day. Marketing spends hours creating variations of the same content. Product teams manually enrich thousands of products. Finance spends days compiling reports. These are problems. AI is simply one possible solution. When you start with the friction instead of the technology, the right implementation often becomes obvious. Sometimes AI is the answer. Sometimes a simple integration or automation is.
Good AI is often invisible
The most valuable AI solutions aren't necessarily the ones customers notice. They're the ones that quietly remove work. A product description that writes itself before anyone asks. Support agents who already have the right information when a customer calls. Content translated automatically without someone remembering to do it. Product data enriched as part of an existing workflow. The user doesn't think, "That's clever AI." They think, "That was easy." That's exactly what you want.
Context matters more than the model
Many companies compare AI models. GPT or Claude? OpenAI or Gemini? Large or small? In reality, the model is often the least important part. What determines the quality of the answer is the context you provide. Does the AI have access to the right product information? Can it retrieve company policies? Does it understand your terminology? Can it distinguish facts from assumptions? Without context, even the most advanced model produces generic answers. With good context, almost any modern model becomes significantly more useful.
Don't build AI because everyone else is
The pressure to "have AI" is understandable. But history is full of technologies that were added simply because they were fashionable. The companies that create lasting value aren't the ones with the most AI. They're the ones that solve real problems more effectively than their competitors. Sometimes AI is the solution. Sometimes it isn't. Knowing the difference is where the value lies.
The real opportunity
Instead of asking where you can add AI, ask where your people spend time doing work that computers are already good at. Start there. Because people rarely buy AI. They buy faster decisions. Better customer experiences. Higher quality. Lower costs. And more time to focus on work that actually requires people. AI just happens to be one of the tools that can help get you there.
