The Expensive Mistake Everyone’s Making
Here’s what I’m seeing happen across almost every industry right now. CEOs read headlines about AI transforming business, feel pressure from shareholders, and rush to buy enterprise licenses for the latest AI platforms. They spend six figures on tools, announce their AI initiative in the next quarterly meeting, and then… nothing happens.
The licenses sit unused. Employees ignore the new tools. Six months later, leadership is scratching their heads, wondering why their massive AI investment produced zero results.
Sound familiar?
Where It All Goes Wrong
The problem isn’t the technology. I’ve worked with dozens of companies implementing AI, and I can tell you the pattern is almost identical every time. Leadership buys the tools but skips the most critical step: training their people how to actually use them.
They’re making a huge assumption. They think their team already knows about AI, is excited about it, and is just waiting for a license to start using it in their day-to-day work. But that’s not reality.
Most people still aren’t using these tools, especially if there’s no clear reason or push to do so. We’re past the early adopters now, those tech enthusiasts who try everything, and we’re moving into the early majority. These are professionals focused on getting their current work done efficiently, and they need guidance on how AI fits into what they’re already doing.
The ROI Question Nobody Can Answer
You’ve probably seen that MIT article claiming 95% of AI projects produce zero return on investment. That’s a pretty wild statement, and it’s fueling speculation that AI is just another tech bubble. But here’s what that statistic is really telling us: we’re measuring wrong, and we’re implementing wrong.
The challenge with AI ROI is that it’s not always tangible. How do you measure someone becoming more efficient, more productive, more capable at their job? How do you quantify having a super-smart assistant that helps you think through problems and enhances your existing skills?
Traditional ROI metrics don’t capture that value. But that doesn’t mean the value isn’t there.
What Actually Works: Building from the Foundation
After working with companies ranging from small businesses to enterprise organizations, I can tell you what actually produces results. Everyone, and I mean everyone, from the C-suite to entry-level employees, needs foundational AI literacy training.
This isn’t about turning your workforce into AI engineers. It’s about giving them enough knowledge to make informed decisions about when and how to use these tools.
Here’s what that training needs to cover.
Understanding the Technology (Without the Technical Jargon)
People need to understand what large language models actually are and how they work. Not at a PhD level, but enough to know what they’re dealing with. I walk people through the evolution: ChatGPT 3.5 could only handle text. Then we got the ability to upload PDFs and documents. GPT-4 brought limited reasoning capabilities. Now we have models with PhD-level intelligence that can solve complex problems in minutes that would take a human hours or days.
Understanding this progression helps people see both where we are now and where we’re headed. It also helps them think strategically about applications instead of just using AI as a fancy search engine.
But here’s the critical part: you also need to teach the limitations honestly. AI models hallucinate. They have knowledge cutoffs. They struggle with certain types of math and reasoning. They can perpetuate biases from their training data.
When people understand these limitations, they use AI more effectively because they know when to trust the output, when to verify information, and when to rely on their own judgment instead.
The Security Issue Nobody’s Talking About
This is huge, and most companies are completely ignoring it. Your employees need to understand the difference between free consumer AI tools and enterprise platforms when it comes to data privacy and security.
Free models often use your input data for future training. That means if your well-meaning employee pastes sensitive company information into a free ChatGPT account, that data could potentially end up training the next version of the model. Trade secrets, customer data, strategic plans – all potentially exposed.
I’ve seen this happen. Employees are trying to be productive, not realizing they’re creating massive security risks. The solution isn’t to ban AI tools, which just drives usage underground. The solution is education.
People need to know what constitutes sensitive information, how different AI platforms handle data, and which tools are approved for which types of work. They need to understand the reasoning behind these restrictions, not just follow blind rules.
Teaching Prompting (The Right Way)
Here’s the good news: you don’t need to turn everyone into a prompt engineering expert. As these models get more intelligent, they’re getting better at understanding natural language. You don’t need complex tricks and techniques.
What people do need is the foundation of how to communicate clearly with AI systems and how to iterate when they’re not getting the results they want. This means understanding how to provide context, specify formats, and refine their requests based on what comes back.
I teach people to think of it like working with a really smart but literal-minded assistant. You need to be clear about what you want, provide relevant background information, and be willing to adjust your approach if the first attempt doesn’t hit the mark.
The training should progress from basic prompts to more advanced techniques like breaking complex tasks into smaller pieces, using multi-turn conversations that build on context, and requesting step-by-step reasoning when needed.
The Low-Hanging Fruit: Custom AI Assistants
This is where I see the fastest, most measurable ROI. Custom AI assistants, whether you call them custom GPTs, agents, or something else, are designed to handle specific, repetitive tasks.
Think about tasks you or your team do multiple times per week that take 30-45 minutes each time. Now imagine cutting that down to 10-15 minutes by creating an AI assistant with predefined instructions, specialized knowledge, and the right tools.
That’s not theoretical. I’ve built these for clients across different industries, and the time savings are real and measurable. From customer service templates, report generation, data analysis, and content creation, the applications are endless.
The key is that these don’t require coding skills. Once people understand the fundamentals of AI through proper literacy training, they can identify their own repetitive workflows and design assistants to streamline them. This is bottom-up innovation at its best.
Unlocking Innovation You Didn’t Know You Had
Here’s something that surprised me when I started doing company-wide AI training: the best ideas don’t come from executives or consultants. They come from the people actually doing the work.
The customer service rep knows their call patterns and pain points better than anyone in management. The financial analyst knows which monthly reports involve tedious but rule-based work. The HR manager recognizes which parts of candidate screening could be consistently automated with AI assistance.
When you train your entire workforce on AI capabilities and limitations, you’re creating a distributed innovation engine. These employees can now articulate their ideas, propose solutions, and collaborate on implementation. AI transforms from an executive mandate that people have to adapt to into a tool that employees actively shape around their needs.
I’ve seen companies go from zero AI adoption to dozens of custom solutions within months of implementing proper training. The ideas were always there – people just didn’t have the framework to express them or the knowledge to implement them.
What This Means for 2026
As we head into 2026, you need to make a choice. You can keep doing what most companies are doing, buying licenses and hoping for the best. Or you can recognize that the licensing agreement is just the first step, and the real investment is in education.
The current AI technology is powerful enough to deliver substantial value right now if properly applied to existing business challenges. We don’t need to wait for the next breakthrough model. What we need is for organizations to take the time to train their people, establish clear guidelines, and create an environment where employees feel empowered to experiment and innovate.
Think about it this way: you’re investing significant money in AI tools. Why wouldn’t you invest in making sure people know how to use them effectively?
The Competitive Advantage Is Education
Companies that invest in comprehensive AI literacy now will have a massive competitive advantage over those that continue with the “tools without training” approach. The difference between AI success and failure isn’t about which platform you choose or how much you spend on licenses.
It’s about whether your people understand how to transform these powerful capabilities into practical solutions for the real problems they face every day.
I’ve seen this play out repeatedly. Two companies in the same industry, same size, same AI tools. One trains their people properly, the other doesn’t. Six months later, the trained company has implemented dozens of efficiency improvements, their employees are engaged and innovative, and they’re seeing measurable returns. The other company is questioning whether AI was worth the investment.
The difference wasn’t the technology. It was the education.
Making It Happen
If you’re a leader reading this, here’s what I want you to take away: AI literacy isn’t a nice-to-have training program. It’s the foundation that everything else is built on. You can’t skip this step and expect results.
Start with leadership. Make sure executives and managers understand the technology well enough to make informed strategic decisions, not just react to competitor announcements or analyst pressure. Then cascade that training down through the entire organization.
Make it comprehensive. Cover how the technology works, its capabilities and limitations, privacy and security considerations, effective prompting, and practical applications specific to your industry. Give people time to practice and experiment in a safe environment.
And here’s the most important part: create a culture where people feel empowered to identify AI opportunities in their own work. The best implementations come from the people closest to the problems.
The Bottom Line
The question for 2026 isn’t whether to adopt AI, as that decision has largely been made. The question is whether you’ll give your people the knowledge and skills they need to make that adoption successful.
Your competitors are either figuring this out or they’re not. The companies that invest in AI literacy now will be the ones pulling ahead while everyone else is still trying to figure out why their expensive AI tools aren’t delivering results.
The technology is ready. The opportunity is massive. The only question is: are you going to equip your people to take advantage of it?
