Feeling Overwhelmed by AI? Here’s How Big Companies Can Use It the Smart Way
Hey everyone, John here! It’s great to have you back on the blog. We’ve all been hearing about Artificial Intelligence (AI) constantly. It feels like every company is racing to use it, sometimes by just trying anything and everything to see what works. But that “throw spaghetti at the wall” approach is starting to feel a bit last year, don’t you think?
The truth is, while AI is incredibly exciting, a lot of these projects are running into trouble—they’re failing, costing way too much, or just not delivering the amazing results we were promised. For 2025 and beyond, businesses need a smarter, more practical plan. They need to see real, predictable results from their investments.
So today, let’s break down a sensible, step-by-step guide for how companies can get the most out of AI without all the chaos. It’s all about being pragmatic.
Step 1: Start on the Sidelines, Not in the Center of the Field
Imagine your business is a professional sports team. You wouldn’t try out a brand-new, unproven play for the first time during the championship game, right? That would be incredibly risky! Instead, you’d practice it on the sidelines during training sessions, where the stakes are low.
The same idea applies to using AI in a business. Many companies get so excited they want to use AI to transform their most important, “core” operations right away. But that can be a recipe for disaster if you’re not an AI expert yet. A much safer approach is to start at the “periphery” of the business.
Lila: “Hi John! What do you mean by the ‘periphery’ of a business? That sounds a little vague.”
John: “That’s a fantastic question, Lila! Think of a business like a big, bustling city. The ‘core’ is downtown—the main power plants, the central government, the major banks. These are the critical operations that keep the city running. A problem here would be a catastrophe! The ‘periphery’ is like the suburbs. It has important things like local libraries, community centers, or parks departments—in a business, this could be the internal employee help desk, the human resources department, or marketing efforts. By starting AI projects in these areas, a company can learn and practice. If something goes wrong, it’s a small problem, not a city-wide blackout. Once you’ve ‘exercised your AI muscle’ in the suburbs, you’re ready to bring that experience downtown to the core.”
Step 2: Set the Right Expectations (AI Isn’t a Magic Wand)
The potential of AI feels almost limitless, and it’s easy to get carried away with the hype. But it’s crucial to be realistic. AI is not a magic switch you can just flip to instantly solve all your problems or replace your entire staff overnight. It’s a powerful tool, but it requires effort.
Getting real value from AI is a process. It follows a cycle:
- Plan what you want to achieve.
- Develop the AI solution.
- Implement it in the business.
- Assess how well it’s working.
- Adjust your approach based on what you learned.
- Repeat!
It takes time, hard work, and collaboration with the right people to see results. So, it’s important for business leaders to set these realistic expectations for everyone involved.
Step 3: Figure Out the Payoff (Your Return on Investment)
Okay, so you’re spending time and money on an AI project. How do you know if it’s actually worth it? This is where a business term called “ROI” comes in.
Lila: “John, I’ve heard that term before. What exactly is ROI?”
John: “Great question! ROI stands for Return on Investment. It’s a simple but powerful idea: for every dollar you put into something, how many dollars (or how much value) do you get back out? If a coffee shop spends $100 on flyers and gets $500 in new sales from them, that’s a great return! With AI, the ‘return’ might not just be money. It could be saving employees hundreds of hours of tedious work or making customers happier. The key is to define what success looks like before you start. For example, a goal might be: ‘Use AI to help our marketing team generate 20% more new leads.’ When you can clearly measure and show how AI helped you hit that goal, it’s much easier to justify doing more AI projects in the future.”
Step 4: Don’t Try to Fix All Your Data at Once
To work well, AI needs data. Lots of it. And this is where many companies panic. They think, “Oh no, our data is a total mess! It’s stored everywhere, it’s disorganized… we can’t possibly do an AI project!”
This is where the practical approach comes in handy again. You don’t need to fix your entire “data estate” all at once.
Lila: “Hold on, what’s a ‘data estate’?”
John: “Think of it like a real estate property. Your ‘data estate’ is all the data a company owns—customer records, sales numbers, internal documents, emails, everything. And just like a real estate property might have a main house, a guest cottage, and a messy garage, a company’s data is often spread out and in different states of organization. The article’s advice is brilliant: don’t feel like you have to clean and organize the entire property at once. If your goal is to use AI to improve sales, just focus on cleaning up the sales data you need for that one project. You can clean out the messy garage later.”
Step 5: Train Everyone, Not Just the Tech Geeks
Successfully using AI is a team effort. It’s not something that only the Information Technology (IT) department needs to understand. Everyone who interacts with the new AI tools needs to be educated on how to use them properly.
This training is a two-way street. The tech teams need the skills to build and maintain the AI systems. But the “end users”—the employees in marketing, sales, or customer service who will use the AI every day—also need training. This includes teaching them how to ask the AI good questions (sometimes called “prompt engineering”) and, very importantly, how to spot when the AI gives a weird or incorrect answer.
Lila: “The article mentioned ‘AI hallucinations.’ That sounds a little spooky! What does that mean?”
John: “It does sound like something from a sci-fi movie! But an ‘AI hallucination’ is simply the term for when an AI makes something up and states it as a fact. Because AI is designed to be helpful, it will sometimes invent an answer if it doesn’t know the real one. It might create a fake historical date, a non-existent legal case, or a made-up customer quote. It’s not doing it maliciously; it’s just a flaw in the technology. That’s why training is so important—it helps employees develop a healthy skepticism and learn to double-check the AI’s answers, especially when something feels a little ‘off’.”
Step 6: Find a Good Guide (The Right Kind of Partner)
Getting started with AI can feel like walking into a giant, intimidating gym for the first time. You see all this complex equipment, and you have no idea where to begin. What do you do? You hire a personal trainer! They assess your goals, create a plan, and guide you every step of the way.
The same logic applies to a business starting its AI journey. It’s wise to find an expert partner. But here’s the crucial part: you don’t just want a partner who knows the technology. You need a partner who also has deep expertise in your specific industry. If you run a hospital, you want a partner who understands healthcare. If you’re a bank, you want one who understands finance.
Why? Because a partner who understands your business knows what’s actually important to you and what problems you’re trying to solve. Without that industry knowledge, you could end up just “throwing a bunch of technology at a problem,” which is like being stuck on a hamster wheel—you’re spending a lot of money and effort but not actually moving your business forward.
My Final Thoughts
John: This pragmatic approach just feels right. In a world buzzing with AI hype, it’s refreshing to see advice that’s grounded in reality. Taking small, deliberate steps, learning as you go, and focusing on real results is so much smarter than making a risky, all-or-nothing bet. It’s about building a solid foundation for long-term success.
Lila: As someone still learning about all this, the idea of starting small is a huge relief! The thought of a company trying to change everything at once with a brand-new technology sounds so stressful. The personal trainer analogy really made sense to me—it shows how important it is to have a guide who really gets you and your goals.
This article is based on the following original source, summarized from the author’s perspective:
A pragmatic approach to enterprise AI