5 steps to incorporate AI into your organization

By Ross Hartmann

AI isn’t the future anymore — it’s the present. Whether you realize it or not, your competitors already use AI to automate mundane tasks, streamline decision-making and enhance customer experiences. So, the real question isn’t whether AI will impact your business but how quickly you will take advantage of it.

So, the real question is: Will you let them leave you behind?

Fortunately, you don’t need a Ph.D. in computer science to start incorporating AI into your organization. You don’t need to turn your company upside down, and you definitely don’t need a bottomless budget. In fact, you can start with a few smart moves — without all the techie jargon or headaches.

But, and this is important, you do need a plan. AI is a powerful tool, but if you don’t know how to use it strategically, it’s like having a sports car and not knowing how to drive a stick. Lucky for you, I’ve boiled it down to 5 practical steps that any executive can follow. Let’s get started.

Step 1: Establish an AI use policy (and set clear boundaries)

Before you hand over the keys to AI, you’ll want to make sure everyone knows the rules of the road. This isn’t about teaching your team how to use AI — it’s about setting boundaries and ensuring that everyone understands what’s acceptable regarding data and AI. Think of your AI use policy as the guardrails that keep everyone driving in the same lane and, more importantly, away from any cliffs.

Why is this important? Because AI can process a lot of data fast. But that also means it can accidentally chew up and spit out sensitive information if you’re not careful. And the last thing you want is your AI spouting off company secrets in places they shouldn’t be.

Your AI use policy should cover the basics of what’s allowed and what’s off-limits. Here’s what you’ll want to include:

• Which AI tools will you use?: First things first. Decide which AI tools are acceptable for your company to use. Maybe it’s ChatGPT for content generation or a more specialized business analytics platform. The key here is consistency. You don’t want each team experimenting with random AI tools, especially if they don’t meet your data privacy standards.
• Who has access?: Not everyone in your organization needs a front-row seat to every AI tool. Decide who gets access based on roles and responsibilities. Your marketing team might need one set of tools, while your legal or finance departments might need different ones (and much stricter rules around them).
• Data governance: This is the big one. What data can be shared with AI tools, and what absolutely cannot? Whether it’s customer data, intellectual property, or even internal strategies, you must decide where the line is drawn. And don’t forget to think about whether you want AI to be trained on your proprietary data. If you don’t, make sure your data stays within your walls and doesn’t become a learning resource for the AI models of the world.

Remember, an AI use policy isn’t there to walk your team through the ins and outs of every AI tool — it’s about drawing clear boundaries when it comes to data.

Think of it as handing out a map before a long road trip: Your team can take plenty of detours, explore different routes and get creative with their driving, but you’ve made sure they won’t veer off a cliff. They have the freedom to innovate, but you’ve put up the guardrails to prevent chaos.

Step 2: Identify the most effective use cases (Don’t try to do it all at once.)

So, now that you’ve got your AI use policy in place, the next step is figuring out where AI can actually make a difference in your business. Spoiler alert: AI doesn’t need to do everything right out of the gate. The key is to start small, identify the high-impact areas, and let AI do what it does best — while you focus on leading.

Here’s where some people trip up: They see AI as this magic solution that can solve all their business problems overnight. But here’s the thing: AI shines when applied strategically.

Think of it like hiring a specialist. You wouldn’t ask your marketing guru to fix the office printer, right? AI’s the same. It’s great at some things but not so great at others.

Here’s what to keep in mind:

• Whole organization initiative: AI isn’t just an IT thing. Every team in your organization should be encouraged to experiment with AI within the bounds of your new AI use policy. Let each department figure out where AI can take over repetitive tasks, automate manual work or give them new insights through data analysis. This makes AI adoption a team effort, not a top-down mandate.
• Leverage AI’s strengths: AI excels in areas like data analysis, automation, data ingestion and mass customization. If you’re swamped with reports, data crunching or repetitive workflows, AI is your new best friend. Tasks like generating detailed reports, automating customer responses, or even analyzing market trends? That’s AI’s sweet spot.
• Know AI’s weaknesses: But (and this is a big one), AI (or at least ChatGPT) isn’t great at everything. It won’t optimize your supply chain or design a new building site. AI struggles with tasks that require estimation, optimization, creative design or long-term planning. Those are still very much in the human domain (or are more suitable for specialized AI models in machine learning).

Now, here’s where it gets interesting: When you ask different teams to experiment with AI, you’ll uncover opportunities you might not have thought of.

Maybe HR finds that AI can streamline recruitment by analyzing resumes at lightning speed. Or maybe your sales team uses AI to personalize outreach at scale so they can focus on closing deals instead of writing emails all day.

The goal is to identify high-impact use cases where AI can add the most value and start there. It’s like boosting your business in the areas that matter most.

(Pro tip: Don’t try to tackle everything at once. Pick one or two departments, roll out AI in specific areas, and learn as you go. As your teams get more comfortable with the tools, you can scale up and explore more use cases.)

Step 3: Train your team to get the most from AI (Don’t let tools go to waste.)

Now that you’ve picked your AI use cases, it’s time to equip your team to actually use these tools effectively. Because here’s the unfortunate truth: Even the best AI in the world won’t help you if no one knows how to leverage it effectively. It’s like handing someone a Ferrari without teaching them how to drive. Sure, it looks cool in the garage, but it’s not getting you anywhere.

Training your team is crucial to making AI a real asset for your business. The goal isn’t just to tell them what buttons to push but to help them understand how to get the most out of AI and avoid some common pitfalls along the way.

Here’s how to get your team AI-ready:

• Understand AI’s limitations: First, make sure your team knows what AI can and can’t do. AI is impressive, but it’s not magic. For example, AI tools like large language models (e.g., ChatGPT, Claude, Gemini, etc.) sometimes come up with answers that sound confident but are completely wrong. This is what we call “hallucinations” in AI, and trust me, it’s a thing. Your team needs to understand how to spot these mistakes and not take everything the AI says as gospel. Let AI do the heavy lifting with data, but humans are still very much needed for critical thinking and decision-making.
• Prompt engineering: Here’s the secret sauce to making AI work for you: Ask better questions. AI’s effectiveness depends a lot on how you communicate with it. Your team will need to learn the art of prompt engineering — which is just a fancy way of saying they need to ask AI the right things in the right way. A clear, well-structured prompt will get you the result you want, while a vague or unclear question might leave you scratching your head. Teach your team how to give AI-specific instructions, and they’ll get more accurate, useful outputs in return.
• Build AI habits: AI isn’t a one-and-done thing—it’s a tool that can (and should) become part of your team’s daily workflow. Encourage everyone to use AI consistently by setting it up where they already work. Set tools like ChatGPT, Claude, or Gemini as default homepages in browsers, create an AI-dedicated Slack or Teams channel and encourage sharing use cases across departments. This makes AI a habit, not a novelty.

Here’s a little insider tip: The more your team uses AI, the more confident they’ll get. And the better they get, the more value AI will bring to your organization. It’s a flywheel.

(Pro tip: Celebrate small AI wins along the way. Maybe your customer service team just cut down on their email response time, or your marketing department’s new AI-powered content generator is already bringing in leads. Recognize those victories to build momentum and keep the team excited about what’s possible)

Step 4: Automate what you can — Save time, money and energy

Let’s face it: There are some tasks in your business that, quite frankly, no one really wants to do. They’re repetitive, time-consuming, and sometimes error-prone. But what if I told you those tasks could be taken off your plate — forever?

Automation isn’t just about saving time (though, trust me, it will). It’s about freeing up your team to focus on the work that actually drives your business forward — strategizing, innovating and, you know, the stuff that humans are best at. So, let’s talk about how to unleash AI in the areas where it can make the biggest impact.

Here’s how to find your automation opportunities:

• Identify time-intensive, error-prone tasks: Start by looking at the tasks your team spends way too much time on. Maybe it’s manually entering data into spreadsheets, answering the same customer inquiries over and over or processing routine paperwork. These are exactly the kinds of tasks most suited to AI. And not only will AI perform these tasks faster, but it’ll also reduce the risk of human error. Who wouldn’t want fewer typos in those 50-page reports?
• What’s repeatable and done at scale?: The best automation targets are tasks that follow clear rules and can be repeated at scale. If your team does something regularly — like invoicing, order processing or even onboarding new employees — AI can step in and streamline the entire process. Think of it as having a super-efficient assistant who never takes a break, never makes a mistake, and doesn’t mind handling the tedious stuff.
• Focus on High-ROI areas: Before you automate everything in sight, take a moment to consider the ROI (Return on Investment). Ask yourself, “How much time and money are we spending on this task, and what’s it really costing us?” The key is to focus on the areas where automation can deliver the biggest bang for your buck. For example, automating customer service responses might drastically reduce response times and improve customer satisfaction. In short, when you’re weighing what to automate, don’t just think about time saved — think about value added.

Here’s where things get really fun: Automation doesn’t just free up your team’s time; it creates better customer experiences. Imagine your customers getting faster, more personalized service because your AI tools are handling queries or automating orders. That’s not just good business—that’s a competitive edge.

(Pro Tip: As with everything, start small. Automate one or two tasks, see the results and scale from there. Like anything, automation gets better with time, and you’ll see more opportunities as your team gets more comfortable with the tools.)

Step 5: Get your data ready for AI — make It work for you

So, you’ve got your AI tools picked out, your team trained, and you’re ready to start automating. But before AI can really work its magic, there’s one more crucial piece of the puzzle: data.

Again, think of AI as a race car—it’s fast, powerful, and impressive, but it won’t go anywhere without fuel. In this case, data is the fuel that powers your AI systems, and the quality of your data determines how well AI performs.

Let’s talk about how to get your data in shape so AI can do what it does best.

• Document your knowledge: The first step in getting your data AI-ready is to make sure all the valuable knowledge inside your organization is documented. This might sound tedious, but it’s crucial. Consider having employees verbally explain their processes — what they do, how they do it, and what challenges they run into. Then, convert those explanations into written, accessible data that AI can use. It’s like taking everything in people’s heads and putting it in a format AI can understand and learn from. (Bonus: This is a great way to preserve institutional knowledge in the long term.)
• Audit your permissions: AI is a powerful tool, but you don’t want it accessing sensitive data willy-nilly. Take a close look at who has access to what within your company. If AI is going to give people easier access to your data, make sure the right people have the right permissions—and that no one has access to things they shouldn’t. Imagine someone pulling up financial data they shouldn’t see, all because AI was given too much freedom. No one wants that kind of mess.
• Separate your data into categories: Not all data is created equal, so it’s essential to separate your data into three buckets:Regulatory information: This is your sensitive data, such as HIPAA-protected information or anything bound by strict regulations. Keep it under lock and key and away from AI unless you’re absolutely sure your tools meet regulatory standards.Proprietary and confidential information: This includes internal business strategies, trade secrets, or any confidential plans. You might let AI in on some of this information — but be selective and make sure it doesn’t get shared in ways you don’t intend.Low-risk information: This is your general, low-stakes data that AI can freely access. Think of publicly available market data, general reports or non-sensitive information. This is where you can let AI really stretch its legs and start learning.

(Pro tip: If you want your AI to be smart, give it the data it needs — but not more than it should have. The cleaner, more organized and well-categorized your data is, the better your AI will perform. It’s all about balance.)

Your AI journey starts now

There you have it — five simple, practical steps to bring AI into your organization without turning your world upside down. From setting boundaries with a solid AI use policy to training your team and automating the tasks no one will miss, you’re now ready to start leveraging AI in ways that actually make sense for your business.

AI is here, accessible, and — most importantly — ready to work today. You don’t need to be a tech guru to get it off the ground. With the right approach, a few strategic moves, and a focus on getting your data in order, you’ll lead your team into a more efficient, data-driven future.

But let’s not sugarcoat it — there’s a learning curve. The good news is you’re not alone in this. Whether you’re figuring out the best AI tools, training your team on prompt engineering or identifying high-impact areas for automation, the path is clear, and the potential rewards are huge. Think about it: less time on repetitive tasks, better decision-making powered by insights, and an edge over the competition. Sounds pretty good, right?

Remember: Start small. Pick one department, process, or task and let AI show you what it can do. As the results roll in, you’ll wonder how you ever ran your business without it.

(Final thought: The future of business is here, and AI powers it. The only question left is — are you ready to leap?)

Want to learn more? Then, be sure to register for our discussion, Leveraging AI for Executive Success: Practical Strategies for Transforming Your Business, with Ross Hartmann. The discussion will include a facilitated Q&A session with Vistage Chair Aviva Leebow.

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