5 Things Your Business Should Be Working On in the Age of AI

AI is everywhere right now. It’s in the news, in your inbox, and probably in at least one tool your team already uses. If you run a business with 20 to 500 people, you’ve likely asked yourself some version of this question: “What am I supposed to actually do about all this?”

That’s a fair question, and you deserve a straight answer.

At Novatech, we work with companies across many different industries, and we hear the same worries over and over. Leaders want to take advantage of AI, but they don’t want to waste money on tools their team won’t use. They don’t want to create security problems. And they don’t want to fall behind competitors who figure it out first.

The good news is this: you don’t need to “do AI” all at once. You need to do a few specific things well. Here are the five we recommend focusing on right now.

1. Get Your Data in Order

AI tools are only as smart as the information you give them. If your company files are scattered across old servers, personal laptops, shared drives, and three different cloud services, AI can’t help you much. It also can’t tell what’s safe to share and what isn’t.

Before you buy an AI tool, take time to clean up where your data lives and who can see it. This is sometimes called “data readiness.” It means knowing what information you have, where it’s stored, and who should have access.

Manufacturing: You likely have data in many places: machine sensors, inventory systems, accounting software, and paper records on the shop floor. Start by picking one area, like production downtime or quality control, and getting that data organized first. Small wins build momentum.

Religious organizations: Member information is sensitive. Donation records, prayer requests, counseling notes, and family details should never be fed into public AI tools by mistake. Make sure you know where this information is stored and who can access it before anyone on your staff starts using AI assistants.

Healthcare: HIPAA rules do not pause for AI. Patient information needs the same protection it always has, which means most public AI tools are off-limits for anything involving patient data. Start by mapping where protected health information lives and setting clear rules about what can and cannot be used with AI.

2. Train Your People Before You Buy More Tools

Here’s something most AI vendors won’t tell you: the biggest reason AI projects fail isn’t the technology. It’s that employees either don’t use the tool or use it incorrectly.

You can buy the best AI software on the market, but if your team doesn’t know how to write a good prompt, when to double-check the answers, or when not to use it at all, you’ve wasted your money.

Start with basic AI training for everyone. Not a one-time seminar, but regular short sessions. Teach people how to use tools they already have, like the AI features built into Microsoft 365 or Google Workspace. Once your team is comfortable, you can think about adding specialized tools.

Manufacturing: Floor workers often get skipped in technology training, but they’re the ones who know where time is really wasted. Include them. Someone who runs a machine every day can tell you exactly which reports or checklists could be automated.

Religious organizations: Staff and volunteers have very different comfort levels with technology. Offer training at more than one skill level, and be patient. A church secretary who learns to draft newsletters with AI can save hours every week.

Healthcare: Clinical staff are already stretched thin. Keep training short, practical, and focused on tasks that give back time, like summarizing notes or drafting patient communication. If training feels like extra work, it won’t stick.

3. Take Cybersecurity More Seriously Than You Did Last Year

AI is a powerful tool for attackers, not just for businesses. Phishing emails are now almost impossible to spot by bad spelling, because AI writes them perfectly. Fake voice calls that sound like your CEO are real and happening. Deepfake video is close behind.

If your cybersecurity plan looks the same as it did two years ago, you are more exposed than you realize. This is not about buying more software. It’s about making sure you have the basics covered and that your team knows what to watch for.

At a minimum, you should have multi-factor authentication everywhere, regular phishing training that includes AI-generated examples, a tested backup plan, and a written response plan for when something goes wrong. If any of those are missing, that’s where to start.

Manufacturing: Ransomware attacks on manufacturers have grown a lot in the last few years because downtime is so expensive. Attackers know you’ll pay to get running again. Make sure your operational technology, like the systems that run your machines, is separated from your office network.

Religious organizations: Many smaller organizations think they’re too small to be targeted. They are wrong. Wire fraud targeting church bookkeepers and fake emails pretending to be the senior pastor are common. Train staff to verify money requests by phone, always.

Healthcare: Healthcare is the most attacked industry in the country, and it has been for years. A single breach can cost millions and put patient safety at risk. If you haven’t had a third-party security assessment in the last 12 months, that’s your starting point.

4. Pick One Real Problem to Solve With AI

A lot of companies make the same mistake: they try to “use AI” in general, which means they use it nowhere in particular. Then six months later, they wonder why nothing changed.

The better approach is to pick one specific, painful problem and solve it. Something measurable. Something your team complains about often.

Good examples include cutting down the time spent writing proposals, speeding up customer email responses, automating a weekly report that someone builds by hand, or answering common customer questions on your website. Start small, measure the result, then expand.

Manufacturing: A common win is using AI to help with production scheduling or to predict when a machine is likely to need maintenance. Another is automating quality control photos or reports. Pick the headache that costs you the most hours each week.

Religious organizations: Many organizations use AI to help with sermon research, newsletter writing, social media content, and answering routine questions from members. Start with communication tasks. They’re lower risk and save real time.

Healthcare: Documentation is the number one burnout driver for clinicians. AI scribing tools that listen to a visit and draft the notes can give providers hours back every week. This is one of the clearest wins in healthcare right now, but it must be done with HIPAA-compliant tools only.

5. Have a Clear Policy So People Know the Rules

Right now, there’s a good chance someone on your team is pasting company information into a free AI chatbot. They’re not trying to cause harm. They just want to get their work done faster. But that information may now be sitting on someone else’s servers, and you have no control over it.

You need a written AI policy. It doesn’t have to be long. It should answer the basic questions your team is already wondering about: Which tools are approved? What kind of information can go into them? What can’t? Who do I ask if I’m not sure?

A clear policy protects your business and gives your team permission to use AI the right way. Without one, you’re relying on each person to guess, and guesses don’t scale.

Manufacturing: Include rules about product designs, customer lists, and supplier pricing. These are often the most valuable things in your business, and they should never be pasted into a public AI tool.

Religious organizations: Be specific about member and donor information, counseling conversations, and any personal details shared in confidence. Your people trust you with sensitive things. Your policy should reflect that trust.

Healthcare: Your AI policy must line up with your HIPAA policies. Name which tools are approved, what can be entered, and what the consequences are for violations. Train on it, then train again.

Where to Start

If this list feels like a lot, start with two things: get a written AI policy in place and make sure your cybersecurity basics are solid. Those two steps protect you while you figure out the rest.

After that, pick one problem that costs your team time every week and try solving it with a tool you already have. Measure what changes. Then do it again with the next problem.

You don’t need to become an AI company. You need to run your business better, and AI is one tool that can help you do that, when you use it on purpose.

If you’d like help thinking through where to start, or you want a second opinion on the AI tools your team is already using, that’s the kind of conversation we have with clients every day. Reach out anytime.