What Growing Companies Get Wrong About AI Adoption
Business owners need practical guardrails to make sure adoption does more good than harm
AI is already showing up in most company’s day-to-day work, long before leadership has made any formal decision about it.
It usually starts small. Someone uses it to clean up an email. Someone else uses it to summarize meeting notes. A manager tests it to help with research, reporting or internal communication. On the surface, it looks harmless. In some cases, it is helpful.
But that is also how businesses end up behind the curve.
The mistake is not that companies are interested in AI. The mistake is assuming they can wait to think seriously about it. By the time many owners decide it is worth discussing, employees may already be using public tools in ways that affect confidentiality, accuracy, consistency and risk.
That is one reason this topic matters so much right now. As Brendan Giesick explained in recent podcast conversation, AI is not something businesses should treat casually just because it feels accessible.
For business owners, the pressure is real. You are hearing about AI everywhere. Vendors are adding it to software platforms. Competitors are talking about it. Employees are experimenting with it. The temptation is to move quickly just so you are not left behind.
That is exactly where many companies get off track.
Buying an AI Tool is not the Same as Having an AI Plan
One of the biggest mistakes growing companies make is confusing access with strategy.
There is a difference between having AI available and knowing how it should fit into your business. If your workflows are inconsistent, your documentation is weak, your people are unclear on expectations or your data is spread across too many places, AI will not quietly fix those problems in the background. In many cases, it will expose them faster.
That is why Brendan’s point matters: “AI is not a strategy.”
A lot of owners start by asking, “What AI tool should we buy?” That feels like the practical question. But it is usually too early. The better question is, “What problem are we actually trying to solve?”
If your team is losing time on repetitive internal tasks, that may be a good place to explore AI. If your staff struggles to find information buried across systems, that may be another. If managers are spending too much time drafting routine internal communication, there may be an opportunity there too.
But the companies that get real value from AI are usually not the ones chasing the loudest new tool. They are the ones connecting it to a specific business problem and putting some structure around how it gets used.
A lot of Businesses Already Have a Shadow AI Problem
Many companies believe they have not adopted AI because they have not rolled out an official tool. That assumption is often wrong. Employees are already trying public AI platforms on their own because they want to save time, get unstuck or move faster.
That is understandable. It is also risky.
The risk is not always bad intent. Usually, it is a good employee trying to be more efficient. They paste a customer email into a public chatbot to help write a response. They upload internal notes to get a summary. They use AI to rewrite something that contains financial details, contract language or private business information.
In the moment, that may not feel like a major issue. But if the company has not approved the tool, trained employees on acceptable use or set clear boundaries around what can and cannot be entered, the problem grows quietly.
Brendan put it plainly: “Shadow AI will become a problem in the coming years.” He added, “I love AI, but it’s the Wild West right now with this technology.”
That is the reality many business owners are dealing with, especially in growing companies where policies are informal, teams are stretched thin and people wear multiple hats. AI use tends to spread faster in those environments because there is less centralized oversight and less time to stop and build policy after the fact.
The real issue is not the tool. It is the lack of guardrails.
The first move is not choosing a platform. It is deciding what rules apply. That does not mean you need a giant policy manual or a formal AI committee. But you do need some basic answers.
What tools are approved for work use? What information is off-limits? What requires human review? Who should employees ask before testing a new use case? What kind of work can AI help draft, and what kind of work should never be handed to it without careful oversight?
Those are basic leadership questions now. They are not optional questions for later.
As Brendan has warned publicly, businesses should be “building and communicating with your team AI acceptable use policies and procedures.”
That advice is especially important for owners who are trying to balance growth with limited internal IT capacity. Many smaller companies do not have time to clean up after a bad AI decision. They do not have room for a privacy issue, a compliance problem or a customer-facing mistake caused by staff using tools inconsistently.
A practical acceptable use policy does not have to be bloated. It just has to be clear. It should tell employees what tools are approved, what data they should never enter, what output still needs human review and where to go with questions.
Without that clarity, one employee may avoid useful tools entirely because they do not want to make a mistake. Another may use AI every day in ways leadership does not even know about. Neither outcome is good for the business.
AI Should Support your People, not Replace Their Judgment
This is where some businesses start to lose their footing.
They begin with a reasonable goal. They want to save time. They want their teams spending less time on repetitive work and more time on work that matters. That is a smart goal. But somewhere along the way, some teams begin treating AI output like finished work instead of draft material.
That is where quality slips.
Brendan said it well: “There always needs to be the human element to massage and bring a genuine feel to the message.”
That idea applies to a lot more than marketing copy.
AI can help organize information. It can speed up a first draft. It can help employees get started faster. But it does not understand the history behind a client relationship. It does not know when a message is technically correct but strategically off. It does not carry accountability. It does not know when tone, nuance or judgment matter more than speed.
That matters for every business, but especially for owner-led companies where trust, service and reputation are a big part of growth. If your team is using AI to help summarize internal meetings or clean up rough internal notes, that may be a smart use of the tool. If they are relying on it to send customer-facing communication, make sensitive recommendations or produce work that no one reviews carefully, that is a different story.
The goal should not be to remove people from the process. The goal should be to make good people more efficient without weakening judgment.
Most Businesses Should Start Smaller Than They Think
A lot of business owners assume AI adoption has to begin with a huge rollout. That assumption keeps some of them stuck.
For most small and mid-size companies, the better path is narrower and more practical. Start with one or two low-risk use cases that solve a real problem and are easy to review.
That could include internal summaries, recurring documentation, brainstorming support, rough internal drafts or knowledge retrieval. It probably should not start with anything involving sensitive financial data, customer records, HR issues, legal language or work that goes out the door without human review.
Starting smaller gives leadership something more valuable than hype. It gives you a chance to see how the tool behaves inside your actual business.
That matters because AI adoption is not really about looking modern. It is about reducing friction without creating new headaches.
What Business Owners Should do Now
If you run a growing business, this is not something to leave floating in the background. You do not need an overly complicated AI roadmap. But you do need to get ahead of casual, unplanned adoption before it turns into a bigger technology and policy problem.
- Start by getting honest about whether your team is already using AI. In many businesses, the answer is yes.
- Then decide what data is off-limits.
- Approve or reject specific tools. Set expectations for review.
- Put someone in charge of oversight.
- Choose one or two practical use cases and test them deliberately instead of letting adoption happen by accident.
That is not bureaucracy. That is leadership.
Questions?
If your business is trying to figure out where AI fits, what risks need attention and how to put practical guardrails in place, Adams Brown Technology Specialists can help.
From acceptable use policies and cybersecurity considerations to technology planning and day-to-day operational support, our team helps growing businesses make smarter decisions about AI and the systems around it.
To hear more from Brendan Giesick on this topic, listen to Part Two of the podcast here.