Pay BillThe Top 12 Errors, Risks, and Mistakes in the Use of AI
Written by Micah Murphy, CPA, BMSS AI Advisory
In Summary:
AI adoption in business is no longer a question of if. It is a question of how well. Across industries, organizations are integrating AI into daily operations at an accelerated pace. The potential is real, but so are the risks. This article identifies twelve of the most common and consequential mistakes businesses make when putting AI to work, along with practical guidance on how to avoid them.
Whether your organization has been experimenting with tools like Copilot, ChatGPT, Gemini, or Claude, or you are just beginning to explore how AI fits into your operations, the learning curve is real. After working with businesses at various stages of AI adoption, we have identified twelve of the most frequent—and most avoidable—missteps. Some are technical. Some are cultural. All of them have real consequences if left unaddressed.
1. Trusting AI Output Without Verification
AI models produce responses that read with authority, whether or not the underlying information is accurate. In any business, this is a significant risk. A fabricated regulatory citation or a miscalculated financial ratio can cascade into flawed reporting and poor decision-making. Every AI-generated output that informs a business decision should be verified against an authoritative source before it moves forward.
2. Feeding Sensitive Data into Consumer-Grade AI Tools
Tools like Copilot, ChatGPT, Gemini, or Claude, all offer powerful capabilities, but their free or consumer-grade versions may process and retain your inputs in ways that create data exposure. Pasting confidential information into these platforms without understanding the terms of use is a risk many organizations are taking without realizing it. Before any data enters an AI tool, you should know where it goes, who has access, and whether your usage agreement permits it.
3. Operating Without an AI Use Policy
If your team is using AI and you do not have a written policy in place, you have a policy problem. Without an approved tools list, data classification guidelines, and clear accountability, every employee is making their own risk decisions on behalf of your organization. A practical AI use policy does not need to be fifty pages or overcomplicated. It needs to answer three questions: what tools are approved, what data can be used, and who is responsible when something goes wrong.
4. Copy-Paste Culture
AI can draft an email, a memo, or a client report in seconds. However, sending that output without reviewing and editing it is a credibility risk. AI-generated content tends to sound generically polished, often misses important context, and may include subtle inaccuracies. Treat every AI output as a first draft, never a final product. Your voice, your judgment, and your understanding of the situation are what give the communication its value.
5. Ignoring AI’s Confidence Blind Spot
One of the most underappreciated risks of AI is that it delivers incorrect answers with the same tone and formatting as correct answers. There is no warning indicator when a model fabricates a fact, a figure, or a citation. Users who mistake confident delivery for accuracy are the people most likely to be impacted. Building a habit of healthy skepticism is essential, particularly when the output involves financial data, legal references, or technical claims.
6. Automating a Broken Process
AI is an accelerator, not a corrective tool. If your accounts payable workflow has approval gaps, or your job costing process relies on inconsistent manual entries, layering AI on top will only produce unreliable results at a faster pace. Before you automate, take the time to map the process, identify where it breaks down, and address the underlying issues. Then let AI amplify a process that actually works.
7. Removing the Human from the Loop
Eliminating human review from AI-assisted decisions may seem efficient, but in areas like financial reporting, regulatory compliance, and client communications, the risk outweighs the time saved. A human-in-the-loop is not a bottleneck. It is a control. The goal is to let AI handle repetitive, time-consuming work while a qualified professional validates the output before it reaches anyone outside of your organization.
8. One-and-Done Prompting
A vague, single-sentence prompt will consistently produce a vague, generic response. AI is most effective when you provide clear context, define the intended audience, specify the desired format, and iterate on the result. Think of prompting in the same way you would about delegating to a new team member: the more specific and structured your instructions are up front, the less rework you will face on the back end.
9. Neglecting Version Control and Documentation
When AI contributes to a work product, there should be a clear record of how it was used. Which tool generated the output? What prompt was used? What did a human review or change afterward? Without this documentation, you have an audit trail gap. For professional services firms, regulated industries, and any business subject to external review, this is a necessity. Build simple documentation habits now, before the regulatory environment demands them.
10. Treating AI as a Strategy Instead of a Tool
“We’re doing AI” is not a strategy. AI is a tool and, like any tool, it needs to be tied to a clear business objective to deliver meaningful value. The organizations seeing real returns from AI are not chasing the technology for its own sake. They are identifying specific operational bottlenecks, defining measurable use cases, and tracking results. Start with the problem you are trying to solve, not the product you want to implement.
11. Underestimating Change Management
Deploying a new AI tool without adequate training, communication, and leadership support is a common path to low adoption and wasted investment. People adopt what they understand and trust. A successful AI rollout requires the same change management discipline as any other technology initiative: clear communication about why the change is happening, hands-on training to build confidence, early wins to demonstrate value, and ongoing support to sustain momentum.
12. Failing to Reassess and Iterate
AI capabilities are evolving at a rapid pace. A workflow you built six months ago may already be outdated, and the risk profile you assessed at launch may have shifted. Treat your AI implementations as living systems, not one-time projects. Schedule periodic reviews to evaluate whether the tools, prompts, and controls you have in place still align with your business needs and risk tolerance.
AI is not going away, and neither are the risks that come with using it without intention. The good news is that every item on this list has a practical solution. It starts with awareness, moves to policy and process, and matures through ongoing evaluation and adjustment.
The organizations that will get the most from AI are not necessarily the ones that adopt it the fastest. They are the ones that adopt it the most thoughtfully.
BMSS’ advisory team brings a practical, experience-driven approach to AI adoption by helping businesses move beyond experimentation and into structured, value-focused implementation. Drawing on our experience with various stages of the AI maturity curve, we are able to guide clients in establishing governance frameworks, strengthening internal controls, and aligning AI initiatives with measurable business objectives. From developing thoughtful use policies to refining workflows before automation, our team can help ensure that AI is deployed responsibly and effectively. As the technology continues to evolve, we can partner with our clients to reassess, adapt, and scale their efforts, positioning them to realize the benefits of AI while managing the risks with confidence. Reach out to us by emailing Micah Murphy for more information.