Avoid AI Pitfalls: A Business Guide

AI is empowering many businesses across all sectors. However, as these tools have been known to hallucinate, provide incorrect or outdated information, and sometimes misunderstand prompts (or struggle with agents or poorly written prompts), the likelihood of replicating human error is substantial.

“Human error” is considered a massive vulnerability to account for in most businesses. Even at the top level of professionalism, plans against it have to be made. It’s why many methods to help reduce the accidental likelihood of medical negligence are common in healthcare. For example, a single slip-up by an overtired doctor or surgeon could result in millions of dollars in restitution and damage to their reputation, not to mention the consequences of a changed life.

Yet while that’s perhaps a stark example, it’s not the only one that’s relevant. For example, human error is often the primary cause of cybersecurity breaches, as it is much easier to trick someone into clicking on a phishing email link than to penetrate a robust defense system that has been automated and features multiple layers to flag inappropriate access.

In this post, we’ll mention the significant areas where this could impact you, and provide a few suggestions to limit it:

Published Content

Content pushed out with AI support can drift off course quite easily without you spotting it, so it’s helpful to slow the pace and look at every line with a clear head. You might have something that appears fine on the surface, including an important claim without a source, a fabricated number, or a tone that doesn’t align with your approved style.

This can run the gamut from making a blog post feel sloppily put together, or much worse, making an implied promise in a piece of ad copy that you can’t deliver on at all. You can be sure that any flaw like this will be noticed sooner or later, even if a customer doesn’t react to it. At the very least, a human should closely review the AI content you put out to make sure it reads well and fits your publishing goals. If you don’t have a publishing guide, create one so you can adhere to a specific standard.

Data Privacy

AI tools often pull from pools of information that you never get to see, so be extremely cautious with anything you feed into them. Even top-flight professionals have been burned by adding drafts, client information, internal conversations, or private plans that should have really stayed outside those boxes. A quick copy and paste is easy to do, but once it’s in, you lose control over where that information travels or how it’s processed. If you have a privacy policy, as most companies are required to, that’s a direct breach of it.

Always keep sensitive information in a separate, secure space. You can still use AI to shape your thoughts, but you can describe the outline without sharing the private core, which is a habit that should limit the chance of personal details slipping into a training set or surfacing in a later prompt. This also means you gain insight into the tool without handing over the parts of your work that you can’t possibly risk getting out. We’d suggest you don’t even risk it.

Regulatory Upkeep

It’s fair to say that regulations are constantly evolving across various sectors, including finance, healthcare, data handling, and crypto regulation. AI tools usually speak with confidence about guidelines that changed last month, but if it’s wrong and you take it at face value, you would only spot the problem later. That “later” is usually quite expensive. 

That’s why it’s helpful to keep a simple reference spot where the current rules are checked before you trust anything you’re shown.

Developing Asset Management

Developing new assets, whether that’s a piece of software, a creative design, or a financial model, is another area where AI errors can sneak in and potentially cause you tough outcomes you have to damage control. If you use AI to generate code, for example, a subtle mistake in the AI’s output, which is often what programmers call “garbage code,” might introduce a vulnerability or a bug that takes days or weeks for a human to track down.

It is obvious that it wastes money and time, but it also limits morale and team collaboration. That’s not to say there’s no place for AI, but it can certainly cause issues if you’re not extremely careful about where it shows up and how. Discipline is often the keyword, because ultimately the assets you use are expected to deliver for your company continually; therefore, don’t build them on poorer foundations than you should.

With this advice, we hope you can avoid AI integrations landing you in hot water.