When you’re about to buy an AI tool, it always comes to mind whether that tool is really AI-based or it is just another marketing gimmick. You can seek help from an AI specialist and engineers, but they have hefty fees that sometimes cost you more than the actual tool you are going to buy. When you initiate a search on the internet, you do not see a real use case or evidence that it is real or fake. This is really confusing. But don’t worry, we have answered this critical question: how to evaluate AI technology during due diligence.
If you want to learn about AI due diligence, check out our details and step-by-step guide to help you move forward from step 2. This part focuses straight on the steps and the process to actually evaluate an AI tool and find out the truth. Let’s begin and see what the prerequisites are and how you can dig up the real story of an AI tool you are about to pay for.
1. Define the Scope and Assemble the Right Team
First, someone needs to define exactly what “AI technology” means in the deal up for review. The phrase might describe wildly different company elements. A recommendation system that probably drives the company’s earnings changes everything. Maybe the technology only helps staff look up policies about work and time off. Skip this simple framing step and confusion might waste days on a background tool while the heart of the operation goes unchecked. Ask early if artificial intelligence powers a main product that users might pay for, or if it quietly helps in the background. That answer almost always shapes how deep the investigation should dive. Anyone who wants the fuller picture of what artificial intelligence due diligence actually means before scoping a specific deal should start with that foundational explainer.
Now, consider the people. Who fills the room can make all the difference. A single reviewer might miss too much. A technology expert may catch shortcuts in the work and see through documentation. Some legal experience will probably spot missing licenses or questionable data use, things a coder may not notice. One person who grasps the business vision grounds the review in the real needs of paying customers. Leave out any one voice and blind spots show up fast. An engineer could step over a legal trap. A lawyer might approve a tool that simply fails expectations. The overlap gives protection, and that overlap is the safeguard.
2. Request the Right Documentation First
Paperwork comes before testing even starts. Documentation can show how seriously a company might treat its own inventions. Both the speed and care of the reply will probably say even more. A provider with clean, tidy records probably handles the system carefully. Someone who hesitates or throws together a reply on the spot may hide weak spots. Make sure to ask for certain records:
- Model documentation: A written description of the architecture, the intended use, and the known limitations. Good documentation admits what the model does poorly, not just what it does well.
- Training data sources: Where the data came from, and whether it was licensed, purchased, or scraped from the open web. Scraped data can carry hidden legal risk.
- Licensing agreements: Written confirmation that the company holds the rights to use and commercialize the underlying model, especially if a third party built part of it.
- Incident history: A record of past failures, customer complaints, or corrections the company has had to make. Silence here is rarely a good sign.
- Prior audit or assessment reports: Any earlier review of the system by an outside party. Someone else may have already done part of your work.
Some firms cannot show those records. Watch the reasons. A small business may just not have formal certifications. Few blame that. Hesitation or flat refusal to show where data came from or explain legal rights speaks louder. Any partial reply should feel like a prompt for more questions. Refusal probably serves as a warning not to ignore.
3. Test the System Directly, Do Not Just Read About It
A public demo might look like a show on a stage. The team probably picked every example with care. Many hours may have gone into practice runs. Awkward slips likely landed on the cutting room floor. Reading long documents about an AI tool can create distance from reality. Very detailed reports might still hide surprises. You probably remain outside the real experience, one step away from the truth. Try using the tool by yourself. Use problems pulled from life, not just samples created in slide decks. Tests should feel rough. Messy requests mimic those users send every day. Strange or half-completed ideas probably reveal the core strengths and weaknesses.
During your test, watch a few signals. The system might give different answers when you repeat a question; big changes may show weakness. Sensible failure messages mean a system can react to mistakes. Made-up answers create a different problem. Tech experts call those false outputs “hallucinations.” These surprises sound convincing but come from fantasy, not fact. Claims in reports deserve a comparison. Outputs should line up with promises. The gap between sales talk and reality often speaks louder than any glossy ad.
4. Check Who Owns the Data and Who Controls the Model
Many people focus on performance checks and skip ownership questions. Fast-moving teams may forget to think about who owns what. That habit can cost a lot later. Ownership may affect finance and law as much as engineering. Sometimes an AI tool works really well, but the team based it on borrowed pieces. These borrowed models may not have much real value. Ask about these topics before you become committed to the tech pitch.
- Data ownership: Who legally holds the rights to the training data and the operational data the system generates. Ownership decides what you actually get to keep.
- Model dependency: Whether the system leans on a third-party foundation model the target company does not control. A price change or shutdown upstream could break everything overnight.
- Key-person risk: Whether the handful of people who understand and can retrain the model are still employed. Knowledge that walks out the door is hard to replace.
- Data portability: What happens to the data and the model if the deal collapses or the partnership ends. Clarify the exit before the entrance.
- Update and maintenance rights: Who is allowed to modify, retrain, or patch the model going forward? A frozen model slowly rots as the world moves around it.
The facts behind ownership may change the true value. Teams that hold their own data and models probably offer a real asset for users. Companies using rented tools or borrowed data look more like subscription sellers than product creators. Use this detail to guide your pricing and negotiations.
5. Assess Legal, Compliance, and Regulatory Exposure
Now think about the rules for this AI. No law school degree needed here, just useful questions. Does the AI touch private data? If yes, does the design follow the local rules for privacy in the places where it will operate? Groups like the Federal Trade Commission have paid close attention to claims about machine-driven tools. Honest marketing probably matters a lot. Certain markets follow extra boundaries. Health, banking, or hiring tools may come with special restrictions. Ask if the team has faced any questions from regulators. Find out how they responded.
Following laws protects more than your comfort. AI built carelessly or used where it should not be may lose value quickly if a regulatory inspector appears. Fines may pop up, but larger problems can come from products stuck on the shelf or deleted information. Checking rule-following early on might protect your deal’s worth after everyone signs the contract.
6. Score the Findings and Decide What to Do Next
All the digging and checking leads nowhere unless someone finally makes a choice. A scoring method works best when it stays clear. Every result probably falls into one of three piles. One group shows no trouble and usually means a green light. Another group seems less simple, calling for a second look or maybe a line in the agreement, but not a total stop. The last group raises real doubt and probably changes the whole plan or forces tougher talks. No secret formula needed here. Using these three everyday groups shapes vague concern into something real that leaders may use.
So what might a bright red warning really mean? Not instant rejection, which catches many by surprise. A red signal marks the start of a new conversation, not the end. Someone may need to review the price to match the danger. Extra questions probably make sense before signing anything. The worst-case scenario means backing out. Every red marker gives direction. Random facts spread across pages almost never help someone choose wisely. A short, simple score sheet gives people what they need. Anyone who would rather hand this work to someone else can turn to AI due diligence companies for investment firms instead of building the process alone.
Common Mistakes to Avoid
Even people with real experience often trip over the same classic mistakes. The usual errors might come from rushing, trusting the wrong sign, or letting one person’s excitement carry the group. Expecting these trouble spots could help block disaster before it starts. A handful of traps tend to catch most buyers out.
- Treating the demo as the product: A polished demo shows the best case. Real users create the worst case daily, and the gap is where value hides.
- Skipping the data question: Chasing performance while ignoring who owns the data leaves you exposed to a legal surprise later.
- Assuming compliance equals safety: A system can meet today’s rules and still behave badly or drift over time. Model drift, where accuracy slowly degrades as real-world data shifts, does not show up on a compliance checklist.
- Letting one department own the review: A single-team review inherits that team’s blind spots. Mixed perspectives catch more.
- Not revisiting tools already in use: Systems bought years ago rarely get re-examined, yet they carry the same risks as any new purchase.
- Confusing AI marketing for AI capability: The label on the box tells you nothing about what is inside. Test the contents.
Effort fits the situation. A tiny young company and a massive high-stakes agreement should never get equal attention. Deep review probably matches the size and risk in front of you. Too much focus on a small buy wastes days and weeks. Too little on a major deal could mean real pain later. Quiet wisdom comes in knowing how much to dig.
The 6-Step AI Due Diligence Checklist at a Glance
Reading through a full evaluation process is one thing, but having it in front of you at a single glance is another. Use this table to see the whole framework at once before working through each step in detail below.
Conclusion
Looking closely at AI during a checkup phase might not need experts from the start. The order and the shape of questions matter most. Start by finding out what the AI really does. Put together the records. Try out the tools yourself. Figure out who actually controls the code and the information. Think about possible legal headaches, then give each answer a quick score. With steps in this order, even a small player probably makes a strong choice without a big review crew.
Hunting for total certainty never happens here. Nobody promises a perfect answer. The true goal shines through: a clear picture of the real value and the biggest risks. None of this has to be improvised from scratch, either: researchers have already proposed structured audit frameworks for evaluating algorithmic systems that formalize much of what a careful buyer should be checking. The questions in this guide, matched to the size of the deal, already put most people ahead of the crowd who just trust flash and hope for luck.
Haroon writes about contact management and keeping your Outlook data clean. He covers how to find and remove duplicate contacts, calendars, and tasks the easy way.