How to Evaluate a Generative AI Platform for Your Business: A 5-Step Checklist

When This Checklist Is For You

If you're the person in charge of finding, testing, or approving new software for your team—especially something like a generative AI tool—this is your guide. I'm an office administrator for a 150-person tech services company. I manage all our software subscriptions and tool evaluations, which is about $85,000 annually across maybe 15 different vendors. I report to both operations and finance, so I need tools that make teams productive and keep the accountants happy.

You'd think picking an AI tool is just about who gives the best answers. It's not. The real question is: which one fits into our actual workflow without creating more problems than it solves? After 5 years of managing these vendor relationships, I've learned that lesson the hard way.

This checklist has 5 concrete steps. It's what I wish I had in 2023 when we first started looking at these platforms. Follow it, and you'll avoid the common traps that make a "productivity" tool a time-sink.

The 5-Step Evaluation Checklist

Step 1: Define the Single, Core Use Case (Not "Everything")

This is where most teams go wrong immediately. They get excited and say, "Let's use AI for everything!" That's a recipe for wasted budget and confusion.

Start with one, specific, high-frequency task. Is it:

• Drafting first versions of client email responses?
• Summarizing long meeting notes into action items?
• Checking and polishing internal documentation?
• Generating ideas for social media posts?

Pick one. Write it down. For us, it was "drafting standard service proposal language." We were spending hours tweaking the same basic paragraphs. By focusing there, we could actually measure if the tool helped.

The check: Can you describe the use case in one sentence without using the word "assist" or "help"? If not, it's too vague. Refine it.

Step 2: Audit the Real Cost—Look Beyond the Login Page

In my opinion, transparent pricing beats a cheap intro price that balloons later. Every time. When I took over purchasing in 2020, I learned this the hard way with a project management tool. The per-user price was low, but we needed the "premium" add-ons for basic features like time tracking. The surprise wasn't the price difference. It was how much we had to spend to get what we actually needed.

For AI platforms, you need to ask:

• User-based or usage-based? Is it a flat fee per seat (like many business plans for ChatGPT or Gemini), or do you pay per query/word (common with API-heavy platforms)? If your team uses it sporadically, usage-based might be cheaper. If it's a daily driver, a seat license is probably better.
• What's the team management overhead? Some platforms make it easy to manage users, roll out prompts, and control costs centrally. Others are just a collection of individual accounts. The admin time has a cost, too.
• Are there minimums or commitments? I've seen quotes that look great until you read the fine print about annual commitments or minimum monthly spends.

The check: Calculate the total estimated cost for your pilot team for 3 months. Include every fee you can find. If a vendor is vague, that's a red flag. The vendor who lists all fees upfront—even if the total looks higher—usually costs less in the end.

Step 3: Test the Output Quality on *Your* Actual Work

Don't just ask it to write a poem about your industry. That proves nothing. You need to test it on the exact task from Step 1.

Here's a concrete method:

1. Take 3-5 real examples of the work product you want to generate or improve (e.g., three old client emails, two meeting summaries).
2. Use the same exact prompt across all the platforms you're testing (JPT-Chat, ChatGPT, Gemini, etc.). Make the prompt detailed. Instead of "write an email," try "Write a polite, professional email responding to a client's concern about a missed deadline. Acknowledge the issue, apologize briefly, state the new delivery date (Friday EOD), and ask if they need any interim updates. Keep it under 150 words."
3. Have the actual person who does this work review the outputs blindly (without knowing which tool created which). Ask them: Which one is most usable with the least editing? Which tone matches ours?

Looking back, I should have done this blind review from the start. At the time, I just picked the one that sounded "smartest" to me. But I'm not the one using it daily. The team's preference matters more.

The check: You should have a ranked list of outputs from the end-user's perspective. If they all say "they need heavy editing," maybe the use case isn't a good fit for AI right now. That's a valuable finding, too.

Step 4: Evaluate the Integration & Security Headache Factor

This is the step most people ignore. They get wowed by the demo and forget the logistics. A tool that lives in a separate browser tab is a tool that won't get used.

Ask these questions:

• Where will people use it? Does it have a desktop app, a browser extension, or a Slack/Teams integration? The best tool is the one that's right where the work happens. If they have to copy-paste constantly, adoption will plummet.
• What does your IT or security team need to know? Do a pre-check. In our 2024 vendor consolidation project, we had to ensure any new SaaS tool met our basic standards: SSO (Single Sign-On) capability, data encryption, and a clear data processing agreement. Getting this approved later can kill a project. Involve them early.
• How is company data handled? According to most platform's terms of service, inputs may be used for model training unless you opt for a business plan with data privacy guarantees. This is non-negotiable for many businesses. Verify.

The check: Have a 15-minute conversation with someone from IT/security. Show them the vendor's security page or SOC 2 report. Get a preliminary thumbs-up or a list of concerns. Do this before you fall in love with a platform.

Step 5: Plan a Realistic, No-Pressure Pilot

Never roll out a new tool to the whole company. You'll get overwhelmed with feedback and won't know what's a real issue versus a learning curve problem.

Run a 4-week pilot with a small, willing team. Give them clear goals:

1. Goal: Use the tool for the Step 1 use case at least X times per week.
2. Success Metric: Save Y minutes per task, or reduce edits by Z%.
3. Feedback: Weekly, 10-minute stand-up: What worked? What broke? What was confusing?

The most frustrating part of software pilots? When there's no structure. You'd think people would just try it and report back, but everyone's busy. You need to create the channel for feedback.

At the end, you're not just deciding "yes or no" on the tool. You're answering: For this specific use case, with this team, did the benefits outweigh the costs and friction? If yes, you have a business case to expand. If no, you saved the company a larger, wasted investment.

The check: You should have a one-page summary after 4 weeks with: (1) time/quality metrics (even if anecdotal), (2) direct quotes from pilot users, and (3) a clear recommendation to stop, continue the pilot, or scale.

Common Pitfalls to Avoid

Pitfall 1: Chasing the "Smartest" Tool. The AI with the highest benchmark scores might be overkill for your simple task and more expensive. Choose the tool that's "good enough" and fits your workflow.

Pitfall 2: Ignoring the Learning Curve. These tools require skill in prompt engineering. Budget time for training. A one-hour workshop on writing good prompts pays off more than choosing a marginally "smarter" model.

Pitfall 3: No Exit Strategy. What happens if the price doubles next year? Is your content trapped? Avoid becoming overly reliant on a platform-specific feature you can't replicate. Keep your good prompts documented in a simple text file, not just inside the tool.

Remember: You're not just buying an AI. You're introducing a new process. The tool is only 20% of the battle. The other 80% is how you integrate it, train on it, and measure it. Focus there, and you'll make a choice that actually sticks.

Prices and features as of May 2024; verify current offerings directly with vendors.