A practical guide to evaluating AI tools for non engineers

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TL;DR

• Start with the problem, not the product: write down the task, who does it today and what good looks like
• Build a small test set of real examples and score every tool on the same set, checking accuracy, not just fluency
• Read the data terms carefully: does the vendor train on your data, how long is it kept, and where is it stored
• Check security and governance basics such as single sign on, admin controls, audit logs, SOC 2 or ISO 27001, and increasingly ISO/IEC 42001
• Run a time boxed pilot with clear success measures, then decide based on evidence rather than demos
• Plan for human oversight, staff training and an exit route before you commit

Why this guide is for non engineers

AI tools are now sold directly to marketing teams, HR departments, finance staff, lawyers, teachers and small business owners. Many of these buyers have no engineering background, yet they are making decisions that affect data protection, quality of work and budgets. Vendor demos are polished and persuasive, and it can be hard to know what questions to ask. This guide offers a structured, jargon free way to evaluate AI tools with confidence

You do not need to understand neural networks to evaluate an AI product well. You need to understand your own work, ask clear questions and test carefully. Those are skills most professionals already have. The steps below simply apply them to a new kind of software

The approach also helps with governance. Organisations increasingly need to show that AI tools were chosen thoughtfully, especially where personal data or important decisions are involved. A documented evaluation makes conversations with managers, auditors and regulators much easier. It also protects you if a tool later disappoints

Step 1: Define the problem

Write a short description of the task you want help with. Who does it today, how long it takes, what inputs they use and what a good result looks like. Be specific, for example summarising customer complaints into weekly themes or drafting first replies to routine supplier emails. Vague goals such as using AI to be more productive make evaluation impossible

Decide what matters most. For some tasks, accuracy is everything and speed is secondary. For others, a rough first draft that saves an hour is valuable even if a human rewrites half of it. Knowing your priorities prevents being dazzled by features that do not matter to you

Consider the stakes. A tool that suggests social media captions carries little risk if it makes a mistake. A tool that screens job applicants, summarises medical notes or drafts legal advice carries much more. Higher stakes call for more rigorous testing, stronger oversight and closer attention to regulation

Step 2: Build a test set

The single most valuable evaluation technique is a small test set of real examples. Gather twenty to fifty representative tasks from your actual work, including some easy ones, some typical ones and some tricky edge cases. Remove or anonymise any sensitive information unless the tool is already approved for it. For each example, note what a good answer would contain

Run every candidate tool on the same test set. Score each output against simple criteria such as correct, partially correct or wrong, and note any errors that would cause real problems. Pay attention to whether mistakes are obvious or subtle, because subtle errors are more dangerous. Fluent, confident writing can hide inaccurate content

Involve the people who do the work. They know which errors matter and which shortcuts are acceptable. Their involvement also builds trust in whatever decision you make. Tools chosen without frontline input often go unused

Repeat the test occasionally after purchase. AI products change frequently as vendors update underlying models, and performance can improve or degrade. A quick rerun of your test set every few months catches surprises. It also gives you evidence when negotiating renewals

Step 3: Check accuracy and reliability

Ask how the tool handles facts. Does it cite sources you can check, or does it produce answers from the model's memory alone? Tools that ground answers in your documents or in linked sources are easier to verify. Even then, check citations, because models sometimes misquote or misattribute

Test how the tool behaves when it does not know. Ask questions it cannot answer from the information available and see whether it admits uncertainty or invents something plausible. Good tools say they are unsure or ask for more information. Tools that always produce a confident answer need closer supervision

Developer surveys show why verification matters. In Stack Overflow's 2026 Developer Survey, nearly half of respondents said they trust AI output they can easily verify, but only about 6.6 percent said they would trust it for important decisions. Those are experienced technical users. Non technical buyers should apply at least the same caution

Step 4: Read the data terms

Data handling is where many organisations get caught out. Ask whether the vendor uses your inputs and outputs to train its models, and whether you can opt out. Many business plans exclude customer data from training by default, while some consumer plans do not. The difference matters enormously for confidential information

Ask how long data is retained, where it is stored and who can access it. Data residency can matter for privacy law, public sector rules or customer contracts. Ask whether the vendor uses subprocessors, such as the underlying model provider, and what terms apply to them. A clear data processing agreement should answer these questions

If the tool will handle personal data, involve your data protection lead early. Under laws such as the UK GDPR and EU GDPR, you may need a data protection impact assessment, particularly for new technologies or high risk processing. Doing this early avoids delays later. It also helps identify safer configurations

Step 5: Check security and governance

Basic security features distinguish business ready tools from consumer apps. Look for single sign on with your existing identity provider, role based access, admin controls, audit logs and the ability to remove users promptly. Independent assurance such as a SOC 2 Type II report or ISO/IEC 27001 certification shows that the vendor's security has been examined. Ask for evidence rather than accepting marketing claims

AI specific governance standards are emerging too. ISO/IEC 42001, published in December 2023, specifies requirements for an AI management system, and some vendors now hold certification against it. The US NIST AI Risk Management Framework, released in January 2023, and its Generative AI Profile from July 2024 offer practical guidance many organisations use. Vendors who can describe how they apply these frameworks are generally more mature

Ask about security testing specific to AI. Has the vendor tested the product against prompt injection and data leakage, and do they follow guidance such as the OWASP Top 10 for LLM Applications? If the tool connects to your email, files or other systems, ask exactly what it can access and whether permissions can be narrowed. Broad access combined with weak controls is a common source of incidents

Step 6: Understand costs

AI pricing can be confusing. Some tools charge per user per month, others charge by usage, often measured in tokens, messages or tasks. Usage based pricing can grow quickly if a tool becomes popular or if automated features run frequently. Ask for realistic estimates based on your expected volume

Include hidden costs in your comparison. Staff time for training, setup, integration, review of outputs and ongoing administration all add up. A cheaper tool that needs heavy checking may cost more overall than a pricier one that is reliably accurate. Your test set results help estimate review effort

Step 7: Pilot, then decide

Run a time boxed pilot with a small group of users, typically four to eight weeks. Define success measures up front, such as time saved per task, error rates, user satisfaction or customer outcomes. Collect feedback regularly and keep notes on problems. At the end, compare results against your original goals

Be honest about the results. Sometimes a pilot shows that a tool saves time but introduces errors that offset the benefit. Sometimes it reveals that the real bottleneck was elsewhere. A pilot that leads to a clear no is a success, because it prevented a poor investment

Step 8: Plan for people and the future

Decide how humans will oversee the tool. Who reviews outputs, how errors are reported and when people must take over are questions to answer before launch. In some jurisdictions and sectors, rules require meaningful human oversight for certain AI uses, and the EU AI Act places obligations on deployers of high risk systems. Clear oversight plans keep you on the right side of those rules

Invest in training. Staff need to know what the tool does well, where it struggles, what data they may enter and how to check results. Short practical sessions beat long policy documents. Encourage people to share tips and problems openly

Finally, plan an exit. Ask how you would export your data and configurations if you switched vendors, and avoid designs that lock you in unnecessarily. The AI market changes fast, and today's leader may not be tomorrow's. A clean exit route keeps your options open

A one page evaluation checklist

• Problem defined, stakes assessed and priorities agreed
• Test set of 20 to 50 real examples scored for every candidate
• Accuracy, citations and uncertainty behaviour checked
• Data terms reviewed: training, retention, residency, subprocessors
• Security reviewed: SSO, admin controls, audit logs, SOC 2 or ISO 27001, AI specific testing
• Costs estimated, including review time and training
• Pilot run with clear success measures and honest conclusions
• Oversight, training and exit plans agreed before launch

Closing thoughts

Evaluating AI tools well is mostly common sense applied with discipline. Define the problem, test with real examples, read the data terms, check security and pilot before committing. None of this requires engineering expertise, and all of it makes AI adoption safer and more effective. Organisations that evaluate carefully tend to get far more value from AI than those that buy on enthusiasm

Please share your evaluation experiences and favourite questions for vendors in the replies. Examples from different sectors would help many readers. Together we can build a practical resource for buyers everywhere


Key references for citation

• NIST AI Risk Management Framework 1.0, January 2023, and Generative AI Profile (NIST AI 600-1), July 2024
• ISO/IEC 42001:2023, Artificial intelligence management system
• OWASP Top 10 for LLM Applications 2025
• Stack Overflow, 2026 Developer Survey
• UK Information Commissioner's Office guidance on AI and data protection