Every category you might buy from now has AI in the feature list, and many pricing pages have moved it behind a higher tier or a separate credit bundle. The badge tells you nothing. Two tools can both say "AI-powered" when one summarises a call transcript and the other quietly takes actions in your pipeline — completely different value, completely different risk.
The takeaway: evaluate an AI feature exactly the way you evaluate any feature — by the job it does, the cost shape it carries, and what happens when it is wrong. The novelty makes people skip the criteria step. It should not; these are the same criteria that decide every other software purchase, plus two specific to AI. What follows is a method, not a ranking — which tools do it well is a per-category question with sourced answers on the comparison pages.
First, classify what the feature actually is
"AI" in business software almost always falls into one of three shapes, and the shape determines how much scrutiny it needs.
Assistive — it drafts, you approve. Subject-line suggestions, a first-pass reply, a meeting summary, a content brief. The human is in the loop by design, so failure is mild: you edit or discard. Most shipped AI features sit here.
Classifying — it sorts, scores, or routes. Lead scoring, ticket categorisation, sentiment tagging, keyword clustering. Failure is quieter and more expensive, because a bad score does not announce itself — it just sends a good lead to the bottom of a queue nobody reads.
Agentic — it acts. Updates records, sends messages, triggers workflows, resolves a chat without a human. Highest value and highest risk, because an error becomes a customer-visible event rather than a bad suggestion.
Ask which of the three you are buying. If a demo blurs the line — showing an agentic capability while the shipping product is assistive — catch it before the contract, not after.
The six criteria
1. Does it replace a task you actually do? The only honest baseline is your current manual process. If nobody writes call summaries today, an AI that writes them saves zero hours — it produces artefacts nobody reads. Name the task, estimate its weekly cost, and check the feature addresses that task rather than an adjacent one.
2. What is the acceptance rate on your data? Vendor demos run on clean, representative examples. Your data is neither. The measurable question: what proportion of outputs would you ship with no edit, with light edits, and not at all? A feature you rewrite every time is a slower way to do the task yourself.
3. How is it billed? The criterion that varies most between vendors, covered below.
4. Where does your data go? Whether inputs train models, how long they are retained, which sub-processors are involved, and what region processing happens in. For regulated data or client confidentiality, this is a gate rather than a preference.
5. Is it reversible? Can you switch it off per user or per workflow? Do AI-generated fields land in normal, exportable fields, or in a proprietary layer that leaves nothing behind if you cancel? An agentic feature with no off switch and no audit log is a poor bet regardless of quality.
6. Is the AI riding along with what you actually need? Often it sits on a tier that also carries the reporting, permissions, or automation limits you were going to need anyway. Price the tier as a whole bundle: sometimes the AI is effectively free, sometimes you are paying a large uplift for one feature and nothing else new.
Decoding the pricing shapes
AI features introduced a genuinely new billing pattern into categories that used to be simple, and it deserves its own read. The common SaaS pricing models still apply underneath — this sits on top of them.
- Bundled into a tier. AI is included above a certain plan. Simplest to forecast; the cost is the tier jump, and you should evaluate the whole tier rather than the badge.
- Credit or token bundles. You buy a pool of units consumed per action. The mechanics to establish before signing: what consumes a credit, whether a failed or regenerated output consumes another, whether unused credits roll over, and what happens when the pool runs dry — does the feature stop, or does it auto-purchase more?
- Per-seat AI add-on. A per-user surcharge on top of the base seat. Predictable, but expensive at scale when only a subset of users will actually use it. Ask whether you can license the add-on for some seats and not others.
- Usage-metered. Billed by volume processed — conversations handled, records enriched, words generated. Cheapest to start, hardest to forecast, and it scales with your growth rather than your headcount.
The general trap is the same one in every consumption-priced tool: the cost you pay at pilot volume tells you almost nothing about the cost you pay at production volume. Model it at two or three times your current volume before you commit, and find out where the cliff is.
How to test it in a trial
Trials are for producing evidence, not for browsing the interface — the general protocol is in our guide to running a software trial that actually tells you something. For AI features specifically, add these steps.
- Bring real, messy data. Import a genuine sample: your actual records, your actual tickets, your actual writing. Never evaluate on the vendor's sample dataset, which was chosen because it works.
- Fix a sample size in advance. Twenty to fifty real items is usually enough to see a pattern and small enough to get through in an afternoon. Decide the number before you start so you cannot stop at the flattering point.
- Score each output into three buckets — ship as-is, edit lightly, discard. Do this in a spreadsheet as you go. The percentages are your acceptance rate, and they are directly comparable between two tools.
- Time the same task manually. Without a baseline, "it felt faster" is all you will have.
- Test the edge cases deliberately. Feed it your jargon, your abbreviations, a non-English record if you have one, an incomplete record, and something genuinely ambiguous. The failure behaviour matters more than the success behaviour: does it flag uncertainty, or does it produce a confident wrong answer?
- Check the audit trail. Can you see what the AI changed, when, and on whose behalf? For anything classifying or agentic, no audit trail is a hard no.
- Test the export door. Generate a batch of AI outputs, then export. If summaries, scores, or tags do not come out in the export, they are not yours in any meaningful sense — which is exactly the switching cost you should be pricing in on day one.
What this looks like across categories
The criteria stay constant; the specifics change with what the tool is for.
- Email marketing. Copy and subject-line generation on the assistive side; send-time optimisation and segmentation on the classifying side. Ask whether the segmentation logic is inspectable — a segment you cannot explain is a segment you cannot fix.
- CRM. Call summarisation, next-step suggestions, and predictive lead scoring. Scrutinise the scoring: what signals feed it, whether you can see per-record reasoning, and how much history it needs before the scores mean anything.
- SEO tools. Keyword clustering, intent classification, and content briefs. Judge these against your own manual grouping of the same keyword set — the easiest AI feature to benchmark honestly, because you already know the right answer.
- Live chat and support. Deflection bots, suggested replies, conversation summaries. The decisive criterion is the handoff: how cleanly the bot escalates, whether the customer repeats themselves, and whether the bot knows what it does not know. Our guide to AI chatbots for customer support goes deeper.
FAQ
Are AI features worth paying extra for?
Only when they replace a task you genuinely perform and the acceptance rate on your own data is high enough that you are not redoing the work. Measure both in a trial rather than reasoning about it in the abstract.
What are AI credits and how do they work?
A credit is a prepaid unit consumed per AI action. Before buying, establish what consumes one, whether regenerating consumes another, whether unused credits expire, and what happens at zero — the feature stopping and the account auto-topping-up are very different outcomes.
How much of my data does an AI feature see?
Ask directly, and get it in writing: what inputs are sent to a model, whether they are used for training, retention period, processing region, and which sub-processors are involved. For regulated or client-confidential data this is a gate, not a preference.
Should I switch tools just to get AI features?
Rarely on that basis alone. Switching carries migration, retraining, and integration-rebuild costs that usually exceed the value of one feature. The incumbent has to be failing on your core criteria by more than the cost of moving.
How do I compare two vendors' AI fairly?
Run the same fixed sample of your own records through both, score outputs into ship / edit / discard, and compare the percentages alongside the total cost at your projected volume. Identical inputs are what makes the comparison mean anything.
Make the call on criteria
Classify the feature, name the task it replaces, measure acceptance on your own data, model the billing at real volume, and confirm you can turn it off and take your data with you. That is a software evaluation — the AI part changes the questions about data and failure modes, not the method. Build it into the same criteria-first framework you would use for any purchase.
Once your criteria are set, compare the tools in your category side by side on Nexuswoot, where pricing, plans, and per-criterion scores sit in one sourced table. (Disclosure: Nexuswoot may earn a commission from some of the tools it compares; rankings follow the published criteria, not payouts.)
