Every category Bettaso covers — email marketing, CRM, help desk, project management — now has a plan with "AI" in the name, an AI add-on priced per seat, or a pool of AI credits stapled to the tier you already pay for. The upsell arrives at renewal, and most buyers approve it because refusing feels like falling behind.
The short version: treat the AI feature as a separate purchase with its own criteria. Judge it on one task you genuinely do every week, price it on how much you would actually consume rather than on the banner, and let it break a tie between finalists — never let it decide the purchase. A tool that is worse at the job you bought it for does not become better because it can also draft an email.
That framing matters because AI features are the hardest thing on a modern feature grid to compare. They are described in verbs ("summarises", "suggests", "automates") rather than specifications, priced in units most buyers have never budgeted before, and demoed on the vendor's own tidy data.
Why AI features break the usual comparison
Ordinary features are binary enough to score: the tool either has two-way email sync or it doesn't. AI features are quality claims. Two products can both list "AI reply suggestions" and deliver wildly different results, and no feature grid tells you which is which.
Three things follow from that:
- Checkmarks carry almost no information. "Has AI summarisation" is a category, not a capability. The comparison has to happen on output you can read.
- The cost is variable, not fixed. For the first time in most software budgets, using a feature more makes it cost more — your invoice now depends on team behaviour.
- The value is time saved minus time spent checking. An output you must re-read line by line before sending carries a review cost, subtracted from the saving. Sometimes it is the whole saving.
That last point is the one buyers skip, and it belongs in the same family as the errors catalogued in our guide to common buying mistakes: a benefit gets counted, its matching cost doesn't.
The four ways vendors price AI — and what to check in each
Most AI pricing you will meet is one of four shapes, or a combination. Identify the shape before you compare prices at all, because the same headline number means very different things across them.
| Pricing shape | How it works | What to check | Common trap |
|---|---|---|---|
| Bundled into a higher tier | AI arrives with a tier upgrade you pay per seat | What else is in that tier — you may be buying four features to get one | The tier jump is priced for the whole bundle but sold on the AI |
| Per-seat AI add-on | A separate line item on top of each licensed user | Whether every seat needs it, or only a few people will use it | Add-ons that must be bought for all seats, not just the ones who use it |
| Credit or token pool | A monthly allowance consumed per action | What one action costs in credits, whether unused credits roll over, what happens at zero | Allowances that reset monthly and actions whose credit cost is unpublished |
| Metered usage | Billed on actual consumption, no fixed pool | Whether you can set a hard cap or only an alert | Alerts that notify you after the spend, with no ceiling |
Two questions cut through all four. What does one real unit of work cost me here? — one summarised ticket, one drafted campaign, one generated project update. And what happens when I run out? A tool that degrades gracefully (the feature pauses) is a different risk from one that keeps billing.
Then apply the rule from our guide to what a purchase really costs before you buy: price the tier that holds your must-haves at your real seat count, for the second year, not the promotional first one. AI add-ons are frequently discounted on introduction. Ask what the renewal rate is and treat that as the price.
The one-task test
Do not evaluate an AI feature by playing with it. Evaluate it the way you would evaluate any other criterion — as a structured trial with a pass/fail you decide in advance.
- Pick one task you actually do weekly, and pick the boring one. Not "write a blog post" but "summarise the week's support tickets into a status note" or "draft the follow-up email after a discovery call". The mundane repeated task is where automation pays; the creative one is where you will end up rewriting everything anyway.
- Time the manual version honestly. Not your best-case guess — do it once with a timer. This number is the ceiling of what the feature can save you.
- Feed it your own data, not the sample workspace. Import a real slice: your actual tickets, your actual deal notes, your actual project. Vendor demo data is clean, well-structured, and unrepresentative of yours.
- Run the task ten times, not once. One good output is a coincidence. What you are measuring is the failure rate, and failure rate only shows up across repetitions.
- Record the edit distance. For each output, note whether you shipped it as-is, edited it lightly, rewrote it, or discarded it. Four buckets, ten runs. That tally is your evidence.
- Time the reviewed version. Manual time minus (generation time plus review time) is the real saving. If it is near zero, the feature is a novelty at any price.
- Check the data terms in writing. Where does your customer data go, is it retained, is it used to train models, can you turn that off, and does turning it off disable the feature? Get this from the documentation or the contract, not from a salesperson's reassurance.
Ten runs on one task takes an afternoon and replaces an argument with a tally. That tally is exactly the kind of evidence that belongs in a scorecard — score AI alongside every other criterion using the method in our guide to comparing products with a weighted scorecard, and give it a weight that reflects how much of your week the task actually occupies.
Criteria that separate a useful AI feature from a demo
Beyond output quality, these are the dimensions that decide whether a feature survives contact with a real team:
- Where it sits in the workflow. A suggestion that appears inside the reply box gets used. One that lives behind a separate button, in a separate panel, gets used twice and forgotten.
- What context it can see. A drafting feature with access to the customer's full history writes something usable; one that sees only the current message writes filler. Ask specifically what the feature reads.
- Correction memory. If you fix the same mistake weekly and it recurs weekly, you have bought a permanent chore.
- Who can turn it off. Some teams need it disabled for sensitive records — check whether that control exists at the record level, not just account-wide.
- Portability. Anything the feature generates should export with the rest of your data. Summaries and AI-written notes that live only in the vendor's proprietary layer are a switching cost you are quietly accumulating.
How much weight should the AI column get?
Less than the demo implies. A practical rule: an AI feature is a nice-to-have until the one-task test proves otherwise, and a nice-to-have never vetoes. If a product wins on pipeline modelling, reporting, integrations, and price, but loses on AI drafting, it still wins. The core job is what you use every hour; the AI feature is what you use for one task.
The exception is when the AI feature is the job — a support team where triage and first-draft replies fill the day. Then it stops being an add-on and becomes a core criterion with real weight. Be honest about which situation you are in; vendors will happily assume the second.
FAQ
Should I upgrade to the AI plan at renewal?
Only if you can name the task it will do and you have run that task through it. Renewal is a bad moment to decide, because you are deciding under time pressure with the incumbent's framing. Ask for the add-on on trial before the renewal date; if that isn't possible, renew without it and evaluate on your own schedule.
How do I budget for AI credits I've never used before?
Estimate the number of real actions per week — tickets summarised, emails drafted — then find out how many credits one action consumes and multiply. If the vendor cannot tell you what an action costs in credits, that is an answer in itself. Set a hard spending cap if the platform offers one.
Is a free AI feature better than a paid one?
Not necessarily better, but it is often good enough for low-stakes tasks, and it doesn't create a renewal decision. What free tiers usually limit is volume, context depth, and the ability to turn off data retention. Check those three before assuming the free version is the same feature with a smaller quota.
Compare software on criteria, not on the AI badge
The method holds for any add-on: identify the pricing shape, run one real task ten times, measure the saving after review, and weight it honestly against the criteria that made you shop in the first place. The slow part is researching the candidates — that's what Bettaso does. Our category pages score options against explicit weighted criteria with side-by-side spec and pricing tables and a clearly labelled Bettaso pick per use-case. (Disclosure: Bettaso may earn an affiliate commission when you buy through our comparisons — it never changes the scores.)
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