Make your product more useful with AI.
A "Summarise" button alone does not make a product better. The strongest teams use AI to make the main job inside their software easier, not to add a chatbot to the side.
The competitive baseline for software just shifted.
A customer logs into legacy software, navigates four screens, fills a form, exports to CSV, and finishes the job in a spreadsheet or a Slack thread. A competitor founded in the last 18 months does the same job in one prompt and a confirm. Churn reports don't always show the damage yet, but sales calls do — every demo now opens with "how is your AI different from theirs?" Bolt-on chatbots and a sparkle icon on the header buy a quarter or two of cover, no more.
Let the product do more of the work.
The shift is simple: instead of making people do every step and record it in the product, the product can suggest the work and let people approve it. Search becomes answers. Filters become plain-language requests. Reports become questions. A PDF, voice note, or email can become usable data. That helps people get value from the product sooner and gives them more reasons to keep using it.
How this can work in real businesses.
Everyday situations where this can make a real difference.
A vertical SaaS for accounting and tax compliance.
The product lets accountants file tax returns and reconcile invoices. Today the user uploads invoices and reconciles line items by hand. With the right AI built in, the product reads the invoice, classifies line items, flags mismatches against tax-authority data, and presents a queue of exceptions. The accountant's job changes from data entry to judgement. Throughput per accountant doubles.
An HRtech recruitment platform.
Recruiters spend their day moving candidates through stages and writing the same five emails. With the right AI built in, the product reads the JD and the resume, drafts the screen-call summary from the recording, schedules the next round in the candidate's timezone, and surfaces the three candidates most worth a partner's time today. The recruiter runs a desk twice the size.
A D2C brand’s internal merchandising tool.
The merch lead asks the dashboard "why did conversion drop in a specific region in the last fortnight?" and gets an answer with the cohort, the SKU, the funnel step, and the likely cause — not a chart she has to interpret. The product has become an analyst.
A legal-tech SaaS.
The platform helps in-house counsel manage commercial contracts. With the right AI built in, a redline arrives, the product proposes the position based on the company's playbook, flags the three clauses that diverge from the standard, and drafts the response email. The lawyer reviews and sends. Cycle time drops from days to hours, and that becomes the demo.
A modern PM tool competing with Linear.
A project lead asks "what is blocking the release?" and the product traces the dependency graph, the recent comments, the CI failures, and the open design questions to give a real answer — not a list of tickets. Native AI turns a tracker into a co-pilot for shipping.
A clinical workflow product.
Clinicians dictate; the product structures the note into the right template, codes it for billing, flags the differential the clinician should consider, and pre-fills the next-visit plan. The clinician's documentation time drops by half. That's table stakes for renewal now.
What can get better.
Ranges teams commonly see. These are patterns, not promises.
- Activation rate (sign-up to first meaningful action) lifts 1.5–3x when onboarding is conversational instead of form-driven
- Time-to-value per workflow drops 40–80% inside features that were re-architected, not decorated
- Net revenue retention recovers 5–15 points among accounts where useful AI features replace manual work
- Pricing power: AI modules can sustain a 20–50% premium when the value is clear
- Sales cycles can shorten by 30–50% when AI features make the difference obvious in a demo
- Roadmap velocity for the AI surface area itself: 2–3x faster than legacy module velocity once the platform pattern is in place
Have a problem like this?
Talk about Product AI →Who this can help.
When product AI is the wrong answer.
When the core differentiator is regulatory positioning, a network effect, or a data moat a competitor can't replicate, AI is upside — not survival. AI is also the wrong investment before product-market fit. AI accelerates what works; it doesn't invent demand that was never there. We've walked away from "add AI to save the product" briefs more than once.
Questions people ask.
We already have a chatbot and a summarise button. Isn’t that AI in the product?
That’s a feature added to the side. It may help for a while, but the bigger opportunity is to improve the main job your customer came to the product to do. The difference shows up in whether people keep using the product, not just in a screenshot.
Will customers actually trust AI inside our product with their data?
They will if the boundaries are clear: per-tenant context, no cross-customer training, audit logs for every model call, and a way to switch the assistance off. Trust is an engineering problem, not a copy problem.
How do we price AI features without giving away margin?
Outcome-based pricing (per resolution, per processed document, per qualified lead) works when the value is measurable. Seat-based pricing works when AI raises the ceiling on what a seat can do. We help model it before we build it.
Our engineering team can do this in-house. Why hire OLN?
They can — eventually. We’ve built and operated this pattern across several products, so the platform decisions (evals, retrieval, guardrails, observability, cost ceilings) are not first-attempt decisions. We compress the learning curve and hand the system back.
Have a problem like this in mind?
Tell us what is getting in the way. We come back within one to two business days — including whether AI is the right tool for it.