Illustrative Scenarios: What AI Delivery Actually Looks Like
Most AI marketing runs on vague claims — ‘transformative’, ‘game-changing’, ‘10x’. This page goes the other way: composite scenarios built from the kinds of problems we are asked to fix, written the way we would want to read them as a buyer. What breaks, and in what kind of company. What the fix involves, and what it costs. What it can return, and how to measure it honestly. These are illustrative examples, not client testimonials. The companies are composites and the figures are indicative ranges, not measured results from named clients. Where a real engagement can be documented and the client consents, we will publish it here as a genuine case study — and nothing on this page should be read as one until that happens.
What you'll find in every scenario
Each scenario follows the same discipline, because comparability is what makes them useful. The starting point: what was broken or manual, quantified where possible — hours, error rates, costs as they were. The build: what was actually implemented, in plain language, including what we chose not to build and why. The investment: indicative cost ranges and timeline, because scenarios without prices are brochures. The return: measured outcomes using the baseline method described in our ROI guide — never vibes. And the honest margins: what went wrong during the project, what we'd do differently, and what didn't deliver as hoped. That last section is the one that matters most. Any vendor can show you a success. Only an honest one shows you the variance.
Three illustrative scenarios
Scenario 1 — Order processing, distribution. A wholesaler taking orders by email in whatever format each customer prefers, with a team keying them in by hand. The fix: extraction plus ERP integration, with human review of exceptions. Investments of this kind sit in the mid five figures; in the scenario we use for planning, the system handles the large majority of orders untouched and the team moves to exceptions and customer relationships. Scenario 2 — Support response, professional services. A firm whose contact form produced replies in days. The fix: grounded AI over their own knowledge base, with clean human handover. In this scenario, time-to-first-response falls from hours to seconds, and after-hours enquiries that were previously lost entirely become a genuine source of new conversations. Scenario 3 — Pilot rescue, light manufacturing. A proof of concept eighteen months old and written off internally. The fix: production hardening against a standard gap checklist, shipped in under three months. Each of these is a composite built from the shape of work we do. We have deliberately attached no client name, no logo, and no percentage we cannot evidence. When we can publish a documented engagement, it will replace one of these scenarios and be labelled as a real case study.
How to read anyone's case studies
Because you'll read many vendors' case studies while choosing a partner, some defense equipment: Ask where the baseline came from. A claimed saving with no stated before-measurement is an estimate wearing a number's clothing. Ask what's excluded from the costs — build only, or subscriptions, maintenance, and internal time too? Ask about the failures; how a vendor discusses projects that underdelivered predicts your own worst-case experience. And check the timescale — results measured in month one, during the honeymoon of attention, decay; ask what month six looked like. We welcome every one of these questions about the work behind these scenarios. They're not hostile due diligence; they're the questions of a buyer who will be a pleasure to work with, because they understand what they're buying.
Your project could anchor this page
A standing offer, and we mean it: for clients willing to be documented — numbers, setbacks, and all, anonymized as much as needed — we offer preferential commercial terms in exchange for the honesty. It's a fair trade. You get measurable results at a better price; future clients get proof instead of promises; and we get the marketing asset money genuinely can't buy: a public record of measured work. If you have a process that frustrates you — something manual, repetitive, and quietly expensive — that frustration is the beginning of a case study. Start with a free consultation, and let's find out whether the numbers justify the story.
Related pages
- AI project failing
- AI implementation ROI guide coming soon
- Services
- Contact
Frequently asked questions
Are these real client case studies?+
No. They are illustrative composites built from the shape of work we do — not named clients and not measured results. We publish a genuine case study only when a client consents and the numbers can be evidenced, and it will be labelled as such.
Why are the companies not named?+
Because they are composites rather than specific clients, so there is no company to name. When a documented engagement is publishable, it replaces a scenario here and carries the client name if they agree to it.
So how do we know you can actually do this?+
Ask us for a reference call. Where a client is willing, we arrange one, and we will say plainly when none is available rather than imply one. You can also judge the work by what we publish: a vendor willing to describe failures is easier to trust than one showing only wins.
If a project of ours underdelivers, will it end up here?+
Only with your agreement — but we will ask, and we are willing to publish the complicated ones. Ask us directly about a project that went badly and we will walk you through what went wrong and what changed as a result.