Hands-on AI training and rollout for professional teams in any sector: finance, property, investment, legal and operations. Taught by a founder with three exits behind him, including a NASDAQ listing, who builds these systems every day. Most AI training is commentary. This is testimony.
Accountancy, advisory and finance teams, with client confidentiality and sign-off designed in.
See this sectorLandlords, estates and planning teams, from conditions to completed works.
See this sectorFunds and their portfolio companies, from private-market diligence to public-market communication.
See this sectorBring a workflow. If it runs on documents and judgement, the same method applies.
See how it worksBuilt on your own records during the session. A messy schedule, a report or a pipeline becomes something that runs, by the end of the day.
Per-user model and spend limits, existing permissions respected, your data kept in your environment.
A repeatable method: plan first, stack the tasks, check the output. Used in every session, not improvised.
A verdict on every common use case, each marked proven now, needs guardrails or approach with care.
Every business is buying AI. Very few are deploying it well. The failures are setup failures, not model failures: tools bought before strategy, usage with no cost controls, pilots that never leave the innovation team, and output nobody would put their name to. They are avoidable, and they show up differently by function.
Client and company numbers pasted into tools nobody approved, and drafts no partner will put a name to.
Consents, leases and enquiries managed in spreadsheets while AI pilots stall in the innovation team.
No view of who is using what, and no answer when a buyer or an investor asks about your AI capability.
Most AI training is a slide deck about a topic. This is a small, repeatable method for turning a real piece of your work into a system that keeps running after the room empties. It has a name because it is used the same way every time, not reinvented per client.
Quality of output equals quality of input. A vague prompt gets a generic answer, the same way a new starter given no context on day one is useless. Fifteen minutes of proper briefing, and they fly. A model is exactly the same.
An AI assistant that does the work, a voice tool that captures every call and dictated brief, and a live connection to wherever your records already sit. Dictate a request, the assistant acts on it and saves the result, a call gets captured automatically, you ask for the transcript to update the tracker. Repeat.
Before anything non-trivial, the instruction is simple: show the plan first, ask any clarifying questions, don't start the work yet. It catches a misunderstanding in thirty seconds instead of after the work is already done.
"Before you do anything, show me the plan and ask me any clarifying questions."Most people send one small instruction at a time. Bundling several into one instruction is where the real time saving shows up: one request that consolidates, flags and lists, rather than three separate ones run back to back.
Ten people in a room rarely have one shared problem. After a shared foundation hour, the room splits into tracks by real day-to-day priority, each building a genuine working system against their own work, then reconvening to show each other what they made.
A system a team built once and never touched again was never really adopted. Every rollout carries a fixed period of direct follow-up afterwards, for the questions that only surface once people are using it on their own work.
Each of these was built by the client's own team, on their own data, in a single session, and was still running weeks later. Clients and properties are withheld by agreement.
A manually tracked set of records became a single schedule with risk dates flagged automatically.
A list of sectors became a scored, prioritised outreach pipeline with first-contact messages drafted for review.
Conditions on two consents became one sequenced method statement, and the works were completed.
Most AI training comes from people who have never shipped an AI product. Every area below is taught from systems I have built, deployed or certified.
What agents reliably do now, versus what vendors claim, and where people must stay in the loop. Taught from agentic systems I run in production today, from AI-led lead qualification to agentic CRM.
Where they fail and what that means for your use case and your budget. Taught by someone who has built and deployed large language models across autonomous vehicles, legal and public markets.
Hands-on: how to brief a model like a colleague. Tone-of-voice files, guardrails, reusable skills and context design, built on your own work so your team uses it the next morning rather than filing it away.
AI is only as good as the data underneath it. The pattern for making data trusted and usable is teachable, and I have built it three times, on three different kinds of data.
Controls that satisfy your IT vendor and your clients: permissions, data handling, provider risk and regulation. Taught by someone who built and certified global data platforms to ISO 27001, Cyber Essentials Plus and IASME Gold.
Producing on-brand written and visual content that does not read like slop: voice files, review routines, and an honest view of where AI video is ready and where it is not.
Where defensible value sits in an AI business, how to tell a genuine technical moat from a wrapper, and when to file. A dated, filed application is a better signal of technical seriousness than a claims page, and the same discipline applies to your AI investments.
Every list of AI use cases circulating on social media promises that all of it works. Not all of it does. Here is the same territory with verdicts attached, from someone who builds and runs these systems. Sessions and advisory engagements prioritise this map against your business: which of these pays back first for you, which needs guardrails, and which to leave alone this year.
| Professional services, finance and property | ||
| Contract and lease schedules | Extract dates, rent reviews and obligations from documents into a working schedule built from your own records. | Proven now |
| Planning and consent conditions | Turn conditions into a register, a programme and a drafted method statement, checked against the exact wording. | Proven now |
| Research memos and briefings | First drafts from your own documents and public sources, with every claim traceable to its source. | Proven now |
| Management accounts commentary | First-draft narrative from the live numbers. Every figure is traced to its source before it leaves the building. | Needs guardrails |
| Due diligence and data room review | Query a data room instead of reading it page by page. Findings still need a professional to confirm. | Needs guardrails |
| Client onboarding and file review | Check documents against a checklist and flag gaps for the reviewing partner. | Needs guardrails |
| Tax positions and audit judgement | Research and drafting support only. The judgement and the sign-off stay with the qualified professional. | Approach with care |
| Operations and automation | ||
| Email and workflow automation | Triage, drafting and multi-step processes across your existing stack. | Proven now |
| Meeting capture and recap | Transcription to actions, briefings and CRM entries without anyone typing notes. | Proven now |
| CRM enrichment and lead qualification | Scoring, enrichment and AI-led qualification. I run this in production. | Proven now |
| Document and data processing | Invoices, extraction, reconciliation, with human sign-off where money moves. | Proven now |
| Proposal and report generation | Drafting in your house voice from your inputs. The guardrail is voice and fact control. | Needs guardrails |
| Customer support assistants | First-line response works. The guardrail is escalation design: knowing what the system must not answer. | Needs guardrails |
| Recruitment screening | Technically feasible, legally exposed: a high-risk category under the EU AI Act. Get advice before you deploy. | Approach with care |
| Data and decision support | ||
| Market and competitive intelligence | Monitoring, synthesis and briefing at a daily cadence no analyst team can match. | Proven now |
| Sales analytics and dashboards | Live reporting with AI-flagged trends, replacing stale decks. | Proven now |
| Customer sentiment analysis | Reviews, tickets and social, read at scale and turned into decisions. | Proven now |
| Churn prediction and demand forecasting | Only ever as good as the data underneath it. Fix provenance first, then predict. | Needs guardrails |
| Pricing and financial forecasting | Strong decision support. The judgement, and the accountability, stays human. | Needs guardrails |
| Content and growth | ||
| Creative variants at scale | Dozens of tested, rotated variants a week where one hero asset used to run for months. | Proven now |
| Personalised outreach at scale | Specific beats generic on reply rates. Personalisation is the work; AI is the tool that makes it affordable. | Proven now |
| Brand-coherent AI video | Holding character, voice and look consistent across a production run. Possible, but only with real controls. Without them you are making slop. | Needs guardrails |
Every common AI use case, rated proven now, needs guardrails or approach with care. One page, yours to keep.
30 minutes. You tell me the decisions your team faces. I tell you straight whether a session is worth your money, and propose a scope if it is. No obligation.
On site or remote, maximum 10 attendees so everyone works and nobody hides. Live demonstrations on current tools, applied to your business.
A written summary: what was covered, what to do next, what to ignore. Then two weeks of direct email access as your team puts it to work.
| Engagement | Format | Includes |
|---|---|---|
| Discovery call | 30 minutes, remote. Free, no obligation | Your challenges mapped, quick wins identified, a proposed scope, a straight answer on whether to book. |
| Half-day training | Four hours, on site or remote, max 10 | Your team leaves knowing what to do: strategy, live demonstrations, hands-on prompt and context engineering, written summary, two weeks of follow-up support. Nothing installed. |
| Full-day training | Full day, on site or remote, max 10 | Everything in the half-day, plus hands-on build time, one follow-up session and 30 days of support. |
| Team rollout | Four-hour setup and training visit, then phased | Your team leaves running AI: workspace and folder structure built, tone-of-voice and guardrail files for each user, per-user model and spend limits set, permissions coordinated with your IT vendor. AI usage is paid to the provider at cost, never marked up. Check-ins billed for time spent. |
| Ongoing advisory | Monthly | AI readiness assessment, vendor evaluation, ROI and pilot planning, governance frameworks, monthly strategy calls, priority access, quarterly board updates. |
| Forward-deployed engineering | Multi-week, embedded in your team | Embedded delivery inside your systems and team: agents, workflows and content pipelines built to production, controls and handover included. Delivered personally or with vetted engineers under my oversight. |
The distinction from training is the deliverable. A training session leaves your team knowing what to do; a rollout leaves them doing it. Same length of visit, different outcome: workspace structure, a tone-of-voice file so output sounds like you, guardrail files for each user, and hands-on training against your actual documents and systems, all live when I leave the building.
Two costs, kept separate. My fee covers the work. AI usage is a third-party cost you pay directly to the model provider. It is never billed through me and never marked up, and per-user limits are set from day one so it stays predictable. If an adviser earns on your usage, question the advice.
Start with six to eight users under tight controls, rather than opening access company-wide on day one.
A few weeks of real use, then reconvene on your team's specific questions rather than a second generic training session.
Widen access once the first group is comfortable and the controls are proven in practice.
The discovery call is 30 minutes, free, and carries no obligation. If a session is not the right answer for you, I will say so on the call.
Or use the form for anything else. I reply within two working days with a proposed scope and available dates.
Prefer email? richard@outrun.ai
LinkedIn: Richard Barlow