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Case Studies

Real-world examples of AI productivity transformation. Each engagement below is recorded verbatim: the brief, the diagnostic, the call, and what the client did next.

01Wealth Management

When the Firm Is Already Building It, Quietly and Correctly

ClientUK wealth management partner practice within a national network of approximately 4,500 advisers
ParticipantsPrincipal and Investment Adviser, representing a team of ten (five advisers, three paraplanners, one admin, one operations coordinator)
LocationUnited Kingdom

Challenge

The practice operates inside a tightly regulated environment: closed-model toolset only, national-network compliance sign-off on all client-facing output, and a mandated upload of documentation to the national CRM surface. Within those constraints, the Principal had built something quietly unusual. The paraplanning team was running seven operational agents on the approved closed-model stack, handling PDF extraction, letter-of-authority chase, annual review preparation, and adviser-voice email drafting. A compliance pre-check agent was four weeks from go-live.

The Principal sat at Level 201 with emerging 301. The paraplanners at 201. The adviser cohort at 101, by deliberate sequencing, not drift. It was well-governed AI adoption. What it did not yet have was a named plan for what comes next.

Solution

Matthew Thistle delivered an AI Business MOT: a sixty-minute in-person diagnostic scoped entirely inside the national-network compliance envelope.

The session covered:

  • Ten targeted questions about workflows, team structure, approved toolset, and build pipeline
  • Analysis against the four-tier AI adoption framework (101 to 401), mapped cohort by cohort
  • Inventory of the existing agent stack: live, in-build, and still-manual
  • Blocker diagnosis across skills, permission, and workflow categories

Results

The report surfaced an atypical profile. Where most SMEs present with a dominant skills-gap blocker, this practice did not. The skills position was partial by design. The binding constraints were permission-shaped and workflow-shaped, both already managed rather than unresolved.

The diagnostic identified two high-leverage opportunities. First, a six-week adviser adoption pathway covering three defined use cases only: meeting debrief, client-voice email, and post-meeting action capture. This comes before paraplanner-led throughput makes the adviser layer the next capacity ceiling. Depth over breadth.

Second, protective instrumentation for the compliance pre-check agent before go-live: a lightweight rework-request log to baseline the four-week build window, and a trace-rendering agent that auto-generates an external-facing document from the pre-check agent's own operation. The log proves the value; the trace answers the question the regulator or network might eventually ask.

"I think we are a long way ahead. I know what our peers do, and they are not using anything."Principal, during the diagnostic
Most AI diagnostics tell a firm what to buy.

This one told a firm what it had already built, and what it needed to do next to protect it.

The 301 Insight

The most consequential finding was not a recommendation to adopt new tooling. It was naming what the practice had already built, and sequencing the finish. Most firms at this stage are told what to deploy. This one needed the opposite: a structured account of what it had done correctly, and a protective plan for the single build that mattered most.

Diagnostic Highlights

Service TypeAI Business MOT (60-minute in-person diagnostic)
ParticipantsPrincipal and Investment Adviser, representing a ten-person team
Primary ChallengesStrong existing AI adoption with no named plan for adviser-cohort transition; single highest-value agent build approaching go-live without protective instrumentation; regulated environment constraining tool choice and documentation flow.
Key FindingsPrincipal at Level 201 with emerging 301; paraplanning team at 201; adviser cohort at 101 by deliberate sequencing; ten-item AI stack inventory (seven live, three in build); atypical blocker profile with skills partial and compliance dominant.
OutcomeTen-page diagnostic report delivered within hours of the session. Two prioritised opportunities, a ninety-day sequence, and four follow-on engagement options presented to the Principal.
02Fintech

When Leadership Gets It but the Team Is Not There Yet

ClientConstruction Fintech: Automated Payment Platform
ParticipantsCEO + 6 team members
LocationBirmingham, UK

Challenge

A construction fintech startup had built an elegant product: a biometrically verified, ring-fenced payment platform for high-value construction supply chains. The leadership team was already using AI tools with real sophistication. They had a working compliance FAQ built in NotebookLM. Their core payment workflows used structured exception handling that reflected advanced architectural thinking. They were not afraid of AI. They were hungry for it.

But the operational team had not been brought along. The gap between what leadership understood and what the wider team could specify, build, or maintain was the company's most consequential bottleneck. And with only one live customer project and FCA regulation on the horizon, the window to get the whole team aligned was narrowing.

Solution

Matthew Thistle delivered an AI Business MOT: a strategic diagnostic session covering the full scope of the company's operations, AI maturity, data protection posture, and growth trajectory.

The session covered:

  • 10 targeted questions about current workflows, team structure, and operational constraints
  • Analysis against the four-tier AI adoption framework (101 to 401)
  • Assessment of data protection alignment for a regulated financial services environment
  • Identification of the first viable agentic deployment opportunity

The diagnostic revealed something unusual. Unlike most businesses that sit at a single adoption level, this company operated across multiple levels simultaneously. Leadership was at Level 201 with emerging 301 capability. Operations remained at Level 101. That split is the bottleneck.

Results

The AI Business MOT report identified five high-impact processes and mapped each to an Automation Priority Matrix. The most significant finding was the contextual briefing use case: when a team member is unavailable and a colleague must step in on a million-pound transaction, there is no rapid briefing mechanism. This was identified as the highest-leverage internal use case and a strong candidate for the company's first agentic system deployment.

The report also established four governing principles for AI deployment within the company's regulated data environment, preserving the existing Google Workspace containment boundary while creating clear rules for when and how agentic systems can take external action.

A critical insight from the diagnostic: the only way the first go-live AI project succeeds is if the operational team provides 90% of the input. If leadership guesses what operations needs, they will build the wrong thing. The recommended engagement is structured around that principle.

The report has been shared with the full leadership team for review, with a follow-up conversation to determine next steps.

5

high-impact processes mapped to an Automation Priority Matrix. Leadership sitting at 201/301. Operations sitting at 101. The split was the bottleneck.

The 301 Insight

This company does not need to be convinced that AI matters. It needs a structured pathway that brings the operational team to the same level as leadership, so the first go-live project is selected by the people who will use it. The diagnostic surfaced that the real risk is not inaction but misalignment: leadership building what they think operations needs, rather than what operations actually requires.

Diagnostic Highlights

Service TypeAI Business MOT (Strategic Diagnostic)
ParticipantsCEO and leadership team
Primary ChallengesSkills gap between leadership AI ambition and operational team capability; no structured pathway from experimentation to embedded integration; regulated environment requiring explicit data governance before AI deployment; scaling pressure with only one live customer.
Key FindingsMulti-level AI maturity: leadership at 201/301, operations at 101; five high-impact processes mapped to Automation Priority Matrix; contextual briefing identified as highest-leverage use case and first agentic deployment candidate; four data protection principles established for regulated AI deployment.
OutcomeStrategic diagnostic report delivered to leadership team. Next step: bespoke team alignment session combining shared competence development with collective first-project selection.
03Trade

When the Real Bottleneck Is Not What You Expect

ClientElectrical, Gas, Plumbing & Compliance Services
ParticipantsDirector + 13 employees
LocationGloucestershire, UK

Challenge

A well-established trade services company managing approximately 4,000 properties was cycling through 400 to 500 jobs per month with 150 open at any given time. The director knew the business needed to modernise but assumed the scaling constraint was engineering capacity. Every process was built around manual systems: paper-based job tracking, hand-assembled invoices, a spreadsheet for gas safety renewals, and an office team absorbing the friction of duplicated data entry across every stage of the job lifecycle.

The director had a clear growth ambition toward 20 staff. But one question in the diagnostic changed the entire conversation.

Solution

Matthew Thistle delivered an AI Business MOT: a structured 45-minute diagnostic designed to map where the business sits on the AI adoption curve and identify the highest-impact opportunities.

The session covered:

  • 10 targeted questions about current workflows, team structure, and operational bottlenecks
  • Analysis against the four-tier AI adoption framework (101 to 401)
  • A direct question: "If 200 new landlord properties arrived tomorrow, what breaks first?"

The director's answer was immediate: not the engineers. The admin.

That single answer reframed the entire diagnostic. The constraint on growth was not technical capacity but administrative throughput.

Results

The AI Business MOT report identified five high-impact processes, with invoicing and job completion as the number one priority. The director confirmed this was the change he would make if he could wave a magic wand. The report mapped each process to an Automation Priority Matrix showing what to automate now, what requires human oversight, and what to leave alone.

Critically, the diagnostic also identified that a team member had been building a bespoke internal job management system over the past 18 months. The recommendations were designed to complement that initiative, not compete with it. This is the kind of nuance a generic AI consultation would miss entirely.

The director is now progressing through the recommended development pathway, with a team-wide AI productivity workshop as the next step.

The office, not the engineers.

The one-line reframing that changed every subsequent investment decision.

The 201 Insight

The most valuable finding was not a technology recommendation. It was the reframing of where the actual constraint sits. The director believed the business needed more engineers to grow. The diagnostic revealed that the office team would break first. That distinction changes every subsequent decision about where to invest.

Diagnostic Highlights

Service TypeAI Business MOT (45-minute diagnostic)
InvestmentCredited toward any follow-on engagement
Primary Challenges400 to 500 jobs per month through manual systems; office team absorbing duplicated data entry across the job lifecycle; growth ambition constrained by admin throughput, not engineering capacity.
Key FindingsBusiness operating at Level 101 (individual experimentation, no structured integration); administrative bottleneck identified as the primary scaling constraint; five high-impact processes mapped to Automation Priority Matrix; internal system build already underway, requiring complementary recommendations.
OutcomeClear, prioritised report delivered generally within 90 minutes of the session. Director progressing toward team-wide AI productivity workshop.
04Automotive

When the Diagnostic Reveals the Real Problem

ClientAutomotive Services
ParticipantsDirector + 2 employees
LocationGloucestershire, UK

Challenge

The director of a busy automotive services business runs an operation with two employees manually processing a high volume of vehicles. Every aspect of their workflow is paper-based. He recognised that to scale significantly, something fundamental needed to change. He had heard about AI but had no clear picture of where it could fit or what the first step should look like.

Solution

After connecting at a local business networking group, the director and Matthew had an initial conversation about his operations. When the discussion moved into detailed workflow territory, Matthew proposed the AI Business MOT: a structured 45-minute diagnostic designed to map where a business sits on the AI adoption curve and identify the highest-impact opportunities.

The director's response was immediate. He booked on the spot.

The diagnostic included:

  • A 10-question structured interview covering current workflows, pain points, and operational bottlenecks
  • Analysis against the four-tier AI adoption framework (101 to 401)
  • Identification of manual processes consuming the most staff time
  • A clear, prioritised report delivered generally within 90 minutes of the session

Results

The AI Business MOT report identified significant time savings available through automation of the paper-based processes. The report recommended a productivity workshop as the logical next step.

However, the conversation that followed revealed something more important than any single recommendation. The business runs on well-established manual processes, and introducing AI training without first understanding how those processes work in practice would risk solving the wrong problem.

Matthew recommended pausing before committing to a workshop and instead taking time to identify exactly which bottleneck, if automated, would unlock the most capacity. Sometimes the most valuable outcome of a diagnostic is knowing what not to do next. The director is now considering his options, including the possibility of a turnkey automation that solves his scaling bottleneck without disrupting existing workflows.

Not every business needs AI training.

The honest finding of the diagnostic.

The Lesson

Not every business needs AI training. Some need a solution. Some need time. The AI Business MOT is designed to surface the truth about where a business actually stands, not to sell the next engagement. In this case, the most valuable thing the diagnostic produced was clarity about what not to do next.

Diagnostic Highlights

Service TypeAI Business MOT (45-minute diagnostic)
InvestmentCredited toward any follow-on work.
Primary ChallengesEntirely paper-based workflows; high-volume vehicle processing by a small team; scaling ambition blocked by manual processes.
Key FindingsBusiness operating at 101 level (tool awareness); significant automation potential in core workflow; organisational readiness is the primary consideration, not skills or budget.
OutcomeHonest recommendation: understand existing processes before committing to training.
The 201 InsightThe diagnostic revealed that the real question is organisational, not technical. The right next step is a conversation, not a course.
05Software

A Software Developer's "Eye-Opening" AI Workshop

ClientBespoke Software Development
ParticipantsDirector + 1 team member
LocationGloucestershire, UK

Challenge

The director of a small bespoke software development firm knew AI was reshaping his industry but had not found a structured way to integrate the latest models into his daily workflow. Like many technically capable professionals, he had experimented on his own but lacked a clear framework for identifying where AI could deliver the most value across his operations.

Solution

Matthew Thistle delivered a hands-on AI Productivity Workshop for the director and his team member, covering:

  • Task identification methodology applied directly to their existing software development workflow
  • Live demonstration of agentic AI tools and how they integrate into a developer's daily process
  • The AIMED framework for structuring AI interactions to produce reliable, repeatable outputs
  • Practical exercises mapping their current workload to identify the highest-impact automation opportunities

Results

The workshop produced an immediate shift in how the director approaches his work. Within days, he had adopted new AI development tools into his workflow and began implementing techniques covered in the session.

The impact was significant enough that he independently referred Matthew to a major UK hosting provider, writing in his referral email:

"He is a real AI expert and is bang up to date on the very latest models and how to get the most from these. The workshop really opened my eyes and we have begun to implement some of the aspects covered."Software Development Director

He also provided an unsolicited public referral at a local business networking group, recommending the workshop to fellow business owners.

Eye-opening.

Two unsolicited referrals inside a week. One by email to a major UK hosting provider. One in person at a local business networking group.

Long-term Engagement

The director is now exploring ongoing AI enablement support to help him scale his consultancy offering, with a specific goal of increasing his revenue through AI-enhanced service delivery. He continues to adopt tools introduced in the workshop and is progressing through the adoption framework at his own pace.

Workshop Highlights

Workshop TypeAI Productivity Workshop (half-day)
Participants2 (Director + team member)
Primary ChallengesNo structured AI adoption framework; awareness of AI potential but no clear integration path; needed practical, developer-relevant guidance.
Key OutcomesImmediate adoption of AI development tools; unsolicited referral to a major UK hosting provider; public referral at local business networking group; ongoing engagement for AI enablement support.
ValidationUnsolicited third-party referral email describing workshop as "eye-opening".
06Education

Bristol University Workshop: AI Productivity Implementation

ClientUniversity of Bristol
EngagementFaculty AI Productivity Workshop
LocationUnited Kingdom

Challenge

Bristol University Business School faculty sought to implement AI productivity tools to enhance their research output and teaching effectiveness. The primary challenge was helping faculty members identify concrete automation opportunities within their existing workflows and providing them with practical skills to implement AI solutions effectively.

Solution

Matthew Thistle delivered a comprehensive workshop introducing the AIMED framework for AI productivity, focusing on:

  • Task identification methodology for academic research and teaching contexts
  • Practical prompt engineering techniques for academic content development
  • Workflow integration strategies for research and administrative tasks
  • Hands-on automation task identification exercises

Results

The workshop successfully engaged Business School faculty and students in practical AI productivity techniques. Participants were actively involved in hands-on exercises to identify automation opportunities within their academic workflows. The session demonstrated clear applications of the AIMED framework to real-world academic challenges and provided participants with actionable strategies for immediate implementation.

Commissioned. Then commissioned again.

The Business School's follow-on request.

Bristol Workshop Highlights

Workshop TypeAI Productivity Implementation Workshop
ParticipantsBusiness School faculty and students
Key Focus AreasTask identification methodology; hands-on automation exercises; practical prompt engineering.
Key OutcomesActive participant engagement; practical automation identification; framework implementation understanding.
07Education

KFUPM Workshop: AI Productivity Transformation

ClientKing Fahd University of Petroleum and Minerals
EngagementFaculty AI Productivity Workshop
LocationSaudi Arabia

Challenge

KFUPM faculty members were struggling to effectively incorporate AI tools into their teaching and research workflows. Despite having access to advanced AI platforms, many were experiencing inconsistent results, wasting significant time on trial and error, and lacking a systematic approach to AI implementation.

Solution

Matthew Thistle delivered a comprehensive workshop introducing the AIMED framework for AI productivity. The session focused on:

  • Task identification methodology specifically tailored to academic contexts
  • Prompt engineering techniques for research and educational content development
  • Workflow integration strategies for faculty administrative tasks
  • Introduction to the AutoGrader tool for assessment efficiency

Results

The workshop was met with exceptional enthusiasm from faculty members, who consistently praised both the content delivery and practical applicability of the AIMED framework. Participants immediately recognized the value of structured AI integration approaches and expressed strong interest in continued learning opportunities.

A workshop became a course.

KFUPM's first prompt engineering course, designed by the speaker they invited for a two-hour session.

Feedback Highlights

"Best event I have attended in 20 years! Excellent speaker — he should make a university course to be taken by students instead of COE 292"Faculty Member
"The subject was very relevant. The session was very engaging. We need more talks from the same speaker about advanced use of LLMs."Education Department
"It was an eye opener workshop to me. The speaker did a wonderful job. I do wish we could have had more time!"Faculty Member
"Very good, very interesting and relevant to current developments. Thank you for the excellent presentation."Academic Development

Long-term Impact

Following the workshop's success, Matthew Thistle was appointed as an AI Productivity Consultant to KFUPM and tasked with formulating the university's first course on prompt engineering. He is currently delivering an evolved version of this workshop to all leaders of KFUPM through the Department of Academic Development. Additionally, negotiations are ongoing for a benchmarking contract to evaluate the AutoGrader tool's potential implementation at the university.

Case Study Highlights

Workshop Type2-hour AI Productivity Mastery
Participants35 faculty members across departments
Primary ChallengesInconsistent AI results; inefficient workflows; lack of structured approach.
Key OutcomesImproved AI confidence; successful workflow implementation; productivity gains across departments.
Follow-up InitiativesAI Productivity Consultant appointment; first prompt engineering course development; leadership workshop rollout; AutoGrader benchmarking negotiations.
08Claims

The Denied Boarding Claim

Why High Cognitive Load Tasks Defeat Knowledge Workers

The Scenario

A passenger is denied boarding on an international flight. They have paid for a ticket, extras, and insurance. The total recoverable amount is between £200 and £920. This is not a complex legal matter. It is not intellectually difficult. Yet the passenger delayed filing for 30 days and nearly abandoned the claim entirely, losing money they were legally entitled to.

Not because they lacked intelligence. Because the task is not one task. It is a chain of 15+ subtasks spanning 6+ platforms, multiple logins, file formats, legal frameworks, and communication channels, all with no single interface to manage them.

Cognitive load, not complexity.

15+ subtasks across 6+ platforms. Most people abandon, not because the task is hard, but because it is diffuse.

Why People Abandon Workflows Like This

  • Context switching cost: the task spans email, phone camera, image editor, web search, word processor, airline portal, and calendar. Each platform switch carries a cognitive reset penalty.
  • No single interface: there is no dashboard or system managing this end to end. The human brain is the project manager, and it has other projects.
  • Ambiguity at every step: which law applies? Which entity to claim against? What document type to select? Each ambiguity is a micro-decision that consumes willpower.
  • Emotional friction: the event itself was stressful. Revisiting it for administrative purposes reactivates that stress.
  • No immediate reward: the money arrives weeks or months later. The effort is required now.

The 201 Lesson

Of the 15+ subtasks in this workflow, current AI can fully handle roughly half: legal research, drafting formal letters, extracting data from emails, resizing documents, and setting reminders. It partially handles portal navigation and form filling. It cannot perform physical actions or authenticate on your behalf.

The effective operator does not try to do everything manually or trust AI with everything. They delegate the high cognitive load research and writing to AI, then execute the portal submissions and physical steps themselves. That judgment, knowing which parts to delegate and which to keep, is the 201 skill most people are missing.

Case Study Highlights

Workflow TypeMulti-platform administrative claim
Subtasks Identified15+
Platforms Involved6+
AI Capability (2026)Full on 6 tasks, partial on 3, human-only on 3
Claim Value£200 to £920
Time Delayed30 days
Root CauseCognitive load, not complexity
Key InsightThe 201 skill is knowing which parts to delegate and which to keep.

This is the kind of workflow I map in the Diagnostic Audit. Two weeks, fixed scope, a written deliverable your leadership can act on. If you want a shorter view first, the AI Business MOT is a single-session diagnostic that feeds into the Audit.

Scope a Diagnostic AuditBook the MOT