01 — ASSESS
Score observable capability
Use interviews, artefacts and working practices — not ambition or technology spend.
The strategy
Six lenses.
One honest baseline.
Each capability is scored from 1 to 5 using observable evidence. The six scores are equally weighted because sustainable AI adoption depends on the whole operating system — data, processes, technology, controls and people.
The overall score shows direction. The lowest capability determines the first conversation.
Explore the scoring model
Move each capability to see how the readiness picture changes.
Data Readiness
Quality, access, ownership and usable structure.2 / 5
15
Evidence: defined owners, quality controls, accessible priority datasets.
Process Mapping
Clarity of current work, decisions and exceptions.2 / 5
15
Evidence: documented flows, pain points, hand-offs and exception paths.
Automation
Reliable automation of repeatable work.2 / 5
15
Evidence: stable workflows, monitoring, ownership and exception handling.
Agentic AI
Ability to delegate bounded outcomes to AI agents.1 / 5
15
Evidence: suitable use cases, human checkpoints, evaluation and escalation.
Governance
Proportionate control of risk, data and accountability.2 / 5
15
Evidence: clear policies, risk owners, approval routes and auditability.
People & Skills
Confidence, capability and ownership across the workforce.2 / 5
15
Evidence: role-based learning, change capacity, leadership and adoption measures.
1UnstructuredAd hoc, reactive and largely undocumented.
2EmergingSome foundations exist, but practice is inconsistent.
3DefinedRepeatable approaches are documented and owned.
4IntegratedCapability is embedded, measured and connected.
5AdaptiveContinuously improved and ready to scale safely.
How scoring stays credible
Evidence first.
Judgement second.
02 — CALIBRATE
Challenge the evidence
Agree what is repeatable across the organisation and record why each score was awarded.
03 — PRIORITISE
Act on the constraint
Translate the weakest capabilities into a practical roadmap with owners and measures.
The readiness guardrail
A high average must not hide a critical weakness. Before scaling higher-risk AI, Data Readiness and Governance should both be demonstrably defined, with accountable owners and evidence of consistent practice.
Experience in practice
Built it.
Scaled it.
These case studies show the thinking behind Project Reboot: understand the real problem, simplify the operating model and build capability that lasts beyond the initial delivery.
Finding better ways to work
Different industries and roles established the same habit: observe how work happens, question unnecessary effort and use data or technology to improve it.
- Challenge
- Manual, repetitive work across manufacturing, administration, public-sector analysis and financial services.
- Contribution
- Improved a production process, built business applications and automated analysis and reporting using Access, ASP, JavaScript, VBA and SQL.
Manual effort reducedReporting acceleratedProblem-solving foundations
Lesson: understand the work first. Technology is valuable when it removes a real constraint.
Turning transformation into measurable value
Complex supplier arrangements and inefficient processes were increasing costs and creating frustrating experiences.
- Contribution
- Led change across HR, payroll, telephony, IT services and secure payments, simplified suppliers and supported Azure migrations and system modernisation.
- Solution
- A leaner operating model with stronger governance, simpler supplier relationships and more scalable systems.
£££k savingsThousands of hours savedReduced riskBetter user experience
Lesson: transformation matters when it delivers real business value — focus on outcomes, not outputs.
Scaling a data platform from 15 to 300+ retailers
A fast-growing business was constrained by manual processes, limited infrastructure and systems that could not scale.
- Contribution
- Led the 2017 move to AWS, designed automated ETL and API processes, introduced Jira and agile delivery and built an eight-person development team.
- Solution
- A cloud-first data and marketing platform with automated workflows, scalable pipelines and resilient delivery.
300+ retailersMillions of records daily8 developersCloud-first platform
Lesson: the biggest transformation was building a team capable of delivering more than any individual could.
Helping shape an early Automotive Cloud solution
A customer needed to modernise engagement and operations while adopting an emerging platform with little real-world precedent.
- Contribution
- Connected customer, vehicle, sales and aftersales journeys, shaped processes and operating models and bridged operational, product and technology teams.
- Solution
- A modern Automotive Cloud platform providing clearer customer and vehicle visibility and foundations for Auto360.
Early European implementationApprox. 180 spreadsheets removedScalable foundation
Lesson: successful transformation solves meaningful business problems and brings people with it.
Connecting automotive systems through GetAUTO
Multiple systems needed to exchange information reliably through one platform that could support continued product growth.
- Contribution
- Owned the product direction, defined requirements for leads, compliance and payments and aligned developers, architects and business stakeholders.
- Solution
- Scalable middleware and a structured data model connecting automotive platforms with Salesforce.
Improved integrationsLess manual movementReusable architectureGrowth enabled
Lesson: release something usable, prove the concept and evolve it through real-world learning.
Building the capability to deliver repeatedly
Auto360 and Marketing360 had no established implementation methodology or dedicated delivery capability.
- Contribution
- Led the first implementation, created project plans, templates and Jira assets, established governance and built the Professional Services team.
- Solution
- A repeatable delivery framework enabling consistent, efficient customer implementations and continuity beyond one person.
First Auto360 rolloutProfessional Services builtDelivery standardisedReusable framework
Lesson: great products need great delivery so customers can realise value consistently and repeatedly.
Start with a conversation
Still curious?
Every business is different. Tell me what you’re trying to improve and we’ll explore where an AI Readiness Assessment could add practical value.
No obligation and no sales pitch — just a useful first conversation about your goals, constraints and opportunities.