AI Services Case Study — Performing an AI Capability and Maturity Assessment
- Jan 29
- 5 min read
Updated: 3 days ago
1. Organisational Problem
As interest in artificial intelligence continues to grow, many leadership teams recognise the potential of AI but lack a clear understanding of their organisation's capability and maturity to adopt it effectively.
This case study describes a situation faced by a UK-based professional services firm that wanted to understand its organisational readiness for AI before committing to further investment or large-scale initiatives.
In the Orr Consulting AI Transformation Process, this case study demonstrates the Discover-stage role of an AI Capability and Maturity Assessment in helping organisations establish an evidence-based baseline before progressing to further AI investment, strategy or delivery.
2. Situation
The firm operated in a professional services environment with an established digital presence and a strong focus on client delivery.
The board recognised that AI was becoming an increasingly important topic across the professional services landscape. New tools and capabilities were emerging rapidly, and the firm had begun considering how artificial intelligence might affect its future operating model, service development and internal ways of working.
However, leadership discussions quickly revealed an important question:
"How ready are we as an organisation to adopt AI responsibly and effectively?"
The board and senior leadership team also held different assumptions about the organisation’s readiness. Some believed the firm was already well positioned due to its strong data and digital environment. Others were more cautious, highlighting potential challenges around governance, skills and organisational understanding of AI.
Before committing to strategy development or new initiatives, the board wanted a clearer, evidence-based view of the organisation’s current AI capability and maturity.
The organisation therefore engaged Orr Consulting to establish an objective baseline, identify priority capability gaps and provide a clearer basis for future investment and transformation decisions.
3. Background
Like many professional services organisations, the firm had already encountered AI in several ways.
Generative AI tools were beginning to appear in day-to-day professional work. AI-assisted drafting, analysis and research capabilities were becoming increasingly visible across the professional services sector. At the same time, many commonly used software platforms were beginning to introduce AI-enabled features such as predictive analytics and workflow automation.
While these developments were promising, they were occurring without a clear view of:
organisational capability to adopt AI safely
leadership understanding of AI opportunities and risks
governance arrangements for responsible AI use
readiness across data, people and delivery capabilities
The board therefore commissioned an AI Capability and Maturity Assessment to provide an objective baseline before further decisions were taken.
4. Action Taken
Orr Consulting was appointed by the senior team to conduct a structured AI Capability and Maturity Assessment using the five AI capability pillars defined within the Orr Consulting AI Transformation Process.
The assessment took place over a two-week period and examined organisational readiness across the following capability pillars:
Functional / Technical Capability
Education and Training Capability
Governance and Assurance Capability
Data Readiness Capability
Strategy and Culture
Evidence was gathered through leadership questionnaires and discussions, review of existing processes and examination of current digital and governance arrangements.
Each capability pillar was assessed using a 0–5 maturity scale, allowing the organisation to establish a consistent baseline across all five dimensions.
0 — Informal — Little to no formal capability exists.
1 — Incidental — Very limited capability exists. 2 — Limited — Capability exists in pockets but remains immature. 3 — Emerging — Capability is functioning and repeatable in defined areas. 4 — Leading — Capability is strong, well-governed and consistently applied.
5 — Best Practice — Capability is mature, embedded and continuously improving.
The objective was not to produce a theoretical score. It was to develop a practical, shared and evidence-based understanding of current capability across the organisation.
The assessment was also designed to identify relative strengths, priority gaps and the actions required to improve readiness for future AI adoption.
5. Outcomes
The assessment created several notable outcomes, observations and lessons learned.
5.1 Evidence-Based Baseline
The assessment established a consistent baseline across the five organisational capability pillars:
Functional / Technical Capability — Limited (2)
Education and Training Capability — Limited (2)
Governance and Assurance Capability — Incidental (1)
Data Readiness Capability — Emerging (3)
Strategy and Culture — Limited (2)
The results were visualised through a capability radar and presented to the board.
The organisation’s overall AI Maturity Score was 10 out of 25, placing it in the Limited Capability bracket within the Orr Consulting AI Capability and Maturity model.
The radar indicated that while elements of AI capability were beginning to emerge, the organisation was still at an early stage of structured adoption.
5.2 Relative Strengths Identified
Data readiness represented a relative organisational strength, reflecting an established digital environment and access to structured organisational data.
This gave the board greater confidence that some of the foundations required for future AI adoption were already in place.
5.3 Priority Gaps Clarified
Governance and assurance capability was the least developed area, highlighting immediate risks requiring attention.
Functional / technical capability, education and training, and strategy and culture were also assessed as developing but not yet mature.
This gave leadership a clearer understanding of where capability improvement should be prioritised before AI was adopted at greater scale.
5.4 Leadership Alignment Improved
The assessment replaced differing assumptions about organisational readiness with a shared evidence-based view.
This improved alignment across the board and senior leadership team regarding current capability, material risks and the actions required before further investment.
5.5 Clear Improvement Priorities
The assessment identified several immediate priorities:
Strengthening AI Governance and Assurance arrangements
Building shared organisational understanding through AI Education and Training
Progressing structured AI Use Case Discovery
Preparing for subsequent AI Strategy and Roadmap development
This gave the organisation a sequenced and proportionate path for improving maturity rather than attempting to address every capability area at once.
5.6 Informed Decision Making
The board gained a clearer basis for deciding where additional capability development, governance activity and investment were required.
The assessment reduced the risk of progressing into strategy or delivery on the basis of assumptions about readiness.
A structured AI Benefits Realisation approach could then be used to define how capability improvements, risk reduction and stronger investment discipline would be evidenced over time.
5.7 Baseline Before Scale
The assessment reinforced an important lesson: organisations should establish a clear and shared view of current capability before attempting to scale AI adoption.
A maturity score did not provide the answer on its own. Its value came from identifying where the organisation was ready, where it was exposed and what needed to improve next.
Without this baseline, the organisation risked progressing into AI strategy, investment or delivery with untested assumptions about readiness, overlooked governance gaps and insufficient capability to support adoption at scale.
6. Final Thoughts
Many organisations recognise the potential of AI but remain uncertain about how prepared they are to adopt it effectively.
An AI Capability and Maturity Assessment provides a structured way to replace assumptions with evidence.
By establishing a clear baseline across technical capability, education and training, governance and assurance, data readiness, and strategy and culture, leadership teams can identify relative strengths, prioritise material gaps and plan improvement more effectively.
In this case, the most important outcome was not the maturity score itself. It was the shared evidence-based understanding that enabled the board to agree where capability was strongest, where risk was greatest and what should happen next.
This AI Services Case Study is part of the Orr Consulting AI Insights Library — structured thinking for AI transformation leaders and decision makers.
If your organisation would benefit from a clearer understanding of its current AI capability, maturity and readiness, we would be pleased to discuss your next AI steps.
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