ILLUSTRATIVE · Operational Intelligence Audit™ · Deliverable 01 of 07
Vital SignsOPERATIONAL AI MATURITY SCORECARD
Assessing current operational maturity across bid, compliance, commercial, reporting, systems and governance domains to establish the baseline for AI workflow implementation.
Hartwell M&E Group's aggregate AI maturity score across all six operational domains, benchmarked against a five-tier maturity scale.
2.4
Aggregate maturity: 2.4 / 5.0
Band: Reactive — AI usage is informal, unstructured and inconsistent across the business. Individual tools are used but without governance, workflow integration or measurable outcomes.
1.0–1.9 Ad Hoc
2.0–2.9 Reactive
3.0–3.4 Structured
3.5–4.4 Managed
4.5–5.0 Optimised
What this means. Hartwell is typical of a mid-market M&E contractor at this stage. Individual team members use AI tools informally (ChatGPT for drafting, email summaries, ad hoc document help) but there is no consistent workflow, no governance framework, no data classification and no measurable improvement programme. The opportunity is significant because the business has the operational complexity, document volumes and commercial processes where structured AI workflows can deliver measurable value.
Section 02
Domain Scores
Maturity scores across six operational domains, from the weakest (Commercial Control, 1.8) to the strongest (Systems & Governance, 3.0).
Bid & Tender
2.3
Reactive
Compliance & RAMS
1.9
Ad Hoc
Commercial Control
1.8
Ad Hoc
Reporting & Visibility
2.5
Reactive
Service Delivery
2.4
Reactive
Systems & Governance
3.0
Structured
Section 03
Domain Assessments
Detailed five-factor assessment for each domain, scoring process consistency, documentation quality, governance, data readiness and AI opportunity.
Bid & Tender
Score: 2.3 / 5.0 — Reactive
2.3
Factor
Finding
Score
Process consistency
Tender production follows an informal process led by the Estimating Lead. No standardised workflow or checklist. Approach varies by individual and tender complexity.
2
Documentation quality
Scope extraction is manual and thorough but inconsistent. Assumptions are drafted from scratch each time. Clarifications are captured ad hoc. No bid risk register produced.
2
Governance
Commercial Director reviews high-value bids but there is no formal approval gate, QA checklist or submission compliance check process.
2
Data readiness
Tender packs are self-contained and well-organised. Company data for PQQs exists but is scattered across documents. No centralised assumptions or compliance library.
3
AI opportunity
High. 40% of tender time is admin. 11 tenders declined for capacity. Scope extraction, assumptions, clarifications and QA are all structurally suitable for AI assistance.
5
Compliance & RAMS
Score: 1.9 / 5.0 — Ad Hoc
1.9
Factor
Finding
Score
Process consistency
RAMS are produced by copying and adapting previous documents. Template quality varies significantly. Task sequences are often generic rather than job-specific.
1
Documentation quality
Hazard identification is reasonable but control measures are often boilerplate. COSHH and permit information is disconnected from the main RAMS document. Briefing records are incomplete.
2
Governance
HSQE Manager reviews RAMS but the review is often compressed by mobilisation timelines. No structured approval gate or version control system.
2
Data readiness
Job information exists in various forms but is not structured for automated processing. Task libraries and hazard registers are maintained in spreadsheets with inconsistent formatting.
2
AI opportunity
Very high. RAMS production consumes 1,730 hours per year. Task-based generation, hazard/control mapping and COSHH indexing are all addressable through structured AI workflows.
5
Commercial Control
Score: 1.8 / 5.0 — Ad Hoc
1.8
Factor
Finding
Score
Process consistency
Change events are captured verbally and logged retrospectively. No structured capture process. Notice discipline is inconsistent — average 18 working days vs 10–14 day contractual windows.
1
Documentation quality
Variation narratives are written manually with variable quality. Evidence is fragmented across WhatsApp, email and personal folders. 38% of submissions returned for further justification.
2
Governance
Commercial Director oversees the variation process but pipeline visibility is limited to a monthly spreadsheet. No real-time view of uncommercialised events.
2
Data readiness
Variation log exists with 142 events across 23 contracts. Evidence exists but is not indexed. Data is sufficient for AI workflow deployment with minor structuring.
3
AI opportunity
Critical. £620k in annual leakage from missed recovery. Change capture, notice automation, evidence indexing and narrative generation are the highest-value AI opportunities identified.
5
Reporting & Visibility
Score: 2.5 / 5.0 — Reactive
2.5
Factor
Finding
Score
Process consistency
Monthly reports and QBRs are produced on a regular cadence. The process is established but heavily manual — 70–80% of effort is data gathering and formatting rather than analysis.
3
Documentation quality
Report structure is reasonable but narrative is often copied from previous months with minor updates. Actionable insights are limited. Board reports lack forward-looking indicators.
2
Governance
Reporting cadence is defined but there is no structured action-tracking or ownership mechanism. Actions from QBRs are not systematically followed up.
3
Data readiness
Sage 200 CVR exports are available but require manual reconciliation. CAFM data is exportable. Multiple data sources need joining manually each month.
2
AI opportunity
High. 480 hours per month on reporting. Automated margin snapshots, WIP tracking, CNI dashboards and board narrative generation would release significant capacity for analysis and decision-making.
4
Service Delivery
Score: 2.4 / 5.0 — Reactive
2.4
Factor
Finding
Score
Process consistency
Helpdesk operates during business hours with OOH call handling. Ticket creation is manual. Priority classification varies between operators and does not always align with contractual SLA tiers.
2
Documentation quality
Ticket records exist in Concept CAFM but quality is inconsistent. Response evidence relies on engineer self-reporting. SLA evidence packs are assembled retrospectively for client reporting.
2
Governance
SLA compliance is tracked monthly but deduction challenges lack structured evidence. No automated escalation or countdown tracking.
3
Data readiness
Concept CAFM contains 4,280 tickets with response times, SLA flags and deduction records. Data is exportable but requires cleaning and mapping to contractual SLA definitions.
2
AI opportunity
Moderate. £100k in SLA deductions. Priority classification, dispatch ticketing and SLA evidence are suitable for AI assistance but require CAFM integration for maximum effect.
3
Systems & Governance
Score: 3.0 / 5.0 — Structured
3.0
Factor
Finding
Score
Process consistency
Core systems (Sage 200, Concept CAFM) are established and used consistently. Document control is reasonable. IT infrastructure is stable and cloud-accessible.
3
Documentation quality
System documentation is adequate. User guides exist for core processes. Data exports are available but not automated. No API integrations currently in use.
3
Governance
No formal AI governance framework exists. No data classification model. No approval gates for AI-assisted outputs. No procurement disclosure position defined. Cyber Essentials certification in progress.
3
Data readiness
Core financial and operational data is centrally held. Export formats are standard (CSV, Excel). Data quality is reasonable but joins between systems are manual.
3
AI opportunity
Moderate. Governance framework is needed as a foundation for all other workflows. Data classification, approval gates and audit trail infrastructure are prerequisites, not standalone AI use cases.
3
Section 04
Priority Matrix
The gap between current maturity and AI opportunity for each domain. The largest gaps indicate the highest-priority areas for AI workflow implementation.
#
Domain
Current score
AI opportunity
Gap
Priority rationale
1
Commercial Control
1.8
5.0
3.2
Largest financial leakage (£620k). Highest gap score. Change capture and variation recovery are immediately addressable.
2
Compliance & RAMS
1.9
5.0
3.1
Second-largest gap. 1,730 hours on RAMS production. Requires governance framework before deployment (higher H&S approval burden).
3
Bid & Tender
2.3
5.0
2.7
Highest data readiness. Fastest speed to benefit. Best prototype candidate. £340k admin burden + £836k declined pipeline.
4
Reporting & Visibility
2.5
4.0
1.5
Strong efficiency case (480 hrs/month). Benefits from Phase 1 data improvements. Best deployed in Phase 2.
5
Service Delivery
2.4
3.0
0.6
Smallest financial impact (£100k). Requires CAFM integration. Best deployed in Phase 3 after foundational workflows established.
6
Systems & Governance
3.0
3.0
0.0
No gap — but governance is a prerequisite. The framework (Deliverable 05) must be established before any workflow deployment begins.
Key insight. Commercial Control and Compliance & RAMS have the largest maturity gaps, but Bid & Tender has the highest implementation readiness. The Opportunity Roadmap (Deliverable 03) resolves this by recommending Bid & Tender and Commercial Control for Phase 1 (highest combined value + readiness), with RAMS following in Phase 2 once governance controls are established.
Section 05
Methodology & Sources
How the scorecard was compiled and what evidence it draws on.
Half-day observation of estimating team (tender production cycle), HSQE review process, and helpdesk operation
8 May 2026
Deliverable linkage. This scorecard is Deliverable 01 of 07 from the Operational Intelligence Audit™. The findings feed directly into Deliverable 02 — Bleed Rate™ (Margin Leakage & Efficiency Analysis), Deliverable 03 — Heat Map™ (Prioritised AI Opportunity Roadmap) and Deliverable 07 — Green Light™ (Phase 1 Implementation Proposal). All seven deliverables should be read together.