Enterprise AI, written to be cited.

To establish what enables enterprise AI to create sustained production value, which early assumptions the public record has confirmed and which it has recalibrated — and to set the frame, the maturity baseline, and the forward research agenda for the five-pillar programme.

• P1-O1Examine the relationship between headline AI adoption and measured production impact, and test the widely cited (and contested) estimates of pilot-to-P&L conversion.
• P1-O2Examine which structural conditions enable organisations to convert AI investment into capability — building capability rather than buying tools; line ownership alongside central expertise — and what is absent where progress slows.
• P1-O3Characterise the maturity ladder, examine where organisations locate themselves on it, and identify what characterises movement between rungs.
• P1-O4Establish the forward research agenda for the programme, and identify the questions practitioners most need answered.

To examine how architecture shapes production AI outcomes, and to give technical leaders an evidence-based framework for moving from demo to reliable production.

• P2-O1Document the retrieval architecture patterns: naive RAG → hybrid → reranking → hierarchical/graph.
• P2-O2Map agentic maturity across a five-tier taxonomy (Tier 1: deterministic-with-LLM-glue → Tier 5: fully autonomous).
• P2-O3Examine which tiers are production-ready for which workloads, and what enables organisations to operate at higher tiers safely.
• P2-O4Provide a decision framework keyed to corpus characteristics, query complexity, latency budget, and error tolerance.

To examine how the operating model shapes enterprise AI outcomes, and to define the archetypes, components, and conditions that enable repeatable success across different organisational contexts.

• P3-O1Define the four operating-model archetypes: Centralised Platform, Centralised Delivery, Federated Platform, Federated Delivery/CoE.
• P3-O2Examine the six recurring conditions of success — production reach, evaluation in production, systems-of-record integration, governance integrated with delivery, talent retention, executive sponsorship — and how far each is present in practice.
• P3-O3Ground the analysis in documented enterprise programmes — including sustained deployments (JPMorgan Chase, Sanofi, Walmart) and publicly reported course corrections (Klarna, McDonald’s).
• P3-O4Examine the five components of an AI operating model — design authority, build capacity, governance regime, run model, funding flow — and which are load-bearing in practice.

To give enterprise decision-makers a fully-loaded view of what production AI actually costs, and a vendor-neutral basis for choosing platforms on the factors that genuinely determine fit — data residency, regulatory accreditation, existing stack, and language — rather than on headline model capability or per-token price.

• P4-O1Decompose the enterprise-AI cost stack across eight categories and test the proposition that inference is a minority of run-rate cost.
• P4-O2Map the proportional cost shape across the five workload archetypes and the pilot / department / enterprise scale tiers.
• P4-O3Examine the global platform landscape — Western, Chinese, Indian, Korean, Japanese, Middle-Eastern — against capability, cost, data residency, and accreditation.
• P4-O4Examine which factors govern platform selection in practice, and test the proposition that platform, identity, and regulatory gravity outweigh raw capability.

To examine evaluation and governance as a single operational system, and to give enterprises an inspectable maturity yardstick and a set of operational components and regulatory mappings that turn AI trust from aspiration into verifiable practice.

• P5-O1Examine the seven evaluation dimensions and five methods proposed as a production evaluation practice, and which are used in practice.
• P5-O2Examine the seven operational components of a working governance system, and the test that distinguishes governance which is operationally enacted from governance which is documented but not implemented.
• P5-O3Map enterprise obligations across the EU AI Act, NIST AI RMF, and ISO/IEC 42001 to concrete engineering work items.
• P5-O4Provide a combined maturity framework that locates a programme on a four-level ladder by inspection, and establish where enterprise programmes actually sit.