Aims, Objectives & Stakeholder Map

This document is the programme spine. It maps each pillar to the stakeholders who own its findings and states the aim and objectives each pillar establishes. Every objective carries an ID (e.g. P2-O1) so the two-tier interview questionnaire can link every question, sub-question, and probe back to a specific objective. It is aligned to the final Pillar 1–5 publications.

Programme thesis. Enterprise AI success is determined not by which model an organisation uses, but by the architecture, operating model, economics, and governance it wraps around that model — the applied layer. Pillar 1 establishes the thesis and the evidence; Pillars 2–5 each develop one load-bearing component for the stakeholder who owns it.

Stakeholder Map

Figure 1. Who owns each pillar. Each pillar is written for the senior stakeholder who must act on it; supporting roles are the secondary audience.

Task 1 · Stakeholder Mapping

Pillar	Primary stakeholders	Secondary / supporting	Focus
P1 · Beyond the Model	CEO, Board of Directors, Chief Strategy Officer (and all senior leaders)	Chief People Officer, Head of Innovation, Chief of Staff	Synthesis & strategy
P2 · Production AI Architecture	Chief Technology Officer (CTO), VP / Head of Engineering, Chief Architect, Head of Data & AI	CIO, Head of Product, Platform Engineering Leads, MLOps Leads	How systems are built
P3 · Operating Models	Chief Operating Officer (COO), Chief People Officer (CPO/CHRO), Head of Transformation	Business Unit Leaders, Head of Change Management, Strategy Director	How AI is delivered
P4 · Cost & Platform Landscape	CFO / Finance Director and FinOps leads who own the AI business case	CIO / CTO, Head of AI or Platform Engineering, procurement, and compliance	What it truly costs
P5 · Trust, Evaluation & Governance	General Counsel, Chief Risk Officer (CRO), Chief Compliance Officer — and the Head of AI Governance who must make the obligations operational	CISO, Head of ML / AI Platform Engineering, internal audit, and delivery leads	Risk & accountability

Detailed Stakeholder Profiles & Objectives

Each profile gives the primary and supporting stakeholders, the key idea, the aim, the objectives (with IDs and the interview theme each drives), and why the stakeholder needs the pillar.

PILLAR 1 · Beyond the Model Synthesis & strategy

Primary stakeholders. CEO, Board of Directors, Chief Strategy Officer (and all senior leaders)

Secondary / supporting. Chief People Officer, Head of Innovation, Chief of Staff

Key idea. A cross-cutting synthesis of what enterprise AI has proved, falsified, and where the evidence points next — documenting the widening gap between organisations doing AI seriously and those treating it as procurement.

Aim. To establish, from the public record, what enterprise AI has proved and falsified in its first sustained period in production — and to set the frame, the maturity baseline, and the forward research agenda for the five-pillar programme.

Obj.	Objective — what the pillar must establish	Interview theme it drives
P1-O1	Assess headline AI adoption rates against actual production impact (e.g. ~95% of pilots showing no measured P&L impact — NANDA/MIT).	Strategy & leadership
P1-O2	Identify the structural failure modes: buying tools vs building capability; central AI labs vs line-manager ownership.	Pilot vs production
P1-O3	Document the ‘maturity ladder’ and where most organisations sit (stalled at scaled pilots).	Pilot vs production / stratification
P1-O4	Establish the forward research agenda for the publication and the organisation.	Stratification & competitive awareness

Why this stakeholder needs this pillar. Every senior leader needs this as context for organisational AI investment decisions. It prevents strategy based on hype rather than evidence.

PILLAR 2 · Production AI Architecture How systems are built

Primary stakeholders. Chief Technology Officer (CTO), VP / Head of Engineering, Chief Architect, Head of Data & AI

Secondary / supporting. CIO, Head of Product, Platform Engineering Leads, MLOps Leads

Key idea. Enterprise AI outcomes are determined by architecture, not model choice. The patterns wrapped around the model — retrieval, query rewriting, reranking, orchestration, evaluation — dominate system quality.

Aim. To demonstrate that production AI outcomes are determined by the architecture wrapped around the model, and to give technical leaders an evidence-based framework for moving from demo to reliable production.

Obj.	Objective — what the pillar must establish	Interview theme it drives
P2-O1	Document the retrieval architecture patterns: naive RAG → hybrid → reranking → hierarchical/graph.	Data & retrieval
P2-O2	Map agentic maturity across a five-tier taxonomy (Tier 1: deterministic-with-LLM-glue → Tier 5: fully autonomous).	AI tools & automation
P2-O3	Identify which tiers are production-ready for which workloads.	AI tools & automation
P2-O4	Provide a decision framework keyed to corpus characteristics, query complexity, latency budget, and error tolerance.	Infrastructure & integration

Why this stakeholder needs this pillar. CTOs and architects need this to move from demo to production. Model-upgrade ROI is negligible without fixing the retrieval and architecture layers first.

PILLAR 3 · Operating Models How AI is delivered

Primary stakeholders. Chief Operating Officer (COO), Chief People Officer (CPO/CHRO), Head of Transformation

Secondary / supporting. Business Unit Leaders, Head of Change Management, Strategy Director

Key idea. Operating model dominates technology choice as the determinant of enterprise AI outcomes. The same model in two different operating structures produces opposite results.

Aim. To demonstrate that the operating model — not the technology choice — determines enterprise AI outcomes, and to define the archetypes, components, and conditions that separate success from failure.

Obj.	Objective — what the pillar must establish	Interview theme it drives
P3-O1	Define the four operating-model archetypes: Centralised Platform, Centralised Delivery, Federated Platform, Federated Delivery/CoE.	Design authority & ownership
P3-O2	Articulate the six conditions of success: production reach, evaluation in production, systems-of-record integration, governance + delivery, talent retention, executive sponsorship.	Across all P3 themes
P3-O3	Ground the analysis in case studies: JPMorgan Chase, Sanofi, Walmart, the Klarna reversal, the McDonald’s failure.	Design authority / funding & sponsorship
P3-O4	Identify the five components of an AI operating model: design authority, build capacity, governance regime, run model, funding flow.	Talent & capability; funding & sponsorship

Why this stakeholder needs this pillar. COOs own the operating model. Without this, AI becomes a technology project rather than an operational transformation. Klarna and McDonald’s are the cautionary tales.

PILLAR 4 · Cost & Platform Landscape What it truly costs

Primary stakeholders. CFO / Finance Director and FinOps leads who own the AI business case

Secondary / supporting. CIO / CTO, Head of AI or Platform Engineering, procurement, and compliance

Key idea. The dollar that matters most is rarely the per-token dollar. Model inference is only 20–40% of run-rate AI cost; retrieval, evaluation, observability, governance, and human review consume the rest — yet are invisible in headline pricing calculators.

Aim. 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.

Obj.	Objective — what the pillar must establish	Interview theme it drives
P4-O1	Decompose the full enterprise-AI cost stack across eight categories and establish that inference is only 20–40% of run-rate cost.	How organisations budget, meter, and track the full cost of AI in production.
P4-O2	Map the proportional cost shape across the five workload archetypes and the pilot / department / enterprise scale tiers.	Which workloads dominate spend, and where budgets systematically break down.
P4-O3	Audit the global platform landscape — Western, Chinese, Indian, Korean, Japanese, Middle-Eastern — against capability, cost, data residency, and accreditation.	How platform shortlists are actually formed, and which evidence buyers trust.
P4-O4	Establish that platform choice is governed by platform, identity, and regulatory gravity rather than raw capability.	The decisive non-model factors in vendor selection and portfolio design.

Why this stakeholder needs this pillar. To build business cases on fully-loaded cost, anticipate the categories that overrun, and select platforms on the factors that actually decide fit — connecting the architecture of Pillar 2, the operating model of Pillar 3, and the governance of Pillar 5 to a defensible economic frame.

PILLAR 5 · Trust, Evaluation & Governance Risk & accountability

Primary stakeholders. General Counsel, Chief Risk Officer (CRO), Chief Compliance Officer — and the Head of AI Governance who must make the obligations operational

Secondary / supporting. CISO, Head of ML / AI Platform Engineering, internal audit, and delivery leads

Key idea. Evaluation and governance are the same operational system: documents without evaluation, and dashboards without governance, both fail.

Aim. To establish that evaluation and governance are 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.

Obj.	Objective — what the pillar must establish	Interview theme it drives
P5-O1	Establish the seven evaluation dimensions and five methods that constitute a production evaluation practice.	How organisations measure whether AI systems perform as intended once in production.
P5-O2	Define the seven operational components of a working governance system and the test that distinguishes governance from compliance theatre.	What governance is actually implemented in delivery versus what is merely documented.
P5-O3	Map enterprise obligations across the EU AI Act, NIST AI RMF, and ISO/IEC 42001 to concrete engineering work items.	How regulatory obligations are translated into delivery, and where teams get stuck.
P5-O4	Provide a combined maturity framework that locates a programme on a four-level ladder by inspection.	Where organisations sit on the evaluation–governance maturity ladder, and what blocks progress.

Why this stakeholder needs this pillar. To earn operational trust and meet binding obligations — EU AI Act high-risk duties from August 2026 — without producing documents no one acts on or dashboards no one reads, closing the loop opened by the architecture of Pillar 2, the operating model of Pillar 3, and the economics of Pillar 4.

From aims to evidence. Each pillar’s objectives drive a two-tier field-interview instrument: a holistic session (~30 min) that sweeps all five pillars for breadth, and a granular session (~45 min) that goes deep on the respondent’s home pillar. Every question is mapped to the objective IDs above; the full instrument is in the accompanying Stakeholder Map & Questionnaire document.