Concept card

Agentic AI Delivery Model

As of 2026-07-24T14:00:00Z

Edited by Cédric-Olivier Mâry, sole editor and publication director — 27 years in the SAP ecosystem. How these pages are produced, and where automation is used.

What is Agentic AI Delivery Model?

The agentic delivery model's core claim is that the data-quality acceptance bar must shift from 'good enough for reporting' to 'good enough for autonomous action' — and that cost must be priced into the SOW upfront.

The agentic AI delivery model describes how an SAP analytics programme has to be structured when the actual deliverable is an autonomous multi-step workflow rather than a dashboard, a report, or a data model. In the conventional BI delivery model, the consultant's job ends at building the information architecture — a human looks at the number and decides what to do about it. In the agentic model, the agent itself executes the decision inside guardrails the consultant helped design. That single shift — from informing a decision to executing it — changes the project's scope, its risk register, its acceptance criteria, and the consultant's liability exposure all at once, which is why it deserves to be treated as a distinct delivery model rather than a BI project with an AI feature bolted on.

The Five Layers of an Autonomous Workflow

Why it matters

An incorrect vendor name on a dashboard is cosmetic; the same error causing an agent to pay the wrong vendor is a material loss
Five structural layers (trigger, context retrieval, reasoning, action, governance gate) replace the old 'architect the report, human decides' model
UAT criteria must shift from 'does it look right' to 'did the agent's action match the correct outcome'

Key points

Agentic delivery model: the deliverable is an autonomous multi-step workflow, not a dashboard — scope, risk, and acceptance criteria are fundamentally different.
Five structural layers: trigger → context retrieval (Knowledge Graph) → reasoning (LLM via Model Gateway) → action (Joule Skill) → governance gate (HITL / audit).
Data quality gate shifts from 'good enough for reporting' to 'good enough for autonomous action' — acceptance criteria must be tighter and costed explicitly.
Agent UAT suite: happy-path + exception-path + HITL escalation + EU AI Act conformity evidence — not conventional BI UAT.
Gartner 40% cancellation rate maps to four failure points: data quality, HITL staffing, EU AI Act compliance, outcome pricing mismatch.
Outcome pricing is the 2026-2028 delivery model frontier — price per agent-resolved decision, not per consultant-day.
EY 30% delivery-time reduction / KPMG 20% sprint speedup are the anchor proof points for outcome-adjacent contracts (SAP Q1 FY2026 earnings call, 2026-04-22).
Agentic AI Delivery Model is mastered only when it changes a named buyer decision.
Start with the semantic contract and control model before demonstrating the tool.
Use current SAP, analyst, study, KG, and news signals as evidence, not decoration.

Terms used on this page

Multi-step autonomous workflow: An agent pipeline that chains multiple Skills, context-retrieval steps, and decision nodes to complete a business process without human initiation at each step.
HITL (Human-in-the-Loop): Governance pattern that pauses agent execution at defined conditions and routes to a human approver before the action executes — mandatory for EU AI Act Annex III systems.
Data quality gate: Acceptance criterion applied to the underlying entity data (vendor master, customer master, material master) before an agent is allowed to take autonomous action on it.
Outcome pricing: Commercial model where the consulting firm or software vendor charges per resolved business decision (invoice matched, payment released) rather than per time unit or per licence seat.
Decision owner: The accountable person who accepts the trade-off and funds the next action.
Semantic contract: The shared definition of business terms, metrics, entities, and access rules used by tools and teams.
Control plane: The layer that applies policy, access, lineage, monitoring, and escalation across the operating model.
Evidence grade: A label that separates verified fact, directional signal, modeled assumption, and field observation.

Sources

Full card available to members. What the full card adds: the full decision framework · the SAP vs Snowflake / Databricks / Fabric comparison · the common pitfalls and their fix · the cheat sheet · the architecture schemas · the code blocks · the facts worth quoting.

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