Analytics Legends The knowledge platform for SAP Analytics
Academy module

Scoping and Pricing SAP AI Engagements

Scoping and Pricing SAP AI Engagements — Analytics Legends section illustration for the SAP Analytics knowledge base (concepts, studies, Academy)

As of 2026-09-25

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

Gives freelance and boutique-firm SAP consultants a method for scoping and pricing SAP AI engagements that survives contact with a real invoice. Covers four engagement shapes (discovery, pilot, production rollout, managed run) and the pricing model that fits each; the discipline of costing delivery fee and platform consumption (AI Units vs GenAI tokens/capacity units, per SAP Note 3437766 and SAP for Me) as separate lines; four risk-premium factors grounded in SAP's own documentation and Value of AI 2026 research; the real, verifiable SAP Generative AI Developer certification (C_AIG_2604) and a demonstrable portfolio as differentiators; three statement-of-work clauses that prevent the most common scope disputes; and where time-and-materials, fixed-fee and outcome-based pricing each break on SAP AI work. Three exercises and a self-assessment gate the move to M383.

What you will learn

  • Classify an SAP AI opportunity into one of four engagement shapes (discovery, pilot, production rollout, managed run) and select the pricing model that fits each
  • Separate delivery fee from platform consumption (AI Units or GenAI tokens/capacity units) as two distinct, sourced lines in a proposal
  • Name the four risk-premium factors (model lifecycle, governance immaturity, evaluation as a deliverable, adoption risk) that justify pricing above a comparable SAP Analytics engagement
  • Cite a real, verifiable SAP generative-AI certification (C_AIG_2604) and a demonstrable portfolio as differentiators, without inventing credentials
  • Write the three statement-of-work clauses that prevent the most common SAP AI scope disputes: model/rate snapshot, fee-vs-consumption separation, and evaluation-set ownership
  • Explain why time-and-materials, fixed-fee and outcome-based pricing each break under specific SAP AI conditions

Module overview

Who this is for. You can build the business case (M378) and you know the delivery lifecycle (M379). This module is about the proposal that gets you hired to run either one: how to scope an SAP AI engagement so the statement of work survives contact with reality, and how to price it without quoting a number you cannot defend. It is written for the freelance or boutique-firm consultant who scopes their own work, but the discipline applies equally to a practice lead pricing a team's engagement.

Prerequisites

  • M378 (Building the SAP AI Business Case) and M379 (From PoC to Production)
  • Comfort writing or reviewing a statement of work or proposal
  • Optional: M334 (From SAP Analytics to SAP AI — the 90-day reskilling path)

Outcomes

  • Produce a scoped SAP AI proposal that classifies the engagement shape, separates fee from platform consumption, and states a defensible price.
  • Explain to a prospect, without notes, why AI Units and GenAI tokens/capacity units must be quoted as separate lines.
  • Justify a risk premium over a comparable SAP Analytics engagement using four named, specific factors rather than a vague markup.
  • Draft the three statement-of-work clauses (model/rate snapshot, fee/consumption separation, evaluation-set ownership) that prevent the most common SAP AI scope dispute.

Full module available to members. The full module adds: the decision framework · the end-to-end scenario walkthrough · the KPI scorecard · the anti-patterns · the code blocks · the knowledge check · the diagrams.

Open in the app →

Next in this track