Concept card

AI Is Everywhere In GTM. Customer Value Isn’t.

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 AI Is Everywhere In GTM. Customer Value Isn’t.?

The gap between AI marketing and measured customer value is a measurement and honesty gap, not a technology gap — test any AI claim on whether it names a specific mechanism or just an assertion.

Every enterprise software vendor now leads its go-to-market messaging with AI. Feature lists enumerate AI-powered forecasting, AI-driven recommendations, AI-assisted workflows and AI-enhanced insights; procurement committees score AI maturity; analyst evaluation criteria increasingly weight AI roadmap breadth. Yet when organisations try to translate AI feature adoption into measured customer or business value, the correlation is frequently weak. Diagnosing why — and building the judgment to tell a genuine capability from a marketing claim — is now a core analytical skill for anyone advising on SAP analytics or AI strategy, because the gap between what is announced and what is delivered shapes almost every technology decision a client makes in this cycle.

The gap between AI-in-marketing and AI-delivering-value is not primarily a technology problem. It is a measurement gap, an implementation-discipline gap, and in some cases simply an honesty gap, and each type calls for different advice.

Why the gap exists

AI features are easy to announce and hard to retire. Once a capability ships, it persists in the feature list across releases regardless of adoption rate or measured value, because removing it looks like regression even when nobody uses it. Feature count and AI sophistication have become a proxy signal for vendor maturity in competitive evaluations, which creates a direct incentive to announce broadly even when a capability's practical applicability is narrow.

Why it matters

AI features are easy to announce and hard to retire — they persist through releases regardless of adoption rate, because feature count is a proxy signal in analyst evaluations.
'Our AI analyses purchase history' is an assertion; 'our AI uses collaborative filtering on 36 months of transaction history to rank cross-sell recommendations' is a testable mechanism.
Outcome measurement fails most often because the organisation never established a baseline before rollout — absence of measured value is not proof of absent value, but it is proof of absent rigour.

Key points

Customer-outcome KPIs (TTFV, NRR, expansion) ABOVE activity metrics on every dashboard
Bake outcome KPIs into the Datasphere analytic model so SAC + Joule inherit
Without semantic enforcement, GTM reverts to activity dashboards within 2 quarters
Sort GenAI use cases by which customer outcomes they serve
AI Is Everywhere In GTM. Customer Value Isn’t. 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.
Separate verified facts from directional trends and modeled assumptions.
Define owner, metric, threshold, support path, and rollback before scaling.
For AI use cases, measure reliability, cost, latency, safety, and human validation.

Terms used on this page

TTFV: Time-to-first-value — interval from purchase to first measurable customer benefit.
NRR: Net Revenue Retention — expansion minus churn from existing customers.
Semantic enforcement: Locking KPI hierarchy in the analytic model so downstream tools inherit.
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.
Adoption metric: The measurable behavior proving that the concept changed actual work after go-live.

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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