Concept card

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

As of 2026-09-27

Edited by Cédric Mary, 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
Forrester's own source survey (30 GTM leaders, 2026-05-07): 88% agree AI's benefits will outweigh risks, yet most cited initiatives remain internal-efficiency-focused rather than customer-outcome-focused — the same gap this card diagnoses, independently confirmed.
Forrester names three GTM AI initiative types: applying AI to existing workflows (risk: automating what should instead be removed), rethinking roles/skills (new roles: marketing engineer, AI system owner, governance owner), and experimenting with market-facing AI for three simultaneous audiences.
The three-audience framing (humans, buyer agents, answer engines) is a 2026 design constraint: a buyer's own AI agent, reached via A2A or MCP, may now be the actual consumer of a data product or generated summary — a KPI or content structure built only for human comprehension may not be agent-actionable.
Forrester's recommended shift: move from engagement metrics (MQLs, marketing pipeline volume) to a 'return-on-objectives' model — directly reinforcing this card's own guidance to lead every dashboard with outcome KPIs, not activity counts.

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.
Return-on-objectives model: Forrester's recommended GTM measurement shift: scoring AI-driven marketing/sales work against stated business and customer objectives rather than engagement metrics (MQLs, pipeline volume) alone.
Three-audience framing: Forrester's model for who consumes market-facing content/data in 2026: humans, buyer-side AI agents (reached via A2A/MCP), and answer engines — each may need a structurally different, separately validated output.
Marketing engineer: An emerging GTM role Forrester names alongside AI system owner and governance owner — a technically fluent role responsible for embedding and maintaining AI capability inside marketing/sales workflows, distinct from a traditional marketing operations role.

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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