76% of workers say their GenAI tools lack business context—why lifecycle and RevOps teams hit a ceiling. Here’s a practical blueprint to wire context into your customer engagement stack without slowing delivery.

Context Is the Real GenAI Bottleneck in Lifecycle Marketing (and How RevOps Can Fix It)

Most lifecycle teams don’t have an “AI problem.” They have a context problem.

Salesforce + YouGov found that 76% of workers say their favorite GenAI tools lack business context, limiting benefits—even as conversational tools like ChatGPT and Slack AI become common at work (Salesforce Newsroom). That explains why many “AI-powered” lifecycle initiatives stall after the pilot: the model can write a subject line, but it can’t reliably answer, “For this customer, in this segment, under these eligibility rules, what’s the right next message?”

The stakes are rising. Salesforce reported $1.29T in global online holiday sales and attributed $262B of 2025 holiday spend to AI and agents (Salesforce Newsroom). If AI is influencing that much spend, lifecycle programs without trustworthy context will increasingly underperform.

Below is a practical approach for lifecycle marketers and RevOps leaders to move from “generic GenAI” to context-aware, measurable customer engagement.

Why lifecycle AI fails: it’s not the copy, it’s missing context

When GenAI “doesn’t work” in lifecycle, the root cause is usually one of these:

Unclear business rules: promotions, eligibility, pricing, regional constraints, and exclusions aren’t accessible to the model—or aren’t up to date.
Fragmented identity + event data: lifecycle decisions require clean joins across product usage, billing, CRM, support, and marketing engagement.
No operational guardrails: approvals, logging, and accountability aren’t defined, so teams throttle usage or keep AI outputs out of production.

Salesforce’s research is explicit: tools lack the business context required to deliver workplace value (Salesforce Newsroom).

Build a “context layer” RevOps can govern (and marketers can use)

Treat context like a product: curated, versioned, permissioned, and measurable.

A useful context layer typically includes:

Identity and account hierarchy: who the customer is, what account they belong to, and how that maps across systems.
Behavior + lifecycle state: product events, web/app behavior, buying stage, retention-risk signals.
Entitlements + eligibility rules: what the customer is allowed to receive (offers, upgrades, content access).
Business objectives and constraints: revenue goals, margin thresholds, regional compliance, frequency caps.

If you’re on Salesforce, RAG-style approaches are increasingly practical. Salesforce Ben outlines how teams can build RAG-powered agents with Salesforce Data Cloud and Agentforce, including a web crawler connection for fast-changing customer-facing information like promotions and eligibility rules—exactly the inputs that cause lifecycle failures when they’re stale or inconsistent (Salesforce Ben).

Note: tool specifics vary by stack (Salesforce, Iterable, Braze, etc.). The pattern—curate context, then retrieve it at decision time—holds across platforms.

A quick litmus test

If you can’t answer these questions from a single source of truth, your AI will hallucinate—or your team will limit autonomy:

What offer is this customer eligible for today?
Which message did they last receive across all channels?
What is the priority right now: conversion, activation, expansion, or retention?

Orchestrate execution: automation is where context becomes revenue

Once context is reliable, you can push it into orchestration:

Triggering (events, milestones, score thresholds)
Decisioning (eligibility, suppression, next best action)
Personalization (content + offer selection)
Measurement (incrementality, cohort lift, pipeline impact)

If you’re operating in Salesforce, Flow is a practical way to automate communications without heavy engineering, but it’s easy to underestimate complexity. Salesforce Ben’s guide to sending emails with Salesforce Flow shows how powerful Flow is—while noting it’s not always “easy,” especially for beginners (Salesforce Ben).

This is where RevOps should lean in: not to “own marketing,” but to ensure automations are observable, auditable, and aligned to revenue processes.

Align teams: the handoff problem becomes an AI problem

Even with the right data and tools, lifecycle programs break when ownership is unclear:

Who defines eligibility rules—and who signs off when they change?
Who owns the taxonomy for lifecycle states?
Who is accountable when AI-driven messaging conflicts with sales motions?

Salesforce Ben’s breakdown of why sales-to-delivery handoffs fail is a useful parallel: cross-functional breakdowns often come from unclear requirements, inconsistent processes, and misaligned expectations (Salesforce Ben). In lifecycle AI, the “handoff” is continuous: data → rules → orchestration → measurement → iteration.

Key actions (do these in the next 30 days)

Inventory your context gaps: list the top 10 decisions your lifecycle programs make (send/suppress, offer selection, channel choice). For each, identify what data/rules are missing or unreliable.
Define a minimum viable context layer: pick 3–5 “must be correct” objects (identity, subscription status, eligibility rules, last-touch history, frequency caps).
Choose a retrieval pattern: use RAG for fast-changing knowledge (promos, policies) and structured lookups for deterministic rules.
Operationalize governance: version business rules, log AI inputs/outputs, and define approval paths for high-risk sends.
Measure lift with a control: require holdouts or matched cohorts so AI-driven improvements are provable.

For broader adoption enablement, Salesforce also published an AI Fluency Playbook focused on preparing workers to collaborate with AI in an “agentic enterprise” (Salesforce Newsroom).

What “good” looks like: fewer surprises, faster cycles, clearer revenue impact

When context is treated as infrastructure:

Marketers ship faster because they aren’t re-litigating rules every campaign.
RevOps gets consistency: the same definitions drive dashboards, routing, and orchestration.
AI becomes safer to scale because the system can cite what it used to decide.

For an external baseline on why context matters in AI systems, see IBM’s overview of retrieval-augmented generation (RAG) and how it grounds outputs in retrieved sources: https://www.ibm.com/topics/retrieval-augmented-generation

CTA

If you’re ready to turn GenAI into measurable lifecycle impact, Engage Evolution can help you design and implement a context layer—from data model and governance to orchestration and experimentation—so your AI and agents operate on your business rules, not generic guesses.

Talk to Engage Evolution about Context Layer Design + Lifecycle Orchestration Services.