Forrester Tells RevOps Leaders to Cut Legacy Tools, Fund AI Data Foundations First

Data orchestration gets budget priority over AI forecasting tools

Forrester's 2026 revenue operations budget guidance tells enterprise leaders to eliminate legacy RevOps investments and fund data foundations before adding AI-first analytics tools. The directive arrives alongside LeanData's survey of 201 enterprise leaders showing an "AI readiness gap" — organizations want AI-driven forecasting and pipeline analytics but lack the unified data models to support them.

The shift affects how enterprises allocate RevOps technology budgets. Forrester explicitly urges operations leaders to reclassify static planning and dashboard tools as technical debt and redirect spending toward adaptive planning platforms, data orchestration middleware, and governance capabilities that can power AI. For buyers evaluating Anaplan, Pigment, Clari, or Gong's RevOps analytics, the message is clear: data quality now determines AI viability later.

LeanData's 201-leader survey quantifies the data problem

LeanData's B2B State of Martech & Revenue Operations 2026 report surveyed 201 enterprise leaders and identified a gap between AI ambitions and data readiness. The finding matters because it moves the conversation from "which AI RevOps tool should we buy" to "do we have the data foundation to make any AI tool work."

The 201-respondent sample provides defensible data for RevOps leaders arguing to boards that budget should move from incremental point tools to lead-to-account matching, routing, and orchestration platforms. LeanData competes directly with Openprise, Tray.io, and Syncari in the data orchestration layer, and the report positions these platforms as required infrastructure before AI co-pilots deliver value.

For enterprise buyers, the implication is immediate: RFPs for CRM and marketing automation platforms now need to include questions about data model openness, orchestration layer compatibility, and support for unified semantic layers. Native routing in Salesforce or HubSpot no longer satisfies the requirement if it locks data into proprietary schemas that block AI tools downstream.

Forrester guidance creates budget line items for AI readiness, not AI features

Forrester's budget planning piece is prescriptive. It directs operations leaders to invest in adaptability and tie AI spending to three specific outcomes: enhancing analytics with buyer-experience indicators, automating account research, and upskilling RevOps staff to lead AI deployment. It warns against "lugging inefficiencies of the past into a more demanding future" and recommends that organizations kill legacy investments blocking adaptive processes.

The practical effect is that CFOs and CIOs now have analyst cover to create AI-specific line items in RevOps budgets tied to account research automation and buyer-experience analytics, not generic "AI transformation." This punishes vendors selling legacy BI or static annual planning tools. Workday Adaptive Planning, Anaplan, and Pigment benefit if they can demonstrate rolling, adaptive planning; vendors locked into annual cycle paradigms face budget cuts.

Forrester also pushes buyers toward data ontologies and semantic layers capable of powering AI, which elevates middleware platforms like Openprise, Tray.io, and Syncari. For enterprise buyers, this means the RevOps stack conversation now starts with data orchestration and ends with AI analytics, not the reverse.

Practitioner standards set new vendor evaluation criteria

RevOps Reality Check published detailed guidance on building AI-ready data foundations, and while not a vendor announcement, it codifies what enterprise buyers should demand in RFPs. The piece insists that RevOps teams define universal records for lead, contact, account, and opportunity and enforce those definitions across all tools. It identifies six firmographic fields — company name, website, employee count, industry, revenue, and geography — as the backbone of every GTM engine.

This creates a clear vendor filter: platforms that cannot enforce a unified data dictionary or ingest the "Super Six" fields without custom code are now architectural risks. Buyers evaluating CRMs, marketing automation platforms, or data warehouses should ask vendors how they handle conflicting definitions of "lead" across sales and marketing and whether the platform supports versioned, governed data dictionaries.

The guidance also elevates orchestration platforms. If a buyer's CRM and MAP cannot natively agree on account definitions, middleware like Openprise or Syncari becomes required infrastructure, not optional tooling.

What to watch

Expect RevOps RFPs in Q1 2026 to include explicit data governance and orchestration requirements. Vendors that position AI forecasting or pipeline analytics without addressing data quality will face skepticism from boards using Forrester's guidance as a checklist. LeanData, Openprise, Tray.io, and Syncari gain leverage as buyers treat orchestration as a prerequisite for AI.

The risk for enterprises is execution. Forrester and LeanData both warn that insufficient data quality blocks AI value, which means organizations face meaningful failure risk if they buy AI-first RevOps tools before fixing foundational data and process issues. Boards should sequence investments: data orchestration and governance first, AI co-pilots second. Any vendor promising AI-driven RevOps outcomes without requiring a data quality audit is selling risk.