Both versions drop the .AI naming convention (modernize.AI, enterprise.AI, decide.AI, etc.) following marketing guidance to avoid domain conflicts and potential legal exposure. They diverge on how descriptive vs. category-aligned the replacement names should be.
Names describe what the offering does. Clear for internal stakeholders and clients unfamiliar with analyst categories. Prioritises immediate comprehension over market shorthand.
Names align with Gartner/Forrester analyst categories and competitor vocabulary. Optimized for search, RFP matching, and executive recognition. Shorter, punchier, category-native.
The .AI naming convention (modernize.AI, enterprise.AI, decide.AI, operate.AI) was the original GTM direction agreed in March 2026 meetings. Marketing subsequently flagged domain availability and legal risk as blockers. All five GTM alignment sessions noted naming was pending final Marketing (Barbara) approval. V2 names were researched against Gartner Magic Quadrant categories, Forrester Wave terminology, and competitor naming conventions (Accenture, Deloitte, McKinsey, Capgemini).
Each offering maintains the same underlying capabilities and service scope. The differences are in positioning, naming, and messaging tone.
| # | Version 1 — Descriptive | Version 2 — Market-Aligned |
|---|---|---|
| 01 |
Data & AI Modernization
Hero: "Build the Foundation for AI-Ready Enterprise Data"
Tagline: AI-accelerated migration from legacy platforms to modern, cloud-native data foundations. |
Data Foundations
Hero: "Make Your Data AI-Ready"
Tagline: Modernize legacy platforms into cloud-native, governed data estates that power AI, analytics, and autonomous operations. |
| 02 |
Applied AI Engineering
Hero: "Design, Build & Scale Enterprise AI Platforms"
Tagline: End-to-end AI platform strategy, governance, and solution delivery. |
AI Engineering
Hero: "Build AI That Runs in Production"
Tagline: End-to-end AI platform strategy, governance, and solution delivery. Engineered for production, not just demos. |
| 03 |
Decision Intelligence
Hero: "From Fragmented Dashboards to Autonomous Decision Systems"
Tagline: Transform analytics into intelligent decision-making. |
Decision Intelligence
Hero: "Turn Analytics into Autonomous Decisions"
Tagline: Transform fragmented dashboards into unified, AI-native decision systems. |
| 04 |
Agentic Process Transformation
Hero: "Redesign Operations with Intelligent Agents"
Tagline: Replace routine exceptions and decisions with reasoning agents that adapt, learn, and improve. |
Process Intelligence
Hero: "Redesign Operations with Intelligent Agents"
Tagline: Go beyond RPA. Redesign workflows for AI-native execution, then deploy reasoning agents. |
| 05 |
Data & AI Managed Services
Hero: "Operate to Transform"
Tagline: Engineering-led managed services that stabilize operations, optimize spend, and continuously evolve your data estate. |
Managed Data & AI
Hero: "Operate to Transform"
Tagline: Engineering-led managed services that stabilize operations, optimize spend, and continuously evolve your data estate. |
Each card shows the key differences in positioning and references the meeting discussions that informed the direction.
The March 12 Modernize.AI GTM session confirmed Data Factory components merge into the modernization offering. The team positioned this as the foundation for enabling Enterprise AI, with a conceptual link to the AI platform pillar. Value proposition agreed: "Establish the foundation for AI and modern analytics." MigWiser 2.0 was confirmed as the primary accelerator (up to 90% faster execution). FinOps-driven TCO improvement of 30-60% was validated as a key metric.
The March 12 Enterprise.AI GTM session deprioritized "AI Factory" naming due to market overuse and vendor-specific overlap (Microsoft, AWS both use the term). Enterprise.AI was preferred as clearer and more scalable. The team agreed on a horizontal offering with verticalized packaging (Life Sciences and FSI as pilot verticals). Commercial model evolution discussed: LLM + engineering blended models, token consumption pricing, and standardized modular pricing templates.
The March 17 Modern Analytics GTM session validated "Decision Intelligence" as the end-state positioning for this offering. The team agreed on a 3-stage evolution: Modern Analytics → Conversational Intelligence → Agentic Enterprise. The offering targets business-focused personas (CMOs, CFOs, domain leaders) — the opposite of typical EPAM offerings that target IT. High market saturation was noted: many products and vendors use the "Decision Intelligence" label, creating risk that clients assume it's a product rather than a service. The team flagged alignment needed with EPAM's investor deck messaging.
The March 19 AI Process Automation GTM session positioned this as a "labor transformation" offering, not just process optimization. The team distinguished between "Autonomous Enterprise" (the vision) and the practical service delivery. The offering name was flagged as not finalized — pending Marketing review. Candidates included operate.ai variants and Intelligent Automation. Key differentiation: EPAM is engineering-first (vs. advisory-only firms) and process-redesign-first (vs. pure RPA vendors who automate existing broken processes). Three-tier engagement model: Discover, Build & Deploy, Operate & Scale.
The March 13 Platform Run GTM session established the "Operate to Transform" philosophy as the core differentiator. The team agreed on two engagement scenarios: Integrated (attached to build engagements) and Standalone (takeover optimization). Commercial model: fixed capacity or Time & Materials with explicit AI/token costs — no outcome-based pricing at this stage. Legacy systems explicitly in scope (MDM, Informatica, Hadoop) for realistic mixed-ecosystem coverage. Naming candidates discussed included operate.ai, runops.ai, dataops.ai, and update.ai — all pending Marketing approval.
Consider these dimensions when evaluating which version to take forward. Both versions can be further refined — the naming direction is the critical decision.
V2 advantage. "Data Foundations," "AI Engineering," and "Process Intelligence" map directly to Gartner/Forrester categories. Helps with RFP matching, analyst briefings, and competitive benchmarking.
V1 advantage. "Agentic Process Transformation" and "Applied AI Engineering" are more distinctive. They signal EPAM's engineering-first, hands-on approach rather than blending into category-standard naming.
V2 advantage. Shorter names with higher search volume. "AI Engineering" and "Data Foundations" match common enterprise search queries and Google keyword research.
V1 slight edge. Descriptive names require less explanation in sales conversations. "Data & AI Managed Services" is instantly clear; "Managed Data & AI" requires a beat to parse.
Both safe. Neither version uses .AI naming convention. No known trademark conflicts for either set of names. V2 names are more generic and thus carry marginally less trademark risk.
V2 advantage. Shorter hero lines, punchier challenge framing, and more provocative taglines ("Build AI That Runs in Production" vs. "Design, Build & Scale Enterprise AI Platforms").
Browse each version's complete site — landing page and all five offering pages — then share your decision.