The Value Migration: What Moves Where in the Agentic Enterprise

The transition from old to new isn’t uniform. Each layer of the old value chain has a specific fate.

What Changes

• System of Record → Dynamic Context Store. The data survives but reshapes for machine consumption. Static schemas become living context graphs. Margin compresses as the semantic layer above becomes the value capture point.
• UI/Dashboards → Exception Handling Only. UIs shift from daily operational tools to exception-handling dashboards where humans supervise and set strategic direction.
• SaaS Applications → Three Fates. Fork into data API (survive at lower margin), agent-native (rebuild around agents), orchestration layer (try to become Tier 1), or decline.
• Workflows → Agent Orchestration. Static workflows become dynamic agent orchestration. Gartner identified agent management platforms as “the most valuable real estate in AI.”
• Per-Seat Pricing → Outcome-Based Pricing. Harvey AI charges for legal work delivered, not seats. If 10 agents replace 100 seats, revenue falls 90% unless the vendor migrates to outcome-based pricing.

The Six Categories of Players

• AI-Native Platforms (OpenAI Frontier, Anthropic Claude Cowork) — Strongest model capabilities. Building trust from scratch in risk-averse enterprises.
• Cloud Infrastructure Players (Microsoft, Google, AWS) — Control compute, deep enterprise relationships. Microsoft’s position is particularly formidable: Azure + Office 365 + OpenAI partnership.
• SaaS Incumbents Pivoting (Salesforce Agentforce, ServiceNow, Workday) — Own the data and workflows. Risk: culture and revenue incentives optimized for UI-driven products.
• Vertical AI-Native Companies (Harvey for legal, Sierra for CX, Writer for content) — Deep domain specialization.
• Process Orchestration Specialists (Camunda, UiPath) — Understand enterprise workflows deeply. Adding AI agent orchestration.
• Emerging AI Agent Platforms (Ema, Clay, Decagon, Glean) — Built agent-first without legacy constraints.

The Counter-Argument

Data gravity is real—enterprises spent decades embedding data into Salesforce, SAP, and Workday. Compliance creates stickiness. The orchestration layer is unproven at scale (80% of AI agents are still chatbots). Incumbents can embed intelligence. Bank of America identified the central paradox: investors simultaneously price in AI capex collapse and AI adoption — as explored in the growing gap between AI tools and AI strategy — making established software obsolete. Both cannot occur at once.

Bottom Line

The SaaS — as explored in the shift from SaaS to agentic service models — value chain doesn’t collapse. It reorganizes. Value migrates from a linear chain to a barbell structure—the SaaS Hourglass. The quiet killer isn’t agents replacing software next quarter. It’s that AI reduces the headcount that uses the software. The barbelled distribution of value is the structural reality. Plan accordingly.

This is part of a comprehensive analysis. Read the full analysis on The Business Engineer.