Everyone Wants to Be AI's Data Layer. Who's Measuring the Work?
Every enterprise AI roadmap eventually runs into the same question: where will the agents get their context? An agent that drafts a staffing plan, routes a request, or answers a manager's question about a team is only as good as the data it can reach. HR technology vendors have recognized that, and over the last few months several of them have made the same pitch to the buyer. For CIOs, the pitch deserves an architectural read rather than a product one: which data does each layer actually hold, and what is still missing?
Why is everyone suddenly claiming to be the data layer?
AI agents have made workforce context valuable infrastructure and four announcements in quick succession show how quickly the category is forming:
- HiBob. On September 1, HiBob announced an investment led by Salesforce and described it as a milestone in its evolution from an HR platform "into an organizational intelligence layer for modern businesses" (HiBob). Reworked reported the round at $166 million (Reworked).
- Salary.com. On September 28, Salary.com launched SalaryTalent, which it calls "an AI-native, compensation-first talent management platform" (Salary.com via GlobeNewswire).
- TechWolf. In its July partnership announcement with WTW, TechWolf described itself as "the data layer for the AI era of work" (TechWolf and WTW via GlobeNewswire).
- Handshake. On September 22, Handshake launched AI Skills Studio, where learners complete projects and "receive a verified skill badge, giving employers evidence of applied AI ability" (Handshake via PR Newswire).
Reworked summed up the pattern in a section heading of its HiBob coverage: "Everyone Wants to Be the Contextual Layer for AI Agents" (Reworked). The same article quotes HiBob CEO Ronni Zehavi conceding the architectural point that matters most to a CIO: "No single system has enough context on its own."
What does each layer actually record?
Each layer records a real and useful part of the workforce picture. Read through a CIO's lens, the differences come down to the type of data each one holds and how current it is.
| Layer | What it records | How the data is produced |
|---|---|---|
| Organizational structure (HiBob) | People, roles, teams, reporting lines, permissions, and plans | Entered and maintained in the HR system of record |
| Compensation (Salary.com) | Pay, job architecture, and market rates | Leveled job data and market benchmarks |
| Skills intelligence (TechWolf) | Skills, work, and labor market data | AI-driven skills, work, and market intelligence models |
| Credentialed skills (Handshake) | Completed projects and verified badges | Learners completing structured missions |
| Observed work (Prodoscore) | Capacity, workload, collaboration, and tool adoption | Measured continuously from activity across the business tools people already use |
Most enterprises will run several of these layers at once, alongside Workday, SAP SuccessFactors, or UKG. The point for architecture planning is what the first four share: each one describes the workforce as designed, priced, or credentialed. Only Prodoscore data describes the workforce as it actually works.
Which layer is nobody claiming?
The layer nobody in this race is claiming is observed work: how people actually spend their time across the tools the business runs on. An org chart says who reports to whom, but not who the team actually depends on. A pay band says what a role is worth, but not whether the role is overloaded. A skills profile or badge says what someone has done or is likely able to do, but not what they are doing this quarter.
That gap is structural rather than a shortcoming of any one vendor. Systems of record are built to hold declared facts: a hire date, a title, a manager, a salary. They aren't built to capture continuous behavioral signal, such as how work flows through CRM, email, collaboration, and desktop applications, where it piles up, and which tools people actually adopt. Most enterprises already feel this as a data silo problem: activity intelligence data is spread across dozens of applications, and none of it reaches the HR stack.
Prodoscore's view is that this behavioral layer is the missing half of productivity intelligence data, and that it complements every layer above rather than competing with any of them. Prodoscore's workforce analytics combines two views of that layer: employee productivity data on capacity and workload, and Skills Intelligence, which measures skills from the work itself instead of inferring them from profiles.
Why do AI agents need to know how work gets done?
AI agents need observed work data because the questions leaders bring to them are usually about work, not structure. Consider a few prompts an agent will be asked in the next year:
- Which team can absorb a new initiative without a new hire?
- Where is workload concentrated on a few people who are at risk of burning out?
- Is the AI tooling we bought in the spring actually changing how people work?
- Who should we move into a role that has been reshaped?
An agent grounded only in org-chart, pay, and profile data can answer these with the workforce as it was documented, which may be months out of date. It can say who holds a title, but not who is at capacity. It can say who earned a badge, but not who uses the skill every day. The real context includes how the organization works in practice, not just how it is drawn. A stale picture built from historical data and an agent with confident language is a risk to decision quality, not an improvement.
How do you add a behavioral layer without creating another silo?
Add it as a feed into the systems you already run rather than as a new destination. Workday, SAP, and UKG manage the workforce as it exists on paper, and they should stay the system of record. A behavioral layer's job is to supply what those systems can't capture and to deliver it where decisions are already made.
In practice that means two integration paths a CIO will recognize:
- An outbound data API that moves activity and workforce data into the warehouse, BI tools, or HRIS dashboards already in place. The Prodoscore Data API is built for that pattern.
- A Model Context Protocol (MCP) connection that lets AI assistants query workforce activity alongside financial, CRM, and planning data. MCP is becoming the standard way to give agents governed access to business data, and Prodoscore's MCP Connector brings the behavioral layer into Claude, ChatGPT, and Gemini (setup walkthrough).
Governance belongs in the same evaluation. Any layer that feeds agents should meet enterprise requirements for encryption, SSO, and data portability, and Prodoscore's runs in a SOC 2 Type 2 compliant environment with SAML 2.0 SSO and MFA. The result is workforce intelligence that sits beside the org chart, the pay data, and the skills profile, and gives agents the one kind of context the others leave out.
Every layer in this race describes part of the workforce. The part your agents will need most is how the work actually gets done. Talk to us about adding the behavioral layer to the systems you already have.
