Four executive sponsors co-owning an initiative is not the same as four executives agreeing on what the initiative is for. The difference doesn't show up until something has to give — and in an HRIS consolidation, everything eventually has to give.
A Fortune 1000 organization had set an OKR to consolidate six separate HR systems into one unified, AI-enabled HRIS platform. The VP of HR, CIO, CISO, and COO were co-sponsoring it. Four months into the initiative, demos had not started, requirements documentation was incomplete, the CISO and IT architecture team were not talking to each other, and the payroll manager had not been asked a single question about her cutover requirements.
The Problems Underneath the Problem
The IT architecture team had been using an AI tool to analyze the existing system landscape and generate architecture options. The tool was producing technically sound recommendations — that had no visibility into the CISO's data classification requirements. Several of the integration patterns it was recommending would have consolidated restricted employee data in ways that violated the organization's data handling policy.
The payroll manager had not been consulted at all. She had lived through a payroll migration at a previous employer and would not authorize cutover to a new system without running parallel payroll for a minimum of three months. That requirement, if surfaced at contract negotiation, would push the live date past the Q4 OKR deadline. If surfaced now, it could be planned for.
Establishing Intent Before Evaluation Began
The COO convened a half-day session with the four sponsors. The outcome they agreed on was specific and ordered: deliver a single unified HRIS platform that measurably improves employee satisfaction with HR interactions, eliminates manual data reconciliation for the HR team, meets the organization's security and compliance requirements, and is live in production by end of Q4. Security compliance was the constraint that could not be traded.
The IT architecture team's AI tool was reconfigured with the CISO's data classification requirements as constraints rather than considerations. When re-run against the same vendor landscape, the recommendation set changed substantially. The tool hadn't been wrong — it had been working with incomplete governing constraints.
What Changed
Vendor evaluation began six weeks after the intent session with a shortlist built against criteria all four sponsors had agreed governed the decision. The parallel payroll requirement was disclosed to shortlisted vendors in the RFP. The platform selected met the CISO's data architecture requirements, supported the AI features within those constraints, and had an implementation timeline the payroll manager approved before the contract was signed. The initiative went live in Q4.
The Intent Statement They Used
Outcome
Deliver a single unified HRIS platform that measurably improves employee satisfaction with HR interactions, eliminates manual data reconciliation, meets the organization's security and compliance requirements, and is live in production by end of Q4. Security and compliance requirements are the governing constraint — no architectural decision that violates them advances regardless of feature or cost implications.
Key Boundaries
- No vendor may be selected whose data architecture requires consolidating restricted-class employee data in ways that violate the organization's data classification policy. Held by: CISO
- Cutover requires a minimum three-month parallel payroll period with sign-off from the payroll manager before go-live. Disclosed to vendors in the RFP. Held by: Payroll Manager / CFO
- The IT architecture AI tool operates with CISO data classification requirements as hard constraints, not scoring variables. Held by: CIO
Decision Authority
- COO owns all cross-sponsor conflicts and final vendor selection authority.
- CISO owns data architecture compliance determination for all vendor options.
- Payroll Manager owns cutover authorization. No go-live date is set without payroll manager approval.
This is how Intent Management™ works in practice — getting the right people to agree on the right things before AI tools make the wrong decisions for them. If your organization is navigating something similar, let's talk.
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