When an AI feature inside an HCM platform underperforms, the instinct is to blame the model. But models get better every few months on their own — what doesn't improve automatically is the messy data and broken workflows sitting underneath them. That mismatch is why Gartner has predicted that roughly 60% of AI projects lacking AI-ready data will be abandoned through 2026. Writing for HRMorning, Ivan Colmenares of AllianceHCM argues that these failures follow a predictable pattern — and offers a five-step framework to prevent them.

Step 1: Get the Data Right Before Anything Else

Stale earnings codes, duplicate records, ghost employees — HR teams often assume AI will paper over these gaps. In reality, a new model won't repair bad data; it will surface it. The recommended approach is to audit records against the six DAMA data-quality dimensions — accuracy, completeness, consistency, timeliness, uniqueness, and validity — assign a named owner to every gap found, and test the tool against real HR questions with verified answers before launch.

Step 2: Train It Like a New Hire

Feeding the tool only the employee handbook is a common shortcut that leaves it underprepared. It needs the full operational picture a competent new hire would get: leave and FMLA procedures, benefits plan rules, pay bands, job descriptions, HR SOPs, HRIS field definitions, and state or local labor-law addenda. Two safeguards matter here: strip or access-gate PII before ingestion (HIPAA, ERISA, and GDPR exposure is real), and version-control every document — an AI grounded on an outdated handbook recreates the very compliance risk it was meant to eliminate.

Step 3: Keep Humans on the Hook

Decisions with legal weight — terminations, accommodations, discipline — should stay with a person who can own the outcome. Accountability can't be delegated to software. The same discipline applies to oversight: logging, audit trails, and human review should be switched on before go-live, not bolted on after the first bad answer reaches an employee.

---

Step 4: Meet People Inside Their Workflow

Adoption dies the moment employees have to leave their normal systems to use a tool. Healthcare offers the proof point: AI took hold quickly in scheduling and documentation because EHR vendors embedded it directly into the software clinicians already lived in, rather than selling it as one more thing on an overloaded plate. HR should copy that playbook.

Step 5: Define Success Early, Then Iterate

Success metrics should be set before training begins. Fast signals — accuracy, hallucination rates, deflection rates — appear within weeks. Slower ones — time saved per transaction, falling ticket volume, cheaper or faster hiring — land around 90 and 180 days, and those are the numbers leadership will ask about at budget time. Every wrong answer caught is a data correction waiting to happen: feed it back, keep the corpus current, and remember that when the data goes stale, the tool goes with it.

The bottom line: the HR teams that see real returns won't be the ones with the newest or priciest technology — they'll be the ones that did the unglamorous groundwork first.