HR AI Needs a Post-Termination Evidence Support SLA
The Email After the Contract Ended
The email arrived eleven months after the system was shut down.
A former candidate wanted to know why he had been rejected from a warehouse supervisor role. He had already asked once, during the hiring process. The employer had reopened the file, found a mismatch between the job requirement and a shift-availability summary, and corrected the record. The recruiter moved on. The vendor support ticket was closed. The pilot ended.
Then the company replaced the recruiting AI vendor.
The new platform had the candidate profile, the requisition, and the final disposition. It did not have the old prompt template, the model route, the scheduling connector trace, the reviewer comments inside the vendor console, or the support engineer’s explanation of which cached policy document had shaped the wrong summary.
Legal asked for the original evidence package. HR operations asked the old vendor to reopen the tenant. Procurement found the termination clause. Security found the data return certificate.
The clause said customer data would be exported or deleted within 30 days.
It did not say who would help explain a disputed AI-assisted employment decision after the export was complete. It did not say how fast the vendor would respond to a regulator, an employee appeal, a candidate complaint, a subpoena, or a litigation hold. It did not say whether the vendor would provide a schema dictionary, a custody affidavit, a model-change history, a tool-call reconstruction, or a technical witness.
The employer had won the exit right. It had lost the support clock.
That is the next gap in HR AI procurement.
Over the past month, the buyer conversation has moved through a chain of controls: evidence packets, audit rooms, kill switches, recovery SLAs, remediation warranties, evidence escrow, decision recall, correction propagation, subprocessor chain of custody, runtime tool approval ledgers, and evidence portability. Each layer answers a real operating question. Can the employer stop the agent? Can it find the bad output? Can it correct every downstream copy? Can it export the evidence when it leaves the vendor?
Post-termination evidence support asks the question that comes after export.
Can the employer still use the evidence when the vendor relationship is over?
In ordinary SaaS, contract termination is mostly a data problem. The customer wants its records returned, deleted, or migrated. In HR AI, termination is also an explanation problem. The records that matter are not only resumes, employee profiles, requisitions, pay files, case notes, and performance documents. They include prompt versions, model routes, tool traces, approval events, human review records, correction receipts, downstream propagation logs, support tickets, schema definitions, and integrity proofs.
Those artifacts do not explain themselves.
An export without support is a box of parts. It may be complete enough for storage and still useless when someone asks what happened to a person.
The next HR AI contract fight will be about the post-exit period: how long the vendor must support evidence requests, how quickly it must respond, what artifacts it must preserve, who pays, and which people remain available when the employer has to answer after the system is gone.
The Law’s Clock Outlasts the Contract
The strongest reason this issue is becoming urgent is that employment records live longer than software subscriptions.
The EU AI Act makes the direction clear. Annex III classifies systems used for recruitment, selection, candidate evaluation, promotion, termination, task allocation, and performance monitoring as high-risk employment or worker-management systems. Article 86 gives an affected person the right to obtain a clear explanation of the role of the high-risk AI system and the main elements of the decision when the output significantly affects them.
The operational burden falls on the deployer. In HR, that is usually the employer.
If an employer changes vendors before the explanation request arrives, the obligation does not become simpler. The employer still has to know what the system did. A vendor portal that worked during the subscription is not enough if the explanation right can be exercised later.
California adds retention pressure. The California Civil Rights Council announced final approval of employment automated-decision system regulations in June 2025, with the rules set to take effect on October 1, 2025. The Civil Rights Department summary says employers and covered entities must keep employment records, including automated-decision data, for at least four years.
Four years is long enough for the original vendor team to change, the customer success manager to leave, the product line to be reorganized, the model provider to be replaced, the data schema to be updated, and the tenant to be archived.
Colorado is moving in the same direction. The SB26-189 bill page describes automated decision-making technology as systems that generate outputs such as predictions, classifications, rankings, scores, or recommendations used to make or assist consequential decisions, including employment decisions. The bill summary says developers and deployers would need to retain records for at least three years. It also says deployers would need to provide a plain-language description of a covered system’s role within 30 days after an adverse consequential decision, while consumers would have rights to correction, meaningful human review, and reconsideration.
That is not only a deployment rule. It is a support-design problem.
The EU Data Act points from a different angle. The European Commission says the Data Act entered into application on September 12, 2025, and includes rules to help customers switch between data-processing service providers. The Commission’s Data Act page frames switching as a way to remove barriers between providers and support data interoperability.
HR AI evidence will test that idea. A customer may be able to switch platforms and still lack the practical support to interpret the exported AI evidence. Data portability solves movement. Evidence support solves use.
NIST gives buyers the clearest contract vocabulary. The NIST AI Risk Management Framework Generative AI Profile tells organizations to document third-party GAI incidents, establish third-party incident response plans, define ownership, rehearse response plans, address redundancy for system artifacts, and review vendor contracts for liability, serious incident notification, response times, and availability of critical support. It also warns that arbitrary or non-standard termination terms can amplify risk.
That last point is the bridge to HR AI.
The vendor can stop providing the live product. It may still need to provide evidence support. The employment decision does not expire when the subscription does.
Adoption Is Creating a Long Evidence Tail
HR teams are adopting AI in exactly the workflows where later evidence requests are likely to appear.
On April 30, 2026, ICIMS and Aptitude Research released survey findings from more than 400 U.S. talent acquisition leaders and practitioners. Sixty-nine percent of companies said they were using AI in some capacity in talent acquisition, but only 18% said they used it broadly across hiring processes. Screening led the use cases at 58%, followed by candidate communication at 54%, assessments at 50%, and sourcing at 46%. Nearly half, 46%, said they were using or planning to use agentic AI in talent acquisition.
Those are not harmless back-office automations. Screening, communication, assessment, and sourcing all generate artifacts a candidate may later question. Agentic workflows make the trace more complex because a single request can call tools, retrieve documents, write statuses, draft messages, and move records across systems.
The same report found that 82% of companies considered transparency and explainability important, while 45% did not yet have a formal AI governance framework. That gap will show up hardest after termination. During the subscription, the vendor can compensate for weak customer governance with account support, dashboard access, and ad hoc exports. After termination, the customer has to stand on the evidence.
SHRM’s 2026 report shows the same weakness inside HR. SHRM surveyed 1,908 HR professionals in December 2025 and found that 39% had AI adopted in HR functions, with another 7% intending to launch during the year. Across organizations, 62% used AI somewhere. Recruiting was the most common HR practice area at 27%, followed by HR technology at 21%, learning and development at 17%, and employee experience at 14%.
Yet 56% of HR professionals said they did not formally measure the success of AI investments at all. Legal and compliance led AI governance in 37% of organizations. HR was often collaborating, but not always steering.
That is how post-termination gaps form. Legal owns the contract. IT owns the integration. Security owns access review. HR owns the business process. Procurement owns renewal. The vendor owns the logs. The candidate or employee owns the grievance.
Nobody owns the period after the contract ends.
Greenhouse’s 2026 recruiting benchmark adds the operating pressure. Greenhouse reported that recruiters were hiring at more than twice the 2022 rate despite higher application volume and a longer time to fill. In the same benchmark series, the company shows the recruiting function absorbing far more volume with leaner teams and more tooling.
That matters because high-volume systems create high-volume evidence. A recruiter may not remember a specific candidate eighteen months later. A manager may have left. The vendor’s account team may have changed. The only stable memory is the record. If the record depends on vendor explanation, the employer needs a post-exit support right before the first dispute arrives.
Grant Thornton gives the enterprise version. Its 2026 AI Impact Survey, based on 950 C-suite and senior business leaders, found that 78% lacked strong confidence that they could pass an independent AI governance audit within 90 days. The report describes organizations scaling AI they cannot explain, measure, or defend.
HR cannot treat that as a general governance problem. Employment decisions have names attached. When a disputed record appears, the audit is not theoretical.
It is one person asking for an answer.
What Breaks After the Vendor Leaves
The cleanest way to understand post-termination support is to list what fails after exit.
The first failure is identity. During the subscription, the vendor knows which tenant, workflow, agent, connector, model route, support ticket, and customer admin relate to a disputed output. After termination, that context may be scattered across archived systems. If the employer exported data without the vendor’s internal identifiers, the old support team may not be able to map an external case ID back to a run, prompt version, or tool invocation.
The second failure is schema memory. A JSON export may include fields such as tool_result_class, review_state, fallback_route, or policy_snapshot_id. Those fields are useful only if someone can explain what they meant at the time of export. Schemas change. Enums are renamed. Deprecated fields disappear from current documentation. A later reviewer may need a data dictionary that describes the old product, not the current one.
The third failure is model and prompt history. A vendor may export the prompt version used for a decision but not the surrounding release history: when the prompt changed, why it changed, which model route it used, whether a fallback model ran, and whether the model provider changed in the same period. Without that history, the employer can show the text of the prompt but not the conditions under which it operated.
The fourth failure is tool reconstruction. Agentic HR workflows rely on connectors, MCP servers, retrieval sources, calendars, assessment systems, HRIS fields, ATS objects, payroll queues, and case workflows. An exported trace may show that a tool was called. It may not show whether the tool schema had changed, whether the data source was stale, whether the returned field was required or optional, or whether the tool had write authority.
The fifth failure is human review evidence. The vendor may hold review metadata that the employer does not store internally: how long the reviewer spent on the decision screen, what warnings were visible, which confidence labels were shown, whether the recruiter expanded the evidence panel, and whether a manager accepted or overrode the recommendation. That evidence determines whether oversight was meaningful or theatrical.
The sixth failure is correction continuity. If a record was corrected before termination, the employer needs to prove the correction was propagated. That may require downstream receipts from the ATS, HRIS, email, case management, payroll, manager packets, analytics datasets, and vendor telemetry. Exporting the final record is not enough. The employer must prove the old output was superseded.
The seventh failure is legal support. An HR team may not only need data. It may need the vendor to sign a declaration that the evidence package was exported from a production system, explain the logging architecture, confirm retention boundaries, identify subprocessors, or provide a technical representative for a regulator call.
None of these tasks are unusual during an active subscription. Vendors already help customers answer audit questions, support security reviews, interpret product logs, produce incident summaries, and respond to escalations.
The unusual part is doing it after the customer is gone.
That is why the support right must be negotiated before termination, not pleaded after it.
Control Planes Can Generate Evidence They Do Not Promise to Support
The platform market is moving toward richer agent evidence. That is good news. It does not close the post-termination gap.
Microsoft’s Agent 365 is the clearest signal from the productivity and security stack. Its Bring Your Own MCP server preview lets organizations register remote MCP servers with Agent 365, route them through the Agent 365 Tooling Gateway, and put them through a developer-to-admin flow. Microsoft describes admin review, approval or rejection, Entra permission grants, runtime enforcement, server-level blocking, declared tool snapshots, and Defender advanced hunting for tool invocations.
That can produce powerful evidence. It can show which agent invoked which MCP server, when it happened, and what metadata surrounded the invocation. In HR, that may become part of the employment decision file.
But Microsoft governing the tool path does not automatically mean the HR AI vendor will support an old recruiting decision after termination. The trace may live in Microsoft. The candidate summary may live in the ATS. The prompt may live in the vendor. The reviewer note may live in an HCM workflow. The correction ticket may live in ServiceNow. Post-termination support has to join those pieces.
ServiceNow is moving from visibility into governed action. At Knowledge 2026, ServiceNow expanded AI Control Tower across discover, observe, govern, secure, and measure. It described 30 new enterprise integrations, runtime observability into agent behavior, NIST and EU AI Act aligned risk frameworks, least-privilege enforcement, and real-time shutdown when an agent exceeds permissions.
The company also opened Action Fabric, exposing governed enterprise actions through a generally available MCP Server across IT, HR, customer service, security, risk and compliance, and app development. ServiceNow says every action runs through AI Control Tower with identity verification, permission scoping, audit trails, session management, and role-based tool packages. Its AI Control Tower documentation also describes approval controls for AI systems, MCP servers, and AI models, including steward review before deployment.
That is close to the support workflow an employer will need. A post-termination request is not just a document export. It is a case with owners, timers, evidence tasks, legal review, vendor escalation, and closure proof.
The contract still has to say the vendor will participate.
Workday starts from the workforce system of record. In February 2025, Workday announced its Agent System of Record to manage Workday and third-party agents in one place, with agent onboarding, role definition, access controls, compliance support, real-time operational visibility, and cost tracking. In September 2025, Workday said its Agent Partner Network had grown more than fourfold to over 50 partners, with partners connecting their agents to Workday ASOR.
Workday has the natural HR advantage: many employment records begin or end in its people, money, and workflow systems. If an agent affects payroll, talent mobility, succession, recruiting, performance, or employee service, Workday may hold the business context other platforms lack.
That also raises the post-exit question. If a third-party agent connected through Workday affects an employee record, and the third-party contract ends, which party supports the evidence request? Workday may have the system-of-record event. The partner may have the prompt and model trace. Microsoft may have the tool identity. ServiceNow may have the case. The employer may have the legal duty.
More control planes mean more evidence. They also mean more support boundaries.
The buyer’s job is to make those boundaries explicit before the old vendor disappears.
The SLA Buyers Will Write
A post-termination evidence support SLA should be written as an operating schedule, not a vague survival clause.
It should start with a simple principle: for employment-impacting AI workflows, evidence support survives termination for the longer of the legally required record period, the customer’s stated retention schedule, or a negotiated minimum support period. Four years will become a common baseline because of California’s automated-decision data retention requirement. Some employers will ask for longer periods where payroll, termination, discrimination, class-action, union, or cross-border records require it.
The SLA should then define support triggers.
| Trigger | Example | Required vendor response |
|---|---|---|
| Affected-person request | Candidate or employee asks for explanation, correction, or reconsideration | Locate evidence package, explain system role, identify missing artifacts |
| Regulator inquiry | Civil rights agency, labor regulator, data protection authority, attorney general | Produce certified evidence, schema dictionary, custody statement, and technical explanation |
| Litigation hold or discovery | Employee claim, candidate claim, class action, subpoena | Preserve remaining records, suspend deletion, identify subprocessors and support contacts |
| Internal audit | Board, internal audit, legal, security, HR compliance review | Validate export integrity, answer control questions, map evidence to policy |
| Historical correction | Prior AI output must be corrected across reports, analytics, or training data | Help interpret old fields, identify affected population, confirm old model and workflow route |
| Vendor-side incident | Vendor finds a defect, model change, security event, or subprocessor issue after termination | Notify former customer, describe impacted workflows, support reassessment |
The response times should be tiered. A regulator deadline or active litigation hold cannot wait behind a routine archive question. A practical contract might use four clocks:
| Request class | Response clock | Deliverable |
|---|---|---|
| Emergency hold | 24 hours | Acknowledgement, preservation action, named support owner |
| Explanation request | 72 hours | Initial evidence location, decision timeline, artifact completeness status |
| Technical reconstruction | 10 business days | Prompt, model, tool, review, correction, and chain-of-custody package |
| Deep support | Negotiated work order | Expert statement, regulator meeting support, deposition preparation, custom data repair |
Those clocks are not universal legal requirements. They are a buying framework. The exact periods will vary by employer size, jurisdiction, workflow risk, vendor tier, and price. The point is to avoid the worst possible clause: “commercially reasonable assistance.”
That phrase is too soft for high-risk HR AI.
The SLA should define artifacts by name. At minimum, the vendor should support:
- Original and corrected outputs.
- Prompt and workflow configuration versions.
- Model route, provider, region, fallback, and material update history.
- Tool and MCP server traces.
- Input-source references and retrieval snapshots where retained.
- Human review records, including reviewer identity, role, action, comment, and elapsed time where captured.
- Approval records for high-risk tool calls or workflow actions.
- Subprocessor and chain-of-custody records.
- Correction, recall, and downstream propagation receipts.
- Support tickets, incident notes, defect notices, and remediation records.
- Export manifest, hash or signature, schema version, and data dictionary.
The SLA should also separate evidence support from product support. The vendor does not have to keep the old product live forever. It does have to keep enough artifact, documentation, and knowledgeable support available for the customer to answer later questions. That could mean archived tenants, cold storage, escrowed documentation, named expert access, support retainers, or a third-party evidence custodian.
The contract should say who can request support. HR alone is not enough. Legal, privacy, security, procurement, records management, internal audit, and an authorized outside counsel may all need access. The vendor should not slow a legal deadline because the original HR admin no longer works at the company.
The contract should say who signs. A regulator may not accept a screenshot from a support portal. The employer may need a statement from a qualified vendor representative that the export came from the relevant production system, that the schema dictionary is accurate for the period, that the artifact set is complete or incomplete for stated reasons, and that no known deletion or migration event altered the evidence.
The contract should say what costs extra. Vendors will resist unlimited post-exit support, and they have a point. Maintaining expert availability for former customers costs money. The answer is not silence. It is pricing: included support for standard requests, rate cards for deep technical reconstruction, pre-purchased incident blocks for high-risk workflows, and penalties when the vendor misses agreed clocks.
The support obligation should survive acquisition, product sunset, and subcontractor change. If the vendor sells the product line, migrates logs, replaces the model provider, changes storage architecture, or terminates a subprocessor, the customer should receive notice when those changes affect historical evidence support.
Without that clause, a vendor can comply with data return and still leave the employer unable to explain a decision.
The Vendor Economics Will Be Uncomfortable
Post-termination support changes the economics of HR AI.
Most vendors price for live service: subscription, usage, implementation, support tier, premium success, and sometimes overage. Evidence support after exit is different. It creates cost after revenue ends. It asks a vendor to retain systems, documents, personnel, and domain knowledge for customers who may have churned.
That is why the first vendor reaction will be narrow.
They will say customer data was returned. They will say logs are retained for a defined period. They will say legal hold support is available only during the term. They will say model providers do not expose enough detail. They will say old prompt versions are proprietary. They will say the customer should have exported everything before termination. They will say professional services can help at standard rates if the data still exists.
Some of that will be true.
It is still not enough for high-risk employment workflows.
The buyer’s leverage comes from timing. Post-termination support must be negotiated before the vendor is selected, not after the renewal fails. A vendor competing for a new HR AI deployment can price the risk. A vendor that has already lost the customer has little incentive to be generous.
There will be at least four pricing models.
The first is included baseline support. The vendor includes a small number of post-termination evidence requests per year for a defined survival period. This will fit enterprise vendors that already have legal, security, and compliance operations.
The second is a support retainer. The customer pays an annual fee after termination to keep evidence assistance, schema documentation, named contacts, and legal-response support available. This looks like termination assistance in outsourcing contracts, but aimed at AI evidence rather than business process continuity.
The third is escrow-backed support. The vendor deposits schema dictionaries, export tools, runbook documentation, and integrity manifests with a third party. If the vendor disappears, is acquired, or refuses support, the customer can access enough material to interpret the evidence.
The fourth is incident pricing. The customer pays only when it needs deep reconstruction, expert statements, or regulator support. This may appeal to smaller vendors, but it creates risk for employers if prices are uncapped during a legal emergency.
Each model has tradeoffs. Included support is simple but may hide the cost in subscription fees. Retainers are explicit but may be hard to justify after churn. Escrow protects against vendor failure but may not preserve human expertise. Incident pricing is flexible but weakens budget predictability.
The important shift is that post-exit support becomes part of total cost of ownership.
That will favor large platforms and mature vendors. Microsoft, Workday, ServiceNow, Oracle, SAP, Salesforce, ADP, and large ATS vendors already understand enterprise evidence, legal hold, audit support, and regulated customer demands. Smaller HR AI vendors may have better products but weaker evidence operations. They will need to decide whether to build support capacity, partner with compliance infrastructure, or avoid high-risk workflows.
This will also change procurement scoring. A vendor that can deliver a beautiful AI workflow but cannot support evidence after termination should lose points against a less elegant vendor with a stronger evidence support schedule.
That sounds harsh until a candidate asks for an explanation two years later.
Then the difference is not elegance. It is defensibility.
The Internal Owner Is Missing Too
Vendors are only half the problem. Employers also need an internal evidence owner after termination.
Today, AI governance often sits across too many rooms. Legal writes the policy. HR owns the workflow. Security reviews access. IT manages integrations. Procurement negotiates terms. Privacy reviews data flows. Internal audit tests controls. Records management owns retention. Business managers consume outputs.
Post-termination evidence cuts across all of them.
If a candidate dispute arrives after the vendor is gone, HR may know the business facts but not the evidence schema. Legal may know the deadline but not the system path. Security may know the identity logs but not the employment context. Procurement may know the contract but not the case. IT may know the migration but not the old model route.
The employer needs an evidence custodian.
That does not have to be a new job title. It may be a role assigned to HR operations, legal operations, privacy, security GRC, or an AI governance office. The custodian’s job is to know where evidence lives, which vendor support clocks apply, which exports are verified, which schemas are still readable, which legal holds are active, which corrections are unresolved, and who can answer when an old AI record is challenged.
The custodian should run three drills.
The first is an exit drill before termination. Can the company export a complete evidence package for three sample decisions, validate hashes, open the files outside the vendor environment, and map each artifact to a human-readable timeline?
The second is a post-exit explanation drill. Thirty days after termination, can HR and legal answer a sample candidate or employee request without logging into the old product?
The third is a vendor escalation drill. Can the company trigger the post-termination SLA, reach the right vendor contact, receive acknowledgement within the required time, and get a usable answer?
Most companies will fail the first time.
That is useful. The point of a drill is to find the missing field before a regulator finds it. Maybe the export lacks prompt versions. Maybe the schema dictionary is incomplete. Maybe the old vendor’s support contact routes to sales. Maybe the exported tool trace uses internal IDs only the vendor can map. Maybe the correction receipt is readable but the downstream system no longer has the referenced packet.
These are fixable problems before exit. They become expensive problems after exit.
The evidence custodian also has to decide what not to keep. Retention is not a license to hoard. AI evidence can include sensitive candidate and employee data, pay records, accommodation material, performance notes, investigation context, and derived scores. Keeping everything forever creates privacy and security risk. The support SLA should align with lawful retention schedules and deletion obligations.
That tension will shape the next stage of HR AI governance. Employers must keep enough evidence to explain and defend decisions. They must not turn AI logs into permanent shadow personnel files.
The post-termination SLA is one way to handle the tension. The employer can keep verified evidence packages and require the vendor to support interpretation within defined windows, without keeping the old live environment open indefinitely.
The Next Fight Is the Evidence Schema
Post-termination support is also a bridge to the next platform fight: evidence format.
Right now, every major control plane is building a different piece of the record. Microsoft can see agent identity, MCP server registration, tool invocation, Entra permission, Defender telemetry, and productivity data. ServiceNow can see AI assets, approvals, workflow actions, cases, SLA timers, and operational context. Workday can see worker data, HR processes, partner agents, roles, skills, money, and workforce events. ATS and assessment vendors can see candidate records, scores, communications, recruiter actions, and disposition logic. Model providers can see model invocation metadata, policy filters, safety events, and sometimes prompt or response logs.
No single vendor owns the whole employment decision.
That means no single vendor can define the evidence package alone.
A post-termination support SLA will need an export manifest. The manifest should list every artifact, its owner, its format, its timestamp, its hash, its schema version, its retention period, its legal hold status, and its relationship to the decision timeline. It should identify missing artifacts and explain why they are unavailable. It should distinguish customer data, vendor operational metadata, model metadata, third-party tool traces, human review records, and downstream correction receipts.
That manifest will become a competitive surface.
If Microsoft makes Agent 365 traces easy to package, Microsoft becomes part of the evidence standard. If ServiceNow makes AI Control Tower cases the best place to coordinate regulator responses, ServiceNow becomes part of the standard. If Workday makes ASOR the natural registry for worker-impacting agent evidence, Workday becomes part of the standard. If ATS vendors and assessment providers expose cleaner candidate decision manifests, they protect their workflow ownership.
The winning schema will not be the most complete engineering trace. It will be the one HR, legal, security, auditors, regulators, and successor vendors can all read.
That requires restraint. Engineers may want every event. Lawyers may want every possible fact. Privacy teams may want less data. HR operations may want a timeline. Auditors may want integrity proof. Vendors may want to protect proprietary logic. Candidates and employees may want plain language.
The evidence schema has to serve all of them without becoming unreadable.
Post-termination support will force the issue because old vendor portals cannot be the long-term interface for every future dispute. Once the relationship ends, the employer needs portable evidence and a support promise around it. The support promise will reveal whether the schema is usable.
If a vendor needs three engineers and two weeks to explain its own export, the export is not mature.
If a successor vendor cannot ingest open appeals, correction receipts, or decision evidence, portability is incomplete.
If legal cannot build a timeline from the manifest, the evidence will not survive stress.
The contract, the schema, and the support operation are becoming one product.
The Closed Tenant
The warehouse candidate’s file was eventually reconstructed.
Not perfectly. The employer found the exported ATS record, the final corrected disposition, the manager email, and the internal HR case. The old vendor produced a support summary after several weeks. A former implementation consultant still had enough context to explain how the scheduling connector had mapped shift rules. The company answered the candidate.
The answer was late.
Nobody had designed the exit around the person who might ask later. The termination checklist had focused on data return, deletion, invoices, access revocation, and migration. It had not asked who would explain an old AI output when the vendor dashboard was gone.
That checklist will change.
HR AI is moving into decisions with long memory: hiring, pay, promotion, scheduling, performance, termination, leave, accommodation, mobility, employee service, and workforce planning. The systems that assist those decisions will change faster than the duties attached to the records. Vendors will be replaced. Models will be upgraded. Agents will be retired. Platforms will consolidate. Support teams will move on.
The evidence will still be needed.
The employer that buys HR AI now is not only buying automation. It is buying the future ability to answer. A post-termination evidence support SLA is the clause that says the answer will not disappear when the tenant closes.
This article provides a deep analysis of post-termination evidence support SLAs for HR AI vendors. Published May 13, 2026.