Sapient Insight Group’s Stacey Harris and West Monroe’s Kim Seals recently explored the state of AI in HR. HR tech buyers are tasked with making “pivotal decisions” about the systems that will drive their AI strategy forward, with a focus on easy integration, advanced security and embedding solutions into the flow of work.
They offer these strategies to evaluate HR tech offerings:
Transparency and explainability: Can the vendor clearly explain how the AI was trained, what data it uses and how it generates results? Is there visibility into the model’s decision process and links to the underlying data? Are AI-driven features clearly labeled in the product for user awareness?
Control and oversight: Do administrators have tools to enable, disable or customize AI features to align with governance and compliance needs? Are there dashboards or audit reports that track AI use and outcomes? Can HR leaders override automated recommendations when necessary?
Bias and fairness: Does the vendor provide documentation or testing that demonstrates bias detection and mitigation? Can the organization test or recalibrate the impact of AI across demographic groups? Does the vendor retest fairness after model updates?
More from Stacey Harris and Kim Seals: Beyond the buzz: Inside AI-enabled HR functions
Integration and data flow: Can the AI securely connect to other enterprise systems and data sources? What APIs or orchestration tools are supported? What data types (transactional, metadata or behavioral signals) are accessed, and can data sharing be restricted by purpose?
Data privacy and sovereignty: Where is workforce data stored and processed, and does it comply with local and global privacy laws such as GDPR or CCPA? How does the vendor handle anonymization (removing personal identifiers) or pseudonymization (replacing them with keys) when models are trained or retrained?
Performance and reliability: Are there measurable benchmarks for accuracy, efficiency or user satisfaction? Does the vendor provide independent testing results and monitor for model drift or performance degradation?
Human oversight: Does the system allow a “human-in-the-loop” process for high-risk decisions such as hiring or pay? Are escalation paths clearly defined when AI results conflict with human judgment?
Usability and readiness: Is the AI intuitive enough for HR staff and managers to use without technical expertise? Does the vendor offer training, in-product education or explainable outputs that help build user trust and adoption?
