Ling-Yi Tsai has spent decades helping organizations weave HR technology into the fabric of daily work. She has guided companies through the shift from traditional HR systems to AI-enabled operating models, stitching together analytics, governance, and frontline execution. As the EU AI Act draws closer, she’s focused on how AI agents become accountable “team members” with clear lines of oversight and performance. In this conversation with Adelaide Taylor, she expands on reporting models, guardrails, and the practical rhythms that make AI adoption both ethical and fast.
As the EU AI Act’s 2025 deadlines near, how are you sequencing compliance steps across intake, validation, deployment, and monitoring, and what timelines or checkpoints work best? Please share a real example with metrics, such as audit findings reduced or deployment cycle time improved.
We work a four-stage lifecycle: intake, validation, deployment, and monitoring. Intake is where we document purpose, data sources, and workforce impact; validation stress-tests fairness, privacy, and explainability; deployment controls rollout and change; monitoring checks drifts and incidents. One client anchored checkpoints to the Act’s 2025 horizon and treated every agent like a product with explicit acceptance criteria tied to fairness and privacy compliance. While I won’t cite numbers beyond those already public, I can say the cadence created visible discipline—teams stopped treating ethics as an afterthought and built it into launch gates, and by the time monitoring kicked in, the conversation had shifted from “if” to “when” we could scale responsibly.
Your quick poll showed HR, Functional Managers, and only 20% choosing a hybrid model. Why might hybrid lag adoption today, and what tipping points move that number? Walk us through a case, including governance cadence, incident handling, and measurable outcomes.
The hybrid model’s 20% result reflects the friction of dual accountability—people imagine double the meetings and twice the bureaucracy. In practice, hybrid works when daily decisions live with functional managers, and HR or a central governance team performs periodic audits. In one case, the tipping point came when HR codified lightweight ethics checklists and the function accepted a recurring audit with pre-agreed artifacts; both sides knew who decided what and when. As the rhythm settled, issues were surfaced and resolved without derailing delivery, and that trust is what nudges adoption beyond the 20% baseline.
For HR-led reporting, how do you operationalize bias and risk management without creating bottlenecks? Describe your workflow, tools, and roles, and share before-and-after metrics on review time, fairness scores, or privacy incidents.
HR-led workflows hum when they’re modular: a standard intake form, a fairness and privacy kit, and a documented sign-off path. We lean on common tooling for reproducible assessments and require that every agent registers its intended use, data lineage, and workforce touchpoints. A small triage group handles most reviews, escalating only the edge cases so the queue doesn’t stall. The effect is tangible—teams perceive HR as an enablement partner because the artifacts are predictable and move fast, which reduces the urge to bypass governance.
In functional reporting, how do domain leaders link agent performance to departmental KPIs while avoiding ethics drift? Give a step-by-step on playbooks, dashboards, and escalation paths, including examples with accuracy, cycle time, or revenue impact.
Functional leaders start by building playbooks that translate departmental KPIs into agent objectives, while adopting baseline ethics standards from the center. Dashboards blend operational signals—like throughput and error patterns—with governance indicators, so leaders never optimize blindly. The escalation path routes anomalies first to the function, then to governance for review, which keeps the response tight and aligned. The real magic is that domain expertise shapes performance while the shared guardrails keep behavior within bounds, avoiding drift as the pace accelerates.
Hybrid models promise dual accountability and scalability. How do you split duties between functional managers and HR/compliance for daily ops vs. periodic audits? Offer a concrete RACI, audit schedule, and an anecdote where this structure prevented a costly error.
In a hybrid, daily ops and configuration sit with functional managers, while HR/compliance owns periodic audits and ethics sign-offs. Responsibilities are clear: functions are accountable for outcomes and incident reporting; HR is accountable for fairness and privacy; both are consulted on changes that affect people. A predictable audit window aligned to business cycles helps, with functions preparing artifacts and HR reviewing against shared standards. We avoided a costly error when an audit flagged a workforce-impact gap before a rollout, prompting a minor configuration change instead of a major remediation downstream—an example of dual accountability catching a misstep early.
You note agents handle data analysis, workflow automation, and transactional decisions. How do you tailor guardrails by task type, and what thresholds trigger human review? Share examples with precision/recall, error budgets, and post-incident adjustments.
We calibrate guardrails by task risk. For analysis, we emphasize transparency and traceability; for workflow automation, we add strict input validation and change control; for transactional decisions, we require approvals for sensitive actions. Human review triggers are tied to deviations from expected behavior, workforce impact, or anomalies in fairness or privacy indicators. After an incident, we tighten controls in the monitoring stage and feed lessons into the validation checklists so repetition is less likely.
Drawing on McKinsey and Gartner, what operating-model patterns actually hold up in practice? Compare two real implementations, including org design, budget ownership, and training hours, and explain what shifted the ROI within six months.
The patterns that endure are those that separate “how we run” from “how we govern.” One implementation gave functions full operational control with a central governance spine; another brought agents under HR for consistency, with functions setting context. Budget ownership followed the work in both cases, while governance was treated as a shared service so standards didn’t splinter. ROI moved within months when leaders accepted that speed and ethics can coexist—embedding training and governance criteria into normal planning rather than treating them as add-ons.
What metrics balance ethics and speed in one scorecard? Please outline governance indicators (fairness, privacy compliance) alongside operational metrics (efficiency, accuracy), and walk through a monthly review with sample numbers and decisions taken.
A balanced scorecard blends governance and execution: fairness, privacy compliance, and explainability alongside efficiency and accuracy. The review looks for patterns: are gains in speed accompanied by stable or better ethics indicators, or are we trading one for the other? When a trend lines up—like improved throughput paired with steady compliance—the team expands scope; if a red flag appears, we adjust guardrails and pause changes. Keeping both sides visible ensures no one metric dominates at the expense of trust.
For controlled pilots, how do you choose scope, exit criteria, and success thresholds? Give a play-by-play from day 0 to day 90, including sample artifacts, stakeholder meetings, and a go/no-go decision with quantified results.
We scope pilots around a contained process with clear outcomes and low regulatory exposure. Day 0 is intake: document purpose, data, workforce impact, and sign-offs; by mid-pilot we run validation exercises, test monitoring, and rehearse incident response; near the end we dry-run the deployment checklist and collect artifacts. The go/no-go meeting includes the function, HR, and governance, and decisions hinge on whether the pilot met its stated thresholds in performance and compliance. If the case is strong, we graduate the agent and schedule the first audit; if not, we iterate with a smaller scope.
Training is pivotal for managers and HR. What curriculum works, who teaches it, and how do you test proficiency? Share a syllabus snapshot, assessment methods, and the measurable behavior changes you’ve seen in production.
The best curriculum spans fundamentals, risk identification, and hands-on practice with real artifacts. Instruction mixes internal experts and external voices so teams see both organizational context and broader operating-model patterns. Proficiency is tested through scenario-based reviews and artifact walkthroughs, forcing people to apply judgment, not just memorize terms. In production, you can tell training worked when teams anticipate governance needs during intake and validation, and audits confirm that standards are living documents, not shelfware.
How do Knowledge Networks, RegulatingAI, and CAIO Connect complement each other across podcasts, webinars, and research? Tell a story of insights moving from discussion to policy to practice, and include adoption or compliance metrics.
These communities create a flywheel from conversation to codification to execution. A podcast explores an emerging practice, a webinar pressure-tests it with practitioners, and research distills it into templates and guidance. A client lifted ideas from those forums into their policies and then used the templates in their pilots, which made adoption smoother and compliance clearer. That progression—talk, refine, adopt—helps close the gap between theory and the choices teams make every day.
When HR lacks deep domain expertise, how do you close the gap without slowing iteration? Describe roles like translators, embedded risk leads, or councils, with examples of decision SLAs and the tools that made collaboration smoother.
We insert translators who speak both domain and governance, and embed risk leads in squads so questions get answered in context. Councils meet on a predictable cadence to harmonize standards and resolve cross-functional issues. Decision SLAs are explicit—who decides, within what window, and with which artifacts—so progress doesn’t stall. Shared workspaces and templates reduce thrash, letting HR and functions co-create without stepping on each other.
What’s your playbook for auditability and traceability in agent decisions, especially for transactional use cases? Walk us through logging, model/version control, and sign-offs, and share a time these records resolved a dispute or regulatory inquiry.
We require rich logging of inputs, outputs, and rationale, with model and version control pinned to deployment checklists. Every change has a ticket, every ticket has a reviewer, and sign-offs are tied to role-based permissions. In a thorny dispute, we traced a transactional decision back through its logs and approvals, which clarified intent and compliance. The records turned an argument into a learning moment and closed the loop with documented improvements.
How do you prevent inconsistent governance when functions move fast? Describe standards, reference architectures, and change control, and give a case where a shared template averted conflicting practices, with cycle-time and defect-rate data.
We use common standards and reference architectures that functions tailor but don’t reinvent. Change control is staged and proportionate to risk, with the same artifacts appearing in every lifecycle. When a shared template replaced ad-hoc approaches, duplication fell and teams converged on a single language for risk and performance. That uniformity cut noise, making it easier to spot true anomalies and course-correct quickly.
The HRTech interview with Stan Suchkov highlights AI-native learning platforms like Evolve. How are you using such tools to scale agent skills and policy knowledge? Share examples of role-based paths, completion metrics, and performance improvements in live agents.
Tools like Evolve help us package role-based learning paths for managers, HR, and technical owners. Content spans operating models, governance, and hands-on labs mapped to the four-stage lifecycle. The platform closes the loop by nudging teams to apply what they’ve learned to real artifacts, so policy knowledge becomes muscle memory. Over time, that scaffolding shows up in smoother intakes, clearer validations, and more predictable monitoring.
Do you have any advice for our readers?
Start small, but start with structure: define the four-stage lifecycle, write down who owns what, and stick to it. Treat reporting as a design choice, not a default, and be explicit about how you’ll balance daily speed with periodic oversight. Use communities like Knowledge Networks, RegulatingAI, and CAIO Connect to benchmark your approach and borrow what works. And remember that the 20% hybrid signal isn’t a ceiling—it’s an invitation to build dual accountability that feels natural in your culture.