Human + Machine: How HR Leaders Are Deciding What to Automate-and What to Keep Human
AI in HR isn't a tech project. It's a leadership decision model: can, should, and shouldn't automate. As usage grows in pockets and stalls elsewhere, the HR leader's job is to create clarity, not hype.
The signal from leading teams is consistent: protect the moments that create trust and connection, automate the repetitive, and measure outcomes like a product team.
Pearson: Draw clear lines and preserve connection
Chief HR Officer Ali Bebo and CTO Dave Treat keep a simple filter on every workflow: can we automate it, should we automate it, and where must we never automate? That framework keeps human connection at the center.
Pearson rolled out "Cara," a chatbot that answers career questions like "how do I get promoted?" and "what skills do I need for that role?" They're testing agentic AI for coaching support, but it won't replace managers. One red line is firm: AI will not make final hiring decisions.
On scale, Treat backs broad access and targeted use cases. Every employee has a Microsoft Copilot license, and agentic AI handles 63% of customer service cases. The principle: start from the outcome and design the best mix of automation plus human judgment.
Intuit: Build AI skills and rethink early-career onboarding
CTO Alex Balazs partners closely with Chief People and Places Officer Caryl Hilliard to map the skills behind the tech roadmap. They balance upskilling internal talent with selective hiring at the intersection of AI and software.
Intuit leans into entry-level hiring. New grads have strong CS fundamentals and use AI coding tools, but they often need help with collaboration and communication. A multi-week onboarding boot camp closes those gaps fast.
West Monroe: Redesign the hire-to-retire workflow
Chief People Officer Tanya Moore rebuilt core HR flows with automation. Scheduling and recruiting tasks now run through tools that cover the work equivalent of three full-time roles-without staff reductions-freeing the team to focus on higher-value work.
An interview feedback automation is on track to save $1.5 million per year. With CIO Kevin Rooney, Moore also launched an internal chatbot that assembles project teams. It asks clarifying questions about skills, speeds staffing from a week to near real time, and helps reduce bias by moving beyond the same go-to network.
Ralliant: Learn by doing, measure what matters
Ralliant formed tech-and-people working councils to embed AI into daily work and share what sticks. They track productivity, reduced friction, speed to market, adoption, engagement, and whether new skills are actually being built.
Chief Technology and Growth Officer Amir Kazmi pushes a learn-by-doing culture-even at the senior level. Everyone experiments. Humility is the point: it's early for agentic capabilities, and progress beats perfection.
A practical playbook HR can use this quarter
- Define "can, should, shouldn't." List workflows, note risks, and mark the non-negotiable human moments (e.g., final hiring decisions, tough performance talks).
- Protect human connection. Use AI to handle FAQs, scheduling, and information retrieval. Save leaders' time for coaching, context, and conflict resolution.
- Start with chatbots and staffing aids. Internal bots for policy, pay, careers, and team assembly remove friction and bias while keeping humans in the loop.
- Pair broad access with targeted wins. Offer companywide tools (e.g., Microsoft Copilot) and stand up focused use cases (service, recruiting, L&D). Track deflection and cycle-time gains.
- Upskill and hire for AI + collaboration. Short boot camps on prompting, data hygiene, and teamwork. Entry-level hires may code well with AI but need people skills. Teach both.
- Bias checks by design. Use bots to broaden candidate and staffing pools. Review data and outcomes regularly with HR, Legal, and IT.
- Keep a human decision-maker. AI can recommend and draft; people decide and own the call-especially in hiring, promotion, and performance.
- Measure like a product team. Pick 5-7 metrics: productivity, time-to-fill, interview cycle time, case deflection, employee engagement, AI adoption, and skills gained.
- Change management is half the work. Communicate where AI helps today, share wins weekly, and publish guardrails. Leaders must model usage.
- Governance that's practical. Clear data privacy rules, vendor due diligence, incident response, and a fast path to approve new use cases.
What this means for HR leaders
The gap isn't tools-it's decisions and habits. The HR-CTO/ CIO partnership sets the standard: outcome-first, human-led, measured rigorously.
If usage is flat on your teams, don't push harder. Make it safer and simpler to try. Small wins build trust, and trust scales adoption.
Level up your team's AI skills
If you're building capability across HR, start with practical training tied to job tasks. These resources can help:
- AI courses by job role for HR, recruiting, L&D, and people analytics.
- Latest AI courses to keep your playbook current.
Quick checklist to get moving
- Pick three workflows to automate in 30 days (policy Q&A, interview notes, staffing requests).
- Publish your "can/should/shouldn't" page and guardrails.
- Enable broad access to a core tool, plus one targeted pilot.
- Report weekly on adoption, time saved, and quality outcomes. Adjust fast.
Keep the human moments sacred. Automate the rest with intent.
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