EMEA leaders want AI-driven ROI. HR holds the key.
AI confidence is high across the UK and EMEA, even as broader economic optimism slides. Leaders believe AI will speed up innovation and free teams for higher-impact work. That belief is fine. But without real investment in people, the promised returns stall.
This is an HR problem disguised as a tech bet. Skills, role design, governance, and change management decide whether AI delivers or drifts. The data backs it up.
Leaders expect AI to create time, ideas, and better decisions
IBM Consulting's "The Race for ROI" report shows what senior leaders want from AI and automation. Among 500 UK leaders, 41% expect AI to drive new ideas and innovation. Another 41% expect more time for creative work. And 39% expect stronger strategic decision-making and planning.
Across EMEA, roughly one in five leaders say they're already hitting current AI ROI goals, and 92% expect to reach them within two years. Meanwhile, 79% are using or planning to use AI to speed up innovation cycles, from scenario simulation to hypothesis testing.
The reality check: skills are the bottleneck
Here's the uncomfortable part. Recent research shows firms often overrate their own innovation outcomes. Many think they're twice as advanced as they are, while fewer than a quarter have actually sold even one technology as part of their offering. Other studies suggest fewer than one in ten companies see a clear return from AI today.
The pattern is consistent: the tech isn't the issue. Capability is. While 62% of organisations say they haven't tapped AI's full potential, only 38% prioritise AI upskilling as part of their transformation. In the UK, just 45% offer company-wide or role-specific AI training.
As Leon Butler, IBM UK & Ireland chief executive, put it: "UK businesses are clearly seeing the productivity benefits of AI, but the real opportunity lies ahead - unlocking even greater value through workforce transformation and upskilling. By investing in AI skills training across all levels, organisations can outperform their peers and build a future-ready workforce that drives innovation and resilience."
What HR should do now
If your company is betting on AI, HR should lead the talent system that makes it pay off. Here's a tight playbook you can put to work.
- Run a skills baseline: Inventory current skills at team and role level. Identify AI-literate, data-aware, and process improvement skills. Tie gaps to specific workflows, not generic categories.
- Redesign roles for AI-enabled work: Define where AI assists, reviews, or automates. Add clear "human-in-the-loop" points. Introduce roles such as AI product owner, AI ops lead, and quality reviewer where needed.
- Set guardrails early: Usage policy, data privacy rules, acceptable prompts and outputs, and escalation paths. Use a simple approval matrix so managers know what's allowed.
- Create a tiered learning path:
- AI literacy for everyone: concepts, safe use, bias basics, and everyday workflows.
- Role-specific depth: procurement, finance, sales, HR, engineering-teach tools and patterns each function will actually use.
- Manager enablement: how to redesign work, set expectations, review outputs, and measure outcomes.
- Launch small, visible pilots: Pick 2-3 processes with clear metrics (e.g., time-to-draft, cycle time, error rate). Train the team, document the playbook, and show before/after results.
- Build coaching and community: Name AI champions per function. Host weekly office hours. Share prompts, workflows, and wins. Normalize safe experimentation.
- Measure proficiency and adoption: Track learning completion, applied use cases per role, output quality, and time saved. Reward teams that document and scale repeatable wins.
A 90-day plan HR can own
- Weeks 0-2: Form a cross-functional steering group. Pick 3 priority workflows. Baseline current performance and risk. Publish a one-page AI use policy.
- Weeks 3-6: Train pilot teams (literacy + role depth). Turn on approved tools with data safeguards. Launch pilots, set weekly checkpoints, and capture lessons learned.
- Weeks 7-12: Expand to adjacent teams. Publish playbooks. Introduce proficiency badges. Present a simple ROI dashboard: time saved, quality lift, risk incidents, and adoption by role.
Budget framing that lands with the CFO
- Reallocate 10-15% of AI platform spend to skills, coaching, and change support. Tools without capability burn cash.
- Fund per-employee learning time (e.g., 2-3 hours per week for 8 weeks) tied to live workflows, not generic courses.
- Negotiate fewer, better licenses. Backfill with internal enablement to cut external services spend over time.
- Hold back a portion of funding until pilots show time or quality improvements on real work.
Common pitfalls HR can prevent
- Buying tools with no playbook or training.
- One-size-fits-all courses that don't match roles.
- Skipping manager training-the most common failure point.
- Ignoring data controls and review steps, then banning tools after a preventable incident.
- Automating customer-facing work before back-office tasks are stable and measured.
Governance made practical
- Publish a simple risk tiering for use cases (low/medium/high) with matching approvals.
- Adopt a plain-language checklist for ethical and safe use. The NIST AI Risk Management Framework is a solid starting point: NIST AI RMF.
- Require human review for high-impact outputs and keep short audit trails for key decisions.
- Review quarterly: what's working, what to retire, and where to double down.
Useful resources
- IBM Institute for Business Value insights on AI ROI and adoption patterns: IBM IBV.
- Curated learning paths by job function to speed role-specific upskilling: Complete AI Training - Courses by Job.
The takeaway for HR
AI won't replace your people. But people who learn to work with AI will outperform those who don't. The companies that win are already treating AI as a workforce change, not a software install.
Shift budget to skills, redesign work, and measure outcomes weekly. Do that, and the ROI leaders talk about stops being a promise and starts showing up in your operating metrics.
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