AI's Next Industrial Shift: What HR Needs to Do Now
The governor of the Bank of England says AI could reshape jobs on a scale comparable to the Industrial Revolution. Andrew Bailey's point is blunt: it won't cause mass unemployment outright, but it will move people out of some roles and into others.
He also called AI the "most likely source of the next leg up" for UK growth. That only happens if training, education, and skills are in place so people can move into work that uses AI well.
What this means for HR
Expect more change at entry level and in process-heavy professional jobs. Your hiring funnel, job design, and learning strategy need an update-fast.
Bailey raised a key worry for HR: "What is it doing to the pipeline of people?" If junior roles shrink or shift, you'll need different on-ramps to build future talent.
The entry-level squeeze is real
Entry-level vacancies have fallen sharply since late 2022, when generative AI went mainstream. Job-matching platform Adzuna reported a drop of nearly a third in early-career opportunities.
Official data shows the UK unemployment rate rose to 5.1% in the three months to October, with 18-24-year-olds hit hardest. According to the ONS, 85,000 more young people were unemployed over that period-the biggest jump since November 2022. Factors include minimum wage increases, higher employer National Insurance, and the spread of AI across admin-heavy roles such as law, accountancy, and operations.
Even big firms are rethinking headcount plans. PwC's global chair, Mohamed Kande, said: "We want to hire, but I don't know if it's going to be the same level of people that we hire - it will be a different set of people."
The scope of automation is wider than most expect
McKinsey Global Institute suggests robots and AI agents could automate around 57% of employees' work hours with technology available today, replacing up to 40% of jobs in the US. That doesn't mean a one-to-one loss, but it does mean tasks will move, and job content will change quickly.
Your 90-day HR action plan
- Audit tasks, not just roles: Map the 5-10 core tasks per role. Flag high-automation tasks (research, summarising, drafting, reconciliation, scheduling, basic analysis). Decide: automate, augment, or stay human-led.
- Protect the pipeline: Create paid internships, apprenticeships, and rotational programs where juniors learn with AI from day one. Move hiring toward skills-based assessments over pedigree.
- Upskill at speed: Set a baseline for AI literacy: prompting, reviewing outputs, data privacy, bias, and brand voice. Build labs where teams practice on real workflows with human-in-the-loop checkpoints. For organised options, explore role-specific programs via Complete AI Training.
- Redesign jobs: Combine AI-first workflows with clear human oversight. Define what "good" looks like for review, escalation, and sign-off.
- Rethink performance and rewards: Measure output quality and time saved, not just volume. If AI lifts productivity, share gains with teams while keeping guardrails for accuracy and ethics.
- Update policy and risk: Approve tools, set data-use rules, and ban sensitive data uploads. Train teams on confidentiality, citations, and IP. Spot-check outputs for bias and errors.
- Plan workforce moves: Build scenarios: automate X% of hours → reskill Y% → hire Z%. Prioritise redeployment paths and targeted hiring over blanket freezes.
- Partner with Finance: Align on wage rises, employer NI costs, and AI investments so savings and redeployment funds are transparent.
Hiring in an AI-heavy market
- Profile shift: Fewer generalists, more AI-augmented specialists with domain judgment.
- Assessment shift: Use work samples that allow AI. Score how candidates brief tools, review outputs, fix errors, cite sources, and protect data.
- Onboarding shift: Teach standard prompts, style guides, and review checklists. Pair juniors with mentors who model AI-assisted workflows.
Rebuild the early-career ladder
- Launch "train-to-hire" programs with colleges and bootcamps, plus short micro-internships tied to real deliverables.
- Use returnships to re-enter the workforce with AI skills. Fund fast, stackable credentials for internal movers.
Governance that actually works
- Maintain an approved tool list with data classifications and usage limits.
- Set audit trails for critical outputs. Run regular red-team tests on quality, bias, and privacy.
- Name accountable owners: HR for skills and policy, IT/Security for tooling, Legal for IP and compliance, Business for outcomes.
What to tell your board
- Growth story: Echo Bailey: AI can lift productivity and growth if skills are funded and measured.
- People plan: Redeploy where possible, hire differently where needed, and build a new early-career funnel.
- Metrics: Hours automated, quality scores, adoption rates, time-to-fill, reskilling throughput, and risk incidents.
Useful sources
The message for HR is clear. AI will shift work, and the winners will build skills, pipelines, and guardrails faster than the market moves. Start now, measure everything, and keep people in the loop.
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