AI Takes Over at Goldman Sachs as Jobs Vanish for Good

AI Cuts At Goldman Signal A New Baseline For HR

Goldman Sachs plans to reduce roles where AI now outperforms people. Many who lose those roles will not get them back. This is not a temporary cycle. It's an industry reset.

If you lead HR, treat this as a blueprint moment. Rebuild job design, skills, and workforce planning around AI as a core tool, not a side project.

What This Means For HR Leaders

• Job recovery is unlikely for tasks that AI can produce faster, cheaper, and with consistent quality.
• The shift is task-first: AI replaces task bundles inside roles before it replaces entire roles.
• Winners reallocate talent to higher-value work instead of only cutting headcount.
• Policy, compliance, and skills will decide whether your AI program saves costs without creating new risk.

Immediate Actions (Next 30-90 Days)

• Map tasks, not titles: For each role, tag tasks as Automate (A), Assist (S), or Human-only (H). Prioritize high-volume A/S tasks for redesign.
• Freeze and redesign: Pause hiring on roles with 30%+ A-tasks. Repost as hybrid roles with clear AI expectations and output targets.
• Set standards: Write "AI-use clauses" into job descriptions, performance reviews, and SOPs. Define who is accountable for final outputs.
• Stand up an AI review board: HR, Legal, Risk, Data, and Procurement. Approve tools, vendors, and monitoring. Log decisions.
• Baseline AI literacy: Run short, role-based training and practice labs. Start with office suites, analysis, and prompt skills. See AI courses by job.

Redeploy, Don't Just Reduce

• Internal mobility first: List A/S task owners at risk and match them to revenue, risk, or client work that needs headcount.
• Skill sprints: 4-6 week micro-credentials for data analysis, workflow automation, and compliance documentation.
• Incentives: Tie redeployment bonuses to completion of projects that show time saved or error-rate cuts.
• Measure ROI: Compare severance cost vs. reskilling plus time-to-productivity. Fund the option with higher long-term value.

Role Redesign Examples (Banking Context)

• Analyst: From building slides and models to validating AI outputs, testing scenarios, and client-ready synthesis.
• Compliance ops: From manual reviews to supervising AI triage, exception handling, and audit trails.
• Research associate: From raw data pulls to query design, fact-checking, source ranking, and risk notes.
• New roles: Automation operations lead, prompt and workflow specialist, AI risk analyst.

Policies, Risk, and Compliance

• Bias and impact checks: Test outputs for disparate impact. Document fixes and retests. See EEOC AI guidance.
• Data controls: Block sensitive data from public tools. Use approved vendors. Require logs and explainability for material decisions.
• Auditability: Keep prompts, outputs, and human approvals for regulated processes.
• Vendor contracts: Include SLAs for accuracy, security, incident response, and model change notices.

Hiring For An AI-First Team

• What to screen: systems thinking, data literacy, prompt writing, judgment under uncertainty, and basic scripting familiarity.
• Practical tests: Give a real workflow, an AI tool, and 60 minutes. Judge output quality, verification steps, and documentation.
• New expectations: Every role ships more output per FTE with AI. Performance plans should show how.

Metrics That Matter

• Percent of tasks by role tagged A/S/H (and updated quarterly)
• Time-to-output and error rate before vs. after AI
• Internal mobility rate for at-risk employees
• Training completion and tool adoption by team
• Incidents: bias, data leakage, policy exceptions
• Cost per deliverable vs. baseline

Communication Playbook

• Be explicit: Share the task map, selection criteria, and timelines. No vague promises.
• Offer paths: Provide clear reskilling tracks with guaranteed interviews for target roles.
• Support exits: Strong severance, coaching, and alumni referrals. Treat people well and protect your brand.

Context You Can Use With Leadership

• Large banks will automate a meaningful slice of research, operations, compliance, and support tasks.
• External estimates indicate broad task exposure to AI across industries. See Goldman Sachs research.

The Bottom Line

AI-driven cuts at Goldman are a preview, not an outlier. Some roles will not return. Your job is to reduce risk, move talent to higher-value work, and set new performance standards.

Move quickly, measure everything, and rebuild roles around AI from the task level up.