How Shell Is Using AI to Navigate a 93,000-Person Workforce Transformation
How Shell Is Using AI to Navigate a 93,000-Person Workforce Transformation
Current date (UTC): 2026-05-20
Current time (UTC): 21:10
When Shell announced plans to cut approximately 10,000 corporate and office roles in 2023-2024, the headline told only half the story. The other half — the part most employers facing sector transitions should study — is what Shell is simultaneously building: an AI-powered workforce intelligence system designed to reskill, redeploy, and reshape a 93,000-person global workforce for the energy transition.
This is not a standard HR automation play. Shell is attempting something few organizations have tried at this scale: using artificial intelligence to map every employee's existing skills against the competencies required for a fundamentally different energy future, then making data-driven decisions about who can be reskilled internally and where external talent acquisition is unavoidable.
The Scale of Shell's Workforce Challenge
Shell's workforce transformation sits at the intersection of two opposing forces. On one side, the company is streamlining legacy operations — the Reuters-reported workforce reduction targeted redundant corporate and office positions as part of a broader cost-efficiency drive (Reuters, 2024). On the other, Shell's Powering Progress strategy commits the company to net-zero emissions from its operations by 2050, which demands an entirely new talent profile across the organization (Shell Powering Progress Strategy).
The skills required for clean energy operations differ fundamentally from those that built Shell's petroleum business. Where the company once needed petroleum engineers, drilling specialists, and refinery operators, the energy transition demands electrical engineers, grid integration specialists, hydrogen technologists, carbon capture experts, and renewable energy project managers. According to LinkedIn Talent Insights data on energy sector workforce trends, this skills mismatch represents one of the largest sectoral reskilling challenges in the global economy (LinkedIn Talent Insights).
Shell's 2024 Annual Report confirms the scale: approximately 93,000 employees worldwide, operating across more than 70 countries, with ongoing investment in workforce development and skills programs to support the transition (Shell Annual Report 2024).
How Shell's AI Skills Intelligence System Works
Shell's approach to this challenge follows a structured, technology-enabled framework that other large employers can study and adapt.
Step 1: AI-Powered Skills Taxonomy
At the foundation is an AI-driven skills taxonomy that categorizes and maps the competencies of Shell's global workforce. Rather than relying on static job descriptions or self-reported skill inventories, the system uses AI to build a dynamic, continuously updated picture of what each employee can do — and what adjacent skills they could realistically develop.
This taxonomy doesn't just catalog current capabilities. It maps them against the skills profiles required for emerging clean energy roles, creating a gap analysis at both the individual and organizational level.
Step 2: Gap Analysis and Pathway Generation
Once the AI system identifies the distance between an employee's current skill set and the requirements of target clean energy roles, it generates reskilling pathways. These pathways consider factors including:
- The size of the skills gap (how many new competencies are needed)
- The adjacency of existing skills (a petroleum engineer's thermodynamics knowledge transfers to hydrogen systems)
- The time and investment required for reskilling versus external hiring
- The employee's career trajectory and development history
This analysis produces a decision framework: for each role transition, the system indicates whether internal reskilling is viable or whether external talent acquisition is the more practical path.
Step 3: Internal Talent Marketplace
Shell uses Phenom's AI-powered talent experience platform to operate an internal talent marketplace — a system that matches employees to open roles, projects, and development opportunities based on their skills profiles and career interests (Phenom People). This marketplace serves as the execution layer for the reskilling strategy: once the AI identifies that an employee in a legacy petroleum role has transferable skills for a clean energy position, the marketplace surfaces relevant opportunities and facilitates internal mobility.
The internal-first approach has a clear strategic logic. Reskilling an existing employee who understands Shell's operations, culture, and safety protocols is typically faster and less expensive than recruiting externally for niche clean energy roles — positions where talent competition is intense across the entire energy sector.
Step 4: External Acquisition for Irreplaceable Gaps
Not every role can be filled internally. Shell's skills gap analysis also identifies positions where external hiring is the only viable option — roles requiring deep specialist expertise (such as advanced hydrogen fuel cell engineering or offshore wind turbine design) that cannot be developed through reskilling programs within practical timeframes.
By using AI to draw this line clearly, Shell avoids two common pitfalls: over-investing in reskilling programs for roles where the skills gap is too wide, and over-hiring externally for positions that could have been filled through internal mobility.
What Makes This Approach Different
Several elements distinguish Shell's AI workforce intelligence system from conventional HR transformation programs.
Scale with precision. Most workforce planning at this scale relies on broad categories — "we need more engineers" or "we're cutting 10% of corporate roles." Shell's AI-driven approach operates at the individual skills level, creating personalized assessments and pathways rather than applying blanket policies.
Continuous adaptation. The energy transition is not a single event but an ongoing shift. Shell's skills taxonomy is designed to evolve as clean energy technologies mature, new roles emerge, and the relative demand for different competencies changes. This is not a one-time restructuring plan but a persistent intelligence system.
Integration of cut and build. Rather than treating workforce reduction and workforce development as separate workstreams, Shell's system connects them. The same AI that identifies redundant roles also identifies reskilling opportunities, ensuring that workforce reduction decisions account for internal mobility potential.
Transferable Lessons for Large Employers
Shell's approach offers a blueprint for any large employer navigating a sector-level transition — whether in automotive (combustion to electric), financial services (traditional banking to fintech), or manufacturing (manual to automated operations).
Build the skills map first. Before making workforce reduction decisions, invest in understanding what skills your current employees actually have. AI-powered skills taxonomies reveal transferable capabilities that traditional job-title-based analysis misses.
Prioritize internal mobility. External hiring for emerging roles is expensive and competitive. An AI-powered internal talent marketplace can surface candidates who are closer to qualification than their current job titles suggest. Shell's use of Phenom's platform demonstrates how technology can make internal-first hiring practical at scale.
Draw clear reskilling boundaries. Not every employee can be reskilled for every role, and pretending otherwise wastes resources and raises false expectations. Use AI gap analysis to identify where reskilling is viable and where external acquisition is the honest answer.
Make it continuous, not episodic. Sector transitions unfold over years or decades. A workforce intelligence system that updates continuously is more valuable than a one-time skills audit that becomes outdated within months.
The Broader Implications
Shell's workforce transformation is a case study in what happens when AI moves beyond automating HR processes and starts informing strategic workforce decisions. The technology is not replacing human judgment — hiring managers and HR leaders still make the final calls on reskilling investments and role assignments. But AI is providing the intelligence layer that makes those decisions informed, personalized, and scalable.
For HR leaders watching the energy transition from adjacent industries, the message is clear: the organizations that invest in AI-powered workforce intelligence now will be better positioned to manage their own sector transitions — whenever they arrive.
Sources: Shell Annual Report 2024 (reports.shell.com); Reuters, "Shell to cut up to 10% of its workforce" (March 2024); Phenom People customer profile (phenom.com/customers); LinkedIn Talent Insights, energy sector skills data; Shell Powering Progress Strategy (shell.com/sustainability).
How is Shell using AI for workforce planning?
Shell uses an AI-powered skills taxonomy to map the competencies of its 93,000 employees against the requirements of clean energy roles. The system generates reskilling pathways for employees with transferable skills and identifies where external hiring is unavoidable.
What platform does Shell use for its internal talent marketplace?
Shell uses Phenom's AI-powered talent experience platform to operate its internal talent marketplace, matching employees to open roles and development opportunities based on skills profiles and career interests.
Why is Shell cutting roles while also investing in reskilling?
Shell's workforce reduction targeted redundant corporate and office positions, while the reskilling investment prepares employees for clean energy roles required by its Powering Progress net-zero strategy. The AI system connects both workstreams — the same gap analysis informing reduction decisions also identifies internal mobility opportunities.
What lessons can other large employers take from Shell's approach?
Key lessons include: build a skills map before making reduction decisions, prioritize internal mobility over external hiring for emerging roles, use AI gap analysis to draw clear reskilling boundaries, and build continuous (not episodic) workforce intelligence systems.