Recruiting & Talent Acquisition
- Resume intelligence
- Candidate matching
- AI interview assistants
- Pipeline forecasting
- Value
- Faster, fairer hiring
- ROI
- High
- TTV
- 3–6 mo
- Complexity
- Medium
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Executive Engagement
HR AI Strategy & Transformation
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01 · Executive Summary
Executive Engagement BriefConfidential · ConocoPhillipsv 1.0 · 2026
The HR AI operating system for a global energy enterprise.
Helping ConocoPhillips define an enterprise-ready HR AI strategy that modernizes workforce operations, elevates employee experience, sharpens decision intelligence, and establishes a scalable, responsible AI-enabled HR operating model.
Engagement type
Strategy · Benchmarking · Pilot Design
Sponsor
CHRO + CIO Joint Sponsorship
Time horizon
6 weeks · with optional 12-month roadmap
Outcome
Enterprise HR AI North Star
Workforce Intelligence
Employee Copilots
Skills Graph
Predictive Retention
Responsible AI
HR Service Automation
Knowledge Capture
Field Workforce Optimization
Talent Forecasting
Decision Intelligence
Workforce Intelligence
Employee Copilots
Skills Graph
Predictive Retention
Responsible AI
HR Service Automation
Knowledge Capture
Field Workforce Optimization
Talent Forecasting
Decision Intelligence
02Market Inflection
The window for HR AI is opening now.
Decisions about HR are already being made around HR — by CEOs anticipating AI productivity, by boards demanding ROI, by employees flooding hiring funnels with AI-generated applications. The gap between AI promise and AI reality is where the HR function gets reorganized — unless the CHRO steps in first.
15%
Org readiness
of orgs prepared for future-of-work shifts (Gartner, 2025)
19%
Talent-risk confidence
of HR leaders confident identifying talent risks
90%
AI ramp expected
of HR pros expect AI to scale at work this year
1 in 50
Disruptive AI value
AI initiatives deliver disruptive value today
Six shifts CHROs can't afford to ignore.
Tailored for ConocoPhillips. These aren't sequential — they're simultaneous, and each one intersects directly with COP's post-Marathon integration, returns-focused discipline, and digital ambition across upstream operations.
Source · Slalom, The Future of the CHRO (2026) · COP overlay by Slalom
- Shift · 01The workforce architect has no blueprint
Only 21% of HR leaders sit inside AI strategy today — yet the CHRO will own what work humans do, and what they don't.
For ConocoPhillipsPost-Marathon, COP is redesigning a ~14,000-person workforce across 14 countries — the blueprint can't be inherited.
- Shift · 02The missing rung
AI absorbs entry-level work faster than orgs redesign pipelines. 76% of HR practitioners expect reduced entry-level hiring.
For ConocoPhillipsEarly-career engineering pipelines feed Willow, Lower 48 and Permian growth — the rung COP can least afford to lose.
- Shift · 03The CHRO owns the algorithm
Algorithmic accountability is the CHRO's biggest legal exposure. EEOC liability attaches regardless of how the AI was built.
For ConocoPhillipsCOP's digital ambition runs through safety-sensitive, regulated environments — algorithmic accountability is non-negotiable.
- Shift · 04Who transforms HR if HR doesn't?
44% of HR leaders plan agentic AI within 12 months. Intent isn't execution — and competitors are already replacing HR roles with agents.
For ConocoPhillipsReturns-focused peers are already deploying agents in HR ops; integration with Marathon is the window to leapfrog, not retrofit.
- Shift · 05The CHRO role will split
People strategy and HR operations are pulling apart. By 2030, most large orgs face this choice — explicitly or by default.
For ConocoPhillipsA combined COP/Marathon HR operating model is the moment to decide — strategy vs. operations — by design, not by default.
- Shift · 06The manager layer is hollowing out
AI absorbs coordination, tracking, and reporting. 41% of employees say their company has already cut managerial layers.
For ConocoPhillipsField supervision, turnaround leads and asset managers shape COP's safety and cost-of-supply outcomes — protect, don't hollow.
03Phase 1 · What we sell
A focused current-state assessment that lands an executive-ready HR AI strategy and business case.
Six to eight weeks. Four moves: assess today, align to Slalom's AI Value Platform of what's possible, prioritize the use cases that matter for ConocoPhillips, and stand up the business case to fund the program.
Indicative current-state view
Where HR AI stands today
HR data architecture
62
Developing
Platform ecosystem
71
Mature
AI governance readiness
38
Foundational
Workforce analytics
55
Developing
Change management
48
Foundational
Security & compliance
78
Mature
Integration capability
65
Developing
Responsible AI policy
41
Foundational
Talent readiness
52
Developing
Business alignment
70
Mature
Assessment deliverables
What you walk away with.
- Current-state assessment of HR + data + AI capability
- Alignment to Slalom's AI Value Platform — what's possible
- Use case alignment & prioritization for ConocoPhillips
- Business case for the HR AI strategy
- Executive alignment workshops (CHRO + CIO + finance)
- Phase 2+ roadmap hand-off
Timebox
6–8 weeks · 3 executive workshops · 1 board-ready readout
04Enterprise Opportunities
Six AI value pools, prioritized for impact and feasibility.
A categorized map of where intelligent automation and copilots create measurable enterprise value across the HR function — each tagged with business value, ROI, time to value, and complexity.
Employee Experience
- HR copilots
- Self-service AI
- Knowledge assistants
- Personalized journeys
- Value
- Consumer-grade EX
- ROI
- High
- TTV
- 2–5 mo
- Complexity
- Low
Learning & Development
- Skills intelligence
- AI-generated learning paths
- Capability mapping
- Personalized training
- Value
- Capability at scale
- ROI
- Medium
- TTV
- 4–8 mo
- Complexity
- Medium
HR Operations
- Workflow automation
- Intelligent doc processing
- Case management AI
- Policy assistants
- Value
- Lower HR cost-to-serve
- ROI
- High
- TTV
- 3–6 mo
- Complexity
- Low
Workforce Planning
- Predictive analytics
- Attrition forecasting
- Succession intelligence
- Organizational modeling
- Value
- Decision intelligence
- ROI
- High
- TTV
- 6–9 mo
- Complexity
- High
Knowledge Management
- Tribal knowledge capture
- Expert systems
- Operational assistants
- Retirement transition
- Value
- Continuity at scale
- ROI
- Strategic
- TTV
- 6–12 mo
- Complexity
- High
05Energy Industry
Potential use cases built for energy, not retrofitted from retail.
Field complexity, safety-sensitive environments, and an aging workforce demand HR AI that respects operational reality. These eight potential use cases anchor early value while building a defensible workforce data foundation.
Field workforce optimization
Predictive scheduling and skills matching for upstream and midstream field crews.
−18% overtime+22% utilization
Craft labor & turnaround planning
AI-assisted forecasting for shutdowns and turnarounds; reduce idle time across crafts.
−12% planning cycle+9% throughput
Knowledge transfer from retiring workforce
Capture tribal knowledge into operational assistants before generational retirements.
1,200+ SMEs onboardedKnowledge retained
Safety & compliance copilots
Front-line copilots that surface SOPs, JSA guidance, and incident learnings in context.
Faster SOP retrievalImproved compliance
Onboarding for remote & field personnel
Personalized, role-aware onboarding journeys across rotations and remote sites.
−35% ramp time+EX score
Learning copilots for operational roles
Generative learning paths mapped to technical competency frameworks.
Personalized at scaleCloses skill gaps
Technical competency mapping
Skills graph for engineering and operations linked to workforce planning.
Unified skills viewPlan vs. supply
Skills gap analysis for digital transformation
Identify capability gaps for cloud, AI, and data programs before they bottleneck delivery.
Targeted reskillingFaster delivery
−24%
HR cost-to-serve
composite enterprise benchmark
+31%
Time-to-fill improvement
critical & technical roles
2.1×
Workforce planning accuracy
vs. spreadsheet baseline
+19
Employee experience pts
rolling 12-month delta
06Next Steps
Together, Slalom and ConocoPhillips can define a practical, scalable, and responsible HR AI strategy that empowers the workforce of the future.
Engagement · 01
HR AI Strategy Workshop
Engagement · 02
Current-State Assessment
Engagement · 03
AI Value Platform Walkthrough
Engagement · 04
Executive Alignment Workshop
Engagement · 05
AI Opportunity Prioritization
Engagement · 06
Business Case Build
Book the executive workshop→Confidential · ConocoPhillips × Slalom
07The Partner
A human-centered partner for enterprise AI transformation.
Slalom brings the rare combination of business strategy, AI engineering, and organizational change in one team — purpose-built for enterprises like ConocoPhillips.
Capabilities
Strategy to implementation — in one team.
- AI strategy
- Workforce transformation
- HR technology modernization
- Data & analytics
- Cloud transformation
- Organizational change management
- Responsible AI governance
- Product & platform engineering
Why Slalom for energy
Credibility, not claims.
- Deep energy industry experience
- Enterprise AI transformation at scale
- Business + technology + change in one team
- Strategy to implementation, in one engagement
A1Addendum · Full Journey
Addendum — the full HR AI journey, after Phase 1.
Reference only. Phase 1 (weeks 1–6/8) is what we propose to sell now; phases 2–5 show the full path so executives can see where the strategy leads. Build/buy and scaling decisions are made after Phase 1 lands.
- Phase 01Weeks 1–6
Current-State, AVP Alignment & Business Case
What we sell first. Land the strategy, align to Slalom's AI Value Platform, and fund the program.
Deliverables- Current-state assessment
- Alignment to AVP (what's possible)
- Use case alignment
- Business case for AI strategy
Business outcomeFunded HR AI strategy - Phase 02Months 2–5
Data Foundation & Build/Buy Decisions
Stand up the workforce data plane and skills ontology. Resolve build vs. buy across platforms, copilots, and point solutions.
Deliverables- Data product blueprint & skills graph v1
- Build/buy decisions across HR AI stack
- Governance & responsible AI framework
Business outcomeTrusted foundation + sourcing decisions - Phase 03Months 4–9
Pilot AI Use Cases
Launch 3–5 high-confidence pilots across EX, recruiting, and operations.
Deliverables- Copilots
- Workflow automations
- Pilot outcomes
Business outcomeProven value, validated patterns - Phase 04Months 9–18
Enterprise Scaling
Industrialize what worked. Embed copilots in the flow of HR work.
Deliverables- Platform model
- Adoption program
- ROI realization
Business outcomeEnterprise-wide capability - Phase 05Month 18+
Intelligent Workforce Optimization
Continuous learning. Decision intelligence. New operating model.
Deliverables- Decision dashboards
- Operating model v2
- Continuous improvement
Business outcomeAI-native HR