Session Outcomes: What Would You Like to Accomplish?
Align on 3-5 tangible outcomes for the next two weeks of engagement
Validated board presentation strategy with clear ROI story
Prioritized 1-2 quick win use cases with feasibility assessment
1-year AI roadmap aligned with "AI Velocity 2030" strategy
Executive sponsorship strategy beyond CAIO role
Data readiness baseline and gap remediation plan
Agenda
0-2mOpening & rapport
2-7mBoard presentation context
7-17mStrategic focus areas
17-25mBlockers & reality check
25-30mSynthesis & next steps
Industry Signals to Mention
67% of supply chain execs have automated key processes
72% of failed AI implementations cite workforce resistance
Only 1 in 5 AI initiatives achieve ROI (Gartner)
20-50% forecast error reduction with AI
Key Assumptions to Validate
Board goal: confidence vs. skepticism
Priority ranking of 4 focus areas
Executive sponsorship beyond CAIO
Data infrastructure state
Blockers: tech vs. organizational
Driver: efficiency vs. resilience
Strategic Focus Areas
GenAI Data Management
Unstructured data processing & insights
Predictive Analytics
Forecasting & demand planning
Smart Warehousing
Robotics & automation
Edge AI
Real-time decision making
Session Notes
Question Bank
"What does success look like for the board?"
"Who's the toughest audience to convince?"
"Which has strongest exec sponsorship?"
"Where is data actually ready?"
"If only one, which moves the needle?"
"Tech vs. organizational readiness?"
"What's behind that?" (dig deeper)
"What would unlock everything else?"
Empathy Phrases
"That's a lot for your first 90 days."
"Makes sense you'd be cautious."
"Inheriting 4 priorities with no ranking is tough."
"Say more about that..."
If They Push Back
"You don't know logistics."
Acknowledge → pivot to supply chain exp → ask what's specific
"Frameworks haven't helped."
"What happened? I'd rather understand what didn't work."
"These aren't my real priorities."
"Good—tell me what is."
Gap Analysis Summary
Key Observations
• Industry leaders typically invested 18-36 months in data infrastructure before seeing results
• All successful cases had executive sponsorship beyond the AI leader
• ROI became measurable only after achieving minimum data quality thresholds
• Workforce readiness was consistently underestimated (72% failure rate when ignored)
Critical Questions for LogiSphere CAIO
Feasibility:
• Do we have the required data infrastructure or a realistic path to build it in 6-12 months?
• Can our IT/engineering teams support AI deployment, or do we need external partners?
• What's our current data quality baseline vs. the 80-90% completeness required?
Viability:
• Which use case delivers measurable ROI within our board presentation timeline (6-9 months)?
• Do we have budget allocation for the $2-5M typical investment per priority?
• How does each use case align with our "AI Velocity 2030" revenue optimization goals?
Desirability:
• Which executives beyond CAIO are willing to champion and resource these initiatives?
• What's the appetite for organizational change and workforce reskilling?
• Which use case resonates most with the board's strategic priorities?
Challenge Questions for Each Client Focus Area
GenAI Data Management: "You mentioned GenAI for data—what specific data problem keeps you up at night? How will you measure if this actually solves it?"
Predictive Analytics: "Walmart took 10+ years to build this capability—what makes you confident you can compress that timeline? What's your fallback if predictions are initially wrong?"
Smart Warehousing: "At $3-5M per facility, how many sites would you pilot? What's the union/workforce reaction plan? DHL took 6 months—can your operations team absorb that disruption?"
Edge AI: "Real-time decisions require real-time data—where's that infrastructure today? If your edge devices fail, what's the manual override process?"
Use Case Comparison: Industry Leaders vs. Client Focus Areas
Compare LogiSphere's proposed focus areas against proven industry implementations.
Click to select cases for detailed comparison.
Client Focus Areas
Client Priority 1
GenAI Data Management
Critical Success Criteria:
• Clean, labeled training data (100K+ examples)
• MLOps infrastructure for deployment
• Data governance framework
• Pilot use case with measurable ROI
Client Priority 2
Predictive Analytics & Forecasting
Critical Success Criteria:
• 2-3 years historical data (demand, weather, events)
• Integration with ERP/demand planning systems
• Statistical baseline for comparison
• Business process redesign for AI outputs
Client Priority 3
Smart Warehousing & Robotics
Critical Success Criteria:
• Warehouse digital twin or simulation
• Capital budget ($2M-5M per facility)
• Worker reskilling program
• Clear automation ROI calculation
Client Priority 4
Edge AI for Real-Time Decisions
Critical Success Criteria:
• IoT sensor infrastructure
• Low-latency network (5G or edge compute)
• Real-time data pipelines
• Clear decision rules and fallback logic
Requirements Met:
• 10+ years of point-of-sale data
• Real-time inventory systems
• Weather & event data integration
• Store-level execution capability
Requirements Met:
• Warehouse management system integration
• $3-5M capital investment per site
• 6-month implementation timeline
• Change management program
Position use cases based on business value potential and implementation complexity.
Drag dots to reflect client's actual context. Compare client priorities (purple) vs. industry benchmarks (green).
Lead
Quick Win
Reconsider
Avoid
High Value
High Complexity
Low Value
Low Complexity
1
2
3
4
D
R
W
M
Client Priorities: 1-GenAI Data, 2-Predictive, 3-Warehouse, 4-Edge AI
Industry Leaders: D-Demand Forecast, R-Route Opt, W-Warehouse, M-Maint
Quick Win (Top-Left)
High value, low complexity - ideal for building momentum
Recommendation: Start here for board credibility Typical timeline: 3-6 months to ROI Key risk: Scope creep into complexity
Minimum Data Requirements:
• 1-2 years historical data
• Single data source integration
• Basic data quality (80%+ completeness)
• Existing reporting infrastructure
Lead (Top-Right)
High value, high complexity - strategic differentiators
Recommendation: Long-term competitive advantage Typical timeline: 12-24 months to value Key risk: Requires sustained exec support
Minimum Data Requirements:
• 3+ years historical data
• Multiple data source integration
• High data quality (90%+ completeness)
• Real-time data pipelines
• Advanced analytics infrastructure
Reconsider (Bottom-Left)
Low value, low complexity - may not be worth the effort
Recommendation: Only if strategic or enabling Typical timeline: Quick to implement Key risk: Opportunity cost vs. higher-value work
Minimum Data Requirements:
• 6-12 months historical data
• Single data source
• Basic data quality (70%+ completeness)
• Manual processes acceptable
Avoid (Bottom-Right)
Low value, high complexity - resource drain
Recommendation: Defer or eliminate Typical outcome: Budget overrun, low adoption Key risk: Credibility damage if pursued
Minimum Data Requirements:
• Often requires new data collection
• Complex multi-source integration
• High data engineering investment
• ROI unlikely to justify effort
Gartner AI Opportunity Radar
Strategic framework mapping AI use cases across four dimensions: from everyday operations to game-changing innovations,
and from internal efficiency to external customer value. Distance from center indicates maturity/adoption readiness.
External Customer-Facing
Internal Operations
Everyday AI
Game-Changing AI
Front Office CX, Sales, Marketing
Product/Services AI-Enhanced Value
Back Office Admin, HR, Legal, Finance, IT
Core Capabilities R&D, Supply Chain, Operations
1
2
3
4
D
R
C
A
Client Priorities: 1-GenAI Data, 2-Predictive Analytics, 3-Smart Warehousing, 4-Edge AI
Industry Examples: D-Demand Forecast, R-Route Opt, C-Customer Service, A-Automation
Distance from center: indicates maturity/readiness level
Proven, established AI applications with clear ROI. Lower risk, faster time to value.
Focus on operational excellence and incremental improvements.
Game-Changing AI (Right Side - Blue)
Transformative, differentiating AI capabilities. Higher risk and investment, but potential
for competitive advantage and market leadership.
1-Year AI Roadmap Sketch
Build this collaboratively during the conversation, then read back at close
Q1 2026
Q2 2026
Q3 2026
Q4 2026
Closing Script
"Based on what you've shared, here's how I'd structure the year: Q1 would focus on [quick win]
plus [foundation], Q2-3 you'd scale that and tackle [next priority],
and Q4 positions you for the Year 2 ask on [bigger bet]. Does that resonate?"
Gartner AI Strategy Execution Roadmap
Interactive roadmap builder based on Gartner's 7-pillar framework. Click activities to select/deselect for your client's roadmap.
Initial Activities
Ramp-up Activities
Advanced Activities
🎯AI Strategy
Declare the vision and ambition for AI
Measure AI maturity
Prioritize initial AI use cases
Define value dimensions for initial AI use cases
Assess external trends and risks
Map and align with other key strategies
Identify priorities for AI portfolio
Set adoption goals for AI roadmap
Obtain buy-in for AI strategy
Communicate the AI strategy
Measure AI strategy success
Establish process to refine strategy
💰AI Value
Prioritize initial AI use cases
Define value dimensions for initial AI use cases
Run initial AI pilots
Introduce product management practices
Identify priorities for AI portfolio
Monitor value of initial use cases
Establish process to prioritize AI portfolio
Implement AI FinOps practices
Set up AI value monitoring system
Launch an initial AI product
Establish an AI product portfolio
👥AI Organization
Create an AI resourcing plan
Form initial external AI partnerships
Set up an initial AI team/center of excellence
Set up an AI community of practice
Establish AI target operating model
Set up process to manage AI partnerships
🎓AI Fluency
Create initial AI awareness campaigns
Create an AI change management plan
Launch an AI literacy program
Set up process to evaluate AI workforce impact
Define business champions to drive AI
Create an initial AI workforce plan
Use AI literacy programs for AI governance
Set up monitoring of employee readiness for AI
Set up cross-functional AI Gov Board
Set up process for review of roles and job redesign
⚖️AI Governance
Identify top AI risks and mitigation
Define initial AI policies
Define decision rights for AI
Establish AI ethical principles
Gain buy-in for AI governance approach
Define AI reference architecture
Set enforcement processes
Define target governance AI operating model
Pilot AI governance tooling
⚙️AI Engineering
Establish build vs. buy framework
Select vendors for initial AI use cases
Set up a sandbox environment
Define library of design patterns
Define AI reference architecture
Create an AI vendor and application strategy
Set up an AI observability system
Establish MLOps/ModelOps practice
Design and embed AI UI/UX best practices
Stand up AI platform engineering
📊AI Data
Assess data readiness for initial AI use cases
Implement data readiness plan
Gain buy-in to evolve data capabilities for AI
Build data analytics for AI
Extend data governance to support AI
Evolve data capabilities for AI
Implement data observability for AI
Adapt metadata practices for AI
Establish an AI data quality framework
Selected Activities Summary
Activities you've selected will appear here. Use this to build your board presentation narrative.
Initial (0-3 months)
No activities selected
Ramp-up (3-9 months)
No activities selected
Advanced (9-12 months)
No activities selected
LogiSphere - Chief AI Officer
First engagement: 16 January 2026 | Status: Active | Next review: Q2 2026
Progress Timeline
16 Jan 2026
Initial Discovery Session
First meeting with newly appointed CAIO. Discussed 4 strategic focus areas and upcoming
board presentation. Key concerns: data readiness, workforce resistance, quick wins needed.
TBD
Board Presentation Support
Follow-up scheduled post-board presentation to review outcomes and refine roadmap.
Key Metrics
Sessions Completed
1
Assumptions Validated
0 / 6
Use Cases Prioritized
4
Roadmap Completeness
0%
Next Milestone
Board Presentation
Session Outcomes: Progress Tracking
Track progress towards agreed outcomes over multiple sessions
Validated board presentation strategy with clear ROI story
Session 1: 25%
Prioritized 1-2 quick win use cases with feasibility assessment
Session 1: 40%
1-year AI roadmap aligned with "AI Velocity 2030" strategy
Session 1: 10%
Executive sponsorship strategy beyond CAIO role
Session 1: 15%
Data readiness baseline and gap remediation plan
Session 1: 20%
Client Context
Organization: LogiSphere (>$100B revenue, global logistics) Client Role: Newly appointed Chief AI Officer Background: AI leadership in tech, new to logistics industry Immediate Pressure: Board presentation in 2 weeks Key Challenge: Building credibility & showing momentum quickly
Engagement Plan
Immediate: Board presentation support & roadmap validation Short-term: Quick win identification & pilot design Medium-term: Data readiness assessment & governance framework Long-term: 7-pillar transformation roadmap execution support
Session History & Notes
Session 1: Initial Discovery
16 Jan 2026, 30 min
Key Takeaways:
• Client is under pressure to deliver quick wins while building long-term foundation
• Four focus areas presented, but no clear prioritization yet
• Board skepticism expected - need strong ROI story and industry benchmarks
• Data infrastructure state unknown - needs validation
Next Actions:
• Share industry use case comparison before board presentation
• Connect client with peer network for logistics AI leaders
• Provide board presentation template & talking points