The EBA Advantage
AWS Experience-Based Acceleration (EBA) is a proven methodology for intensive, hands-on transformation engagements. Hundreds of enterprises have used EBA to achieve in days what traditionally takes months.
Why EBA for AI Strategy?
Traditional AI strategy consulting stretches across months of meetings, slide decks, and committee debates. The EBA model inverts this: immersive, co-located work produces better outcomes faster.
| Traditional Approach | OAIO EBA Approach |
|---|---|
| 9-12 weeks elapsed time | 1 week intensive + 2 weeks pre-work |
| Scattered attention across priorities | Dedicated focus, cross-functional alignment |
| Sequential stakeholder buy-in | Parallel engagement, real-time decisions |
| Deliverables arrive weeks after sessions | Walk out with completed artifacts |
| Momentum fades between touchpoints | Intensity creates lasting change |
The OAIO EBA Structure
Pre-Work (2 Weeks Before)
Minimal virtual preparation maximizes onsite productivity:
| Activity | Duration | Output |
|---|---|---|
| Kickoff call | 1 hour | Scope confirmation, logistics |
| Superintelligent survey deployment | 10-15 min per employee | Organizational signal data |
| Executive pre-read distribution | Self-paced | Primed participants |
| Data inventory preparation | Internal | Ready for Pillar 2 |
Survey as Catalyst: The Superintelligent survey runs during the two weeks before the onsite. By the time the team arrives, we have organizational-wide signal about where AI can help, what barriers exist, and which opportunities have natural champions.
The Intensive Week (4-5 Days Onsite)
Day 1: Discovery and Prioritization (Pillar 1)
Morning: Survey Reveal and Hypothesis Validation
- Present Superintelligent synthesis: what the organization really thinks about AI
- Surface surprising insights that leadership didn't anticipate
- Validate or adjust executive hypotheses against employee reality
Afternoon: 5-Lens Prioritization Workshop
- Apply structured evaluation to candidate AI opportunities
- Score against: Technical Feasibility, Organizational Readiness, Business Value, Risk Profile, Time to Value
- Build consensus through evidence, not opinion
Evening Deliverable: Prioritized opportunity register with 2-3 selected agents and named business owners
Day 2: Data Reality Check (Pillar 2)
Morning: Data Mapping Sessions
- For each selected agent, map required data sources
- Identify data owners (names, not roles)
- Surface access requirements and integration complexity
Afternoon: Readiness Assessment
- Score each data source: Availability, Quality, Accessibility
- Calculate agent readiness scores
- Identify critical gaps and remediation paths
Evening Deliverable: Data readiness matrix with go/no-go recommendations per agent
Day 3: Governance Design (Pillar 3)
Morning: Permission Boundaries Workshop
- Define what each agent CAN do, CANNOT do, and REQUIRES APPROVAL for
- Assign human accountability for every AI action
- Address shadow AI: create sanctioned paths
Afternoon: Trust Architecture
- Design observability requirements (what must be visible)
- Define escalation triggers and procedures
- Draft sanctioned AI policy framework
Evening Deliverable: Governance framework with permission matrices and accountability assignments
Day 4: Experience and Economics (Pillars 4 and 5)
Morning: Interaction Design Sessions
- For each agent, design the human-AI handoff
- Define transparency requirements (confidence, explanations)
- Build the "Experience Test" answer: Why would someone choose this?
Afternoon: Economic Modeling Workshop
- Define economic thesis for each agent (cost reduction vs. revenue enablement)
- Build 12-month cost forecasts with documented assumptions
- Design financial controls (budget caps, circuit breakers, alerts)
Evening Deliverable: Experience specifications and economic models with CFO-ready documentation
Day 5: Integration and Roadmap (Exit)
Morning: Blueprint Assembly
- Compile all artifacts into implementation-ready documentation
- Validate completeness against engineering requirements
- Identify handoff recipients and next steps
Afternoon: Executive Briefing and Commitment
- Present complete blueprint to leadership
- Secure implementation commitments
- Define success metrics and review cadence
Final Deliverable: Complete OAIO Blueprint Package ready for implementation team handoff
The Transformation Effect
The EBA model creates transformation through intensity. Key mechanisms:
Cross-Functional Collision
- Stakeholders who rarely interact work side-by-side for a week
- Silos dissolve when people solve problems together
- Relationships formed during intensity persist afterward
Decision Velocity
- No waiting for the next meeting to resolve questions
- Leadership present to make real-time calls
- Blockers identified and addressed in hours, not weeks
Visible Progress
- Each day ends with tangible deliverables
- Participants see their work take shape
- Momentum builds rather than fading
Commitment Through Co-Creation
- Artifacts created together, not delivered to stakeholders
- Ownership emerges from contribution
- Implementation resistance reduced because people designed it
AWS Partner Economics
As an AWS Partner, Orion can access EBA funding to subsidize engagement costs:
| Component | Standard Cost | With AWS EBA Funding |
|---|---|---|
| Pre-work and survey | Included | Included |
| Intensive week (4-5 days) | $75,000-100,000 | $35,000-50,000 |
| Post-engagement support | Optional | Optional |
Funding Eligibility:
- Customer must be AWS customer or committed to AWS for AI workloads
- Engagement must result in AI agent deployment on AWS (Bedrock, SageMaker, etc.)
- AWS SA may participate in portions of the engagement
Ideal EBA Candidates
The intensive EBA format works best for organizations that:
- Have clear AI interest but lack structured approach
- Can commit cross-functional leadership for a full week
- Are ready to make decisions, not just explore options
- Want to move from strategy to implementation quickly
- Value intensity over extended deliberation
Red Flags for EBA Format:
- Leadership unavailable for full week commitment
- Organization in crisis mode with competing priorities
- Need extensive education before strategy work
- Political dynamics require careful stakeholder sequencing
What Makes OAIO EBA Different
Unlike generic AI strategy engagements:
Evidence Over Opinion
- Superintelligent survey provides organizational-wide signal
- Decisions grounded in data, not executive assumptions
- Prioritization framework forces explicit tradeoff conversations
Named Accountability
- Every agent has a human owner by name
- Every data source has a named steward
- Every governance decision has assigned accountability
Implementation Ready
- Deliverables designed for engineering handoff
- Economic models include actual cost projections
- Governance framework ready for policy drafting
Built for AWS
- Architecture aligned with AWS AI services (Bedrock, SageMaker)
- Economic models use AWS pricing
- Governance patterns leverage AWS security controls
Sample Week Schedule
| Time | Monday | Tuesday | Wednesday | Thursday | Friday |
|---|---|---|---|---|---|
| 8:00-9:00 | Kickoff and Survey Reveal | Data Synthesis | Governance Synthesis | Design Synthesis | Blueprint Review |
| 9:00-12:00 | Hypothesis Validation | Data Mapping | Permission Boundaries | Interaction Design | Integration |
| 12:00-1:00 | Working Lunch | Working Lunch | Working Lunch | Working Lunch | Working Lunch |
| 1:00-5:00 | 5-Lens Prioritization | Readiness Assessment | Trust Architecture | Economic Modeling | Exec Briefing |
| 5:00-6:00 | Day 1 Deliverable Review | Day 2 Deliverable Review | Day 3 Deliverable Review | Day 4 Deliverable Review | Celebration |
Getting Started
Related Resources
- OAIO Methodology Overview — Full pillar delivery guides
- GTM Pilot Framework — Testing approach with friendly customers
- Virtual Alignment Guide — Alternative for distributed engagements
- AWS EBA Program — Official AWS EBA information