AI Transformation Case Sprint
Frame the decision, map Work, assess Capabilities, govern the proposed Solution, model the target, and publish the case.
ORGONAUT
Partner proposition
Build the business case for AI transformation.
Map the Work. Assess Capabilities. Govern the exact AI Solution. Model the target Scenario. Publish a case clients can approve.
What this is
A delivery layer, not an affiliate brochure.
The partner offer is simple: consultants use Orgonaut inside paid client engagements. Orgonaut becomes the system of record for the recommendation: current Work, capability outcomes, governed AI Solutions, target Scenarios, evidence, economics, and approval.
AI value depends on changing work and the operating model around it. Orgonaut keeps that logic inspectable without claiming to restructure the organisation or guarantee the outcome.
What you sell
Three partner-led offers.
Lead with a clear consulting product. Use Orgonaut to make the work visible, repeatable, and easier for leadership to approve.
Operating Model Review
For CTOs and product leaders reviewing team topology, capacity, cost, ownership, platform leverage, and delivery constraints.
Org Efficiency Review
For executive teams and PE-backed companies comparing cost reduction, reallocation, spans, layers, and role-mix options.
Product evidence
Show the model, not a slide.
Use Orgonaut in workshops to move from discussion to a shared baseline, a scenario, and a clear delta against live structure.
Live model: teams, roles, allocations, member kinds, cost visibility.
Scenario workflow
How the engagement runs.
Example: transition an engineering team to an AI SDLC, target 2x delivery performance, and reduce staff cost by 20% without losing accountable ownership.
1
Set the goal
Agree the outcome, scope, constraints, sponsor, and decision standard.
2
Capture Live
Import or model the current org. Validate teams, roles, cost, and ownership.
3
Clone scenario
Create a future-state scenario from Live View so the baseline stays intact.
4
Model changes
Use Orgonaut and its CLI with an AI agent to apply proposed org changes.
5
Promote Live
When accepted, promote the scenario and use Orgonaut as the org source of truth.
Workshop view: inspect the structure while changes are being discussed.
Decision view: compare cost, headcount, FTE, and velocity deltas.
Agent workflow: apply and review changes through the Orgonaut CLI.
White label
Your brand in front. Orgonaut underneath.
The client relationship remains yours. Orgonaut supports the model, scenario comparison, demo tenant, reporting options, and ongoing operating record.
White-label capabilities
- Co-branded or white-label report options
- Partner demo tenant with realistic SaaS data
- Repeatable workshop templates
- Scenario narratives for board and executive review
Data and integration story
Start with manual modelling or import from the systems the client already trusts. Keep the first engagement pragmatic.
BambooHR
Dynamics 365 HR
Microsoft
CLI
Commercial model
Simple economics.
Implementation partners earn recurring commission and keep their advisory fees. Referral partners can introduce clients without delivery responsibility.
| Path | Best fit | Commercial | Partner role |
|---|---|---|---|
| Referral Partner | Advisors who introduce Orgonaut to relevant clients. | 25% recurring monthly commission for the first 12 months. 15% after month 12. | Warm introduction, client-fit signal, no implementation obligation. |
| Implementation Partner | Consultants using Orgonaut inside paid engagements. | Same commission structure, plus your own implementation and advisory fees. | Prepare data, configure tenant, run workshops, interpret outputs. |
| Strategic / White-label Partner | Firms building a repeatable AI transformation or org design practice. | Custom terms for repeatable demand or portfolio use. | Co-branded delivery, packaged offers, partner preview feedback. |
Commercial terms are subject to final written partner agreement.
Demo tenant
Sell with a working example.
Selected Partner Preview participants get a demo tenant with fictional SaaS-company data. Use it to sell the engagement before the client has shared their own org data.
Show rollups and summary views for leadership conversations.
Demo tenant shows
Current org structure
Departments and teams
Cost and capacity rollups
AI SDLC scenario
Scenario deltas
Board-ready summaries
Agreement outline
Draft points for legal review.
This is not the contract. It is the outline partners should expect to see in a formal agreement.
Orgonaut obligations
- Provide agreed product access and partner enablement
- Track eligible referred subscriptions
- Pay commission under the signed agreement
- Support approved demo tenant access
Partner obligations
- Represent Orgonaut accurately
- Own the consulting advice and client change plan
- Protect client data
- Register opportunities through the agreed process
Discovery
Questions that qualify the work.
Use these before proposing the sprint. They keep the conversation tied to a decision, not a generic AI workshop.
Client decision
- What organisational decision are you trying to make?
- What would make the recommendation credible to leadership?
- Who approves the future-state model?
- What happens if no change is made?
Model inputs
- Where is the current org structure stored?
- Which teams, roles, and functions are in scope?
- What cost, capacity, delivery, or HR data is reliable?
- Who will own the model after the engagement?
Outreach
Simple partner copy.
Lead with the business decision. Do not sell "AI transformation" in the abstract.
LinkedIn DM
I am helping leaders turn AI ambition into an inspectable Transformation Case: current Work, capability outcomes, governed Solution design, target Scenario, economics, and evidence. Worth a short conversation?
Workshop framing
This workshop is designed to compare options, not force a restructure. We will model the current structure, define assumptions, create scenarios, and make the trade-offs visible.
For AI consultants
Build the case before your client commits.
Use Orgonaut as the model beneath Work discovery, capability assessment, governed AI design, target scenarios, and reporting.