Elai + ChangeFusion
Predictive AI. Real adoption. Measurable value.
Predict what matters. Change what counts.

Turn one priority business decision into measurable AI value.

Elai brings predictive AI. ChangeFusion helps leaders embed it into workflows, decisions, and operating rhythm—so value shows up in the business, not just in the model.

What executives get

  • A clearer starting point for predictive AI
  • Faster proof using real business data
  • Roles, workflows, and governance that support adoption
  • A practical path from pilot to business value
The Value Gap

AI creates value only when the business can act on it.

The problem is not model creation alone. Value breaks when prediction, workflow, ownership, and adoption do not move together.

1
OutcomeWhat result should move?
2
DecisionWhich choice matters most?
3
DataCan it be predicted?
4
ModelWhat signal is trusted?
5
WorkflowWho acts and when?
6
AdoptionHow is it sustained?
7
ValueWhat changes in performance?
Joint Proposition

One proposition. One integrated path.

Elai proves predictive value. ChangeFusion makes it usable inside the business.

Elai

Rapidly generates, tests, and scales predictive AI models using real business data.

Core role
  • Model feasibility and proof
  • Backtesting and validation
  • Deployment, monitoring, and scale

ChangeFusion

Aligns leaders and redesigns the conditions required for adoption and measurable results.

Core role
  • Leadership alignment and readiness
  • Workflow, governance, and role design
  • Adoption, trust, and value tracking
Services

A practical path forward.

Start where you are. Expand as value becomes clear.

AI Leadership Playbook

Align leaders, prioritize a use case, and define the path to proof.

Duration: 4–6 weeks

3-Month Activation Journey

Launch the first use case and prepare the business to act on it.

Duration: 3 months

6-Month Value Realization Journey

Embed the model into the workflow and track early business value.

Duration: 6 months

9-Month Expansion Journey

Expand use cases, strengthen governance, and deepen adoption.

Duration: 9 months

12-Month Transformation Journey

Scale predictive AI across functions with operating discipline.

Duration: 12 months
Use Cases

Start with a consequential decision.

We begin with the decision the business needs to improve—not with a technology looking for a problem.

Customers

Which customers are most likely to leave, buy, renew, or respond?

Sales

Which leads and opportunities deserve attention now?

Operations

Where are delays, failures, or capacity risks emerging?

Finance

What will demand, cash flow, or payment behavior look like?

Workforce

Where are attrition, absence, skill, or performance risks increasing?

Supply Chain

What inventory, supplier, or stockout risks should we act on?

Consultant Team

Senior advisors who help AI become business value.

Jill Hinson

Jill Hinson

Practice Leader

Leads change strategy, executive alignment, and value-realization architecture.

Marc Reiher

Marc Reiher

Enterprise Transformation

Focuses on operating-model redesign, technology change, and large-scale activation.

Malia Scanlan

Malia Scanlan

Transformation & Design

Supports enterprise readiness, organization design, and workforce transformation.

Liv Olson

Liv Olson

Consultant

Supports client engagements across change strategy, workflow design, and adoption.

Vanessa Hammett

Vanessa Hammett

Communications & Adoption

Leads strategic communications, stakeholder engagement, and adoption support.

Monica Thakrar

Monica Thakrar

Leadership Alignment

Advises on readiness, facilitation, coaching, and leadership transitions.

Claire Meany

Claire Meany

Capability Building

Builds leader and manager capability for sustained AI adoption.

Neeraj Bhagat

Neeraj Bhagat

Executive Advisor

Supports business-case development, operational redesign, and executive coaching.

Why It Matters

Focused for senior executives.

This is not a broad AI transformation story. It is a business-value story built around one decision, one proof, and one path to scale.

ClarityWhere AI should create value first
ProofReal data, faster validation
AdoptionWorkflows, roles, and trust
ValueOutcomes tied to the business

Choose one priority decision.

Test it with real data. Embed it in the business. Build from there.

  • Confirm the executive outcome and value metric
  • Identify the first decision and data sources
  • Define proof scope, workflow changes, and adoption path

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