AI + CX Advisory Work

Putting AI to Work

The AI conversation is moving from experimentation to value realization.

My recent advisory work has focused on helping organizations bridge that gap—translating emerging AI capabilities into practical business applications, customer experiences, operating models and measurable outcomes.

Working with growth-stage technology companies and established enterprises, I help leaders move beyond the question of "What can AI do?" toward the more important question:

"Where can AI create meaningful value for our customers and our business?"

Case Study 01

AI Commercialization & Enterprise Value Realization

Challenge

An emerging enterprise AI platform needed to move from a technically sophisticated product toward a repeatable enterprise adoption and commercialization model.

Approach

Developed an enterprise value-realization framework connecting AI capabilities to functional business use cases, stakeholder outcomes, adoption milestones and measurable economic value.

Focus

Enterprise adoption • Value realization • Functional use cases • Operating model • Commercialization • GTM strategy

Outcome

Created a structured approach for engaging enterprise design partners, validating business value and translating product capabilities into executive-level business outcomes.

Case Study 02

AI Readiness & Market Intelligence

Challenge

A new AI-enabled business concept required a scalable method for evaluating companies, market opportunities and organizational readiness.

Approach

Designed an AI-supported intelligence framework combining structured data, business characteristics and weighted scoring models to evaluate opportunity, strategic fit and execution readiness.

Focus

AI readiness • Market intelligence • Data enrichment • Scoring models • Segmentation • Decision support

Outcome

Converted fragmented market and company data into a repeatable decision framework capable of identifying and prioritizing high-value opportunities.

Case Study 03

Customer Intelligence & Personalized Engagement

Challenge

A consumer services business needed to better understand its customer base and move from broad-based marketing toward more targeted engagement.

Approach

Integrated customer profile, transaction and engagement data to develop behavioral and demographic customer segments, identify service patterns and create targeted lifecycle campaigns.

Focus

Customer intelligence • Segmentation • Lifecycle marketing • Retention • Personalization • Customer value

Outcome

Created a data-driven customer engagement model enabling more relevant targeting, recall campaigns and customer lifecycle management.