Keynotes and executive workshops on AI transformation, adoption, and the human-machine future.
Dr. Irina Raicu has run the programs she speaks about. At Microsoft she led custom Copilot and agentic AI adoption across a 2,000+ person global support organization spanning the Americas, EMEA, and APAC, reaching 90%+ daily active use within nine months. Every talk is built around one decision a leadership team has to make, and the operating change that follows.
15+ years across consulting, AI product delivery, digital transformation, and organizational adoption
Selected audiences
Rooms, stages and studios.
Stages, programs and institutions where this work has been presented. Not clients.
London College of FashionMilano Fashion Institute
London College of FashionMilano Fashion Institute
London College of FashionMilano Fashion Institute
London College of FashionMilano Fashion Institute
Signature keynotes
Four talks, four decisions.
Each keynote is adapted to the room.
AI programs open with a strategy deck and close with a tool rollout. The constraint is rarely the model, it is whether anyone can show the output is good enough for a customer.
Best for
CIO, CDO, and COO audiences. Board and PE operating-team sessions. Enterprise AI summits.
Use case
Sample outputs
Written standardWhat good enough means, in writing
Evaluation
Ship
Revise
Stop
Strategy becomes operational only when the output can be tested.
Once agents absorb real work, the org chart stops describing what happens. This talk draws on a hybrid hub-and-spoke model that linked a central AI Innovation Hub to regional enablement leads, managers, practitioners, and a 65-person champion network.
Best for
CHRO, COO, and transformation audiences. Operations and shared-services leadership. Executive education programs.
AI Innovation HubCentral standards, intake and measurement
Regional enablement
Managers
Practitioners
Champion network
Real workflows moving through the organizationDistributed capability, named decision rights
Central standards. Distributed capability. Named decision rights.
Machines are moving into public view, and the design problem is no longer capability. Trust, attention, and interpretation decide whether people accept a system or work around it.
Best for
Innovation forums, technology summits, design and creative-technology conferences, university lecture series.
Image
Movement
Character
Context
Public interpretationCues shape the reading. They do not settle it.
Trust
Hesitation
Avoidance
People read the system before they understand the technology.
Marketing and brand organizations keep framing AI as a procurement question. The sharper question is which production decisions you will hand to a model, and what check stands behind each one.
Best for
CMO and brand leadership audiences. Marketing and creative industry summits. Agency and holding-company leadership offsites.
Brief
Model-assisted production
Review gate
Factual accuracy
Brand consistency
Rights and permissions
Final approval
Accountable human
Public output
Automation does not remove the decision. It relocates it.
AI programs open with a strategy deck and close with a tool rollout. The constraint is rarely the model, it is whether anyone can show the output is good enough for a customer.
Best for
CIO, CDO, and COO audiences. Board and PE operating-team sessions. Enterprise AI summits.
Use case
Sample outputs
Written standardWhat good enough means, in writing
Evaluation
Ship
Revise
Stop
Strategy becomes operational only when the output can be tested.
Once agents absorb real work, the org chart stops describing what happens. This talk draws on a hybrid hub-and-spoke model that linked a central AI Innovation Hub to regional enablement leads, managers, practitioners, and a 65-person champion network.
Best for
CHRO, COO, and transformation audiences. Operations and shared-services leadership. Executive education programs.
AI Innovation HubCentral standards, intake and measurement
Regional enablement
Managers
Practitioners
Champion network
Real workflows moving through the organizationDistributed capability, named decision rights
Central standards. Distributed capability. Named decision rights.
Machines are moving into public view, and the design problem is no longer capability. Trust, attention, and interpretation decide whether people accept a system or work around it.
Best for
Innovation forums, technology summits, design and creative-technology conferences, university lecture series.
Image
Movement
Character
Context
Public interpretationCues shape the reading. They do not settle it.
Trust
Hesitation
Avoidance
People read the system before they understand the technology.
Marketing and brand organizations keep framing AI as a procurement question. The sharper question is which production decisions you will hand to a model, and what check stands behind each one.
Best for
CMO and brand leadership audiences. Marketing and creative industry summits. Agency and holding-company leadership offsites.
Brief
Model-assisted production
Review gate
Factual accuracy
Brand consistency
Rights and permissions
Final approval
Accountable human
Public output
Automation does not remove the decision. It relocates it.
Panels, fireside formats, and moderated roundtables are available on the same topics. Talk through a keynote ↗
Executive workshops
Three formats. Each one ends with something written down.
These are working sessions, not presentations. Participants bring their own workflows and leave with a document their team can act on the following week.
A scored and sequenced portfolio of candidate use cases, each with a named owner, a value hypothesis, the data it depends on, and the evaluation that decides whether it ships.
02
Designing the AI Operating Model
Audience · CIO, CDO, COO, and transformation leadership.
What participants leave with
A drafted operating model on a single page: what sits central and what sits in the business, the enablement layer, the champion network, the intake path, and the metric each decision forum owns.
03
Redesigning the Work: Human-Agent Collaboration
Audience · A single function or delivery organization, including its front-line managers.
What participants leave with
Two or three core workflows redrawn end to end: what the human keeps, what an agent takes, where review sits, and who is accountable for quality. Scope and sequence are set with the sponsor beforehand.
Dr. Irina Raicu is an enterprise AI transformation executive and advisor with a PhD in AI. Formerly Microsoft Global AI Director, Enterprise AI Adoption & Support Transformation, she led custom Copilot and agentic AI adoption across a 2,000+ person global support organization, reaching 90%+ daily active use within nine months.
Medium bio (100 words)
Dr. Irina Raicu is an enterprise AI transformation executive and advisor with 15+ years across consulting, AI product delivery, digital transformation, and organizational adoption. Formerly Microsoft Global AI Director, Enterprise AI Adoption & Support Transformation, she led custom Copilot and agentic AI adoption across a 2,000+ person global support organization spanning the Americas, EMEA, and APAC, reaching 90%+ daily active use within nine months. She holds a PhD in Artificial Intelligence and works with executive teams on AI operating models, use-case portfolios and the organizational change that adoption requires.