AI Transformation · Human-AI Collaboration · Executive Education
Samuel Zaruba Smith
Helping leaders redesign organizations, work, and decision-making for the age of AI and AI agents.
I combine organizational and behavioral research with hands-on enterprise AI experience to help leaders decide where AI should automate work, where humans must stay in control, and how to redesign the organization around both.
Faculty and Associate Director, Center for Business Data Analytics, University of Kentucky Gatton College of Business and Economics.
How I help
Five ways to work with me
Most organizations have deployed AI without changing how work actually gets done. That gap, not the technology, is where the value is lost.
AI & organizational transformation
Help leadership teams see how AI and AI agents change workflows, decision rights, staffing, and structure.
- Agentic AI adoption and workflow redesign.
- Decision rights: what AI decides, what humans decide.
- AI operating models and organizational readiness.
- Job and role redesign around human-AI teams.
- Governance and accountability that scales with adoption.
Programs for boards and leadership teams
Built for executives who must make AI decisions, not for engineers who build models.
- Leading the AI-Enabled Organization.
- Managing Human-AI Teams.
- Redesigning Work for Agentic AI.
- AI for Executives: What to Automate, What Not to Automate.
- Responsible AI as an Organizational Capability.
- Formats: 60 to 90 minute briefing, half-day, full-day, or multi-session program.
Executive and strategic AI advisory
Ongoing counsel for senior leaders navigating AI decisions with real consequences.
- Where AI agents create real value, and where they do not.
- How to measure AI productivity honestly.
- How to manage organizational resistance and adoption.
- How to structure governance and accountability.
- SaaS and technology company advisory.
Expert witness & litigation support
Independent, credentialed analysis for legal, regulatory, and standards matters.
- Signed written expert opinion to NIST on the AI Risk Management Framework.
- Artificial intelligence, machine learning, generative AI, and AI agents.
- AI governance, responsible AI, and human-AI decision-making.
- Cybersecurity, data security, and IT governance controls.
- AI organizational practices and standards of care.
Keynotes and invited talks
Plain-English sessions for boards, conferences, and executive audiences.
- The Agentic Organization.
- What Happens When Every Employee Has an AI Agent?
- Why AI Transformation Fails.
- Leading Human-AI Teams.
- What Leaders Should Automate, and What They Should Not.
Technical depth, not just frameworks
The advice holds up because the technical work is real.
- Enterprise AI and machine learning delivery at scale.
- Adversarial machine learning and large language model security research.
- NIST AI RMF, NIST RMF, FedRAMP, CMMC, NERC CIP, ISO 27001, SOC 2.
- Responsible and explainable AI, anomaly and fraud detection.
Free diagnostic
Human-AI Organization Readiness Assessment
Ten questions on how your organization is actually adopting AI. You get a preliminary read on where you are strong and where transformation tends to break down. Nothing leaves your browser.
This runs entirely in your browser. No answers are transmitted, recorded, or stored, and no company information is collected. It is a preliminary orientation tool, not a substitute for a full organizational assessment.
Who I work with
The people who call me
CEOs and business-unit leaders
Deciding what AI actually changes about the operating model.
CIOs, CTOs, and Chief AI Officers
Moving from pilots to production without losing control.
CHROs and people leaders
Redesigning roles, teams, and work around AI agents.
Boards and risk committees
Oversight, accountability, and honest measurement of AI programs.
Law firms and litigation teams
Expert analysis on AI, security, and standards of care.
SaaS and technology companies
Product, go-to-market, and governance advisory for AI-native teams.
Financial institutions
AI adoption inside heavily regulated environments.
Utilities and critical infrastructure
AI and automation where reliability and regulation are non-negotiable.
Professional associations
Executive education and certification-track programs.
Selected engagements
Where I have done the work
Enterprise AI adoption, cross-functional leadership, and translation between technical teams and executives.
Directed responsible AI governance and emerging technology portfolios across a regulated enterprise.
Led applied AI research and delivered executive-level AI education to enterprise clients.
Applied data science and machine learning delivery on enterprise cloud infrastructure.
Machine learning and analytics for large-scale telecommunications operations.
Applied AI and data science engagement.
Data governance advisory and audit-focused analytics for enterprise clients.
Emerging technology, data governance, and AI adoption in a regulated utility environment.
Enterprise data governance and analytics supporting grid and operational systems.
Funded doctoral research in adversarial AI and large language model security.
Logos denote prior employment or consulting engagements and do not imply endorsement, sponsorship, or any current relationship. Logos load from a public logo service and remain the property of their respective owners.
See how I teach
Watch a full session before you book one
These free lectures show exactly how I teach AI to business and executive audiences: plain English, no coding background required, and no vendor pitch. If the style fits your team, the executive programs go deeper on your own workflows.
Research
How AI changes people, work, and decisions
My research sits where organizations meet intelligent systems: how people adopt AI, how they trust it, how decisions get divided between humans and machines, and what breaks when that division is wrong.
Organizations and human-AI interaction
- Human-AI collaboration and human oversight of automated systems.
- Algorithmic decision-making and decision rights.
- AI adoption, trust, and organizational change.
- Work design and the future of work under agentic AI.
Recent work includes research on the limits of human supervision in AI anomaly detection, presented at the Commonwealth Computational Summit.
Technical foundations
- Adversarial machine learning and attack transferability across large language models.
- Large language model red-teaming and evaluation.
- Responsible AI, explainability, and AI governance.
- Blockchain auditability for financial and audit professionals.
Selected speaking venues
- The Innovate Summit, Louisville (2026)
- Commonwealth Computational Summit (2024)
- ISACA Podcast (2023)
- Data Science Connect Conference (2023)
- O'Reilly Strata Conference (2017)
Credentials
PhD, Public Policy
Center for Cybersecurity. Committee spanning Economics, Computer Science, and Public Policy.
MBA
Specialization in Accounting Information Systems.
Expert witness to the National Institute of Standards and Technology on the federal AI Risk Management Framework.
Start a conversation
Tell me what you are trying to change
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