The Practice

From idea and research to product, startup, and impact.

Strategy, productization, and governance for researchers, founders, and leaders building systems in domains where governance and getting it right are not optional.

The Thesis

Most technology projects fail not because the underlying idea or technology was wrong, but because the wrong questions were asked before anything was built. Systems are optimized for what is measurable and convenient — not for what actually matters to the people who will use them or be affected by them. The result is products that are technically functional and operationally useless, or worse, quietly harmful in ways the evaluation framework was never designed to catch.

The critical question is rarely, "Does this work?" It is, "Does this work for the right people, in the right context, with the right safeguards?"

My practice is built around that distinction. Strategy comes before code. Productization is a rigorous discipline, not an engineering side-effect. Governance is structural, not a compliance veneer added at the end. The three are inseparable; treating them as separate stages is the most common reason technology investments — in AI and beyond — do not return what was promised.

What I Do

Five practices, one through-line.

Every engagement starts with the same question: what outcome are you trying to produce, and what is at stake if you get it wrong?

Strategy & Advisory

AI strategy, digital transformation roadmaps, product and technology decisions for leadership teams navigating complexity.

Product & Digital Transformation

Taking ideas from concept to deployment. Translating business and research goals into products that work in real environments.

Research & Intelligence

Applied research, white papers, and evidence-based analysis. Intelligence-led decision support for organizations and policymakers.

Speaking & Thought Leadership

Keynotes, panels, workshops, and executive briefings on AI, emerging technology, and digital transformation trends.

Training & Mentoring

Executive education, founder cohorts, curriculum design, and mentoring the next generation of technology leaders.

Sectors served Government · Education · Healthcare · Retail · NGOs · Financial Services
The Three Phases

Strategy, productization, governance.

The phases are sequential in framing and parallel in practice. Most engagements span more than one; some begin in any of them.

— Phase One

Strategy

The decisions made before any code is written. What is worth building, what evaluation looks like, what the risk profile actually is.

This is where most AI investments are won or lost. I work with leadership teams to clarify what success looks like in operational terms, what the right evaluation metrics are for the actual risk surface, and where the proposed architecture has structural weaknesses that will not surface until production. The output is not a deck — it is a defensible decision.

In practice

A regulated-sector platform team has been told by a vendor that their LLM evaluation is "97% accurate." A two-week strategy engagement establishes that the test set excludes the highest-risk category of user query, that the accuracy metric is structurally insensitive to the harms the system actually creates, and that the organization needs a different framework before scaling. The engagement ends with a written architecture review, a recommended evaluation framework, and a clear go / no-go on the deployment.

Deliverables
  • Architectural & evaluation review
  • Risk-aware evaluation framework
  • Risk & governance assessment
  • Build / buy / partner analysis
  • Strategy memo & written recommendation
  • Fractional Chief AI Officer engagement

Who this is for Researchers and founders making early decisions about what to build and how to evaluate it — and executive teams, boards, and product leaders making capital decisions on AI investments in regulated or high-stakes environments where the cost of a wrong call is measured in compliance exposure, reputation, or harm.

— Phase Two

Productization

The discipline of turning research and prototypes into systems that survive contact with real users — taught and applied.

A working prototype is not a product. The gap between the two is where most AI initiatives stall. I work directly with engineering and product teams on the mechanics — evaluation pipelines, release governance, observability, regression discipline — and I run programs that build this capacity inside organizations rather than holding it outside them. Twenty years of implementing software in regulated environments inform this work; the editorial discipline is the same whether the product is a banking system or a clinical-decision tool.

In practice

A founder cohort enters a six-week productization sprint with prototypes that work in demos but fail under uneven inputs. The sprint establishes evaluation harnesses, regression testing, and release-readiness criteria specific to each prototype. The cohort exits with shipped, monitored, governed products — and a productization playbook each team continues to use.

Deliverables
  • Embedded productization advisory
  • Evaluation harness design & build
  • Release governance & regression discipline
  • Founder cohorts & productization sprints
  • Executive education programs
  • Curriculum design & delivery

Who this is for Researchers moving a prototype or framework toward a deployable product, founders and engineering leaders responsible for moving AI work from demonstration to deployment, and learning-and-development teams building the institutional capability to do it repeatedly.

— Phase Three

Governance

For domains where the cost of being wrong is high, technical and policy questions are inseparable. I work on both.

Governance is not a compliance overlay. It is a structural property of the system — present in the choice of evaluation, in what gets logged and reviewed, in who is accountable when an output causes harm. My doctoral research at UMBC's KnACC Lab is directly on this terrain: how AI systems should evaluate content in environments where being wrong is expensive, and what proportionate, risk-aware evaluation looks like in practice. That work informs the advisory.

In practice

A platform faces regulatory pressure on its content-moderation pipeline. A governance engagement maps the system's structural blind spots — including where the evaluation framework systematically silences legitimate speech and misses genuine harm — and produces a revised governance architecture that is both more defensible to regulators and demonstrably less harmful to affected users.

Deliverables
  • AI policy advisory & review
  • Platform governance frameworks
  • Health & financial AI evaluation
  • Content moderation architecture review
  • Expert review & written testimony
  • Standards & regulator engagement support

Who this is for Researchers whose work sits on contested policy or ethical terrain, founders building in regulated sectors, and platforms, regulators, foundations, and public-sector bodies whose AI systems sit on the policy boundary — and who need work that is technically rigorous and policy-fluent at once.

Engagement Formats

Three commitments, scoped to the question.

Engagements are scoped, not priced from a menu. The format is decided after the question is understood. These are the three shapes the work most often takes.

Foundations

What the work draws on.

The advisory is grounded in three things — and the combination is the point. Each on its own is common; together, in one practice, is rare.

Twenty years of implementing software in regulated environments — UK, Nigeria, US — meets active doctoral research on the structural failures of current AI evaluation, framed inside the editorial discipline of someone who has stood in front of regulators, founders, and ministers, and explained why systems fail and what changes when they do not.

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Years delivering software in regulated environments
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Speaking engagements, keynotes and panels since 2014
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Clients served across six sectors at iBez
Doctoral Research
Beyond Epistemic Conformity
UMBC · KnACC Lab. Narrative-aware credibility and risk assessment in online health discourse. IEEE ICDH Best Student Paper, 2025. See the research →
Industry Practice
Twenty years of regulated software
Real Asset Management (accounting and project management software), Lehman Brothers (e-trading mortgage systems), Kaupthing Singer & Friedlander (capital markets, AML), and ten+ years founding and running iBez Consulting serving clients across government, healthcare, education, and enterprise sectors.
Public Practice
100+ speaking engagements since 2014
Keynotes and panels at IEEE, AMIA, AfricaNXT, Africa Women in IT, Founder Institute, and government and ministry-level convenings. See the speaking →
Begin a Conversation

If your AI question is the kind where being wrong is expensive, let's talk.

Send a brief description of the question and the timeline. I will reply within five working days with a frank read on whether — and how — I can be useful.

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