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Public engagements

Speaking, Workshops & Advisory

Dr. Tatianna Gilliam translates enterprise AI, ERP transformation, data architecture, and governed systems into decisions that technical teams and executive leaders can act on.

Engagements are designed around evidence, operating reality, human authority, implementation risk, and the practical work required to move from technology ambition to deployable systems.

Through Brilliant Brainstorm Intelligence, LLC, selected public engagements connect enterprise strategy with architecture, data, governance, and operational execution.

Engagement formats​

Formats below reflect offer-readiness review. Limited means selected public engagements may be discussed through approved channels. Planned means the topic catalog is ready, but open booking is not published here.

Executive Briefings

Limited

Focused sessions for leaders evaluating AI, data, ERP modernization, governance, or operating-model decisions.

Audience

  • Executive sponsors
  • Transformation leaders
  • Architecture and governance owners

Outcomes

  • Clarify the actual decision
  • Identify readiness and evidence gaps
  • Separate implementation risk from technology hype
  • Define human authority and accountability
  • Establish the next bounded action

Technical Workshops

Limited

Working sessions for architecture, data, engineering, ERP, and operations teams designing governed and implementable systems.

Audience

  • Architecture and engineering teams
  • ERP and operations leaders
  • Data and AI practitioners

Outcomes

  • AI readiness and data risk framing
  • Human-in-the-loop architecture patterns
  • Evidence and decision controls
  • ERP data-quality and inventory system reasoning
  • Architecture decision records that survive scrutiny

Strategic Advisory

Limited

Bounded architecture and strategy engagements for organizations navigating enterprise AI, ERP, data quality, human oversight, and implementation risk.

Audience

  • Technology and transformation sponsors
  • Enterprise architects
  • Operating leaders accountable for delivery

Outcomes

  • Map decisions to system boundaries and evidence
  • Surface adoption and data-quality constraints early
  • Define governance and human-authority checkpoints
  • Produce a bounded next-step architecture or review package

Keynotes and Conference Sessions

Planned

Clear, evidence-driven talks on enterprise AI adoption, ERP transformation, human authority, responsible automation, and the systems required to move from experimentation to operational value.

Audience

  • Conference and community audiences
  • Technical and executive mixed rooms

Outcomes

  • Translate architecture into actionable decisions
  • Show where evidence must precede implementation
  • Connect governance to operating reality

Verified topic catalog​

Each topic links only to existing public proof — released repositories, published documentation, or published writing.

Evidence Before Implementation: What Organizations Need Before an AI Build

Limited

Audience

  • Executive sponsors
  • Architecture leads
  • Delivery owners

Core question

What must be true — in decisions, data, authority, and evidence — before an AI build is justified?

Takeaways

  • Ambition is not readiness; readiness is inspectable.
  • Critical gaps in data, authority, or evidence should stop or reshape the build.
  • Public diagnostic methods can make those gaps visible before spend accelerates.

Related public proof

Formats: Executive Briefings · Technical Workshops

Human Authority by Design: Building Human-in-the-Loop Systems That Can Be Trusted

Limited

Audience

  • Architecture and engineering
  • Governance owners
  • Operations leaders

Core question

Where must human authority remain explicit so automation can be trusted under consequence?

Takeaways

  • Human oversight is an interface design problem, not a slogan.
  • Authorization, escalation, and verification must be defined before agents act.
  • Trust grows when failure modes and evidence paths are visible.

Related public proof

Formats: Technical Workshops · Executive Briefings

From ERP Data to AI Readiness: Why Operational Data Quality Determines AI Outcomes

Limited

Audience

  • ERP and operations teams
  • Data owners
  • AI sponsors

Core question

How do warehouse, inventory, costing, and master-data realities constrain AI outcomes?

Takeaways

  • AI quality inherits ERP and operational data quality.
  • Controls and definitions matter more than model novelty early on.
  • Sanitized transformation patterns make the readiness conversation concrete.

Related public proof

Formats: Technical Workshops · Executive Briefings

Governed Agents: Authorization, Evidence, Execution, and Verification

Limited

Audience

  • Platform and architecture teams
  • Security and governance
  • AI product owners

Core question

How do you structure agent work so authorization, evidence, execution, and verification remain inspectable?

Takeaways

  • Agents need permission boundaries before capability demos.
  • Execution without verification is incomplete work.
  • Public patterns should stay free of private operating details.

Related public proof

Formats: Technical Workshops · Strategic Advisory

Why AI Adoption Is an Operating-Model Problem

Limited

Audience

  • Executive leaders
  • Transformation offices
  • Architecture leads

Core question

What operating-model changes are required for AI work to produce durable operational value?

Takeaways

  • Tools do not adopt themselves; roles, controls, and incentives do.
  • Implementation risk often lives outside the model.
  • Evidence and accountability decide whether pilots become systems.

Related public proof

Formats: Executive Briefings · Strategic Advisory

What ERP Transformations Teach Us About Responsible AI

Limited

Audience

  • ERP leaders
  • AI sponsors
  • Change and adoption owners

Core question

Which lessons from ERP transformation — adoption, data quality, controls — transfer to responsible AI?

Takeaways

  • Process reality beats slide-deck ambition.
  • Adoption and data definitions are first-class architecture concerns.
  • Responsible AI inherits the discipline of controlled enterprise change.

Related public proof

Formats: Executive Briefings · Technical Workshops

Communication Across the Organization​

The same system can be explained differently without changing the underlying architecture — so executives, engineers, and operators can act from a shared evidence base.

Executive leaders

Business value, operating risk, investment choices, governance, and adoption.

Architecture and engineering teams

System boundaries, tradeoffs, failure modes, interfaces, evidence, and delivery.

Operations and ERP teams

Process reality, data quality, controls, adoption, and measurable outcomes.

Brilliant Brainstorm Intelligence, LLC​

Brilliant Brainstorm Intelligence develops governed AI, enterprise architecture, ERP transformation, and evidence-first operating tools.

Public engagements connect technical systems to business decisions, implementation controls, and measurable operational work.

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Discuss an Engagement​

For speaking, workshop, advisory, or technical-review inquiries, use the approved BBI contact channel or connect with Dr. Gilliam professionally on LinkedIn.

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