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AI Governance & Context Strategist

When AI outputs break down,
I fix the system behind them.

I help organizations improve AI reliability, consistency, and trust by strengthening the context, evaluation, and governance systems around their models.

What I work on
  • Improving AI output consistency across teams
  • Reducing dependence on analyst interpretation
  • Building governance systems that make AI auditable and scalable
Where I've done this
  • Enterprise AI governance — Dell Technologies
  • AI-powered Business Intelligence — Tilt
  • AI reasoning systems — Kitewing

Enterprise and growth-stage environments

6% → 81% accuracy gain — context architecture redesign only. Zero model changes.
Zero compliance incidents — 33 AI systems, 900+ stakeholders, one operating model. Dell Technologies.
Analyst interpretation layer reduced — 200+ users receiving decision-ready outputs directly. Tilt.
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Typical engagements
2–3 week diagnostic 3–6 month fractional Post-pilot enterprise or growth-stage
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Common Patterns

This is familiar territory if...

These patterns come up consistently in organizations that have moved past the pilot stage and are trying to make AI work reliably at the operational level.

AI outputs vary depending on who asks Different users, different prompts, different answers — because no one built shared standards.
Analysts are still doing the interpretation The AI returns data. A human still has to contextualize it before anyone will act on it. The tool is running but the workflow hasn't changed.
Leadership doesn't act on AI outputs Early inconsistencies eroded confidence, and it hasn't recovered. The system runs. The outputs go unread.
You can't explain why outputs fail No evaluation system, no failure log, no improvement loop. Problems recur without explanation.
Compliance risk is growing quietly AI is deploying across teams with no governance framework. Legal is uncomfortable. Leadership can't audit what they can't predict.
Adoption is stalled despite the investment Budget spent. Training done. Daily usage is low and business impact is invisible.
Root Causes

Most AI reliability problems aren't model problems. They're systems problems.

The same three gaps come up repeatedly in organizations past the pilot stage — and none of them require a new model to fix.

The three gaps I see most often:

  • Missing context — the model has no structured understanding of how the business works, so outputs default to generic responses. Business logic, metric definitions, and data relationships need to be explicitly encoded into the system
  • No evaluation system — without a consistent way to measure output quality, failures are invisible until they compound. There's no improvement loop, no accuracy baseline, no systematic way to fix what's breaking
  • Insufficient governance — teams are building and deploying AI independently, with different standards and no accountability structure. Outputs vary. Trust erodes. Risk accumulates quietly
The Approach

Three systems that address each gap

01

Context Architecture

Embeds your metric definitions, data relationships, and business logic directly into the system — so AI reasons from your context, not guesswork.

02

Evaluation Systems

Measures accuracy and reasoning quality, catches breakdowns before they compound, and creates the feedback loop that drives improvement.

03

Governance Layer

Enforces consistent, auditable behavior across teams and use cases — the layer that makes AI trustworthy enough for leadership to stake decisions on.

System Architecture — From Query to Trusted Output

User Query Context Architecture Business logic · Data context AI Model Processing Evaluation + Governance Accuracy · Safety · Consistency Trusted Output Decision-ready Data Layer
Case Studies

Three Engagements. Documented Outcomes.

Standardized Governance Across 33 Enterprise AI Systems

The Situation: 33 AI systems operating under different standards. Teams were building independently — different prompt conventions, different output formats, different risk profiles. There was no shared governance framework, no accountability structure, and no way for legal or leadership to audit what was being deployed or why.
What I Did
  • Architected a company-wide AI governance framework with clear accountability across product, engineering, design, and legal
  • Standardized prompt and context architecture across all 33 systems — built once, applied consistently
  • Defined responsible AI principles that satisfied compliance requirements without slowing delivery speed
  • Gave leadership a single operating model they could defend, audit, and scale with confidence
Impact
50% Reduction in AI onboarding time
35% Efficiency improvement across AI teams
900+ Stakeholders aligned under one framework

The result: AI scaled across the enterprise within a consistent governance structure. Teams continued building; the framework gave them clear standards to build against.

Reduced Analyst Interpretation Overhead in AI-Powered BI

AI + BI Transformation — from reporting to decision support. The model was functioning. The problem was that every output still required a human to contextualize before anyone would act on it. Analysts were spending significant time translating AI responses into formats leadership could use. The business logic needed to produce usable outputs lived in people's heads, not in the system.
What I Did
  • Built the business context layer that taught AI how the company's data, metrics, and logic actually work — in terms the system could reason with directly
  • Locked in metric definitions, ownership structures, and calculation standards so outputs stopped conflicting with each other
  • Redesigned output formats around decision types — what leadership needs to act, not just what the data can produce
  • Embedded governance and a self-auditing loop so issues surface by design, not silently
Impact
Hours → Min Analysis turnaround time
200+ Users enabled on the platform
30% Reduction in token usage

The result: The model never changed. The system around it did. Analysts spent less time on interpretation. Leadership had outputs structured for direct use.

75-Point Accuracy Gain. Zero Model Changes.

The Situation: Starting accuracy: 6%. The model returned coherent answers that were consistently wrong in context. The problem wasn't the model — it had no structured understanding of the business, the decisions it was supporting, or what "correct" meant in that environment. A context and evaluation problem presenting as a model problem.
What I Did
  • Built a structured 16-prompt evaluation framework that measured accuracy and reasoning quality across every output category
  • Designed reusable context modules embedding business logic, terminology, and decision patterns directly into the system architecture
  • Mapped every failure mode by output type — identified exactly where the system broke down and why it kept repeating
  • Ran systematic improvement cycles: measure, identify root cause, fix, re-test — treating accuracy like an engineering problem with hard targets
Impact
6% → 81% AI accuracy improvement
16 Evaluation prompts designed and benchmarked
+75pts Accuracy gain with no model changes

The result: 75-point accuracy gain. No new model, no new infrastructure — just a system that understood what it was being asked to do, with an evaluation framework that proved it.

Sample Work

AI System Diagnostic — Sample

Evaluation framework, failure mapping, and before/after accuracy results — from a real engagement. Built for hiring teams and organizations scoping a project.

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Instant download. No form.

Engagement Models

How I Engage

Two structured engagement types. Both defined upfront. Both tied to a measurable outcome.

Diagnostic

Find Out Why Your AI Isn't Trusted

2–3 weeks · Fixed scope · Immediate clarity

  • Identify exactly where and why AI outputs are failing — context gaps, evaluation gaps, governance gaps
  • Map exactly what leadership doesn't trust — and why it compounds without intervention
  • Surface compliance exposure before it reaches legal or the board
  • Leave with a prioritized fix roadmap — specific and sequenced
Embedded

Build AI Systems That Produce Usable Outputs

3–6 months · Fractional · Embedded

  • Own the context and evaluation layer that determines whether outputs are usable
  • Drive measurable accuracy and trust improvements tracked against documented baselines
  • Embed standards into your product and data teams so the system outlasts the engagement
  • Give leadership confidence to act on AI — because they've seen it work, consistently
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The Difference

What Makes This Different

Most AI consultants deliver recommendations. I deliver working systems — and the evaluation framework to prove they work.

I don't...

  • ✗ Build or fine-tune models
  • ✗ Run prompt engineering sessions in isolation from system design
  • ✗ Deliver frameworks that require internal teams to implement
  • ✗ Measure success by deliverables handed over
  • ✗ Recommend new AI tools when the existing system is the problem

I do...

  • ✓ Improve the systems that shape how AI interprets business context and produces outputs — the context, evaluation, and governance layer
  • ✓ Reduce analyst interpretation overhead by structuring outputs for direct use
  • ✓ Build evaluation systems that prove improvement with hard numbers — not qualitative assessments
  • ✓ Stay embedded until the outcome is real — not just documented and recommended
  • ✓ Build systems your team owns and maintains after the engagement ends
Point of View

Five Things I Believe About Enterprise AI

01

"Most AI failures are context failures, not model failures."

Companies spend millions fine-tuning models and zero dollars defining what those models need to know about the business. The model is rarely the bottleneck. The surrounding system always is.

02

"If leadership doesn't trust the output, the system has failed — regardless of accuracy."

Technical accuracy is a prerequisite, not an endpoint. An AI system that produces correct answers no one believes is operationally worthless. Trust is a product problem, not an engineering problem.

03

"Prompt engineering without governance is not a strategy — it's a liability."

At scale, without standardization and oversight, you get different users, different prompts, different outputs, different decisions. Governance is what makes AI usable. It's not overhead — it's the operating system.

04

"AI adoption fails at the system level, not the tool level."

Most organizations evaluate AI by the tool — the model, the interface, the vendor. But adoption lives or dies in the systems around the tool: how it understands the business, how its outputs are measured, and how it earns trust over time.

05

"The companies winning with AI aren't using better models. They're using better systems."

Every enterprise has access to the same frontier models. The differentiator is context depth, evaluation rigor, and the governance layer that makes outputs reliable enough to replace human judgment in routine, high-stakes decisions.

Background

Six years on the operational layer of enterprise AI.

I've spent the last six years working on the systems around AI — the context architecture, governance structures, and evaluation frameworks that determine whether outputs are consistent and usable at scale.

At Dell Technologies, I contributed to enterprise AI governance and standards efforts spanning 33 AI systems and 900+ stakeholders. The work focused on building shared accountability structures, standardizing context and prompt conventions, and giving compliance and leadership a framework they could actually audit.

At Tilt, I worked on the context and evaluation layers supporting AI-powered analytics and decision systems — embedding business logic directly into the system, structuring outputs for operational use, and building the feedback loops that make accuracy improvable over time.

I'm most effective in environments where AI adoption has moved past the experimentation stage and the challenge is operational consistency, governance, and trust — not more tooling.

Brandy McCarron — AI Governance & Context Strategist

Enterprise Experience

  • Dell Technologies AI Governance Lead
  • Tilt AI Systems Strategist

Core Expertise

  • AI Context Architecture
  • Governance & Compliance Systems
  • Evaluation Frameworks
  • AI Product Strategy
  • Enterprise Stakeholder Alignment
  • AI BI & Decision Systems
Let's Talk

AI systems become valuable when
people trust the outputs enough to act on them.

I work with organizations that are moving beyond experimentation and need AI systems that are consistent, explainable, and operationally usable across teams.

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