Bradley Campbell Advisory
AI governance

AI governance for organizations moving from experimentation to operational use.

This sprint establishes the policy boundaries, reference patterns, and review discipline needed to use AI responsibly in real delivery environments. It is designed for organizations where AI use is already underway and internal controls need to catch up.

Discuss scope View services
10 business days fixed-scope sprint with executive-ready outputs
3 layers policy boundaries, reference architecture, and review workflow
Human review controls designed around accountability rather than automation theatre
Implementation-oriented built for software organizations, not abstract AI strategy exercises
Core output

Policy, architecture, and review discipline.

The aim is a shared operating model: clear enough for leadership to trust, and practical enough for engineering to follow.

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Usage boundaries

Define approved, experimental, restricted, and prohibited forms of AI use so teams know what is acceptable, what requires review, and what should not happen at all.

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Reference patterns

Establish sane patterns for internal assistants, AI-enabled product features, model access, logging, data handling, and human control points.

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Review workflow

Create checkpoints for privacy, security, legal, and human oversight so AI work is governed as part of the system, not treated as an isolated experiment.

Included in the sprint

Core deliverables

  • inventory of current AI use cases, tools, and undocumented usage patterns
  • risk-tier model for approved, experimental, and prohibited patterns
  • AI policy draft grounded in actual delivery constraints
  • reference architecture for internal AI tools or product-facing AI capabilities
  • review checkpoints for privacy, security, compliance, and human approval
  • 30-60-90 day rollout roadmap for governance, tooling, and enablement
Starting at $12,000
Fit

Where it tends to work well

  • Good fit: SaaS, B2B, regulated, or platform-heavy organizations moving toward repeatable AI use
  • Good fit: CTO, CISO, VP Engineering, or innovation leaders who need a cross-functional operating model
  • Good fit: teams building internal copilots, assistants, automations, or agentic workflows
  • Not fit: companies seeking a generic AI strategy deck with no implementation implications
  • Not fit: organizations interested in AI branding without corresponding governance or execution discipline
Workstreams

What the sprint addresses.

The work is structured to answer the questions leadership and builders both need resolved before AI use becomes routine.

Policy and risk

Define acceptable use in operational terms.

Most organizations do not need an abstract theory of AI governance first. They need a usable model for what data, tools, vendors, prompts, and outputs are acceptable under real business constraints.

  • acceptable-use boundaries by data sensitivity and workflow type
  • approved, experimental, and prohibited usage categories
  • privacy and security review triggers
  • minimum human-review expectations by risk tier
  • third-party model and vendor evaluation criteria
  • guidance for employee tooling and internal enablement
Architecture and patterns

Create a reference model for AI-enabled delivery.

Whether the organization is enabling internal assistants or building AI features into products, teams need common patterns for data handling, approval flow, model access, and human accountability.

  • reference architecture for internal copilots, agents, or AI-enabled products
  • guidance for prompt handling, context injection, and output validation
  • recommended control points for logging, auditing, and escalation
  • security and privacy considerations for model integration
  • build-versus-buy framing for common AI platform choices
  • design principles intended to scale without overengineering
Operating model

Make governance light enough to endure delivery pressure.

If governance becomes ceremonial, teams route around it. The sprint leaves behind a review process, ownership model, and near-term rollout plan that can function in a real software organization.

  • lightweight review workflow for new AI initiatives
  • role clarity across engineering, security, legal, and product
  • decision checkpoints for higher-risk use cases
  • training and enablement priorities for teams and managers
  • sample artifacts and decision templates
  • 30-60-90 day rollout plan with realistic sequencing
Timeline

What 10 business days usually looks like.

The engagement is designed to move from ambiguity to a usable governance model quickly, without pretending that slow process equals rigor.

Days 1–3

Read the current reality

Interview stakeholders, review active AI use, identify undocumented workflows, and understand where current risk and ambiguity live.

Days 4–7

Define the model

Draft guardrails, reference patterns, review criteria, and ownership structure based on the actual delivery environment.

Days 8–10

Finalize and hand off

Deliver the operating model, walk leadership through tradeoffs, and leave the organization with a practical rollout plan.

AI governance usually fails for one of two reasons: it is too vague to guide decisions, or too heavy to survive actual delivery pressure.

This sprint is designed for the useful middle.
Relevant perspective

Architecture-first. Security-aware. Built for delivery.

  • Chief Architect with cross-functional experience across engineering, platform, and security
  • former Information Security Officer and Director of Security Architecture in regulated environments
  • hands-on practitioner working with AI-agent-led software and modern delivery workflows
  • strong fit for organizations that need practical governance rather than hype
Next step

If AI use is outpacing internal controls, this is a sensible starting point.

If useful, I can scope the sprint around current AI usage, delivery constraints, and risk posture.

Contact Agentic delivery workshop