Execution-gap scorecard
A concise view of controls that are automated, manual, partial, undefined or unevidenced.
Turn requirements into systems that work.
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I help regulated technology organisations turn complex product, delivery and AI governance requirements into working operational controls, accountable workflows and evidence they can defend.
Elvis Tapfumanei
Begin with an assessment, continue through implementation leadership or test one controlled workflow through a Colloxa pilot.
AI Governance Execution Assessment
For organisations that have AI policies, approved tools or governance decisions but cannot reliably show how those requirements operate inside a real workflow.
A policy can define what should happen. It cannot, by itself, identify restricted data in a prompt, stop an unapproved model, route an exception to the correct person or preserve evidence of the decision.
This assessment follows one material AI workflow through the systems, actors, decisions, controls and records involved. It identifies where governance depends on informal judgement, where ownership is missing and where evidence cannot be produced without reconstruction.
What leaves the room
Policy-to-workflow map
A traceable connection between approved requirements and the operational points where decisions must occur.
Control and evidence register
Gaps, impact, recommended control treatment, required evidence and proposed owner.
Responsibility map
Clear roles across Product, Engineering, Operations, Security, Privacy, Risk and Compliance.
30/60/90-day backlog
Prioritised work that can move into delivery.
Executive findings session
A decision-focused review of exposure, ownership and next actions.
Format
Typical duration: 2–3 weeks for one workflow. Final scope depends on the number of systems, policies and stakeholder groups involved.
Fractional Technical Delivery Leadership
For regulated technology teams that know what must change but need senior delivery ownership across product, engineering, operations and governance functions.
Complex delivery fails when priorities enter through multiple channels, dependencies remain implicit, ownership is fragmented and leadership receives activity reports instead of decision-ready information.
I establish the operating layer that turns competing requirements into a controlled delivery plan. The work focuses on outcomes, decision rights, dependencies, capacity, risk and evidence.
What leaves the room
One delivery view
Initiatives, milestones, owners, decisions, dependencies and material risks.
Delivery operating model
Intake, prioritisation, planning, review, escalation and reporting.
Capacity model
Clear separation of strategic work, KTLO, incidents, technical debt and unplanned demand.
Decision and accountability log
Named owners, due dates, escalation routes and consequences of delay.
Executive reporting
Progress, risk, decision requirements and forecast confidence.
Implementation leadership
Active coordination across the functions required to deliver the change.
Format
Available as a bounded recovery or implementation engagement, or as fractional leadership for one or two days per week.
Colloxa Enterprise Pilot
For regulated organisations that need to test runtime policy enforcement and evidence generation in a controlled AI workflow.
Some governance gaps cannot be closed through operating models alone. The organisation may need a control layer that evaluates an AI interaction before it reaches a model, applies policy, alters or blocks the request, routes exceptions and preserves an evidence record.
Colloxa is being built for that runtime layer. Pilots remain bounded around one defined use case, control objective and evidence requirement.
- One agreed workflow or AI interaction path
- Defined policy and data-handling rules
- Controlled user group and acceptance criteria
- Evidence requirements and findings report
Next decision
Start with the smallest useful unit of proof.
Select one material workflow, define the outcome and produce evidence that the approach can work.