AI Governance Consulting for Enterprises

Q: What is AI governance?

AI governance is the set of policies, responsibilities and controls that guide how an organization builds, buys and uses AI. It defines who approves a use case, what data it can access, how outputs are reviewed and who remains accountable as its use changes.

Q: How is AI governance different from data governance?

Data governance addresses the quality, ownership, access and management of information. AI governance covers how AI systems use information, how their outputs are evaluated and who is responsible for their use. Cadeon’s data governance consulting supports the data foundations that AI oversight depends on.

Clear Rules for AI Use

Make AI Governance Easier to Manage

Somewhere in your company, someone is pasting internal data into an AI tool. Another team has a pilot running that legal has never seen. Nobody has a full list, and nobody is sure who gets the final say.
We fix that with rules people can follow. Who approves a new tool. What data it can touch. Who answers for the result. The rules are short, written down, and built into the way your teams already work, so every AI project gets a clear yes or no instead of sitting in limbo.

Clear ownership

Controlled data access

Practical review steps

Book a Consultation

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What We Help With

AI Governance Services That Fit Your Business

Start with the gaps that matter to your organization. An AI governance consultant can help clarify responsibilities, review data controls, and define the work needed for new or existing AI use cases.

AI Governance Consulting

Define the policies, ownership, and approval steps that guide how teams select, develop, and use AI across the business.

Data Foundations for AI

Strengthen AI data governance through data governance for AI, covering source quality, ownership, access, and traceability.

Generative AI Governance

Set expectations for assistants and copilots, including permitted inputs, access to company information, output checks, and human review.

AI Risk and Compliance Review

Support your internal risk, privacy, and compliance teams with documented use cases, control gaps, and review requirements before deployment.

Where We Start

Start With the AI Already in Use

The first job is finding out what is actually running. A department has a tool nobody approved. A pilot reads data nobody knew it could reach. A vendor switched on AI features in software you already pay for.

So we start with a short AI governance assessment. We list every AI tool and use case, trace the data behind each one, and find out who owns what. You end up with a clear map and a clear order for what to fix first.

Flowchart diagram with multiple interconnected steps labeled 'Process' on a light blue background.

Data Analytics for Calgary’s Oil and Gas Sector

Cadeon’s energy work includes daily operations reporting, production loss management, and drilling and completions analysis. Our energy and utilities analytics services connect operational records with the questions engineers and management need to answer.

Spotfire® for Operational Dashboards in Energy.

Cadeon helped energy drilling and completion teams streamline daily operational reporting by replacing manual data aggregation with automated, interactive Spotfire dashboards accessible across the organization.

View Case Study

Spotfire® for Loss Management in Oil & Gas

Cadeon implemented a comprehensive loss management analysis solution using Spotfire, helping an oil and gas operator standardize production loss reporting, improve data quality, and support executive decision-making.

View Case Study

Spotfire® for Drilling and Completions Performance Analysis

Cadeon helped an innovative drilling contractor transform high-frequency rig instrumentation data into interactive Spotfire dashboards, giving operators and well owners clear visibility into drilling performance at any point in time.

View Case Study

Our Process

From AI Rules on Paper to a Working Governance Model

A policy needs people, controls, and a review process behind it. We organize the work around your current AI use, the decisions that need oversight, and the systems your teams already manage.

Diagram with interconnected white text boxes labeled mostly 'Strategic planning' and one labeled 'Process', connected by dashed lines and white circles on a light background.

Assess and Define

Establish what needs governance and where responsibilities or controls are missing.

List the AI tools, use cases, and data sources in play

Find the gaps in ownership, risk, and policy

Agree what to fix first

Diagram showing multiple interconnected boxes labeled 'Strategic planning' and one labeled 'Process' connected by dashed lines.

Design and Implement

Turn agreed requirements into responsibilities and working controls that fit your organization.

Write the policy, roles, and approval steps

Set the data and risk controls

Build review into the workflows your teams already use

Flow diagram showing four boxes labeled Process, Strategic planning, Strategic planning, and Strategic planning connected by dotted lines to a central circle labeled Optimization, which is connected to a box labeled Process 1 & 2.

Monitor and Support

Keep governance useful as tools, use cases and business requirements change.

Check how AI is actually used over time

Back up the owners and reviewers

Update the rules as tools and regulations change

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AI Oversight

Give Your Teams a Clear View of AI Use

Rules nobody can see get ignored. Cadeon builds dashboards and reports for a living, so we make AI use visible too.

Dashboards for AI Use and Risk

See which AI tools and use cases are live, who owns each one, and what has been flagged, all on one screen.

Reports Without the Spreadsheet Chase

Leaders and reviewers get a regular update on AI use without anyone rebuilding a spreadsheet by hand.

Dashboard showing Boston Airbnb listings with price and guest filters, and a map with listing locations; and a Heart Disease Prevalence in Canada report including obesity level, blood pressure and cholesterol charts, a patient city treemap, and a map with patient details.

Not Sure Where You Stand With AI?

If your priorities are still unclear, Cadeon’s AI readiness assessment reviews strategy, data, systems, governance, and organizational readiness to identify gaps and practical next steps.

Explore the AI Readiness Assessment

Why Cadeon

Connect AI Governance to the Data Behind It

AI rules need to reflect how information moves through your business. Cadeon brings experience across data governance, integration, analytics, and AI readiness to help teams connect oversight requirements with their actual environment.

Data and AI Under One Team

Review AI use alongside the quality, ownership, and accessibility of its source data. This helps connect governance decisions with the technical work needed to support them.

Experience With Complex Enterprise Data

Cadeon works across enterprise environments with multiple systems, departments, and reporting needs. We bring that context to the dependencies and ownership gaps that can affect AI projects.

Governance Built Into Everyday Work

Connect agreed controls to the places where decisions happen, from granting data access to reviewing a new use case. Give owners clear responsibilities and a practical way to record decisions.

Need a Clearer Plan for AI?

Tell us which AI your teams use today, or plan to use next. We will show you where the gaps are and what to tackle first.

Book a Consultation

Explore Our Services

Questions About AI Governance Consulting

What is AI governance?

AI governance is the set of policies, responsibilities, and controls that guide how an organization builds, buys, and uses AI. It defines who approves a use case, what data it can access, how outputs are reviewed, and who remains accountable as its use changes.

What does an AI governance consultant do?

An AI governance consultant helps an organization review its AI use, identify gaps, and put agreed oversight requirements into practice. Depending on the scope, AI governance services can cover ownership, approval steps, data controls, documentation, and ongoing review processes.

Do we need an AI governance framework before using AI?

Start with basic controls before introducing AI into business workflows, particularly where sensitive information or consequential decisions are involved. Define permitted uses, assign owners and agree on data access and review requirements, then expand governance as the use cases become more complex.

How is AI governance different from data governance?

Data governance addresses the quality, ownership, access, and management of information. AI governance covers how AI systems use information, how their outputs are evaluated, and who is responsible for their use. Cadeon’s data governance consulting supports the data foundations that AI oversight depends on.

How long does an AI governance project take?

The timeline depends on the number of AI use cases, the data involved, existing controls, and the teams needed for review. A focused assessment has a different scope from implementing governance across several departments, so milestones should be agreed after the initial review.

How do we get started?

Book a Consultation to discuss the AI tools you use or plan to introduce, the data involved, and the questions your team needs to resolve. From there, we can discuss an assessment or a more focused governance engagement.