Our AI Strategy: Embedded Governance (part 2)
In our previous piece, we outlined the first part of our AI Strategy - the use-case driven framework. Today we'll pair that framework with a governance model to inform how the strategy is implemented.
Define your “why”
I previously mentioned the framework defines the “what” and the governance model (below) defines the “how”. The “why” - your North Star - is harder to answer, but arguably more important.
Your company’s reason for building or adopting AI is ultimately a question of values. Maybe it’s a clear business opportunity with measurable ROI. Maybe it’s a strategic move (less generously called “a vibe”) from leadership. Either way, articulating your “why” ensures everyone—from engineers to executives to external partners—understands the purpose behind the effort. When parties are all aligned, they derive meaning from the work, partner better together, and increase the likelihood of a successful delivery.
Reaching consensus on the “why” isn’t easy, but it’s essential. It aligns your team, grounds your investment, and gives direction to your AI vendors. Do it early, and you’ll avoid missteps later.
Embedding AI Governance
Rather than relying on a generalized governance policy, we start with the AI development lifecycle and embed governance activities into each phase. This approach ensures the right controls, checks, and balances exist throughout the process of building and delivering AI solutions.
Governance exists throughout the model development lifecycle. Note that development
steps can run in parallel if needed, i.e. Designing models while still collecting data.
Governance activities
While we tailor our governance activities to each client’s needs, some that we see over and over again include:
Organization structure: Ensures the team working to develop, manage and use AI applications are trained and enabled. Defined roles and responsibilities exist.
Data Security: Protects the company’s data (both internal and external) from unauthorized access.
Data quality: Uses best practices to ingest, structure and serve reliable and trust-worthy data for AI/ML models and stakeholders.
Vendor assessment: Defines a framework to assess AI vendors, focusing on criteria such as vendor cost, solution set, and risk of going out of business.
Model Reliability: Establishes a framework to assess and manage issues like hallucinations, bias, and inaccuracies in AI models.
Cost Considerations: How much should an organization invest in AI solutions? Should they build custom solutions, buy existing models or a hybrid approach of both?
Compliance: Ensures AI applications are compliant with relevant regulatory bodies, and incorporates this into an Acceptable Use Policy.
Training and Enablement: Builds a use-case specific training and enablement program for stakeholders across the organization.
These activities (and more) are not created in a vacuum for others to consume. We embed them in the AI development process.
Governance and Development
Strategy & Planning
The first phase of AI development involves defining your AI goals, architectural vision, and resource needs. You need to align AI initiatives with business objectives, secure executive sponsorship, and outline key constraints (legal, ethical, operational). Establish how you’ll integrate AI into existing systems and plan your data infrastructure early. Define roles and responsibilities, and outline a training and enablement program (subject to change once you roll it out) that you deploy throughout the lifecycle as needed. You’ll want to establish a lean, cross functional (include cybersecurity, compliance and technology) AI Center of Excellence to engage throughout the development cycle.
Data Collection & Management
AI’s success depends on the quality of its data. Source, assess, and process data with controls that are specific to your use case. For example, you’ll make different design and modeling choices when building a radiological image recognition model (which may favor minimizing false positives) compared to a computer vision model for a manufacturing line (which may allow for up to 10% failure rates).
Here you’ll create versioned data pipelines, automate data validation, and track metadata. As noted, depending on your use case, you’ll integrate bias checks and privacy protections. This foundation ensures your models train on high-quality, representative data.
Design & Development
In this phase you’re moving from prototype to production with clear development standards. Maintain reproducibility through code and experiment tracking (e.g., Git, MLflow). Perform preliminary performance checks—latency, throughput, and resource usage—to catch bottlenecks early. Leverage the AI CoE to assess risk, fairness, and compliance.
Validation & Testing
AI models, especially early on in the AI maturity of your organization, will need to undergo thorough testing and validation. Test every model extensively: functional accuracy, security, reliability, and performance under stress. Conduct bias audits where relevant. Establish acceptance thresholds and build immediate feedback loops (including human-in-the-loop) to keep issues contained and visible. Only production-ready models advance.
Deployment & Monitoring
Once a model is ready for production, you can roll it out with real-time monitoring for drift, anomalies, and security threats. I like to run models on small samples before letting them loose. Make sure to implement automated alerts tied to performance metrics; define clear retraining triggers. Version each model carefully and maintain rollback plans. Maintain incident response protocols to swiftly address unintended outcomes.
As you can see, throughout the development process, there are governance checks and controls. They should be “forked” (essentially copy and paste) from a centralized governance repository, and augmented for each use case. The centralized repository should be updated with new learnings as governance evolves.
Applying governance to your use case
The embedded governance model applies across all framework layers with context-specific implementation.
For example, when building an “Innovation” AI computer vision model detecting manufacturing anomalies, governance focuses on specialized talent acquisition, strategic data collection, automated dataset creation, and KPI-driven drift monitoring.
Conversely, when rolling out “Enterprise” AI like Gemini in Google Workspace, governance emphasizes establishing an AI center of excellence, workforce enablement through prompt engineering training, acceptable use policies, and continuous improvement through collective learning.
While all elements exist within the governance framework, effective implementation requires identifying and emphasizing the most relevant components for each specific use case.
Putting it all together
Bringing AI into your organization means balancing big-picture objectives with the practical realities of AI development. By combining a use-case driven AI framework with an embedded governance model, you set the stage for growth, innovation, and responsible adoption.
Not every company needs the entire model from the start; you can tailor these elements to your current readiness and risk tolerance. Still, using the framework as a guide helps your AI center of excellence chart a deliberate path toward a more AI-first organization.
Interested in exploring how to apply this framework to your own environment? Let’s talk. We’ve helped organizations at every stage of AI maturity, and we’d love to see how this approach can unlock value for you.