Defining AI: The First Step to Responsible and Scalable AI Governance

Artificial Intelligence (AI) is transforming every industry, accelerating decision-making, enabling new customer experiences, and creating entirely new ways of doing business. But as organizations embrace AI with increasing speed and scale, a critical question is often overlooked: What exactly does “AI” mean in your organization?

Without a shared understanding of what qualifies as AI, what’s in scope for governance, and how different AI systems should be categorized and monitored, even the most well-intentioned governance efforts will struggle to take hold.

In this post, we’ll explore why clear definitions and classifications at the organizational, scope, solution, and pattern levels are not just helpful—but essential to building an effective, risk-aligned, and future-ready AI governance program.

Real-World-Inspired Scenarios: Where the Absence of Definitions Leads to Governance Failures

Let’s begin with three real-world-inspired cases that highlight how failing to define and classify AI can create blind spots:

Finance Team Uses GenAI Without Disclosure: A finance team deploys a generative AI tool to create quarterly revenue forecasts—but because the tool isn’t clearly recognized as “AI” internally, it bypasses risk review. Inaccurate outputs lead to an SEC inquiry and reputational damage.

HR Tool Misclassifies Candidates: HR adopts a résumé screening tool marketed as “analytics,” which deprioritizes candidates from underrepresented groups due to biased training data. With no internal classification scheme, the tool escapes fairness review—until a whistleblower sounds the alarm.

Autonomous Agent Crashes Infrastructure: An agentic AI model is deployed to manage cloud performance. It incorrectly deletes a config file, causing major downtime. No one realized it should’ve been governed under “agentic AI” with safety guardrails.

What do these have in common? They all lacked a clear definition of AI, a structured governance scope, and solution-level or pattern-based classifications.

Understanding AI: The Organizational Level Definition

“AI” means different things to different people—ranging from simple automation to advanced autonomous agents. Without an internal definition, confusion reigns. The first step in implementing AI governance is defining what AI means at the organizational level. This definition lays the foundation for how AI initiatives are perceived, communicated, and executed within the company. It involves identifying AI's strategic role—whether it's a tool for process automation, an enabler for data-driven decision-making, or a driver for new business models. A clear definition helps align AI initiatives with organizational objectives, ensuring that AI projects are not just innovative but also strategically relevant. Key components include:

• Mission Statement: A clear statement outlining your organization's AI vision and purpose. For example: "To leverage AI ethically to enhance customer experience, optimize operations, and drive sustainable growth."
• Scope of AI Initiatives: Defining what falls under the AI umbrella. This clarifies whether AI encompasses machine learning, deep learning, natural language processing (NLP), robotic process automation (RPA), or other technologies. Clear boundaries prevent ambiguity and streamline governance.
• AI Principles: A documented set of ethical guidelines governing data privacy, fairness, transparency, accountability, and compliance with relevant regulations. This should align with overall organizational values.

Why It Matters:

• Strategic Alignment: It ensures that AI initiatives support the organization's mission and goals.
• Consistent Communication: Provides a common language for stakeholders to discuss AI, reducing misunderstandings and misalignments.
• Investment Decisions: Guides decisions on resource allocation and prioritization of AI projects.

Scoping AI: Know What’s in (and out) of AI Governance

Once AI is concretely defined within the organization, the next step is to determine the scope of AI projects. Mapping all AI systems in use or under development is essential. This process clarifies which tools and solutions fall under the governance program, their functionalities, data sources, dependencies, and integration points. For each initiative, detailed information is crucial:

• Data Sources: Complete identification of data used to train and operate AI models to ensure data quality, privacy, and security.
• Model Development Lifecycle: A comprehensive understanding of the AI model's development, from data collection and preprocessing to training, evaluation, deployment, monitoring, and retraining. Governance should establish best practices at every stage.
• Impact Assessment: A thorough analysis of the AI system's potential impact on various stakeholders (customers, employees, partners) and the associated risks and benefits to inform mitigation strategies and ethical considerations.

Why It Matters:

• Resource Management: Clearly scoped projects enable precise budget allocation, time management, and efficient use of resources.
• Risk Mitigation: Identifying potential risks and setting boundaries prevents scope creep and reduces unforeseen challenges.
• Performance Measurement: Defined objectives and success metrics allow for effective tracking and evaluation of AI project outcomes.

Defining AI Solution Levels: Apply the Right Governance Based on Risk

One of the most critical elements in operationalizing AI governance is recognizing that not all AI solutions are created equal—and therefore, not all require the same level of oversight, assurance, and control. This is where solution-level classification comes into play.

By categorizing AI systems based on their purpose, complexity, and risk, organizations can apply proportionate governance—ensuring higher-risk solutions receive deeper scrutiny, while lower-risk solutions can move faster under lighter controls.

For example, AI solutions can be tiered into:

• Foundational AI: Embedded or prebuilt solutions integrated with business productivity apps.
• Applied AI: Applied AI – Custom or fine-tuned models built for business-specific purposes.
• Advanced AI:  AI systems capable of autonomous action, planning, or collaboration.

When classifying an AI system, consider the Autonomy of Decision-Making, Impact of the System, Model Type and Complexity, Data Sensitivity, Level of Human Oversight

Why It Matters:

By applying this tiered structure, organizations can:

• Align governance controls to actual risk, not just technical sophistication.
• Streamline AI intake and approvals using risk-informed workflows.
• Assign clear accountability for higher-risk solutions that require stronger oversight (e.g., independent validation, ethics review).
• Enable innovation by reducing friction for low-risk, well-understood AI use cases.

Use Patterns and Classifications: Scale Governance with Consistency

As AI adoption grows, organizations face an increasing variety of use cases—from simple classification models to complex, multi-step agentic workflows. While each use case may feel unique, many AI applications follow repeatable patterns in terms of objectives, architecture, and risk profile. AI solution patterns are common use case blueprints that describe how AI is used across the business. A pattern reflects a recurring combination of business objective, technical architecture, data dependencies, and risk factors.

Think of them as templates that:

• Standardize risk assessments and control requirements.
• Accelerate solution design, review, and deployment.
• Enable governance teams to scale with growing demand.

Patterns can be grouped by a higher level of AI functionality such analysis, language, interaction, and generation.

By identifying and documenting these AI solution patterns and functionalities, organizations can apply consistent, repeatable governance controls—driving both speed and safety.

Here are some examples:

Risk Tier:

• Low – Minimal business impact, no autonomy, limited data exposure.
• Medium – Moderate impact, indirect decision influence, may process sensitive data.
• High – Direct decision-making, high autonomy, potential compliance, or reputational risk.

Why It Matters:

By defining AI patterns and linking them to functionality, solution levels and risk tiers, organizations can:

• Reduce review timelines.
• Avoid reinventing governance steps for every project.
• Ensure consistent compliance with policies and regulations.
• Empower business teams to innovate with confidence and clarity.

Conclusion: Don’t Start Governance Without Defining the Ground Rules

Defining AI at the organizational, scope, solution, and pattern levels may seem like a foundational step—but it is also the most strategic one. With these in place, you enable:

• Scalable and risk-based oversight
• Efficient collaboration between business, tech, and risk
• Trustworthy, compliant, and value-driven AI systems

As AI continues to evolve and impact all facets of business operations, clarity, and precision in defining AI scope and application are indispensable. By prioritizing these foundational definitions, organizations can not only harness the transformative power of AI but do so responsibly and sustainably, fostering trust among stakeholders and driving sustained success.

Your next move?  - Start with a working group to define and document what AI means across departments.  - Align it with your governance goals, regulatory obligations, and business strategy.

Because if you don’t define AI—someone else in your organization will, and that’s where the real risk begins.

Appendix:

How to Use the Table above

• Define AI functionalities and match patterns. Define minimum risk and control expectations for each pattern.
• During AI intake, match the use case to a pattern to determine default controls and risk expectations.
• For risk tiering, align tier with solution level to apply proportionate reviews.
• In your governance tooling, automate control suggestions, approval routing, and documentation needs based on pattern selection.