An Introduction to
AI Governance
Unlocking the transformational value of Enterprise AI requires effective AI Governance that delivers on business demands while safeguarding the organization from the technology's inherent risks without stifling innovation
The Dual Role of AI Governance in Growth and Risk Mitigation
The goal of effective AI Governance is to accelerate innovation and business growth by 1) increasing the efficiency and efficacy of an organization's AI initiatives and 2) mitigating potential risks with safeguards that help an enterprise enforce policy, regulatory compliance, and ethical standards. AI Governance enables organizations to scale their AI initiatives and operate in a way that is transparent, accountable, robust, safe, fair, compliant, and aligned with societal values such as non-discrimination.
Effective AI Governance will safeguard an organization from AI-related risks, enforce policy and regulatory compliance without stifling innovation, and enable the business to quickly measure and report on governance metrics and key performance indicators (KPIs) related to the risks, performance, health, value, and quantifiable return on investment (ROI) of all AI initiatives across the enterprise. Successfully implementing an AI Governance framework helps organizations deliver responsible AI at scale.
What Is AI Governance?
Artificial Intelligence (AI) Governance is a framework for assigning and assuring organizational accountability, decision rights, risks, policies, and investment decisions for applying AI. In short, AI Governance is asking the right questions and giving the answers to put the right safeguards in place (Source: Gartner). The framework applies to all decision-making models including AI, generative AI (GenAI), Machine Learning (ML), statistical, regression, rules-based, in-house, third-party vendor, open source, and cloud-based. In this context, “AI” is used as a short-hand for the comprehensive list of decision-making models and technology.
In the era of GenAI, traditional governance practices and oversight mechanisms built for software, data, and corporate assets don’t adapt well for supporting AI initiatives. For example, traditional governance may lead to operational bottlenecks that stifle innovation because they can’t keep up with business demands and the inherent risks that AI presents, which may require reviewing constantly changing model outputs that change within a day, hour, or even minute.
This is causing a seismic shift in governance frameworks and means effective AI Governance must be adaptive — meaning it must be dynamic, enterprise-wide, and real-time in order to handle the unique challenges of AI, including explainability. AI Governance leverages best practices and policies to guide the development and use of AI initiatives, ensuring that these technologies can be brought to market efficiently, responsibly, and conform to all ethical principles.
Why Enterprises Need
AI Governance Software
Streamlining AI Governance in the Generative AI Era
The Shift from Manual Processes and DIY Systems to Purpose-Built AI Governance Platforms
Hidden Costs of Manual and In-House Solutions
Accelerating AI Initiatives with Commercial AI Governance Software
Why Effective
AI Governance Matters
Generative AI ushered in a new era of AI model development. Recent years have seen a massive increase in AI investment and related innovation. Alongside the growth in AI has come increasing concerns over ethical and responsible AI use.
R&D Investment
Growth Opportunities
Societal Impact
Regulatory Environment
The Changing AI Landscape
The size of enterprise investments associated with AI use is growing rapidly.
These numbers fail to account for the fury of investments that kicked off late in 2022 following the introduction of ChatGPT.
At the same time, the pervasive use of AI models combined with their potential impact on society is increasingly becoming the concern of regulatory bodies.
global corporate AI spend in 2021
a sixfold increase over the level of investment seen in 2016
the number of AI models in the enterprise is increasing by 20 to 30% each year
From Guidance to Regulation
The EU AI Act of 2024 began its life as a set of guidelines released in 2019 by the EU High Level Expert Group on AI. The Act is the world’s first comprehensive legal framework targeting AI use in business. The passage of this Act ushers in a new world of legal regulation specific to AI use.
With so many AI use guidance documents being issued by so many governmental entities around the globe, it seems certain that more governments will follow the path taken in the EU - evolving guidance into AI specific regulations that will have the force of law.
Non-AI specific regulations such as GDPR, HIPAA and PCI are also likely to play a big role in regulating AI use. These regulations focus on sensitive data and data privacy rights. The data intensive nature of AI model building means that there will likely be overlap between data governance and AI governance regulations
Top AI Governance Challenges
The expected growth in AI use along with the increased interest of governments looking to regulate this space is forcing enterprises to take a fresh look at their ability to deal with comprehensive regulations related to AI use.
Lack of Visibility
Into what is being built, for what purpose, who owns it and whether it conforms to all internal and external requirements
Manual, time-consuming
Efforts associated with ensuring that AI meets internal and external standards
Reactive
Responses to both internal and external inquiries for information related to the portfolio of AI initiatives
Inconsistent
Processes and policy enforcement across teams leading to increased complexity and associated risks
The High Cost of
Failed Governance
The risks associated with having an inadequate approach to the governance of AI use can be catastrophic
Regulations that are being adopted by governmental entities can have significant penalties for incidents of non-compliance
Customers that are negatively impacted or harmed by a business application using AI pose a risk of expensive litigation.
Ungoverned AI model development presents significant risks to brand equity that may have been earned over many years.
When the validity and accuracy of AI models is not appropriately proven, organizations risk the failure of major revenue generation or cost reduction initiatives.
Build The Right Governance Framework
Visibility
Robust inventory management capability to keep track of all essential meta and artifactual data associated for each AI model
Orchestration
An overarching control function that ensures the continuous and automatic enforcement of all relevant compliance requirement
Transparency
Routine and systematic reporting on AI performance relative to performance, value, security, validity, fairness, and bias
Automation
Integration of the AI tool chain and tech stack to support the standartization and automation of the compliance process
AI Governance Insights from 2024 - Pilots and Exploration
Below are key AI governance insights from 2024, and trends that will shape 2025 — pulling from ModelOp’s experience over the past year working with large enterprises and multi-national corporations who are tackling the rapidly evolving challenges of bringing AI to market, responsibly.
1. AI Production Gap: Pilots Take Off, but Production Expectations Fall Short
2024 witnessed a proliferation of AI pilots across enterprises. From generative AI chatbots to predictive analytics tools, many organizations explored the potential of AI with great enthusiasm. However, transitioning these projects from pilot phases into full-scale production environments proved difficult. Common barriers included insufficient infrastructure, unclear ownership, and a lack of governance processes to mitigate risk. While AI projects generated buzz, fewer delivered measurable ROI, exposing the gap between ambition and execution. Driving home this point, Battery Ventures’ 2024 State of Enterprise Tech Spending Survey showed a 42% delta between expected and actual AI usage.
The challenge for executives: Build the governance and operational frameworks to scale AI beyond pilots and prove its business value.
2. Third-Party and Embedded AI: In Use but Unmanaged
For those AI systems in use, a substantial portion of these AI systems were from third-party vendors. These include SaaS-based AI platforms, existing software with embedded generative AI (e.g. AI-driven CRMs and analytics tools), and APIs offering AI capabilities. However, many organizations lacked oversight into how these systems operate, who owns their governance, and how risks are assessed.
The takeaway: Enterprises must govern not just their in-house AI but also third-party AI solutions to avoid vulnerabilities and compliance issues. Read more about the uses cases and model types that enterprises implemented in 2024 here.
3. AI Regulatory Landscape is Fragmented: EU, US States & Agencies Lead the Way
In 2024, regulatory developments around AI intensified globally, albeit in a fragmented fashion. The EU’s AI Act entered into force in August, setting a high bar for AI risk management and transparency. In the United States, state-level regulations emerged, while federal agencies issued guidance on AI safety and accountability. This patchwork of regulations created complexity for Fortune 500 executives managing multinational operations.
The key insight: Staying ahead of regulatory changes will require adaptable AI governance frameworks that comply with evolving global standards. Learn more about the EU AI Act, governance themes, and compliance requirements here.
4. Unclear AI Accountability: Governance Ownership Still Up for Grabs
One of the clearest gaps in 2024 was the lack of clarity around AI accountability. Who owns AI governance? While CAIOs and CDAOs often led AI initiatives, oversight responsibilities involving legal, compliance, IT, and operational teams remained unclear. Many organizations struggled to align governance priorities across departments and business units.
The opportunity: Define roles and responsibilities for AI governance—from oversight to implementation—to ensure accountability and cohesion. Technical and business leaders need to work together to prioritize effective and efficient governance. Learn how FINRA approached jump starting governance here.
5. Understanding DIY Governance: Steep and Hidden Costs
Some organizations attempted to build custom AI governance frameworks internally. While this “DIY” approach may seem cost-effective initially, hidden expenses quickly surfaced: resource drain, technical complexities, legal risks, and incomplete coverage of AI risks. Enterprises discovered that ad hoc solutions were financially unsustainable and technically unmaintainable as AI use cases proliferated.
The lesson: Investing in scalable, purpose-built AI governance strategies can save significant costs and prevent AI-related disruptions down the line. Read more about the steep costs of building a homegrown governance system here.
Trends and Goals for 2025 - Achieving AI Value and Accountability
1. AI Portfolio Intelligence and Minimum Viable Governance (MVG): Business Priorities
In 2025, organizations will need to move beyond experimentation to demonstrate AI’s tangible value. AI Portfolio Intelligence—a strategy to track, realize, and optimize AI assets like a financial portfolio—will emerge as a best practice and best-in-class AI Governance software will provide the capabilities to deliver it. Alongside this, implementing Minimum Viable Governance (MVG) will allow businesses to balance oversight with innovation, focusing on critical AI use cases first.
2. Regulatory Pressure and Public Awareness: Growing Teeth
While there is some uncertainty and there are cases in which AI legislation didn’t move forward (e.g. California’s SB-1047), regulators are gaining momentum, and 2025 will see enforcement efforts intensify. The EU AI Act is now in force and during the 2024 legislative session, at least 45 states introduced AI bills, and 31 states adopted resolutions or enacted legislation. Enterprises can no longer treat governance as optional. Simultaneously, public awareness around AI ethics and risks is increasing. Organizations that fail to govern AI responsibly risk losing customer trust and market credibility.
Proactive governance: Businesses must anticipate and align with evolving regulations, prioritizing ethical AI deployment to build trust and avoid penalties.
3. The Rise of Agentic AI, Trust-Centric Governance, and AI Ownership
2025 will bring the next wave of AI capabilities—including agentic AI systems capable of autonomous decision-making. These advancements introduce new risks, including accountability gaps and unforeseen consequences.
To succeed, organizations will need trust-centric governance models that ensure AI systems are transparent, auditable, and aligned with business objectives. Ownership of AI initiatives will become formalized, with CAIOs and C-level executives playing central roles in governance leadership.
Part 3: Business Implications
What's Your GenAI Exposure? A Cautionary Tale
A recent story underscores the risks of AI. This is obviously a sensitive and unfortunate example, but it underscores the problems a single adverse event can cause. Optum’s AI chatbot, used internally by employees (which appears to have been part of a pilot program) to ask sensitive questions about claims, was inadvertently exposed to the internet. This misconfiguration revealed a serious lapse in governance and exposed the company to reputational damage, security risks, and regulatory scrutiny at an unprecedentedly challenging time for the company (source).
The message is clear: AI exposure without robust governance can lead to significant consequences. Missteps with AI systems, particularly generative AI, can erode years of trust and cause harm to a brand’s reputation at any time.
Conclusion and Takeaways - 2024 to 2025: AI Pilots Need to Show Results in Production
As we look ahead to 2025, the imperative for executives is to bridge the gap between AI exploration and enterprise value realization. Here are three key takeaways:
1. Prepare to Manage AI Like a Portfolio
To show measurable ROI, AI initiatives must be treated as a strategic portfolio. Leveraging AI Portfolio Intelligence allows leaders to prioritize high-impact projects, track performance, and demonstrate value to stakeholders.
2. Don’t Wait for an Adverse AI Event
AI-related incidents, such as misconfigurations or misuse, can cause rapid and lasting damage to a company’s reputation and bottom line. Proactively implementing AI governance frameworks—before something goes wrong—is critical.
3. Focus on Priority Use Cases with Minimum Viable Governance (MVG)
Organizations don’t need every AI governance capability overnight. Adopting an MVG approach allows businesses to focus governance efforts on their most critical AI use cases, ensuring compliance, mitigating risk, and maximizing impact.
Final Thought
2025 will be a defining year for AI governance. Fortune 500 executives who act now to operationalize AI governance, scale pilots into production, and build trust will set their organizations up for long-term success. The stakes are high, but the rewards are transformative: AI done right will drive growth, innovation, and competitive advantage well into the future.
Govern and Scale All Your Enterprise AI Initiatives with ModelOp Center
ModelOp is the leading AI Governance software for enterprises and helps safeguard all AI initiatives — including both traditional and generative AI, whether built in-house or by third-party vendors — without stifling innovation.
Through automation and integrations, empowers enterprises to quickly address the critical governance and scale challenges necessary to protect and fully unlock the transformational value of enterprise AI — resulting in effective and responsible AI systems.
To See How ModelOp Center Can Help You Scale Your Approach to AI Governance