It’s only been three years since ChatGPT was unveiled to the public. While three years isn’t so long in the grand scheme of things, ChatGPT has already made a huge impact. According to a recent report from OpenAI, roughly 700 million users had already used ChatGPT by July 2025. To give you an idea of the scale, that’s roughly 1/10th of the entire adult population on Earth! And, when it comes to artificial intelligence, this seems to be just the tip of the iceberg.
Businesses, recognizing the opportunity of AI, are racing to upskill their teams and integrate the technology into workflows. Several companies have already driven sales, enhanced productivity, boosted revenue, and achieved cost savings with AI.
However, it’s not all roses. While AI provides a lot of upside, organizations are now encountering new challenges pertaining to compliance, regulation, and bias.
This is where the evolving field of AI governance enters the equation. Keep reading to learn more about what enterprise AI governance is, how to get started, and some of the risks, challenges, and associated tools.
What is enterprise AI governance?
Enterprise AI governance provides a structured framework that protects organizations against AI risks by establishing consistent, ethically grounded guardrails, policies, and procedures. These drive organizations toward the responsible, transparent, and fair development and use of AI systems. This framework ensures that the adoption of AI aligns well with the values and principles of the company, is in tune with its strategic goals, and adheres to local laws and regulations at all times.
Simply put, enterprise AI governance acts as the rule book and the referee for the organization, offering structured policies and oversight. The framework intends to provide support with practical implementation throughout the AI system lifecycle.
Why is AI governance so important?
Organizations are increasingly prioritizing AI governance for two key reasons: they’re using AI more and more, and they need to comply with ever-evolving regulations.
1. Growing organizational demand
Companies understand that they can’t just turn on AI and let it do its own thing. Human oversight, backed by AI governance, is required. Otherwise, things can go wrong, and the company’s bottom line and reputation can take a hit.
Case in point? In 2021, an AI-powered feature caused Zillow to buy properties at inflated prices. The result: a $304 million dollar write off and laying off 25% of the workforces. Needless to say, the move also shattered customer trust on top of being bad for the business.
2. Public and regulatory pressures
Governments and the public are wary of the impact AI will have on society, and rightfully so. To safeguard societal structure, laws and regulations continue to evolve. Some of the few notable ones in recent years include:
- EU AI Act 2024 (European Union)
- National Artificial Intelligence Initiative Act of 2020 (United States)
- AI Promotion Act 2025 (Japan)
Enterprises are waking up to the risks of AI and are making efforts to tackle data privacy, curb bias, and fulfill legal requirements. Failure to maintain compliance can result in millions of dollars in fines and, depending on how bad the situation is, even cause operations to shut down.
How do organizations implement AI governance?
While the importance of AI governance is self-evident, the journey each organization takes to implement it is unique. That said, following this general framework should help your company get started on the right foot.
Assess current status and identify risk
Start by figuring out how your team is using AI currently and how they intend to use it in the future. Once you understand all of the tools involved in handling, storing, and manipulating your data, you can get a better idea of the current lay of the land, including the most pressing risks your organization faces.
Form a team and create buy-in
Create a cross-functional committee that provides oversight and governance for AI projects. The committee should not be limited to technical departments only.
To make sure no key areas of operations are overlooked, include members from finance, HR, legal, IT, operations, and leadership. Bring in talent, especially those who already have AI skills or are eager to learn.
Once you’ve gathered the players, set up a regular cadence and delegate roles and responsibilities. The committee should have visibility into KPIs across ROI, compliance, and productivity.
Personally, I’ve seen significant concerns and ultimately resistance from workers about the adoption of AI. Many people are concerned about losing their jobs to technology.
This is no doubt an understandable perspective. What helps here is to educate your employees about the promise of AI, instilling assurance about how the technology will make their jobs easier and help them do their best work. You’ll also want to offer training so that your team can learn how to use the tools productively.
Being empathetic goes a long way. Don’t invalidate their concerns. Instead, show them the bigger picture and help them understand that AI adoption is inevitable.
By helping employees be a part of the journey and the future of the business, you can eventually convince employees to buy into the technology and maybe even get optimistic about it.
Develop policies
Once you have your team ready, formulate policies that define:
- Ethical guidelines
- Data privacy standards
- Transparency requirements
Since AI evolves rapidly, your policies aren’t static documents. Instead, the committee should review what’s working and what isn’t and continue to refine policies in order to adhere to what’s best for stakeholders, employees, and compliance purposes.
Execution
One could plot and plan all they want. But if they can’t execute, what’s the point?
If the team is inexperienced, bring in mentors, coaches, and AI project managers to better help plan, manage risk, and deploy AI initiatives. Leadership needs to understand that AI initiatives are largely experimental and take time to deliver results.
Even then, scaling an initiative doesn’t always go as planned, so organizations should set aside extra funding to make sure they can pay for potentially higher than expected costs.
Automated tools should be used to continuously monitor AI model behavior, performance metrics, and compliance. AI systems are learning and evolving all the time, and 24/7 human monitoring isn’t really practical.
Hence, automated tools that can capture and report important events and anomalies can help keep AI systems in check. By conducting regular audits, your organization can help ensure AI tools are operating as expected, protecting you from a rogue AI agent causing havoc.
Foster a culture of continuous improvement
Since AI continues to evolve rapidly, it’s important to instill a culture of continuous improvement where employees are constantly learning the new AI trends. Teams should be encouraged to share learnings with their colleagues, and everyone should be committed to helping the organization’s AI function become stronger every day.
Challenges in AI Governance
While AI can absolutely transform the way teams work, organizations run into several challenges while implementing AI governance.
1. A dynamic AI landscape
It might be hard to believe it, but AI is still relatively nascent and is in its growth phase as technological breakthroughs occur every few days. New tools and techniques hit the market much faster than any policy can accommodate.
2. Sheer complexity
AI models — especially deep learning models — are often opaque and difficult to decipher, which can cause AI models to act as “black boxes.” This means that no one can explain how a model arrived at its conclusion or why.
Deep technical expertise is needed to develop, maintain, and troubleshoot such models. A best practice is to keep AI models well-documented and “explainable,” at least as much as you can.
3. Data quality
AI models are only as good as the data used to train them. Sourcing and using unbiased, error-free, and representative data is difficult.
4. Uncertainty with legal and regulatory changes
Rapidly evolving laws and regulations make it hard to navigate an already uncertain path. To succeed, organizations need to be agile.
What tools or platforms help with AI governance
There’s no shortage of AI governance tools on the market today. The trick is finding the appropriate one for your organization’s unique needs. With that in mind, let’s explore a few different types of tools you should consider.
Audit and monitoring tools
AI models can face model drift, causing performance degradation over time. Additionally, they can also suffer “silent failures” where the model breaks down in subtle ways. Tools like Fiddler.ai enable real-time model performance and drift monitoring capabilities, helping protect against these problems.
Model governance platforms
Depending on where your data comes from, it will likely be biased. IBM’s Watson OpenScale helps mitigate the risks and provides bias detection, fairness metrics, and transparency reports.
Data governance solutions
As an organization grows, keeping data cataloged becomes critical. This is where tools like Collibra and Alation can be a game-changer by helping manage an organization’s data catalogs, lineage, and compliance.
Explainability frameworks
As mentioned earlier, complex models can act as “black boxes.” Libraries such as LIME and SHAP help resolve this challenge by providing explainability.
Enterprise AI governance: Final thoughts
You shouldn’t be surprised if Trader Joe’s announces its own AI governance team and policies in the near future. With AI penetrating conventional businesses as well, each enterprise will sooner or later need one to protect against profound risks.
After all, AI governance is increasingly a strategic necessity that protects organizations, empowers innovation, and most importantly ensures that AI technology benefits society. By orchestrating the right team, policies, tools, and continuous oversight, enterprises can navigate through the challenges and unlock AI’s true potential safely — all while remaining compliant.
To transform AI governance from a chore into a real business advantage, many enterprises are turning to Workato MCP, which provides a secure governance layer that keeps AI workflows safe, transparent, and compliant — allowing organizations to truly reap AI’s benefits.
Learn more about Workato MCP and how it supports enterprise AI governance.
This post was written by Ali Mannan Tirmizi. Ali is a Senior DevOps manager and specializes in SaaS copywriting. He holds a degree in electrical engineering and physics and has held several leadership positions in the Manufacturing IT, DevOps and social impact domains.