AI Ops: The Future of AI Governance and Enterprise Growth

ChatGPT (DALL-E) Generated AI Ops

AI is no longer just a tool, it is becoming fully integrated into the backbone of modern business operations — and in with some industries, it has already been there for years — we just never noticed it. From automation to strategic decision-making, AI is driving innovation at scale. As AI becomes more prolific in an organization, those organizations need to start thinking beyond usage governance, they need to really start working to build out their AI Operations (AI Ops) processes and models.

Organizations that prioritize AI Ops can unlock AI’s full potential while maintaining compliance, governance, and resilience; however, those organizations need to be careful how and where they establish AI Ops — don’t relegate it to be a sub-function of IT Operations. While it may exist within your IT or Engineering organization, be sure to give AI the attention and priority needed for it to truly be successful.

Why AI Ops Matters Now More Than Ever

AI has become an integral part of workflows, customer experiences, and decision-making. Without a structured AI Ops strategy, companies risk inefficiencies, security gaps, and compliance issues. A well-defined AI Ops approach includes:

• Technical Governance & Standardization: Establishing guidelines to ensure AI applications are reliable, ethical, and scalable.
• Data Security & Privacy: Protecting sensitive data while adhering to regulations like GDPR and CCPA.
• Visibility & Monitoring: Leveraging dashboards and alerts to track AI agent activity, performance, and the things that they interface with.
• Access Control & Change Management: Ensuring AI modifications follow structured governance processes and include human overrides.
• Scalability & Resource Optimization: Managing compute, storage, and costs efficiently as AI adoption expands.
• Data Integration Methods: Integrating data and data models into the technical ecosystem including CI/CD pipelines.
• AI Operational Best Practices: Establishing guidelines on topics from advanced data cleaning procedures, transformation techniques, and data design patterns (e.g., Medallion architecture).
• Incident & Problem Management: Describing how to respond to and manage incidents and problems related to AI solutions.
• AI Literacy: Ensuring that everyone in the organization that has the potential to interface with an AI solution has at least foundational knowledge of AI related topics.

Before you get started you should become aware and read up on some of the standards that exist and are in development that cover AI Ops, as well as some of the frameworks and models on how to evaluate your organization’s posture or maturity.

Global Standards for AI Ops

Governments and industry leaders recognize the need for AI governance. Key standards shaping AI Ops include:

• ISO/IEC 42001: specifies requirements for establishing, implementing, maintaining, and continually improving an AI management system within an organization. (iso.org)
• ISO/IEC JTC 1/SC 42: is a subcommittee focused on standardization in the field of artificial intelligence, serving as the focal point for AI standardization within ISO and IEC. (iso.org)

How to Assess Your AI Maturity

Organizations differ in their AI adoption journey. AI maturity models help benchmark progress and provide structured pathways for improvement:

• MITRE AI Maturity Model: Evaluates AI readiness across ethics, data, and performance. (MITRE)
• Gartner’s AI Maturity Model: Guides businesses through AI implementation levels. (BMC)
• TM Forum’s AI Maturity Model: Assesses AI integration in strategy, operations, and technology. (TM Forum)
• Microsoft’s LLMOps Maturity Model: Provides insights for scaling large language models. (Azure)

While adopting one of these models may be overwhelming at first, or even more than what you really need, they are good sources to reference when thinking about AI operational maturity. You may even be able to create a tailored version that aligns with your comany or organization’s needs; however, I highly recommend finding a partner that can help you along your journey, especially in the early stages.

How Leading Companies Are Embracing AI Ops

Top organizations across industries are leveraging AI Ops to drive efficiency and innovation; here are some use cases to consider.

Finance: Automating contract review, fraud detection, and risk assessment; using AI to maintain compliance and resilience.

Healthcare: Implementing AI-driven diagnostics to enhance patient care while AI Ops ensures ethical AI usage and data security.

Education: Personalizing education and facilitating research partnerships through AI-powered platforms; AI Ops may cover ensuring models are operating within expectations and reducing potential for hallucinations.

Aerospace: Deploying AI for predictive maintenance and operational efficiency; AI Ops ensures reliability (precision, accuracy, control) in mission-critical applications.

Consumer Goods: Using AI to optimize demand forecasting and supply chain management; AI Ops may focus on standardizing model deployment globally.

Logistics: AI-driven route optimization and predictive analytics improve delivery times; AI Ops supports scalable and secure logistics operations.

Manufacturing: AI-powered quality control (inspection) detects errors (anomaly detection) in real-time.

• A real world example is BMW Group’s AIQX and Car2X platforms.

AI Ops may be implemented to streamline automation and deployment of models across global production facilities.

Who’s Leading the Conversation on AI Ops?

There are key leaders in the AI space that are shaping AI governance and how we interact with it both today and in the future. To stay aware of what is going on, I recommend that you check them out and start following them. Here are a few people that are actively working on these topics.

• Andrew Ng — AI educator, co-founder of Coursera and Landing AI.
• Aishwarya Srinivasan — Google AI strategist specializing in AI governance.
• Rana el Kaliouby — AI ethics advocate, focused on emotionally intelligent AI.
• Cassie Kozyrkov — Chief Decision Scientist at Google, democratizing AI adoption.
• Britney Muller — AI researcher and advocate for responsible AI.
• Lila Ibrahim — COO of Google DeepMind, overseeing AI ethics and governance.
• Rama Akkiraju — AI/ML VP at Nvidia, leading enterprise AI strategies.
• Matt Garman — CEO of AWS, advancing AI cloud innovations.
• Carolina Dybeck Happe — Microsoft COO, accelerating enterprise AI adoption.
• Timnit Gebru — Founder & Executive Director at The Distributed AI Research Institute
• Benjamin Manning — AI, ML & Data Science Leader and advocate for Accessibility throughout STEM

AI Ops Isn’t Optional

AI is rapidly expanding and at some point in the near future it will be as ubiquitous as web and mobile. Regardless of the industry, AI and AI Operations is coming and organizations need to be ready to adopt it. Companies that are not thinking about AI Ops now are surely going to face major hurdles in the future as they work to retrofit policy and procedures into already existing systems. Fortunately many of us are also working to figure it out and there are plenty of resources for us to get help — even from an AI!

Getting AI Ops right today will determine tomorrow’s success.