This webinar addressed the critical need for governance frameworks as enterprises deploy autonomous AI agents capable of taking actions across connected systems. With real-world vulnerabilities already emerging (like Asana's recent MCP server breach), organizations must implement proper controls before widespread adoption to avoid costly security incidents and operational disruptions.

👉 Download the full slide deck to revisit key frameworks, controls, and live demo takeaways.

As enterprises begin deploying autonomous AI agents, new governance challenges are emerging fast. From agent-to-agent communication risks to unclear accountability, the cost of poor oversight is only getting higher.

In this exclusive webinar replay, ModelOp CTO Jim Olsen explores how to bring safe, scalable governance to agentic AI—sharing practical strategies and live tooling examples, including:

• Why agent-to-agent interactions are a hidden vector for data leakage and risk
• The autonomy dilemma: how much control is too much—or not enough
• How enterprises are using Agent Service and MCP protocols to enforce guardrails

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Want more expert insights like this? Register for the Good Decisions webinar series to stay ahead of the curve with 30-minute sessions designed for enterprise AI leaders.

Agentic AI is here. The risks are real—and avoidable.

Organizations are experiencing a massive imbalance between AI ambition and execution. While there's an overwhelming number of proposed AI use cases and initiatives, very few actually make it to production. Most remain stuck in the proposal phase, creating a significant bottleneck that prevents companies from realizing AI's value at scale.

What’s holding enterprise AI back? The data tells a clear story.

The 2025 AI Governance Benchmark Report revealed a growing commitment to responsible AI—with 36% of enterprises budgeting over $1M for governance software. But investment alone isn’t enough. In this exclusive webinar replay, ModelOp’s experts dig into the report’s most surprising—and actionable—findings, including:

• Why fragmented systems and manual processes remain the #1 blockers to governance adoption
• The critical training gap that’s slowing AI scale across industries
• How top-performing organizations are aligning governance with business value

Download the full slide deck to review the stats, charts, and takeaways shared during the session.

Want more expert insights like this? Register for the Good Decisions webinar series to stay ahead of the curve with monthly 30-minute sessions designed for enterprise AI leaders.

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Model cards are a breakthrough for AI transparency—offering critical insights into how models were trained, their intended use, and their limitations. But transparency alone isn’t enough. To drive real-world results in healthcare, organizations need to move beyond reading model cards to actively using them for governance, compliance, and risk management.

Is your organization ready to turn model card transparency into operational advantage?

Join Dave Trier, VP of Product at ModelOp, and Amanpreet Kaur, Ph.D., Implementation Engineer, for a 30-minute session focused on bridging the gap between documentation and action. You’ll learn how leading healthcare organizations are importing model cards, integrating them into compliance workflows, and automating governance processes to scale AI safely and efficiently. See how to turn transparency into a practical, repeatable process that accelerates responsible AI deployment.

🔍 What You’ll Learn:

• How to import and evaluate model cards—including those from CHAI and other healthcare standards bodies
• Why transparency isn’t enough—and how to use model cards in real governance workflows
• How to centralize model tracking for compliance, audits, and risk reporting
• Ways to automate governance processes without slowing down AI adoption
• What leading healthcare teams are doing to scale AI responsibly and efficiently

Register for the series.

Just like nutrition labels transformed food transparency by providing standardized information about ingredients and nutrients, model cards serve as "nutrition labels" for AI systems. They provide essential transparency into what AI models contain, how they work, their intended use cases, limitations, and potential risks - enabling stakeholders to make informed decisions about AI deployment.

‍Register for the series.

SLMs and Agentic AI are redefining enterprise AI strategies—offering more efficient, specialized models and autonomous capabilities that can drive real business value. But with these advancements come new governance challenges. Is your organization prepared to manage the complexity, visibility, and oversight required to scale responsibly?

Join us for an insightful session led by Jim Olsen, CTO of ModelOp, as he breaks down the technical and operational implications of SLMs, model distillation, and Agentic AI architectures. Discover how modern governance strategies can empower your organization to embrace innovation while maintaining control, compliance, and long-term scalability.

What You’ll Learn:

• SLMs vs. LLMs: How Small Language Models provide cost and privacy advantages over traditional LLMs—and why they’re better suited for agentic use cases.
• The Role of Model Distillation: What distinguishes distillation from reinforcement learning, and how tools like DeepSeek and Gemma 3 enable lightweight expert agents.
• Managing Agentic AI Architectures: How to govern large-scale model ensembles and expert models using tools like ModelOp’s Model Inventory and Approval Workflows.
• Operationalizing Governance: How enterprises are applying continuous monitoring, traceability, and structured approvals to stay ahead of compliance and performance risk.

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The emergence of small language models (SLMs) is making Agentic AI more accessible and practical for enterprises. Unlike large language models that require massive computational resources and expensive GPUs, SLMs can run on standard desktop hardware with just 4GB of GPU memory. This shift enables organizations to deploy specialized AI agents locally without sending sensitive data to external vendors, making Agentic AI cost-effective and secure for enterprise use.

GenAI and Agentic AI are revolutionizing how businesses approach data management, but they also introduce unprecedented challenges. Is your organization ready to navigate the risks, compliance demands, and governance complexities these technologies bring?

Join us for an insightful discussion led by experts from ModelOp, Macula Systems, and Mercy . Together, we explore how data and AI governance must align to enable innovation while safeguarding compliance and organizational readiness.

What You’ll Learn:

• The Role of Governance: How integrating data governance with AI governance ensures visibility, trust, and compliance.
• Practical Use Cases: Success stories on data and AI governance from Doug Graham, Director of Enterprise Data Governance at Mercy.
• Scalable Solutions: Frameworks to manage data and AI at scale, avoiding pitfalls in innovation and compliance.
• Actionable Insights: Quick-start governance strategies to prepare for the future of GenAI.

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Kick off 2025 with a webinar designed for Fortune 500 AI and governance leaders to explore how AI Portfolio Intelligence empowers organizations to track, realize, and optimize the value of their AI initiatives including generative AI. Discover why treating AI as a strategic portfolio is essential for demonstrating ROI and aligning innovation with business goals. Learn how AI Governance, focusing on the Minimum Viable Governance (MVG) approach, works in parallel with AI Portfolio Intelligence to unlock AI’s full potential by ensuring accountability, compliance, and sustainable growth. Don’t miss this opportunity to gain actionable insights and accelerate your AI strategy in 2025.

What is agentic AI—and how do you keep it from going off the rails?

In this episode of Good Decisions, Jay Combs chats with Dave Trier, VP of Product at ModelOp, about Salesforce’s Agentforce 2.0 and the rise of agentic AI. These systems don’t just make predictions—they take action. And while that’s exciting, it also introduces a whole new layer of risk.

Jay and Dave unpack why governance for agentic AI is so critical, especially when you’re dealing with tools that can automate decisions in customer service, sales, and operations. They dig into how organizations can stay in control, manage compliance, and avoid surprises by integrating governance directly into platforms like Salesforce.

If you’re navigating the shift toward autonomous AI agents, this one’s a must-listen.

2024 has been a year of rapid growth, rising expectations, and new challenges for AI Governance. As AI adoption accelerates, organizations are still facing critical hurdles:

• The AI production gap: Pilots take off, but production results fall short.
• Third-party and embedded AI: Widely in use, but still unmanaged and ungoverned.
• A fragmented regulatory landscape: EU, U.S. states, and agencies are setting the pace.
• Unclear accountability: Who truly owns AI Governance in your organization?
• The steep, hidden costs of DIY governance.

What’s next in 2025?
In this webinar, we discuss the trends and strategies that will shape AI Governance in the year ahead:

• AI Portfolio Management and Minimum Viable Governance (MVG): Practical approaches to balance innovation and compliance.
• Regulatory pressure: Growing real teeth as public awareness increases.
• The rise of agentic AI and trust-centric governance—turning hype into action.

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