GRC survival guide: Thriving in the era of AI SaaS

Recent headlines paint a dire picture: Powerful AI models are eroding the market capitalization of major SaaS vendors. The narrative suggests AI will replace traditional software, but this perspective misses something fundamental: AI won't make SaaS obsolete, it will make SaaS fully actualized.

The truth about surviving and thriving in this new era rests on the realization that large language models (LLMs) will become a commodity layer, chosen less for uniqueness and more for performance, efficiency, and availability. AI agents will move fluidly across models, selecting the right one for each task without human intervention. The LLMs will become invisible infrastructure.

Beware the false prophets

Many vendors are responding to AI anxiety by bolting chatbots onto legacy systems filled with fragmented, unstructured data. Policies are scattered across SharePoint, controls are buried in spreadsheets, and frameworks are trapped in PDFs. Adding AI to this chaos doesn't create innovation, it creates confusion. A chatbot that can't find the truth faster isn't intelligent; it's just expensive.

Trusted data and intelligent workflows

What determines who thrives is investment in trusted data and intelligent workflows that connect AI to how organizations actually operate. Trusted data brings context, accuracy, and a deeper understanding. Applications provide the workflows. AI agents tie it all together with unprecedented speed, insight, and productivity at a scale no human team could achieve alone.

But here's the critical prerequisite: Your GRC data must be normalized, structured, and connected across policies, controls, frameworks, and requirements. Without this foundation, AI can't help you, it can only amplify your existing dysfunction.

A defining moment for GRC

For GRC teams, this isn't just industry evolution, it’s a defining opportunity. Compliance, Risk, and Audit teams stand at the critical intersection of AI governance and AI adoption, uniquely positioned to become the AI enablers for the modern enterprise.

Related reading: AI governance and the future of GRC

This moment requires mastering two imperatives simultaneously.

First, GRC teams must enable enterprises to adopt AI quickly, securely, and with appropriate risk awareness. In a rapidly changing competitive landscape, speed isn't optional. World-class AI governance means saying "yes, and here's how we do it safely," not defaulting to "no." GRC team’s ability to govern AI adoption will make them indispensable.

Second, ensure your own GRC data is AI-ready. Structure it. Normalize it. Make it coherent and accessible. The organizations facing the most complex risks are precisely the ones that need AI-powered GRC most, but only if the underlying data can support it.

The rise of agentic SaaS

Imagine AI agents that answer "are we compliant with this new regulation?" in seconds, not weeks. Agents that identify control gaps across frameworks instantly and surface risk patterns invisible to human analysis.

This isn't science fiction, it's what becomes possible when AI meets clean, connected, trustworthy GRC data. When your compliance posture isn't locked in silos but flows through intelligent workflows purpose-built for how assurance actually works.

Related reading: How to transform your GRC strategy with AI-driven tools

The choice ahead

The GRC platforms that will thrive aren't racing to build proprietary models. They're the ones that already structured their data right, built workflows that reflect how governance actually happens, and created ecosystems where AI agents can operate with both power and precision.

The question isn't whether AI will transform GRC, it's whether your platform was built for this moment. Choose accordingly.

About the authors

Daniil Karp is a SaaS business professional with over a decade helping
organizations bring revolutionary new practices and technologies into
the fields of IT security and Compliance, HR/recruiting, and collaborative
work management. Prior to joining AuditBoard Daniil worked in go-to-market at companies including Asana and 6sense.