Shoppers of compliance tech are increasingly turning to AI-driven operating models as boards demand faster, broader assurance. This matters because GRC teams are stretched thin across resilience, third‑party risk, cyber, privacy and AI oversight , and scaling execution, not just visibility, is now the business priority.
Essential Takeaways
- Capacity crunch: GRC teams face expanding mandates without matching headcount, so backlogs and compliance fatigue are rising.
- Spreadsheets persist: Many organisations still rely on manual tools and inbox workflows, which slow execution and increase risk.
- AI beyond assistants: The shift is from generative helpers to AI acting as codified, role‑based contributors within workflows.
- Codified expertise: Embedding institutional know‑how into systems preserves standards, reduces single‑person dependency and speeds audits.
- Governance first: Clear permissions, transparent reasoning and human sign‑offs are essential when AI participates in regulated decisions.
Why execution capacity, not insight, is the real problem
Boards can see the risks , dashboards make problems visible , but seeing isn’t the same as fixing. The everyday headache in regulated industries is that the list of things to check and report on has ballooned while budgets and teams haven’t. That mismatch creates slower review cycles and growing backlogs, which feel like the business being held back rather than protected.
For years firms papered over the gap with spreadsheets and heroic staff, but those workarounds are brittle. Industry commentary and reports show organisations still using manual processes alongside paid systems, a clear sign that visibility tools haven’t solved execution. The practical upshot: you need to measure capacity to execute as much as you measure compliance posture.
How AI can be more than a policy helper
Many early AI tools in compliance have been clever summarists , they draft, they answer questions, they speed a single task. That’s useful, but it leaves the human responsible for running the whole process. The more valuable model is when AI operates like a virtual specialist: a vendor manager, auditor or control owner that follows the organisation’s rules and workflows.
That shift gives you operating leverage. Instead of hiring more experienced people to do every assessment, you have scalable, repeatable agents that carry out defined tasks at pace. The trick is to codify the methods experts use today so the AI doesn’t just produce output, it applies your standards consistently across hundreds of assessments.
Codifying expertise: how to turn people’s know‑how into a business asset
Most of the best compliance judgement lives in people’s heads , what good evidence looks like, how to score a supplier, when to escalate. Capturing that as codified rules and workflows changes the game. It makes knowledge portable, repeatable and auditable, so you’re not left naked when a senior lead moves on.
Practically, this means building templates, decision trees and scoring rubrics into the tools that run your processes. You get continuity, faster onboarding and fewer one‑off calls to senior staff. It’s not about replacing experts, it’s about amplifying them so the whole team works at a higher standard.
Governance and explainability: non‑negotiables for AI in regulated work
Boards won’t accept speed at the expense of accountability. If AI takes actions in regulated flows, you must be able to show what it did, why it did it and who overruled it. Role‑based permissions, transparent records of reasoning and mandatory human confirmation for high‑risk decisions are basic controls, not optional extras.
Design these controls into the architecture from day one. That approach builds confidence across compliance, legal and the boardroom, and it makes regulators’ lives easier when they ask for an audit trail. In short: trust in automation is built on explainability and sensible limits.
How to move from pilots to a scaled GRC operating model
Start with the highest‑value bottlenecks , the assessments that create the greatest risk if delayed, or the vendor reviews that always lag. Codify the decision logic for those tasks, embed it into workflow, and introduce AI agents to carry out routine elements under human oversight. Monitor outcomes, tweak the rules and expand incrementally.
Combine that with targeted hiring where nuance matters, and consider specialist partners for overflow rather than expecting headcount alone to close the gap. The organisations that win are those that reframe GRC from a cost centre into an execution engine that supports growth and resilience.
It’s a small change that can make every compliance programme move faster without losing its bearings.
Source Reference Map
Story idea inspired by: [1]
Sources by paragraph:
Noah Fact Check Pro
The draft above was created using the information available at the time the story first
emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed
below. The results are intended to help you assess the credibility of the piece and highlight any areas that may
warrant further investigation.
Freshness check
Score:
8
Notes:
The article was published on 7 May 2026, making it current. However, similar themes have been discussed in recent publications, such as Deloitte’s ‘Adaptive by design: The next operating model for government’ (March 2026) ([deloitte.com](https://www.deloitte.com/us/en/insights/industry/government-public-sector-services/government-trends/2026/modern-operating-model-government-ai-era.html?utm_source=openai)) and McKinsey’s ‘How agile operating models benefit risk and compliance functions’ (September 2023) ([mckinsey.com](https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/how-agile-operating-models-benefit-risk-and-compliance-functions?utm_source=openai)). While these sources cover related topics, they do not appear to be direct replications of the TEISS article.
Quotes check
Score:
7
Notes:
The article includes direct quotes from SureCloud’s research, such as ’60 per cent of UK enterprises continue to use spreadsheets daily alongside their paid tools.’ However, these quotes cannot be independently verified through the provided sources, raising concerns about their authenticity.
Source reliability
Score:
6
Notes:
The article is published on teiss.co.uk, a UK-based cybersecurity news platform. While it is a niche publication, it is not widely recognised as a major news organisation. The article cites SureCloud’s research, but without access to the original study, the reliability of these claims cannot be fully assessed.
Plausibility check
Score:
7
Notes:
The article discusses the challenges faced by Governance, Risk, and Compliance (GRC) departments, such as increased demands without corresponding increases in resources. This aligns with industry trends and is plausible. However, the specific statistics and claims made are not independently verifiable, which diminishes their credibility.
Overall assessment
Verdict (FAIL, OPEN, PASS): FAIL
Confidence (LOW, MEDIUM, HIGH): MEDIUM
Summary:
The article presents current challenges in Governance, Risk, and Compliance (GRC) functions, citing recent research and industry trends. However, the reliance on unverified quotes and the lack of access to the original research diminish its credibility. The source, teiss.co.uk, is a niche publication, and the verification sources lack independence. Therefore, the content cannot be fully verified, leading to a FAIL verdict with MEDIUM confidence.
