Leaders exploring AI-powered Regulatory Change Management (RCM) solutions face a growing number of complex choices. Supra Appikonda, co-founder and COO at 4CRisk.ai, offers key insights for compliance and risk professionals evaluating such tools, emphasising the importance of asking vendors the right questions to future-proof investments.
With decades of experience in enterprise AI deployments, Appikonda outlines critical considerations for selecting a platform tailored to the needs of regulatory, risk, and compliance teams.
A key starting point is understanding the product’s AI capabilities and the concrete value it offers. Buyers should scrutinise whether vendors provide ROI models based on real-world volumes and timeframes, and whether these models are backed by customer case studies. AI should go beyond simple automation—vendors should explain how their models handle horizon scanning, obligation mapping, and taxonomy creation with precision.
Another core area of differentiation is intelligent content curation. Vendors must explain how they aggregate, ingest, and analyse regulatory information across jurisdictions, formats, and languages. Appikonda stresses the importance of tools that reduce noise, enhance signals, and maintain an always-updated inventory of obligations from over 2,500 global sources.
Equally critical is how the AI system assesses relevance and applies context. Does it accurately map obligations to internal policies, procedures, and controls? Buyers should understand whether the approach is rules-based, machine-learning-driven, or hybrid, and expect clear explanations for how relevance is determined.
Integration and workflow flexibility are also pivotal. Buyers should explore how well the AI platform connects to existing systems via APIs and how it enables configurable, AI-powered workflows. Solutions with “co-pilot” features, such as secure, instant query responses, can drive significant time savings—up to 90% in some cases.
Evaluation of AI models must include scrutiny of the training data, model explainability, and governance. Buyers should understand where training data originates, whether public LLMs are used, and if their own data is secure and excluded from training processes. Transparency and auditability—like confidence scores and access to AI reasoning—should be non-negotiables.
Further, ongoing model performance and governance practices must be in place. Vendors should offer defined lifecycle processes, retraining protocols, and human-in-the-loop mechanisms for oversight and refinement. Privacy and security must extend beyond baseline compliance, addressing AI-specific risks like evasion or poisoning attacks.
Scalability and compliance readiness are essential. Vendors must demonstrate the ability to process high volumes of regulatory data while meeting standards such as GDPR, SOC2, and ISO 27001. Private SaaS environments and secure infrastructure should be standard.
Successful implementation requires more than just technology—it depends on the vendor’s expertise and support. Prospective buyers should ask about implementation timelines, templates, and training, as well as user-friendly design tailored to compliance professionals who aren’t data scientists.
It’s also vital to understand the vendor’s roadmap. A strong commitment to innovation, demonstrated through case studies and recent awards, reflects the vendor’s readiness for the evolving AI and regulatory landscape. Ethical AI practices, model explainability, and published guidance on AI governance should also be part of the offering.
Finally, leaders should consider the strategic benefits. AI-powered RCM enables proactive compliance, faster market adaptation, and greater operational efficiency. It can reduce manual workloads, enhance audit outcomes, mitigate risks, and boost an organisation’s competitive edge.
For more a mode detailed breakdown of what to consider when seeking AI-powered change management solutions, read the full story here.
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