As artificial intelligence continues to reshape the compliance and risk management landscape, many organisations are grappling with how to distinguish genuine AI-driven solutions from traditional software products dressed up with analytics.
Shwetha Shantharam, AVP and product head at 4CRisk.ai, with two decades of software experience—five of those focused on AI innovation—recently offered a guide to help buyers make informed, future-proof decisions when selecting regulatory and compliance tools.
A key shift lies in the emergence of AI agents and co-pilots, which differ significantly from legacy applications. While traditional apps rely on predefined workflows and human input, AI agents operate autonomously, learn from their environment, and focus on delivering outcomes. Buyers should ask vendors whether their tools have been deployed as actual AI agents, support human-in-the-loop functions, offer explainable ROI, and provide ownership of AI-generated outputs. Equally important is understanding if their AI agents are connected via AI-powered workflows that align with business processes.
Another distinction is how AI technology improves on traditional analytics. Legacy apps often require manual data collection and offer retrospective insights. In contrast, AI apps use predictive analytics to automate decisions, process both structured and unstructured data, and respond in real time. Buyers should probe vendors on whether their systems truly leverage natural language processing, decision-management frameworks, and retrieval augmented generation, and whether those technologies are verified by awards or independent assessments.
Language models also represent a fundamental evolution. Large and small language models enable natural, conversational interaction with compliance software, enhancing accessibility and relevance. However, not all LMs are built equally. Buyers should ask whether vendors use domain-specific models, how they handle hallucinations and bias, whether public or private environments are used, and if client data is reused in training models.
The underlying platforms supporting these AI capabilities must also be scrutinised. AI-native platforms are data-centric, cloud-based, and built for scalability using advanced infrastructure like GPUs. Key features to check include support for single sign-on, robust access controls, audit trails, and certification such as SOC II. Buyers should also consider the ability of the platform to handle large-scale data analysis and model training without compromising security or performance.
4CRisk.ai offers a suite of AI tools specifically tailored to regulatory, risk, and compliance teams. These include products for regulatory research, compliance mapping, change management, and the Ask ARIA Co-Pilot. These tools significantly reduce the time spent on manual analysis and provide rapid, actionable insights. For example, Ask ARIA Co-Pilot acts as an always-on assistant, answering complex internal queries and saving up to 90% of time compared to manual research.
For organisations looking to evaluate or adopt AI tools in 2025, the message is clear: ask the right questions, validate true AI capabilities, and choose vendors offering transparency, performance, and proven impact in the compliance domain.
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