What Leaders Should Consider when Buying AI-Powered Regulatory Change Management Solutions

Introducing Our Author

In this blog, Supra Appikonda, Co-Founder and COO at 4CRisk.ai, provides the essentials that compliance, regulatory and risk leaders need to know before investing in AI-powered Regulatory Change Management (RCM) solutions.  These products differ from traditional RCM solutions in key ways, so make sure to ask your potential vendors these questions. Supra has decades of experience deploying large application software solutions for large companies and brings his expertise in specialized AI-powered products for regulatory, compliance and risk teams to this discussion.

Ask your potential vendor these questions to ensure your choice is AI-Future Proof.
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1. Core RCM Use Cases, Functionality and Value Leveraging AI

• Business Case and ROI: Does the vendor provide ROI models and outline the business case for the solution based on realistic use cases, volumes and timelines that align with your needs? Ensure the ROI model is complete and reflects the end-to-end business process and is verifiable through case studies from customers.
  Look for -> Time Reduction/Efficiencies 20- 50x efficiency gain in mapping, analysis, reporting, reviews. Potential for operational cost savings (up to 50-70% in related AI automation areas); ROI Template, instructions, examples, spreadsheet.
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• ‍AI Solving RCM Problems: How specifically does the solution leverage AI/ML (NLP, classification, LLMs, etc.) beyond standard automation?  What unique RCM problems does its AI solve?  Horizon Scans, Rulebooks and Obligations, Assessments and Gap Analysis, Reg Change Management? Go beyond marketing terms. Understand the specific techniques used and their direct application to RCM tasks like horizon scanning, intelligent curated content, summarization and synopsis generation, enterprise compliance taxonomy mapping, obligation extraction or applicability assessment.
  ‍Look for -> Industry recognition through Awards/Top 100 Lists (BOTH RegTech 100, AI Fintech100, as well as industry-specific i.e. Banking Tech Awards, over multiple  years.
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• ‍Intelligent Curated Regulatory Information: Does the product provide AI-powered information from content horizon scans across multiple sources and formats? What sources (regulatory bodies, jurisdictions, news, standards, guidance) does it cover? How is data ingested, correlated and kept current? How does it handle different languages/formats? Assess coverage breadth and depth relevant to your needs. Understand data refresh frequency and processing capabilities for diverse inputs.
  ‍Look for -> Intelligent Regulatory Content Horizon Scanning with proof that noise is reduced, and signal enhanced through aggregated, correlated and curated regulations for a dynamic, always-updated obligation inventory. 2500+ global sources, including state and national registers. Ability to merge similar requirements across sources, build harmonized and common obligations from sources including International Standards such as NSF Cyber Security Framework; % slashing manual tracking and curating costs.
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• AI Relevance, Context and Mappings: How does the AI determine relevance and applicability? Can it create obligations and map them to enterprise taxonomy, internal compliance artifacts such as controls/policies/procedures? Understand the logic (e.g., rules-based, ML model, hybrid). Assess the capability and accuracy of curating rulebooks and obligations mapped to your internal frameworks, policies, procedures, controls and contracts.
  ‍Look for -> AI analysis that reveals Program gaps by mapping regulatory requirements and standards to internal artifacts such as policies, procedures, risks, controls or contracts.
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• ‍AI-Powered Workflow: How configurable is the workflow? What are the specific capabilities/limitations for integrating with existing GRC, risk, legal systems via APIs? Assess flexibility to match your processes. Probe deeply into API functionality, data formats, and documented integration successes/challenges.
  ‍Look for -> AI-supported, highly configurable workflow and Agentic features.
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• ‍Co-Pilot: Does the product offer a co-pilot to answer queries, provide recommendations on both external and internal questions?  A safe and secure knowledge base can provide valuable insights, transforming your policies and procedures into an easily searchable knowledge base.
  ‍Look for -> Instant answers and recommendations to your compliance questions that can save up to 90% of research time and boost the efficacy of the responses.
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• ‍Audit and Compliance: What are the capabilities for demonstrating compliance? How robust and accessible is the audit trail for AI-driven decisions? Evaluate reporting features, examine the detail, accessibility, and immutability of logs, specifically tracking AI analysis and outputs.
  Look for -> Access control, audit trails, private SaaS environment, security and resilience protocols in place.

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2. AI Technical Capabilities and Models

• Data Analysis: How does the solution process unstructured and structured data? What specific AI techniques (NLP, ML models) are employed? Understand the underlying technology stack. Verify capabilities for handling diverse data types relevant to RCM. Understand the effort required to convert such unstructured data into structured format, is it automated or requires human intervention.  
  Look for -> Multiple format types ingested, parsed and correlated with AI. 80% of the data is unstructured. Real compliance insights and risks are buried in the unstructured data.
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• ‍Model Training: Where does the training data come from? Is it relevant, current, high-quality, unbiased, and appropriately licensed for RCM? Is the vendor using a public domain LLM ? Is your vendor using your data to train the models?  Data provenance impacts accuracy and bias. Verify sources, update frequency, quality control processes, licensing rights.
  ‍Look for -> Products that avoid use of public LLMs for training; ensure IP is not violated.
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• ‍Specialized Language Models and Training Process: How are the AI models trained, tested, and validated specifically for the Risk and Compliance domain? Look for domain-specific validation, not just general Machine Learning metrics. Understand testing protocols for accuracy, robustness, and edge cases in RCM.
  ‍Look for -> Small/specialized language models, curated and tuned on a risk, compliance and regulatory corpus, with proven AI model governance and processing steps. Ensure there is a well-defined specialized language model supported by a governance process with steps outlined for Data Clearance and Acquisition, Pre-processing and Tokenization.

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3. AI Governance & Trust

• Explainability and Transparency: How does the vendor ensure transparency and explainability (XAI)? (e.g., confidence scores, evidence linking, specific XAI methods employed)? Is the AI Architecture shared with you, transparent, proven and explainable?   Understand how the system explains its reasoning. Can it link outputs to source text? Assess what methods are used (e.g., LIME, SHAP, attention mechanisms) that are needed for audits/regulators.  Ensure Users are informed through disclaimers that AI is used in the review process.
  ‍Look for -> Confidence scores, links to authoritative sources and customer documents, and human-in-the-loop checkpoints.
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• Accuracy and Validity: How are potential "hallucinations" or incorrect outputs handled/mitigated? Understand safeguards, confidence scoring, and processes for identifying and correcting erroneous AI outputs.  Look at quality assurance protocols, data cleaning and preprocessing, and performance monitoring
  Look for -> Definition of Model governance program process and steps, including attestation of results from prior years. Review vendor’s AI governance model and AI lifecycle policy.
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• Model Governance: What specific steps are taken during data sourcing, model development, and testing to identify and mitigate potential biases? Look for concrete actions, not just statements. Ask about fairness metrics, bias detection tools, diverse data sourcing, and testing across demographic groups if applicable.
  ‍Look for -> Definition of Model governance program process and steps, including attestation of results from prior years.
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• Model Performance: How are the AI models maintained, updated (retrained), and validated over time as regulations and language evolve? How is performance monitored? Understand the model lifecycle management process. Ask about retraining frequency, monitoring for model drift, and ongoing validation protocols.  
  Look for -> Definition of Model performance program process and steps, including attestation of results from prior years.
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• Human Oversight: Is there a mechanism for human oversight and feedback to improve the AI? Human-in-the-loop is vital. How is user feedback collected and used to refine models? Ensure AI-generated results ensure reliability, accuracy and build trust.
  ‍Look for -> Products that incorporate process steps where professionals can verify, collaborate on results, and revise.
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• Privacy and Security: Beyond standard security, how is data privacy maintained within the AI models? How are AI-specific vulnerabilities addressed? Ask about techniques like differential privacy, federated learning (if applicable), and security testing specifically for AI model attacks (e.g., evasion, poisoning)  
  ‍Look for -> Products that adhere to all relevant privacy regulations and standards when handling data for AI development. Ensure robust security measures protect AI systems and business data. ‍

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4. Integration & Scalability

• Scalability: Can the solution handle your projected volume of regulatory data and user load, particularly the computational demands of AI processing?  Assess performance under load. Understand infrastructure requirements and scalability options. Ensure the product is purpose-built for AI with the right performance and scalability needed for large-scale number crunching and analysis that is at the heart of AI
  Look for -> Specific ranges and volumes supported with performance criteria. Support for SSO, RBAC and Audit trails. Platform Certified (i.e. SOC II).
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• ‍Compliance: Does it meet relevant compliance/security standards (e.g., GDPR, SOC2, ISO 27001)? Verify certifications through independent reports. Ensure models reside in secure, private environments and are regularly improved.
  ‍Look for -> Current SOCII Report as a minimum, SaaS environment, private cloud.

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5. Implementation & Support

• Implementation: What AI-specific expertise does the vendor provide for setup, integration, and user training? What is the typical timeline and data requirement? Assess the vendor's implementation team capabilities. Understand prerequisites, timelines, and change management support.
  Look for -> Implementation methodology, templates for project plan with estimates.
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• Ease of Use: Is the platform intuitive for compliance professionals interacting with AI outputs? Does it allow for human oversight and correction, and support distinct Human-in-the-Loop steps that work clearly in tandem with your business process?   Evaluate usability from the perspective of non-data scientists who need to interpret and act on AI results.
  ‍Look for -> Clearly laid out Human in the loop steps, with ability to vote. Best practices outlining AI Strategy, principles, governance in eBooks and blogs.

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6. Vendor Profile & Roadmap

• Roadmap:  What is the vendor's future development plan, particularly regarding AI advancements, model updates, and incorporating user feedback? Assess the vendor's commitment to innovation and staying current with rapidly evolving AI and regulatory landscapes.
  ‍Look for -> Roadmap revealed (NDA would be required).
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• References: Can the vendor provide specific, verifiable references or use cases demonstrating successful AI deployment in RCM for similar organizations? Look for concrete proof points beyond marketing claims. Be critical of potentially biased industry reports.
  ‍Look for -> Seek independent validation where possible through industry-recognized Awards. At least 2 references with accompanying case studies (can be anonymized). Awards granted (RegTech100, AIFinTech100, AI Awards) within 12-24 months.
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• ‍Responsible AI: What is the vendor's approach to responsible AI development and AI ethics? Do they offer guidance on AI governance? Assess the vendor's commitment to ethical principles. Look for best practice resources or frameworks to help clients govern the use of their AI.
  Look for -> Best practices outlining AI Strategy, principles, governance in eBooks, blogs.

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7. Strategic Enablement and Qualitative Benefits Examples to Consider

• Enhanced Strategic Decision Making
  Efficient RCM can become a competitive advantage; demonstrating trustworthy AI practices builds reputation. Proactively keep pace with the velocity of change across all applicable rules, regulations and laws (RRLs) while mitigating compliance risks by aligning policies, procedures and controls with required changes. Insights from AI analysis reveal trends, inform strategy, improve time to market, competitive advantage and differentiation.
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• ‍Faster Adaptation to Regulatory Changes
  Enables quicker market adjustments, and product launches. Seamlessly search authoritative sources and specific regulatory agencies to identify regulations, rules, laws, standards, guidance and news; build curated rule books and business obligations, merged across similar sources, aligned with your legal requirements. Proactive identification and mitigation of issues reduces costly fixes later. Automation handles routine tasks, freeing staff, lowering overhead, reducing administrative burden, and lowering remediation costs.
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• ‍Risk Reduction
  ‍Demonstrable compliance enhances brand trust; avoid damage from public compliance failures. Mitigate risk of non-compliance penalties through timely and accurate compliance. Examples: reduced reputational risk, reduction in compliance errors and oversight, reduction in fines & penalties, improved accuracy and consistency vs. manual review; lower error rates.
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• ‍Resource Optimization
  ‍Secure and private interactions and analyses with significant optimization for governance, risk, and compliance program teams seeking AI-powered automation. Higher compliance rates demonstrated Examples: Enhanced audit trails and evidence, Improved Audit Outcomes, Frees skilled personnel for higher-value strategic tasks, reduced labor costs for routine tasks.

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Check out these related blogs and resources  

• https://www.4crisk.ai/post/five-ways-regulatory-change-management-istransforming-with-ai ‍
• https://www.4crisk.ai/post/regulatory-intelligence-how-the-regtech-sector-is-being-transformed-by-ai-in-regulatory-risk-and-compliance-programs
• ‍https://www.4crisk.ai/post/the-business-value-and-roi-of-rcm-how-ai-transforms-each-step-in-the-process