What To Ask Your Vendors When Purchasing AI Apps

Introducing Our Author

Shwetha Shantharam, AVP and Product Head at 4CRisk.ai has extensive experience in traditional applications, solutions and software development, and over the past 5 years has specialized in AI-powered products for regulatory, compliance and risk teams. She brings her 20 years of experience and wisdom to this blog to help you understand the real differences between traditional, legacy apps and the revolution we are undergoing with modern AI-powered products, agents and co-pilots.  

AI products and Agents are different than legacy apps in keyways that your team needs to know about.  

Ask your vendor these key questions to determine if the AI is real, or simply Analytics.  In this way, you can make sure your choice is AI future-proof.  

Let’s look at these 4 distinct layers: AI Agents, AI Tech, Trusted Language Models and the AI Platform.

1. How are AI Agents and Co-Pilots Different from Traditional Apps

AI agents represent a significant shift from traditional "legacy" applications, offering capabilities that go beyond the rigid structures of older software.  

AI Agents:

• Are designed to be autonomous, meaning they can make decisions and take actions to achieve specific goals without constant human intervention.
• Focus on achieving desired outcomes, rather than just executing predefined steps.
• Can adapt and learn from their interactions, improving their performance over time.
• Utilize machine learning to analyze data, identify patterns, and adapt their behavior.
• Can personalize experiences based on user preferences and past interactions.
• Can handle unexpected situations and learn from errors.

Ask these questions to help you understand your vendors’ AI capabilities:

• Are these products designed and proven with deployments, as AI Agents, with a Co-Pilot, that performs tasks for you, with the ability to be linked together with AI-driven workflow?
• Do the product support distinct Human-in-the-Loop steps, that work clearly in tandem with your business process?
• Is the ROI clear and explainable, with references?
• Do you own the IP of the AI outputs?
• Does the vendor AI transparently explain the results?

2. How is AI Technology Different from Analytics

Traditional apps using analytics may involve manual processes for data collection, analysis, and reporting and can be time-consuming and resource intensive. AI Apps, in contrast, automate repetitive tasks, such as data entry, analysis, and decision-making, improve efficiency, reduce operational costs, and enable real-time responses and faster decision-making. In essence, legacy analytics primarily provide insights into past events, while AI applications provide insights into the future and automate responses.

AI technology:

• Leverages machine learning algorithms to learn from data and adapt over time.
• Can process vast amounts of structured and unstructured data, including real-time data.
• Employs predictive and prescriptive analytics (what will happen and how to respond).
• Automates many data processing and analysis tasks.
• Can forecast future trends, anticipate problems, and recommend optimal actions.
• Continuously learn and improve their performance based on new data.
• Can adapt to dynamic environments and changing user needs.
• Can handle massive datasets of various formats, including text, images, and audio.

Ask these questions to help you understand your vendors’ AI capabilities:

• Does the product parse both unstructured and structured information at a significantly granular level using AI to enable deep analysis and automatic filtering?
• Does the product use AI to complete mappings, tagging and inference, or is manual tagging required?
• When data is scanned and ingested, is AI used to ‘filter out the noise’?
• Are core AI technologies such as NLP (Natural Language Processing), DM (decision-management) or RAG (Retrieval Augmented Generation) used appropriately in AI architecture?
• Is the AI Architecture shared with you, transparent, proven and explainable?
• Have Awards been granted that verify the tech is truly AI?  

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3. How are Trusted Language Models different from Traditional Datastores

The integration of Large Language Models (LLMs) and Small Language Models (SLMs) is fundamentally changing how applications are built and how they interact with users, creating significant differences compared to legacy applications. In essence, LMs are shifting the paradigm from rigid, rule-based applications to dynamic, intelligent systems that can understand and respond to human language. In fact, specialized, small language models significantly reduce bias and hallucinations, as they can be curated on specific domains. This is leading to a new era of applications that are more intuitive, personalized, and powerful.  

AI Apps using Language Models  

• Enable users to interact using natural language, making interfaces more intuitive and accessible.
• Can understand complex queries, nuances, and context, leading to more personalized and dynamic experiences.
• Allows for conversational interfaces, where users can engage in back-and-forth dialogues.

Ask these questions to help you understand your vendors’ AI capabilities:

• Does this product leverage small/specialized language models, curated on a risk, compliance and regulatory corpus, with proven AI model governance and processing steps in place?
• Do the vendor’s models reside in secure, private environments, and are they regularly improved?
• Does the vendor use public domain LLMs?
• Does the vendor use your data to train its models?
• Are your AI models tuned for your domain?
• How does the vendor deal with, measure and minimize hallucinations?
• How is data bias handled?
• What is the accuracy of results?  

4. How are AI Platforms different from Traditional Tech Platforms

The differences between AI platforms and legacy application platforms are significant, reflecting the evolution of software development and the increasing importance of data and intelligence.  

AI Platforms:

• Are inherently data centric. They are designed to ingest, process, and analyze vast amounts of data to train and deploy AI models.
• Focus on extracting patterns, insights, and predictions from data.
• Emphasize machine learning, deep learning, and other AI techniques.
• Often rely on cloud-based infrastructure to handle large volumes of data and complex computations.
• Require specialized hardware, such as GPUs, for training and deploying AI models.
• Are designed for scalability and elasticity.

Ask these questions to help you understand your vendors’ AI capabilities  

• Is this product 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?
• Does the platform support SSO, RBAC and Audit trails?
• Is the AI Platform Certified (i.e. SOC II)?

Check out our secure, private, and domain-specific Gen AI platform, AI Agents and Ask ARIA Co-Pilot for Regulatory, Risk and Compliance teams. Here’s to making 2025 the year when you ensure your new app choices are AI Future-Proofed.    

Check out these related blogs and resources  

• https://www.4crisk.ai/post/compliance-three-key-actions-to-leveraging-ai-successfully-in-2025 ‍
• https://www.4crisk.ai/post/ai-game-changer-specialized-language-models-the-safest-alternative-to-llms-for-regulatory-risk-and-compliance-programs ‍
• https://www.4crisk.ai/whitepapers/adopting-ai-strategy-governance-and-evaluation-best-practices