20 Questions to Ask in Every WealthTech AI Demo

Wealthtech AI demos often present a polished version of reality that hides operational risks. Vendors spend significant resources preparing controlled environments where the data is clean and the workflow is choreographed. The difference between a good technology decision and an expensive mistake lies in what you ask when the scripted presentation ends. Firms that lead with their own requirements rather than the vendor’s feature list find that the universe of viable tools shrinks quickly.

Before asking about specific features, you must understand how the system handles information. Regulators have tightened oversight on software tools that touch client data. The 2024 Regulation S-P amendments require documented vendor oversight for every system that processes personal financial data. Start by asking about data capture and retention.

Related: Raymond James Hires 1 Billion Dollar Team

• What data do you capture from our interactions with your platform, and where does that data go?
• How does your platform handle personally identifiable information and client financial data? What controls prevent that data from being used to train your models or your software team?
• How long do you retain client data, and what happens to it if we terminate our contract?
• What compliance certifications or third-party audits has your AI system undergone in the past twelve months?

This stage reveals whether the AI is real, proprietary, or borrowed. A vendor who becomes evasive at this level often signals that the marketing narrative differs from the technical reality. You need to know exactly where the model lives and how it behaves.

• What AI models power the features you are demonstrating today? Are they proprietary, licensed, or built on a third-party API such as OpenAI or Anthropic?
• What training data did you use to build or fine-tune the model? Does it include any client data from existing customers?
• Where does data processing occur? On your infrastructure, on a third-party cloud, or on our servers?
• How does your system handle hallucinations and bias? What validation exists before an AI output reaches a user?
• What temperature or confidence settings govern the model’s responses, and can we configure those settings for our compliance environment?
• Has your AI system ever produced incorrect output that affected client communications or recommendations? How was that identified and resolved?

Advisor360’s research found that 93% of advisors want final human review authority over any AI-influenced output. If a vendor’s answer to this requirement is vague, that gap between your firm’s expectations and the vendor’s design is something you need to resolve before signing a contract, not after deploying the platform. This creates a gap in internal credibility that is difficult to close later.

Related: Estate Elegance: Timeless Beauty in Vintage Engagement Rings

The most revealing questions often expose the limitations of the tool. You need to see if the system relies on the AI or if it functions as a standard rules-based engine. Asking about the “human in the loop” is particularly important for maintaining client trust.

• If you turned off the AI component entirely, what functionality would remain?
• Can you show us a side-by-side comparison of a workflow completed with the AI active versus without it?
• What can this system accomplish that a rules-based engine could not?
• Where is the human in the loop before an AI output reaches a client account or a client communication?

Some vendors conflate platform-level metrics with AI-specific metrics. You need data on what the AI does, not just what the platform costs or how many users it has. Ask for production references and specific performance data.

Related: Record hot dog consumption expected nationwide

• What performance data do you have on the AI component specifically, not the platform as a whole?
• What is the error rate for the AI’s outputs, and how do you measure it?
• Has your AI’s performance been validated by a third party? Can you share that assessment?
• Can you connect us with a reference client who has deployed this AI feature in production for at least twelve months? Not a pilot. Production.

Firms that get these answers clearly will make better purchasing decisions. If a vendor cannot answer these questions specifically, that inability is itself an answer. It tells you either that the AI is less mature than the demo suggested, or that the sales team is operating ahead of what the product team can actually support. Neither situation produces a good technology outcome for your firm. The firms that build this evaluation discipline now will carry more internal credibility into the next round of adoption.