No one doubts AI’s potential in financial services—but not all AI is built the same. Between overpromising vendors, hidden costs, and security concerns, choosing the right AI solution requires due diligence. Here are the six key questions that will help you cut through the noise.

1. What’s the business case for this AI solution?

AI should do more than just sound impressive—it should deliver measurable results. Will it save advisors hours of manual work? Increase client conversion rates? Improve investment recommendations? Before committing, quantify the expected impact. If the AI doesn’t lead to cost savings, revenue growth, or better client outcomes, it’s just an expensive distraction and other technology you probably don’t need.

✅ Look for: A clear ROI: hours saved, higher client conversion, or improved investment recommendations.

🚩 Red flag: If the vendor can’t quantify benefits, the tool might just be an expensive gadget.

2. How transparent and explainable is the AI model?

AI solutions must be transparent. If advisors can’t explain why the AI suggests a specific action, clients and regulators won’t trust it. A strong AI solution should first diagnose the problem that it is answering before—before recommending a course of action to the advisor.  It’s paramount that you know the logic behind it. 

Ask: Does the AI show why a recommendation is made? Can advisors trace the logic from diagnostics to solutions? Are the assumptions clear? If the AI simply provides an output without explaining its reasoning, it’s a black box—and that’s a risk

✅ Look for: AI that shows its thought process, allows advisors to adjust assumptions, and provides clear explanations.

🚩 Red flag: “Black box” AI that gives answers without explaining why.

3. What data does this AI require, and how is it stored and protected?

The best AI models rely on high-quality data to deliver accurate insights. But how that data is collected, stored, and used matters for compliance and client privacy.

✅ Look for:

• Clear data policies on storage, security, and regulatory compliance (SEC, FINRA, GDPR).

• Strong security measures like encryption and controlled access.

• Transparency on AI learning: Does it retain data to improve, and if so, how is it protected?

🚩 Red flag:

• AI providers that can’t clearly explain data security measures or how they comply with financial regulations.

• Solutions that store client data indefinitely or use it to train models without clear consent and proper anonymization.

4. What Is the Total Cost of Ownership?

AI pricing is shifting toward usage-based models rather than traditional flat fees. This approach makes sense—AI solutions consume computing power and data processing based on how much they’re used. Instead of paying a fixed fee regardless of value, firms now pay per document analyzed, portfolio reviewed, or client processed.

However, pricing can vary significantly depending on the AI models used—for example, closed-source models are often much more expensive than open-source alternatives. That’s why transparency is key to ensuring AI costs align with your firm’s business needs.

✅ Look for:

• Clear, predictable pricing that scales in a way that matches your firm’s usage.

• Pricing tied to business activities (e.g., reports generated, clients onboarded) rather than abstract AI metrics.

• Awareness of model costs—closed-source AI solutions can be significantly more expensive than open-source ones.

🚩 Red flag:

• Opaque pricing structures where costs escalate unexpectedly.

• Hidden fees for standard functionality like accessing data, exporting reports, or maintaining compliance.

A well-designed AI solution should scale with your firm’s needs, remain cost-effective, and clearly tie usage to real business outcomes.

5. Is the AI provider a strong long-term partner?

AI in financial services isn’t just about today—it needs to evolve alongside market changes, regulations, and technological advancements. A good AI provider isn’t just well-established; they’re also innovative, adaptable, and committed to continuous improvement.

Consider:

• Is the provider focused on financial services, with a track record of innovation?

• Are they investing in AI advancements, regulatory updates, and new features?

• Do they offer ongoing support and improvements to keep the solution competitive?

Rather than just looking at a vendor’s size or age, focus on their ability to grow with your firm and the industry.

✅ Look for:

• A track record of innovation in financial service, ongoing investment in AI development and regulatory adaptation and a strong financial backing, especially with younger startups

🚩 Red flag: 

• AI solutions that haven’t been updated in years, lack industry focus, or have no clear roadmap for future improvements.

6. How well does AI fit into your existing workflows?

Not all AI solutions need deep integration to be valuable. Some tools—like AI-driven prospecting, research, content creation, client communication, and market commentary—can function effectively as standalone solutions without complex system connections.

On the other hand, AI for client operations, portfolio management, compliance automation, or financial planning often requires seamless integration with existing systems to ensure data accuracy and regulatory alignment.

Even when direct integrations aren’t available, AI solutions that allow data export/import in standard formats (e.g., CSV, API access, or direct file uploads) can still fit within an advisor’s workflow.

Ask yourself:

• Does this AI tool need to connect to my existing software, or can it operate independently?

• If integration is needed, does the provider offer APIs or connectors for future expansion?

AI should enhance your workflow, whether as a plug-and-play tool for certain tasks or a deeply integrated system for more complex operations. The key is choosing the right level of connectivity for the task at hand.

✅ Look for: 

• AI that fits where you need it most—either as a standalone tool or an integrated system for complex workflows. 

• Flexible data export/import options can also be an alternative when direct integrations aren’t available.

🚩 Red flag: 

• AI that forces unnecessary integrations, lacks easy ways to move data or doesn’t align with your most critical workflows.

Conclusion

AI can be a game-changer in financial services—but only if it’s the right fit. By asking these six key questions, firms can separate hype from real value and choose AI that is transparent, secure, cost-effective, and aligned with their needs. The best AI solutions scale with your business, enhance workflows, and integrate where it matters most. 

At Sherpas, we help financial professionals streamline prospecting, automate diagnostics, and generate actionable insights. See how Sherpas can support your AI journey. Contact us today.