How to Maximize Success with AI for Insurance & Wealth Management

The insurance and wealth management industries are at an inflection point. AI adoption is happening—yet true organizational transformation is lagging. A July 2025 report from MIT’s Project NANDA, The State of AI in Business 2025, found that while over 80% of organizations have piloted AI tools, just 5% of enterprise-grade pilots have reached production with measurable P&L impact. The authors call this phenomenon the GenAI Divide: the stark gap between experimenting with AI and capturing real business value from it.

Why the gap? Because most pilots are select by first looking for problems that GenAI might be able to solve under the leader's domain rather than looking across the Value Chain to reinvent the enterprise. The result is incremental improvements that have not resulted in improved ROI. Further, most business processes require a combination of tasks, some of which can be delegated to GenAI, others may be better suited for a more declarative technology, or a human. GenAI is not always the best tool. The evolution of AI from GenAI to Agentic AI allows for the blending of GenAI and declarative technologies and can be leveraged either for full automation or keeping a human in the loop. For firms like ours—mid-sized, regulated, and deeply relationship-driven—the lesson is clear: AI success starts with business alignment, not technology.

Step One: Select Pilots that Fuel Growth

When executives think about AI pilots, the temptation is to focus either on front-office experiments—such as marketing campaigns or prospecting emails—or cost-takeout in core operations, like claims processing. Both approaches miss the real sweet spot for mid-sized insurance and wealth firms.

The highest-performing organizations, according to MIT’s research, are finding ROI in back-office functions that directly expand sales capacity and productivity without adding headcount. These aren’t “nice-to-have” pilots, but enablers that free relationship managers, underwriters, and advisors to focus on revenue-generating work.

With Agentic AI, the opportunities are even stronger because the systems can adapt to each firm’s workflows and blend technologies:

• Merge Natural Language Processing (dictation) with complex data entry tasks or initiate transactions. Entering new client information can take a significant amount of time from your sales team, especially if you're capturing household and existing financial account information.

• Use Standard Operating Procedures to create workflow logic or ensure data is fully captured. This approach minimizes IT costs from enshrining logic in code and moves the management of that logic back to the business.

• Summarize financial product performance and suggest adjacent financial products or coverages during renewal or annual reviews. Upon review by an Underwriter, Agent, or Advisor, this can be further augmented with marketing content about the new products so that there is an optimized chance for successful cross & up-sell.

  
  

These use cases may not generate flashy headlines, but they unlock growth leverage: more clients served, more deals closed, and stronger relationships built—without increasing fixed costs.

By anchoring your AI pilot here, you set a precedent: AI isn’t about cutting jobs or chasing hype, it’s about creating the capacity for growth—and Agentic systems compound the growth over time.

Step Two: Achieve Readiness in Parallel

One of the most common reasons pilots stall is that organizations wait until after pilot design to address infrastructure and data quality. MIT’s findings reiterate a known business truth, speed is valuable. To that end, Readiness shouldn't wait until after Pilot planning has concluded. Once Pilot Planning has identified the business domain to focus on, Readiness can begin, including implementation efforts. This parallel approach can save weeks or months in overall speed to value.

Agentic AI raises the bar: these systems thrive on clean data, integrated platforms, and user feedback loops. Growth-focused firms treat readiness as an enabler of revenue expansion:

• Data Readiness: Most enterprises have data fragmented across multiple systems. Data issues like fragmentation, duplication or incompleteness will prevent AI from unlocking value. To enable some of the AI use cases, data may need to be combined in ways that it hasn't been previously, like client profitability data with selling hierarchies to determine what coaching a relationship manager should have with an Agency or Advisory Office.

• Infrastructure Readiness: Legacy databases and technology aren't structured properly for AI use cases. Transactional systems will be late to the game and not well suited for Agentic AI. Platforms built for Innovation may not have access to the right data or an upgrade may be needed to unlock the AI capabilities.

• Skills Readiness: Business and IT teams will need to be re-skilled to leverage Agentic AI. This may include documenting those Standard Operating Procedures and ensuring your knowledge base is complete, learning prompt building, and how to properly govern the investment ongoing. Agentic AI use cases should increase the speed at which change can be realized and rebalance the labor mix required to manage the overall solution to rely more heavily on the business.

This parallel-track approach allows infrastructure and people capability to advance while business leaders validate the pilot use case. By the time the pilot is planned, the foundation for scaling—and for growth—is already underway.

Step Three: Partner for Growth Acceleration with Agentic AI

The MIT report also found that external partnerships succeed twice as often as internal builds. For mid-sized financial services organizations, this means success is less about building proprietary tools and more about finding the right partners who understand your workflows and growth objectives. In most cases, your core business isn't training one or more LLM's, but selling and servicing your clients.

With Agentic AI, the stakes are higher. Your labor strategy will likely shape your firm’s success for years to come. That’s why firms must seek trusted partners who can embed Agentic capabilities into platforms like Salesforce, ensuring scalability, compliance, and revenue alignment. Because strategic alignment to ROI and speed is so critical, the Partner must combine industry expertise, technology excellence and a core focus on speed.

At Platinum River INNOVATIONS, we’ve seen that embedding Agentic AI into Salesforce for wealth and insurance clients delivers results far faster and more cost effectively than custom-building in-house. These systems don’t just automate tasks—they grow with your firm, scaling sales capacity without scaling headcount.

What This Means for Insurance & Wealth Leaders

The AI Divide isn’t inevitable. Organizations that succeed do three things differently:

1. Align pilots with mid & back-office functions that expand sales productivity and client capacity.

2. Advance infrastructure, data, and skills readiness in parallel to speed time to value.

3. Select partners who can deliver Agentic AI that learns, adapts, and integrates deeply into workflows.

The takeaway is simple: don’t treat AI pilots as experiments. Treat them as the starting point of an enterprise transformation. Begin with business alignment, build readiness in parallel, and partner wisely. Done right, your first Agentic AI pilot won’t just prove technology—it will prove ROI and fuel growth.