Reimagining AI to Discover the Art of the Possible in Wealth Management

EDISON, N.J., June 25, 2025 /PRNewswire/ -- Mike Tropeano, CFA, AIF – Senior Vice President, Fi-Tek

We have moved past the hype and are arriving at the point where Artificial Intelligence (AI) will have a significant impact on how we do our jobs. Sitting on the sidelines is no longer an option. Celent reported that 62% of wealth managers in the US are either in production, piloting, experimenting, or exploring use cases with generative AI. Engagement is not limited to large firms, with 50% of organizations between $1B–$20B doing something related to AI. Still have doubts? Data from IoT Analytics and BCG found that in the final quarter of 2024, mentions of AI agents on earnings calls increased more than 330% year over year.

Progress is not without challenges. Questions about the accuracy of the results, cost of the solution, nervousness about the privacy of data, and the lack of clear regulations on the use of AI technology have held some back from diving deeper into AI. This shouldn't stop you from understanding what AI is and how it can impact your organization. Harvard Business School professor Karim Lakhani summed it up well:
"AI won't replace humans, but humans with AI will replace humans without AI."
Not learning more about what the future holds and developing a strategy only puts you further behind.

How Can AI Transform and Become Impactful?

Addressing the challenges and clearly defining your value proposition will show how AI can help you improve client engagement and become more efficient in the front, middle, and back offices. We know that leveraging data is the key to delivering actionable and strategic insights for use within your organization. Your firm possesses massive amounts of data, which delivers no value when it is at rest. What are some of the use cases where you can put your data to work?

Transforming Client Engagement Through Hyper-Personalization

Improving the client experience requires a fundamental change from reacting to anticipating a client's needs. Orion's Investors Survey found that the top two ways an advisor can enhance the client experience is through communication and personalized services. Both can be achieved by improving the way you use the data that you have within your ecosystem. We need to anticipate the client's need and bring them to where they want to go. It comes down to improving how we make them feel like a segment of one.

More specifically, organizations can increase scale by using generative AI solutions to support the automation of some client communications, leveraging virtual assistants to synthesize large data sets, and deploying predictive analytics through advisor dashboards to deliver the next best action. Machine learning is another way to personalize online engagement, including the delivery of custom insights.

Driving Scale

Almost all firms are under pressure to become more efficient to address shrinking margins, an aging workforce, and key talent risk. We need to think about how we can improve the effectiveness of traditional automation tools, including expanding the capabilities to address tasks that have been traditionally manual.

Optical Character Recognition (OCR) has been in place for many years. Improvements in accuracy have allowed it to become a bigger part of mainstream technology. Combining OCR with Robotics Process Automation (RPA) or using OCR to feed a transactional API provides the beginnings of an intelligent automation solution.

Improving Your Control Framework

It is a constant battle to stay one step ahead of the fraudsters. As soon as a gap is closed, they find another vulnerability. Organizations are looking for AI-based solutions to develop a stronger control framework and quickly detect anomalies that could be fraudulent transactions.

We rely on humans or rules-based engines to detect irregularities. This only works when datasets are manageable, predictable, or we know what to detect. Schemes to defraud investors are becoming more complex. Deploying multifactor models and predictive analytics can assist in understanding patterns of behavior and provide insights into a potential fraudulent transaction or the detection of a bad actor.

What Do You Need to Consider?

Transforming through emerging technology can be stressful. Simplifying your goal makes the first step easier. Consider starting with defining your organizational value statement for utilizing AI. Focus on the objective versus the outcome as the first step in developing a strategy. As you move forward, leverage a role-based design rather than taking a task-based approach to quantifying what you want to achieve. Work to identify your targeted use cases, prioritizing higher impact, lower effort. This will support the delivery of a more efficient operating model and allow for similar tasks to be solved together. After you establish the foundation, the work continues.

Assess the Regulatory Environment and Internal Compliance Requirements

At this point, organizations should have a policy defining appropriate uses for AI. This may be as simple as: the use of AI is strictly forbidden, is limited to certain functions, and/or requires a set of disclosures. More specifically, you need to define your plan to supervise the use of AI across your organization. When you deploy an AI-based solution, there needs to be a plan to identify and mitigate associated risks such as accuracy or bias. Understand that this document will be fluid as the regulatory environment matures.

While we do not have a fully defined regulatory framework, there are existing laws, regulations, and guidance that are applicable. Regulations exist for the protection of personally identifiable information (PII), your duty as a fiduciary, and what needs to be disclosed when using third-party information. Regardless of how the task is accomplished, the regulatory guidance stays the same. As solutions are deployed, you will want to consider how to use that tool and still ensure compliance with applicable regulatory requirements.

Get Your Data in Order

The ability to analyze large data sets is core to the value of artificial intelligence. Success depends on ensuring that the data used is accurate and available. It seems simple; however, many organizations struggle to identify a gold copy for some information and, if they have one, the accuracy can come into question. Data governance is a process that is consistently discussed, however, few implement. Gaps in quality and quantity can lead to hallucinations or misinterpretations.

You will also need to clearly define what data is needed. Bigger is not always better. Narrowing the scope to only the important data elements not only makes the integration easier, it also limits what could be exposed should a bad actor gain access to your information.

Understand Your Partner's Plan to Integrate AI

Lack of resources and subject matter expertise are two of the top reasons why most organizations fail to adopt emerging technologies. Regardless of size, many firms need to rely on partners to help deploy innovative solutions. AI is maturing at a rapid pace. Innovative firms have both a strategy and results they can point to on how they are using AI to improve their products.

Interested in finding out more? Reach out to the Fi-Tek team.

Media Contacts
Arpita Chatterjee
Director of Marketing and Strategic Alliance, Fi-Tek
732-632-8167
[email protected]

SOURCE Fi-Tek LLC