Just when you thought it was safe to launch your next-gen digital strategy, a newer, faster, cheaper large language model or breakthrough FinTech solution enters the market and upends everything you thought you knew about technology. Whether it’s a market-moving disruption or simply a sales demo for a new AI-powered widget that will change the way you work, the pace of new technology development is moving faster than ever. That’s creating challenges for many firms when it comes to choosing which horses to back, where to double-down on digital transformation, and how to integrate new technologies responsibly.

The fact is it’s still early days in the AI-led digital transformation journey for many firms, and before they start chasing every new product launch, it is important to first figure out what they hope to accomplish with AI and develop a sensible strategy to get there.

Introducing the E-D-U-C-A-T-E Framework

That was the big takeaway from last week’s panel discussion, “Industry Perspectives on Applying AI,” at the Financial Services Institute (FSI) OneVoice 2025 conference, where Broadridge Senior Vice President and Chief Growth Officer, Wealth Management, Ramprasad “RP” Sandilya introduced a new approach to tackling AI overload. He helped put AI usage in context by sharing his advice that financial firms need to spend less time worrying about specific technologies, and more time on organizational readiness. To help with that process, he introduced a new framework to help clients adopt and implement AI strategies that will continue to grow and evolve as new models are continually introduced.

Today’s breakthrough technology is tomorrow’s outdated artifact, and for firms that want to keep pace with each new advancement in a world that’s moving this fast, the old-fashioned approach to software development and underlying technology architecture maintenance needs to evolve to support that change. To build a foundation that can consistently grow, without veering off course, firms need to follow the E-D-U-C-A-T-E framework, which Sandilya defined as follows:

• Ethics are paramount when working with AI.  The first questions firms need to ask themselves about AI are whether underlying models have been developed in an ethical and responsible way including the use of personal information, and whether guardrails have been put in place to ensure that they continue to be deployed safely. The answers to those questions form the foundation to enduring trust in AI.

• Data governance is critical.  Regardless of the large language models being used or the specific tasks being deployed, professional-grade AI-driven technologies are nothing without data, and, in heavily regulated industries like financial services, it is essential that strict data governance controls be put in place to manage where the data is sourced from, and how that data can be used.

• Understanding of AI across the organization is important. Before any technology can deliver value, it needs to be used widely across the entire organization. For AI projects, that means all potential users – even technology hold-outs – need to be brought along on the journey and shown how new tools and resources can help them improve.

• Client-driven use cases deliver the biggest impact. There are dozens of examples financial services firms can use to demonstrate the value of integrating AI into their workflows, but few capture the hearts and minds of advisors, technologists and senior leadership more than client-driven applications, where AI-powered models can be used to surface key data and insights unique to an individual client. Effectively delivering more personalized client insights is the killer app for AI in wealth management, and real-world examples go a long way to proving that.

• Architecture drives the technologies that are used. As we saw with the launch of DeepSeek, new models and new tools are being introduced all the time. The key to staying nimble and being able to benefit from the constant advance of technology is having a flexible architecture in place that can adapt and grow over time. Conversely, taking a tools-based approach that requires reconfiguring the entire enterprise software architecture every time a new capability is introduced is the fastest path to bankrupting an AI strategy.

• Talent, both from an upskilling and retention perspective, is critical. To stay competitive in a world that’s moving this fast, firms need the right combination of cutting-edge technology and industry-specific domain expertise to develop the right tools. Getting this formula right requires investment in upskilling and retention of current employees who will help lead the change from within.

• Experimentation is something the culture must have to best utilize AI solutions. Even the most technologically sophisticated organizations will find themselves navigating some uncharted waters as they embrace the AI revolution. Firms need to get comfortable experimenting, tweaking, and refining to find the strategies and technologies that work best for them.

Thinking Beyond the Bright, Shiny, New Thing

There is no shortage of noise in the AI space, right now, and with much of that attention focused on the financial services industry, and how firms will use the technology in everything from wealth management client engagement to back-office operations, it’s easy to get distracted. Worse, with many of these technologies widely available to consumers and very little barrier to entry prohibiting individuals from experimenting on their own, the risk of misuse and uneven industry adoption is growing every day. To get their AI formulas right, firms need to be able to incorporate guidance and best practices from every level of their organization into their digital strategies. This is much bigger than just giving people access to the latest, greatest large language model; firms need to take a cross-functional approach that is consistent and aligned with firm goals – even if the inputs and underlying technologies keep changing.

To further explore how firms are successfully navigating the complex landscape of AI adoption and to gain insights from industry leaders on refining your own AI strategy, check out the latest perspectives from our experts.