As Credit unions are identifying more and more ways to leverage AI to transform operations and deliver new efficiencies, many are encountering a surprising hurdle: they aren’t ready to implement AI. The challenge is not in the technology itself but in a critical element—data readiness. According to a recent study from AI platform optimization provider Riverbed, 94% of C-suite executives have made AI a key focus, with adoption efforts led from the top. Yet, only 37% say they are fully prepared to implement AI today. This gap between ambition and readiness underscores a fundamental issue—data.
Data as the Core Barrier to AI Success
The Riverbed study highlights that nearly one in four AI projects are underperforming. The culprit? Data. While 85% of decision-makers agree that quality data is vital for AI success, nearly half say that poor-quality internal data is preventing further AI investments. This problem is particularly pronounced in the financial services sector, where only 36% of leaders rate their data as excellent in terms of quality and completeness, and only 34% believe in the accuracy and integrity of their data.
These statistics emphasize that before AI can deliver the benefits organizations expect—whether it’s improving operational efficiency or delivering personalized member experiences—companies must focus on building strong data foundations. Without clean, accurate, and accessible data, AI projects are bound to underdeliver.
Why Data Readiness is Key
Data is the fuel that powers AI. Without accurate and complete data, AI models cannot deliver the predictive power or insights that organizations need. AI relies on high-quality data for training, analysis, and decision-making. If the data fed into AI systems is inaccurate, incomplete, or outdated, the results can be misleading or even damaging. Companies with high-quality data are much more likely to implement AI successfully, improving not only operations but also digital experiences for employees and customers.
Riverbed’s findings highlight that 86% of high-performing organizations—those already successfully using AI—are much more likely to have their data in working order. These companies are using AI to improve digital employee experiences and deliver IT services, making them more efficient, agile, and competitive. In contrast, organizations with poor-quality data continue to struggle to realize the full potential of their AI investments.
Building a Data Strategy Before AI
Credit unions and other financial institutions face more significant data quality challenges, so a clear data strategy is no longer a luxury—it is a necessity. To be truly AI-ready, IT departments and leadership must work together to ensure that data is accurate, available, and in a format that can yield actionable insights.
A robust data strategy involves more than just cleaning up data. It requires setting up proper governance structures, ensuring data privacy and security, and making the data accessible across the organization. Furthermore, leveraging real data (rather than synthetic data) is crucial to AI success, as synthetic data can introduce biases and miss critical nuances found in real-world data.
The Path Forward
The journey toward AI maturity begins with a focus on data. Credit unions, in particular, must prioritize data readiness to harness AI’s transformative potential. As Riverbed’s survey shows, high performers are already reaping the rewards of AI by ensuring their data is in working order. The time is now for all organizations to invest in their data foundations. With the right data strategy in place, AI can fulfill its promise to drive growth, efficiency, and exceptional member experiences.
Is Data Governance on your 2024
to-do list?
Give three hours and get what you need to launch your data governance efforts.
THAT DAY!
Please join the virtual
Deep Dive Data Governance Workshop
Wednesday, October 9, 2024
9:00 am to 12:00 pm (PST)
Leverage your data and your talent to solve members' problems in 90 days or less!
Gathering Data to deliver insights is difficult enough, but getting your talent to leverage it to take action might ACTUALLY be harder.
Luckily, there is 10x the MX.
Your team decides what member problems to solve and we help provide the framework to solve it leveraging the data you have on hand. No new technology and no new talent.
Learn how Corning Credit Union used 10X the MX to:
Increase member experience and satisfaction
Improve organizational efficiency and processes
Empower credit union talent, boost satisfaction, and build critical thinking and execution capability.
Data Strategy
CASE STUDY:
First City Credit Union
The goal of any data strategy is how to leverage it to drive growth, efficiency, intelligence, and empowerment.
Learn how First City Credit Union partnered with THRIVE to help them identify their gaps in existing data efforts, gain clarity on member needs and wants, and build a multi-year roadmap for data success.
Comments