In January, Informatica released its annual data leader survey, CDO Insights 2025: Racing Ahead on GenAI and Data Investments Despite Potential Speed Bumps.

This report on the state of data management surveys 600 chief data officers, and chief data and analytics officers, throughout the U.S., Europe and Asia. It is no surprise in recent years that the spotlight is increasingly on data readiness for AI. That is still true in the 2025 findings; however, we are at a turning point where tomorrow’s AI promises meet the reality of data readiness today.

While the goal of trusted and reliable AI adoption remains a top concern for the C-suite to navigate, it is full speed ahead as organizations tackle historical data challenges to build confidence in AI-ready data, and train employees for enabling responsible data and AI use.

What is clear for 2025: People and technology must align if GenAI is to deliver on its potential.

Where today’s data leaders are putting their focus, from this year’s CDO Insights 2025 survey:

• Investment is growing: 87% who adopted or plan to adopt GenAI will see increased investment from their organizations in 2025
• AI pilots require more confidence: 92% are concerned pilots are moving forward without addressing prior challenges
• Demonstrating business value is critical: 97% of those using or planning to use GenAI face difficulty demonstrating business value of initiatives
• Removing roadblocks requires wide-ranging capabilities: Cybersecurity and privacy compliance (46%), uncertainty over responsible use of AI (45%), reliability of results (43%) and lack of trust in data quality (38%) are difficulties to demonstrate business value.

To put the latest stats in perspective and learn how high-performing organizations are approaching AI-readiness, Informatica recently interviewed a data strategy expert in financial services to gain more perspective on how these metrics impact real-world planning.

Blake Andrews, Chief Data & Analytics Officer for Independent Financial shared insights on this year’s 2025 survey, including how organizations can address the challenges of AI adoption and understanding their readiness. Here are highlights of the conversation (edited for brevity):

Watch the full conversation here for more details!

1. What perspective do you bring to today’s set of AI adoption challenges?

I always enjoy reading the Informatica survey because of the insights. And it helps validate some of your own experiences and see where others across the market and across various industries are, and how they’re positioned. I've had the opportunity to work across a variety of industries oil and gas, banking, insurance; so, having those differences in perspectives, and difference in experience, is always interesting to put in a broader context.

2. Are you finding it difficult to get AI into production?

I think there are a few different hurdles. One is obviously the inflated sense of expectations that the C-suite has, and executives have for, the ROI on AI projects. And then there’s a lack of appreciation for other hurdles and roadblocks that could come up from just introducing such a high-profile, and high visibility, set of new technological capabilities. And then you must look at showing the ROI, but what is the baseline that you’re measuring against?

There’s some trepidation and concerns around, “What are the broader impacts that generative AI could have to our internal workforce?” and “How do you know the knock-on effects of having to up-skill, retool our people?”

3. What is your view of how data quality or AI-ready data play into your AI journey?

Oh, it’s paramount. You know, all AI — but specifically generative AI — tends to be a bit of a magnifying glass on your data quality issues that exist in your environment. It’s going to accentuate those issues every time. And it’s also that data quality requirements for generative AI, and AI in general, seem to be a bit more multi-dimensional.

With traditional data quality use cases — and the requirement that we’ve been tackling for the last several decades now — you’re having to look at the relationships of the quality across multiple data elements, and that interconnectedness, because of the way that the machine is going to infer those relationships. So, it becomes a much more complex issue to manage. And you must be looking at that strategically. When planning out what use cases we’re going to tackle, how are we making sure that we’re coordinating the efforts of our data quality teams to get out in front of those use cases and those requirements for AI projects?

But then even more tactically, your project teams have to be very diligent in identifying those hotspots and those potential issues up front, and coordinating with their data quality counterparts, to make sure that all of that gets synchronized, and everybody’s on the same page… Because if you don’t have good quality practices — or governance practices — GenAI gets more messy as you’re trying to put data into that pipeline, right.

4. Does there need to be more training, or an up-level of skill sets in organizations?

So much of the burden of managing AI as a technology is really falling on the shoulders of your CDOs. And that even gets into organizational development and training, and competencies, and skill improvement across the enterprise. And so, one of the things I would really advocate for is engaging with your HR teams, engaging with your corporate learning and development groups. It comes up in conversation all the time…. We've seen multiple companies, in the early days of ChatGPT and Copilot, people were stubbing their toes, and you had some of these missteps. And a lot of it’s coming from an altruistic place. People are really wanting to leverage the technologies.

It’s just a lack of understanding of some of those pitfalls and a lack of awareness. So, these need to be part of the broader conversation and the broader strategy that a data leader builds around AI to make sure that they’re addressing that learning and development component.

5. How can an integrated set of tools help organizations become AI ready?

I’m a big proponent of that...a unified, integrated approach. I think it’s unrealistic to say that we're going to be able to do everything in one solution. And I think that there’s the Informatica approach, to your credit, you guys have put in the interoperability components. You can bring other tools to the table. But the nice thing about it for our teams, we have one consistent kind of centerpiece. And then we can layer on around that. And that’s kind of my preferred approach. I think that helps simplify the experience for your technical teams.

You spend less time trying to fight through integration and connecting disparate pieces, and parts of the puzzle. And so, I think that there’s a lot of value in trying to have a centralized platform because of so many different skills and components, and capabilities, that you must be able to maintain in a modern data architecture.

6. What is your approach to the cloud: How do you take advantage and how do you think of cloud native?

I think there are some legacy technologies out there that the tail on them is longer than anyone had really anticipated. And it may be another 10 to 15 years on some of this to migrate into cloud environments. And we said that we needed to be there five years ago, and we understand we still have a longer way to go. But you are seeing a lot of progress in the last, I would say, the last 3 to 4 years. I've seen a lot of movement in that space. And I think the broader macroeconomic factors, you’re starting to see more consolidation, more M&A activity, those types of transactional deals have a way of spurring on some of that transformation and cloud migration, as well as you get these bigger organizations and you’re consolidating all of this on-prem infrastructure.

You say, wait a minute, there are too many cost savings to be realized by migrating to the cloud. Let's start to push in that direction. I think that a lot of organizations are going to start to see some real traction in that area. And in the next five years, you’re going to — I would assume that — upwards of 90% of workloads would probably be in the cloud, is what I would expect.

Watch the full conversation here for more details!

It’s clear the path to AI readiness requires an integrated strategy for people, process, and technology to address all phases of bringing trust assurance — from AI pilots to adoption, through scale out. CDO’s are at a crossroads between maintaining legacy infrastructure and modernizing for a cloud future with the added complexities that AI readiness brings to the table, underpinned by reliable data and confidence in the workforce to handle AI responsibly.

How does your organization compare?

• Top obstacles preventing more GenAI initiatives moving from pilot to production: Data (43%), technology (43%), people (35%), process (35%), and regulations (34%)
• 11 months is the average time data leaders whose workforce needs more training say it’ll take to get their employees up to speed on responsible use of GenAI under their current training program
• Data leaders are optimistic that their future will be all-in on cloud data: 95% expect their organization to be fully cloud native in under five years

Organizations can accelerate their generative AI initiatives when AI is managed responsibly. AI-powered data management in the cloud helps get pilot programs to market-ready when performance and ROI concerns are put in the rearview mirror for the C-suite. Solving today’s organizational challenges comprehensively — people, process, and technology — are critical to move from proof of concept to operational programs for generative AI.

Informatica is here to help accelerate your journey! The breadth of capabilities from a single data management cloud that comprehensively spans ecosystems, combined with best of breed services on the industry’s only AI-powered data platform, helps accelerate data and AI initiatives.

The opportunity ahead is tremendous — it’s time to get ready for AI, including your data!

Download the CDO Insights 2025 survey to learn more about industry readiness for AI.