I’m sure you’ve read this a million times: Artificial intelligence (AI) has immense potential, with companies already seeing tangible results from operational gains driven by generative AI (GenAI). But the risks are equally high.

In the CDO Insights 2025 survey by Wakefield and Informatica, 92% of the 600 global respondents are concerned that they are continuing to accelerate AI adoption even as they discover underlying problems with the data and the organizational readiness to use AI responsibly. The reality is that right now, 97% of organizations using or planning to use AI find it difficult to demonstrate business value because of these limitations.

But here is the good news about AI — and more specifically, GenAI initiatives. It is driving so much enthusiasm that business stakeholders are also paying attention. Over the years, we have seen IT teams struggle to get long-standing commitment or engagement from their business counterparts to participate in governance and data quality processes, and for good reason.

Getting everyone on board

These folks are trying to do their day job and these other, data-related tasks do not feel part of it. Now, as AI becomes mainstream, it is becoming part of everyone’s day job. Companies must tackle long-standing data-related issues to drive AI; otherwise, what’s the point?  Bad data equals bad AI, and with AI, the consequences of making mistakes are not trivial. 

To be fair, we have been kicking the data quality and data governance cans down the road for years. But every year, the bar is raised for data privacy and data security, and policy compliance gets harder and more complex. Regulations around data sovereignty are now commonplace across the globe and more recent AI laws and mandates only add to the challenge.

A company’s partners and customers also expect that if they provide data, it will be safeguarded and handled responsibly. Regulations such as the “Right to Be Forgotten” come with serious penalties for any company doing business in the EU, potentially up to €20M for non-compliance. With AI, the ability to avoid the discipline and rigor that comes with robust data quality and governance will only continue to get harder.

From pilot to production: The data dilemma

Many companies I speak with describe the challenges of getting AI projects from pilot to production. This is a struggle echoed by 67% of the respondents in the Wakefield survey who revealed that they have been unable to successfully transition even half of their GenAI pilots to production.

It’s one thing to extract some data and clean it up once for a pilot. It is a whole different ball game when you want to operationalize that into a repeatable process. In fact, 43% of the respondents in this study said that data quality, completeness, and readiness are leading culprits for keeping pilots from taking a victory lap.

Aligning data success metrics with business value

In several recent interviews with Chief Data Officers (CDOs), a common theme emerged. They are starting to align data success metrics, not just with traditional IT goals such as completeness and uniqueness, but also with the objectives of the business stakeholders they are supporting.

For example, if incomplete, duplicative customer data is hurting your customer satisfaction scores in your call center, align the success of your data quality program to increased customer satisfaction. IT leaders are also allocating funding for projects deemed as “AI” to fix the underlying data issues required to be successful.

86% of survey respondents said they plan to increase investments in data management in 2025 with 44% citing data readiness for GenAI as a primary driver to these investments. These investments won’t just benefit AI-related projects; they will improve trust and confidence in data for all use cases ranging from analytics to customer experience, and beyond.

Understanding AI and data

Over the last several years, the call for data literacy, and now AI literacy, has been becoming louder and louder. As GenAI empowers non-technical users, companies must educate their employees and drive an understanding of the meaning of data and how it should be used.

97% of data leaders have already encountered issues with their workforce using GenAI or its outputs in their day-to-day operations. This includes potential risks such as using wrong or incomplete data for inputs, plagiarism, copyright or licensing issues, unauthorized use of sensitive data, or not reviewing inputs for bias, among others.

In this “AI Age,” we can’t depend on a data professional to be the intermediary for these kinds of issues. To be effective, we will have to drive new user training programs and equip our systems to instruct, guide, and protect users right during the moment of engagement. Governance cannot be a destination; it must be operationalized as part of the data journey, wherever the data goes. 

Laying the groundwork for AI success

The consequences of a successful AI strategy are profound, driving improved operational efficiency and better customer and employee experiences. We see companies driving innovation and creating new products or services faster and with confidence. They are enhancing collaboration across business units in more effective ways.

The excitement and optimism for the promise of AI is palpable and warranted, but creating sustainable, scalable processes depends on reliable, responsible data.

It’s time to stop kicking the can down the road and create a data foundation that will provide the necessary ingredients for long-term success with AI, reducing the risk of non-compliance with regulations, and driving alignment on business priorities across the organization to truly achieve the value that all companies seek.

To discover more about the experiences of other data leaders in their AI and data journey, get your copy of this year’s report, “CDO Insights 2025: Racing Ahead on GenAI and Data Investments While Navigating Potential Speed Bumps.”

About the Author

Amy McNee brings over 30 years of experience to her role as the Senior Vice President of Solution Architecture and Technical GTM at Informatica. With a background in the analytics and data management industry, Amy has consistently demonstrated her expertise in driving innovation and facilitating growth for companies, including organizational and GTM transformations. She was recognized as part of the 2024 Global Data Power Women list by CDO Magazine. 

McNee’s thought leadership extends beyond her professional roles. She is a speaker at industry events such as Gartner/Evanta, IDC, MIT, ThoughtWorks, and AWS Innovate. She covers critical topics such as modern data strategy, the impact of AI, practical approaches to building a data mesh architecture, and the pivotal role of people and processes in fostering a data-centric culture within enterprises.