AI’s Competitive Edge: Turning Data Challenges Into Business Success

The artificial intelligence revolution is no longer creeping forward—it’s sprinting. In my recent conversation with Brett Roscoe, Senior Vice President and General Manager of Data Governance and Cloud Operations at Informatica, we explored the findings from their CDO Insights 2025 report that reveal both the explosive growth in AI investment and the obstacles companies are encountering on their journey.

What struck me most during our discussion wasn’t just the sheer scale of financial commitment—with 87% of companies increasing their generative AI investments in 2025—but the widening competitive chasm between early adopters and those hesitating to embrace AI’s transformative potential.

The Investment Stampede Is Real

The enthusiasm for generative AI isn't just talk—it's backed by serious capital. As Roscoe explained during our conversation, "87 percent of our customers... are increasing their investments in gen AI in 2025. Now that was already on top of what they've done to increase it in 2024."

This investment surge isn't happening in isolation. Alongside their AI spending, 86% of organizations are simultaneously boosting their investment in data management practices to support these initiatives. This dual-track approach signals a growing recognition that successful AI implementation requires robust data infrastructure.

What are companies hoping to achieve? According to Roscoe, "the biggest areas were operational efficiency... and enhancing customer experiences or employee experiences." These goals are already materializing in companies like CN Rail, which has slashed data preparation time from months to just two weeks, creating the foundation for accelerated AI project development.

The Early Adopter Advantage Is Widening

Perhaps the most compelling insight from our conversation was the growing evidence that AI early adopters are pulling ahead—and fast. As Roscoe noted, "Early adopters of AI had a big advantage. In fact, to the point where these early adopters saw something like 35 percent cost decreases and 58 percent growth attributed to their Gen AI projects."

This isn't incremental improvement—it's transformative change. When McKinsey surveyed organizations already deploying generative AI, they found that, on average, these companies attributed 20% of their EBITDA to their AI initiatives. Numbers like that aren't just competitive advantages—they're existential threats to laggards.

I've observed this dynamic firsthand across industries: the gap between AI leaders and followers isn't stable—it's expanding daily. Every organization that doesn't see itself as AI-enabled is falling further behind those that do. This creates a powerful incentive for companies to push ahead despite challenges.

The Hidden Obstacles To AI Success

While investment enthusiasm runs high, implementation reality is more sobering. The CDO Insights 2025 report revealed that a staggering 97% of organizations struggle to demonstrate business value from their generative AI investments—a significant roadblock to securing continued funding and executive buy-in.

Technical challenges are equally prevalent, with 92% reporting that issues like data quality, responsible AI use, and compliance concerns are delaying the progression from proof-of-concept to production. As Roscoe emphasized, companies are grappling with questions like: "How will this AI model behave? What risks are associated with putting this into production?"

In my experience working with organizations across sectors, I've observed additional barriers: executive teams not thinking ambitiously enough, scattered pilot projects without strategic cohesion, leadership knowledge gaps, skills shortages, cultural resistance, and data silos that prevent AI from reaching its potential.

Data: The Foundation Of AI Success

Our conversation repeatedly returned to a fundamental truth: without good data, there can be no good AI. This reality is driving a profound shift in how organizations approach data management.

"Gen AI is the most data-hungry thing we've ever seen," Roscoe explained. Traditional approaches of siloed data management simply cannot meet the voracious appetite of generative AI systems that require access to diverse data sources across the enterprise.

This is forcing a paradigm shift from what Roscoe described as "governance" to "data literacy"—moving beyond protecting and restricting data to enabling its responsible flow throughout the organization. As he put it, success means you can "shoot data across your organization in a rapid way and still sleep well at night."

Informatica recommends a four-step process to achieve this balance:

·         inventory (understand what data you have),

·         control (establish appropriate access policies),

·         deliver (create user-friendly interfaces for data discovery), and

·         observe (monitor usage and quality).

This methodology has enabled organizations like Holiday Club's vacation unit to consolidate seven disparate systems into a unified customer view and helped Helix accelerate loan approvals by 300%.

Building An AI-Ready Culture

Technical solutions alone aren't sufficient. Every successful AI implementation I've studied shares a common feature: an organizational culture that embraces data-driven decision-making at every level.

Roscoe emphasized that the most successful organizations take a holistic approach: "Get executive buy-in. Have top-down support for our gen AI programs and projects. Work with our business teams and our constituents and our consumers... collaborate and make sure that everybody understands the tools and the capabilities and the processes we're going to put in place."

This cross-functional collaboration is essential to avoid what Roscoe calls "siloed behavior" and "rogue Gen AI programs" that can lead to problematic outcomes. Informatica exemplifies this approach internally, with CEO-driven initiatives to educate employees, drive cultural change, and build AI capabilities into their products.

The Future Arrives Faster Than We Think

Looking ahead, the pace of AI innovation shows no signs of slowing. Roscoe pointed out the stunning adoption curve of ChatGPT: "It took 5 days for chat to get to 5M users. It took 10 years for people to adopt cloud as a general concept."

This acceleration demands a new organizational mindset—one that can quickly adapt to emerging technologies. As Roscoe advised, "The biggest thing you can be ready for is rapid change... you need to be able to quickly embrace it, quickly understand it, and kind of augment it into your business and your practices."

The Competitive Imperative

The message is clear: generative AI isn't optional—it's imperative. Organizations that fail to harness this technology risk falling irretrievably behind.

"I think Gen AI is probably the biggest disruption we'll see in this space, at least in my lifetime," Roscoe concluded. The companies that succeed in the coming decade will be those that recognize this reality and act accordingly.

The time for cautious experimentation has passed. Now is the moment for bold, strategic implementation of generative AI across the enterprise—backed by robust data management, cross-functional collaboration, and a culture that embraces technological change. Those who hesitate may find the competitive gap impossible to close.

For more insights, download the report CDO Insights 2025.