Artificial intelligence is moving from experimentation to execution at a rapid pace.
Nearly 80% of enterprises are already using AI in some capacity, yet most remain in early or pilot stages. As adoption accelerates, a clear divide is emerging between organizations seeing real impact and those struggling to move beyond experimentation.
That level of transformation creates opportunity. It also raises the bar on what it takes to succeed.
At the center of that shift is one critical factor: data.
“AI is a foundational shift—it’s as big as the internet was in the mid-90s.”
AI is not a shortcut to better decisions. It is an accelerator.
For organizations with strong data foundations, that means faster insights, better targeting, and improved outcomes. For others, it introduces new risks at scale.
Research shows that 63% of organizations lack confidence in their data for AI initiatives, and up to 60% of AI projects fail due to data-related issues.
That risk is not theoretical. It is already shaping outcomes across industries.
“If you don’t have high-quality foundational data, AI will just make the wrong decisions faster.”
Even before AI, data challenges carried significant cost.
According to Gartner, poor data quality costs organizations an average of $12.9 million per year.
AI increases the speed and scale of decision-making, which means those costs can compound quickly when data is inaccurate, incomplete, or disconnected.
This is why organizations that approach AI as a tool-first initiative often struggle to see results.
From fragmented systems to connected ecosystems
To succeed in the AI era, organizations must rethink how they treat data.
It can no longer be a byproduct of operations or a collection of disconnected datasets. It must function as core infrastructure.
This shift requires:
“Data has to be infrastructure. It can’t be disconnected datasets with questionable quality.”
What an AI-ready data strategy looks like
Organizations building for AI success are focused on:
This is not a future-state vision. It is a current requirement.
From brand control to customer control
Customer behavior has evolved faster than most organizations’ data strategies.
Today’s buyers move seamlessly across channels, devices, and environments. They expect consistent, relevant experiences at every touchpoint.
“The idea that brands own the customer journey is obsolete. The person owns the journey…The key is marketing to a person—understanding both their professional and consumer profile, and linking the two.”
Why understanding the full person matters
Meeting those expectations requires a more complete view of the customer.
Connecting B2B and B2C data enables:
Organizations that can unify these perspectives gain a significant advantage.
From weeks to hours
AI is not just improving capabilities. It is redefining timelines.
In an hour, we can do what used to take weeks.
This acceleration impacts:
Why speed without accuracy creates risk
Faster execution only delivers value when it is aligned with the right data.
Organizations must balance:
Those that achieve this balance will outperform competitors still operating at slower, less integrated levels.
“Data isn’t about data—it’s about improving decisions and driving outcomes.”
Despite widespread adoption, few companies are seeing meaningful returns.
Research shows that only about 5% of organizations are achieving measurable value from AI investments.
This gap highlights a critical issue. Many organizations are investing in AI without addressing the foundational requirements needed to support it.
From experimentation to impact
To close this gap, organizations must:
AI must be tied directly to outcomes such as revenue growth, efficiency gains, and customer experience improvements.
Otherwise, it remains a pilot project.
AI adoption is accelerating, but the market is still early in its development.
This creates a window of opportunity for organizations willing to invest in the right foundations now.
What will separate leaders from the rest
The organizations that succeed in this next phase will:
They will not just adopt AI. They will operationalize it.
Andy shares more on how Data Axle is approaching AI, what is working across the market, and how organizations can move from strategy to execution.
30 Minutes With… Episode 5: How a 50 Year Old Company Retools for AI Success
As Content Marketing Manager, Natasia is responsible for helping strategize, produce and execute Data Axle's content. With a passion for writing and an enthusiasm for data management and technology, Natasia creates content that is designed to deliver nuggets of wisdom to help brands and individuals elevate their data governance policies. A native New Yorker, when Natasia is not at work she can be found enjoying New York’s food scene, at one of NYC’s many museums, or at one of the city’s many parks with her two teacup yorkies.