Bridging the hidden hole between knowledge and choices within the age of AI
Everywhere you turn, the conversation about AI includes the same message: success depends on good data. It’s become the mantra of every boardroom and conference stage.
Companies invest millions in cleaning, tagging, and organizing data with the belief that once it’s right, AI transformation will follow.
But that belief is incomplete. Cleaning and collecting data is step zero. Without the engineering, architecture, and operational readiness to use it, even the cleanest data set won’t move the business forward.
Chief Product & Technology Officer, CBTS.
A Gartner survey found that 63% of organizations either don’t have or are unsure if they have the right data management practices for AI.
But even if companies don’t know where to start to get from data to AI transformation, there’s a straightforward strategy that any organization can use to produce business outcomes.
Why progress stalls at step zero
Progress stalls when there’s a gap between any of the layers between data and activation — strategy, engineering, modernization, visualization, and readiness. Some organizations write an ambitious data strategy that never links to measurable business outcomes.
Others collect and store vast amounts of information without a plan for how it will flow between systems. Most often, legacy IT infrastructure makes modernization nearly impossible, while data teams remain siloed from the decision-makers.
Gaps in skillset or experience are another frequent hurdle. Companies may have data analysts who can interpret dashboards, but lack data engineers and architects who can build the pipelines and governance structures that make insights reliable and scalable. When there’s a lack of talent available, organizations remain stuck on one piece of the process.
This blocks more than just a deeper understanding of the numbers; it’s preventing innovation inside these companies. Nearly half of the executives in a survey from IBM said data concerns remain a barrier to agentic AI adoption for their organizations.
When teams can’t trust their data, they can’t use it as the foundation for an AI strategy, even when there’s pressure from the top. AI may be the flashy thing everyone wants to talk about, but the “boring” stuff is what makes it work.
Turning data into true business outcomes
Solving this doesn’t necessarily mean hiring a whole department worth of people or investing in dozens of new data tools, but it demands a shift in how organizations think about readiness. True readiness starts when data operations are designed with business outcomes in mind.
Companies that mature in this area treat engineering and architecture as business disciplines. They define clear ownership of data pipelines, establish governance from the start, and modernize infrastructure so data can move securely and efficiently.
When those pieces are in place, the business outcomes follow. In some organizations, connecting production and maintenance data has shortened downtime cycles and increased throughput — real revenue gains from systems that can finally communicate.
In others, unifying financial and operational data has eliminated duplicate software licenses and reduced infrastructure costs. That could translate to saving tens of thousands of dollars a month. Visibility drives those savings.
Risk also drops dramatically when governance and observability are embedded in daily operations. Leaders trust what they’re seeing and can prove the integrity of every decision. When data is flowing together, it also allows organizations to proactively see vulnerabilities and significantly reduce the likelihood of a cybersecurity breach.
While many enterprises try to piece these layers together internally, most eventually realize they need a partner that can guide the full process — from strategy through architecture, modernization, and AI readiness. The right partner brings the frameworks, talent, and repeatable processes that turn readiness into results.
Speed reigns over size
When organizations have that foundation, they can quickly move from insight to execution. Smaller organizations with modern data architectures are already outpacing much larger competitors that are weighed down by legacy systems. Once data can move freely, decisions accelerate, forecasts sharpen, and automation compounds.
AI literacy is now table stakes. AI execution is what separates the companies moving ahead from those with failing projects. In the race toward AI transformation, the winners won’t have the most data; they’ll be the ones who built the fastest car and knew how to drive it across the finish line.
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