Artificial Intelligence

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In 1973, Motorola engineer Martin Cooper placed the very first public call from a handheld portable cell phone to his counterpart at competitor Bell Labs. This was a groundbreaking moment in the history of technology, but it still took decades before cell phones advanced from brick-like, single-purpose devices to the powerful smartphones we rely on today. Unlike cell phones, artificial intelligence (AI) has advanced much faster, compressing decades of progress into just a few years, with coinciding, rapid adoption. In fact, according to a recent survey from my company, Prosper Insights & Analytics, 54% of executives and business owners now use generative AI applications, such as ChatGPT and CoPilot.

Basic task automation has quickly evolved into sophisticated applications generating human-like text, images, code, and more. Advancements in large language models (LLMs), cloud computing, and data availability have only accelerated innovation. With countless more applications on the horizon, business leaders are looking to profit from the potential of AI to automate workflows, improve decision-making, and uncover new revenue streams.

But beneath the surface of this transformation lies a growing, multi-faceted challenge. As AI adoption accelerates, so does the volume and complexity of data it generates. With global data creation projected to exceed 220 zettabytes in 2026 and AI-generated content on track to surpass human-created content, businesses are struggling to effectively use all of the data they’re generating. Research from Gartner highlights this gap between AI ambition and reality with less than 30% of AI leaders reporting their CEOs are happy with their AI investment return.

The Hidden Cost of Data Silos
AI only works well when it gives an organization visibility across its data. When that happens, it can deliver better insights, support smarter automation, and create strategic advantages. But too often, structured, and unstructured data is stored in disparate silos, by different business units and product groups. When this data is fragmented, incomplete, or inaccessible, AI models won’t have the context they need to deliver meaningful insights.

And the impacts of siloed data can be felt across the enterprise, creating friction throughout the AI lifecycle. Data scientists spend more time locating, cleaning, and reconciling datasets than building models. Business leaders struggle to trust AI outputs when they’re based on partial or inconsistent information. IT teams struggle to manage sprawling, complex storage environments that are costly and difficult to scale.

“Data silos are undeniably the enemy of AI innovation,” says Chris Willis, Chief Design Officer and Futurist at Domo, a cloud software company that helps teams integrate data, automate workflows, and deliver analytics with a unified platform. “Their impact can directly lead to underperforming AI initiatives, wasted time, and missed opportunities.”

In other words, data silos are preventing organizations from generating value from their AI efforts. Not surprisingly, a recent Prosper Insights & Analytics survey found that nearly 40% of executives and business owners are concerned that AI is “hallucinating” or providing wrong information.

AI Success Starts with Rethinking Data
Organizations commonly launch AI initiatives by first identifying a use case, then deploying a tool, and finally expecting results. This approach can deliver wins, particularly when it comes to automating repetitive tasks.

Yet, the long-term value of AI requires generating insights across the enterprise. That takes a data-first mindset that prioritizes accessibility, integration, and governance, and ensures that data can flow across systems, teams, and environments. Without this foundation, even the most advanced AI tools will be a disappointment.

Domo understands firsthand that companies struggle with data management and often don’t leverage AI consistently across the enterprise. Their platform enables users to prepare, visualize, automate, distribute, and build end-to-end data products that provide solutions across the entire data journey. Hundreds of Domo customers’ AI use cases reveal that AI doesn’t become valuable when a model gets smarter. It becomes valuable when it’s connected to a business and becomes a system of action. Providing users with the ability to build data products is what generates measurable value for the business.

The Case for Unified Infrastructure
To achieve real gains from AI, organizations must move beyond fragmented architectures and invest in unified data infrastructure. This means consolidating data across on-premises systems, cloud platforms, and edge environments into one cohesive ecosystem.

Unified infrastructure offers several key benefits:

• Improved data accessibility: Teams can access the data they need, when and where they need it.
• Enhanced data quality: Centralized governance reduces inconsistencies and ensures that AI models are trained on reliable datasets.
• Faster time to insight: Integrated data pipelines enable real-time analytics and more responsive decision-making.
• Cost efficiency: Eliminating redundant storage and streamlining data management reduces operational expenses.

Integration Is Everything
As data volumes continue to grow, integration becomes even more important for AI success. Organizations that can connect data across applications, platforms, and environments will gain the most value from their investment.

“The organizations that scale successfully are prioritizing integration and rethinking it as a foundation, not plumbing,” says Willis. “More than ever, it means adopting a shared approach to integration that includes common patterns, shared ownership, and flexibility for teams to move without introducing risk.”

Modern data integration solutions make it possible, by unifying structured and unstructured data, automating data pipelines, and maintaining consistency across systems. These are the very capabilities that are essential for scaling AI initiatives beyond isolated use cases.

Security and Governance
As organizations prioritize breaking down data silos, they must also consider security and governance. Robust data governance frameworks are essential to ensure that data is accessible, secure, compliant, and used responsibly. This includes implementing consistent policies for data access, encryption, and lifecycle management across all environments.

AI also introduces other considerations, including protecting sensitive training data and ensuring transparency in how models use information. Organizations that fail to address these issues risk not only financial loss but also reputational damage.

Data as a Strategic Advantage
It’s clear that breaking down data silos is no simple feat. It takes alignment across IT, data teams, and business leaders, as well as a willingness to rethink legacy processes and systems. But the payoff can be substantial.

Organizations that can successfully unify and manage their data gain:

• Accelerated AI adoption and innovation
• Improved decision-making with better insights
• Reduced operational inefficiencies and costs
• Stronger data security and compliance

Most importantly, they can transform data from a fragmented liability into a strategic asset that can evolve over time.

Navigating the Path Forward
Without question, AI is reshaping the competitive landscape, but technology alone is not enough to guarantee success. The real differentiator lies in how organizations manage and leverage their data.

As data volumes continue to grow, the gap between organizations that lead and those that are left behind will widen. Businesses that invest in modern data management and eliminate silos will be better positioned to realize the full value of AI. And those that don’t may find themselves drowning in data, unable to deliver meaningful ROI, and struggling to keep up with the competition.

Disclosure: The consumer sentiment study referenced above was conducted by my company, Prosper Insights & Analytics. This is the same dataset used by the National Retail Federation, and available from Amazon Web Services, Bloomberg, and the London Stock Exchange Group for economic benchmarking.