Why the right data foundation is essential to unlock AI potential [Q&A]

As AI use continues to grow it’s becoming increasingly clear that the real competitive advantage isn’t just in the models, it’s in the data behind them.

Proper data architecture is what enables AI to reason effectively and create real business value. We spoke to Manny Rivelo, CEO of ConnectWise to discuss why the right data foundation is so vital to AI success.

BN: Why is data — rather than the AI model itself — emerging as the true competitive differentiator in this new era of AI adoption?

MR: The power of any AI system lies in the quality, integrity, and accessibility of the data that fuels it. Models can be replicated, improved upon, or even commoditized over time. What remains unique to each organization is its data: the historical, operational, and contextual information that reflects how an AI system actually works.

High-quality, well-structured data provides the context AI needs to make trustworthy, relevant decisions. Without context, even the most advanced model can only generate generic insights. The differentiator comes when a company can securely connect its proprietary data with AI systems to enhance decision-making and efficiency.

The shift from ‘model-centric’ to ‘data-centric’ AI is a sign of maturity in the field. Organizations are realizing that competitive advantage doesn’t come from having the biggest model, but from how effectively you curate, govern, and activate your data.

BN: Many organizations have vast stores of unstructured or siloed data. What are the biggest barriers to transforming that data into a strategic AI asset?

MR: The biggest challenge isn’t just the volume of data, but the lack of structure and governance. In many organizations, data lives in functional silos, trapped in legacy systems or stored in inconsistent formats. That fragmentation makes it difficult to aggregate and prepare data for AI systems that depend on accuracy, completeness, and timeliness.

Another major barrier is data integrity, or ensuring that data is accurate, complete, and consistent throughout its lifecycle. If integrity is compromised, AI outcomes can be skewed or outright wrong. Achieving integrity requires disciplined data hygiene, strong validation processes, and mechanisms to detect and remediate errors or duplication. Security and compliance add additional complexity. As data becomes more widely shared between systems, especially across cloud environments, ensuring privacy, regulatory adherence, and proper access controls becomes a constant balancing act.

Cultural barriers also persist, with many organizations still treating data as a byproduct of operations rather than a core asset. Overcoming that mindset requires leadership alignment, investment in data literacy, and a commitment to cross-functional collaboration.

BN: How can companies design data architectures that balance accessibility and security, especially as AI systems become more deeply embedded in operations?

MR: Balancing accessibility and security begins with thoughtful data architecture — designed to make trusted information available for AI-driven insight without compromising privacy or compliance. The foundation is strong governance: understanding what data exists, who owns it, and how it flows across systems. Modern architectures often use a federated approach, keeping sensitive data protected while allowing anonymized or aggregated information to inform AI models. Role-based access controls, encryption, and continuous validation ensure security, while data catalogs and lineage tracking provide the visibility needed to maintain trust. Above all, this balance is not static; it requires constant adaptation as AI capabilities, regulations, and business needs evolve.

BN: What are some practical steps business and IT leaders can take today to prepare their data for AI-driven transformation?

MR: Flawed data leads to flawed AI. Preparing data for AI starts with trust and structure. Leaders should focus on data quality, removing duplicates, validating accuracy, and maintaining consistency across systems. Visibility is equally key, so map where data lives, classify it by sensitivity, and define clear ownership. Governance must be shared across business, IT, and security teams to ensure alignment on data use and privacy. Automation can sustain integrity through continuous validation, but culture is what keeps it strong, as it educates employees on proper data handling, ethics, and compliance. Above all, treat readiness as an ongoing process; as AI evolves, data structures, policies, and oversight must evolve accordingly. Organizations that embed these habits early will turn data from an operational byproduct into a durable competitive advantage.

BN: Looking ahead, how do you see data management and AI maturity evolving over the next few years — and what will distinguish the leaders from the laggards?

MR: We’re still early in the AI journey, but the next few years will define how organizations turn experimentation into measurable impact. As AI becomes embedded in daily operations, the companies that succeed will be those that elevate data management from a technical concern to a strategic discipline. Leaders will treat data as a core business asset, investing in unified architectures that connect structured and unstructured information securely across departments. They’ll use automation to maintain integrity, integrate governance directly into their AI pipelines, and build systems that learn continuously from feedback rather than static datasets.

Culture is just as critical as technology. Organizations that foster transparency, data literacy, and ethical accountability will move more quickly and with greater confidence. In contrast, laggards will struggle with fragmented data, inconsistent oversight, and a lack of trust in AI-driven outcomes. Over time, the focus will shift from protecting data to enabling it—turning secure, well-contextualized information into a foundation for innovation, adaptability, and human-AI collaboration. Those who master that balance will lead not just in AI adoption, but in redefining how intelligence and decision-making operate across the enterprise.

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