High-quality unified data architecture is essential for AI success
If your company is serious about AI, then your data infrastructure needs to grow up. No shortcuts. Quality outcomes come from quality input, and in AI, that means building a solid foundation of unified data architecture. You need to organize data, structure it, and ensure that every bit of it is usable across your business. That happens through systems like data lakes, data warehouses, or even better, data lakehouses, which combine both structure and flexibility.
This is about control. When you know what data you have, where it’s coming from, and how it’s being processed, then, and only then, can you trust your AI models to deliver results that matter. We’re seeing companies like Skyworks Solutions take exactly this approach. Their CIO, Satya Jayadev, doesn’t waste time. He calls their Databricks-powered data lakehouse “the foundation of a skyscraper.” That’s because it handles massive amounts of data, petabytes collected from factories and other systems, yet organizes it with clear quality tiers like bronze, silver, and gold. That structure allows AI to work without noise or confusion.
This level of infrastructure isn’t optional anymore, it’s mandatory for anyone who wants to compete in AI, from automation to predictive analytics to generative models. Feeding bad data into an AI system can lead to mistakes, compliance issues, and serious reputational damage. But when you get it right, it unlocks exponential value.
According to IDC’s Office of the CDO Survey, companies that put structured governance around their data are further ahead in AI execution. They have higher productivity, better decision-making, and stronger bottom-line performance. That’s where you want to be.
Mature data practices provide a competitive edge in AI deployment
Most companies are collecting data. Fewer are managing it properly. And very few are using it strategically. That’s the difference. Implementing AI that actually works, not just in experiments but in production, starts with data maturity. This isn’t about technical nonsense. It’s just smart business.
Look at the top-performing companies using AI today. They all have one thing in common: a serious approach to how they process and govern data. We’re talking about cataloging, metadata, and consistency across systems. When your teams know where data lives, what it means, and whether it’s trustworthy, you eliminate doubt. From there, AI can add real lift to your customer retention, profit margins, and operational efficiency.
Stewart Bond from IDC puts it plainly: “Organizations are prioritizing data quality to boost the productivity of data workers and enhance the accuracy and relevance of AI-generated outcomes.” In other words, clean up your data, and your AI will deliver results you can use. That means fewer errors, faster insights, and more reliable automation.
According to IDC’s survey, companies with data maturity were five times more likely to have generative AI tools in production. They also reported major gains that executives care about, higher profits, improved customer retention, and greater revenue.
Any executive looking to gain a real advantage in the AI era needs to look hard at their data practices. Audit them. Improve them. Institutionalize them.
Generative AI enhances data quality and operational efficiency
Most companies still think generative AI is just about text generation or chatbots. They’re missing the real opportunity. One of the strongest applications of generative AI is behind the scenes, improving the quality of your data in real time. The technology is being used right now by companies like Gallo and Servier Pharmaceuticals to identify inconsistencies, correct classification errors, and fill gaps in datasets that would otherwise degrade the accuracy of their systems.
Robert Barrios, CIO at Gallo, is using AWS Bedrock to train private large language models. These models scan vast datasets, spot where terms or categories deviate from established standards, and make automatic corrections. For instance, if a data record includes a non-standard product descriptor or customer attribute, gen AI steps in and normalizes it. These systems understand internal naming conventions and make precision-level improvements without exposing sensitive data to the public Internet.
Mark Yunger, Head of IT at Servier Pharmaceuticals, speaks to a similar use case. Servier runs a private version of ChatGPT on Microsoft Azure to maintain control over internal documents while accelerating content creation. Instead of employees spending time generating compliance documents or communications manually, AI drafts high-probability outputs based on secure proprietary data. The gains here are clear: faster turnaround, fewer errors, and tighter security.
You don’t need to overthink it. Generative AI helps clean, structure, and scale your data more quickly than human teams ever could. When it’s implemented using private infrastructure, it also supports compliance and data privacy, two areas that will derail your AI plans fast if left unchecked. If you’re a decision-maker, start testing generative AI where it creates the most leverage: inside your systems, improving what your business already relies on.
Comprehensive data governance and cross-functional collaboration drive AI success
AI doesn’t scale unless your governance scales with it. That’s the simple truth. It’s about how effectively your data is defined, secured, and applied across your organization. Without clear rules, definitions, and ownership, your data becomes unreliable, and that cascades into poor AI results.
Skyworks Solutions’ Satya Jayadev puts it directly: “AI is not traditional IT but a transformational tool, everyone wants access to it.” That demand creates pressure. To get ahead of it, you need a governance strategy that doesn’t restrict innovation but keeps all teams aligned by giving them trusted access to data under a shared framework.
Mark Yunger at Servier Pharmaceuticals has taken the same path. His team spent 18 months building a taxonomy and naming structure to unify how data is used across product teams, R&D, and PR. This wasn’t just back-office work, it let the company deploy AI more confidently, knowing that data inputs were consistently understood throughout the business. He also calls out the biggest challenge: talent. It’s not easy finding professionals with the right mix of governance experience and technical capability. But without that combination, data projects stall or never scale.
At AES, Chief Digital Officer Alejandro Reyes is clear about the balance: You need to enforce governance without slowing down how people work. That’s now non-negotiable for any company building large-scale AI platforms. AES uses tools like Atlan and Qualytics within its CEDAR platform to make sure that operational data can be trusted across engineering, finance, and beyond. They’re running AI in real production environments, with tight controls and no bottlenecks.
Cross-functional coordination is key. AI systems only perform well when business teams and IT are in sync. Governance is the bridge that allows both sides to scale impact without risking security, accuracy, or compliance. If you’re in the C-suite, make sure your governance model is centralized, transparent, and actively maintained. That’s the foundation AI needs to deliver real value.
Tailored data platforms enable sector-specific AI strategies
AI strategies are not one-size-fits-all. What works for a beverage company won’t meet the demands of an energy provider or a pharmaceutical firm. Industry-specific requirements, including regulatory pressure, operational complexity, and speed-to-insight, demand tailored data platforms. These platforms must integrate domain-specific logic, privacy considerations, and functional scalability from the start.
At AES, a company focused on sustainable energy, that approach has materialized in a custom-built data platform called CEDAR, operating on Google Cloud Platform. Chief Digital Officer Alejandro Reyes explains how it unifies operational data from all clean energy sites into a standardized format. Using Atlan for data cataloging and Qualytics for quality checks, CEDAR provides clean, reliable data for AI applications across departments, whether in forecasting energy supply, aligning with market demand, or projecting revenue. CEDAR powers Farseer, AES’s AI tool that was recognized with a 2024 CIO 100 Award for its real-world business impact.
Pharmaceuticals face a different challenge. Servier Pharmaceuticals, known for developing cancer treatments, operates in a tightly regulated space. Mark Yunger, Head of IT, knew they couldn’t afford data privacy mishaps or poor governance. To solve that, Servier created a unified taxonomy within a BigQuery lakehouse environment and built a secure private implementation of ChatGPT on Microsoft Azure. This ensures compliance with laws like the EU AI Act, especially when protecting patient trial data or avoiding infringement on other companies’ patents.
For Robert Barrios, CIO at Gallo, the goal was to make use of both structured and unstructured data. Gallo’s AI environment includes a structured SAP S/4HANA warehouse with data marts split by business function and a flexible Redshift-based lakehouse for metadata-rich processing of non-SAP data. This hybrid setup supports precision marketing, sourcing, and finance strategies tailored to the consumer goods sector, all using their own internal LLMs with AWS Bedrock to maintain control over their data layer.
This is where C-level strategy matters. AI won’t be effective without a data system that understands the business it’s meant to serve. Data models must be informed by industry knowledge, and platform choices must reflect the company’s risk profile and need for scalability. Executives should take a direct role in ensuring their data platforms are designed to support the complexity of their sector, not just technically, but operationally, competitively, and legally. That’s where real value gets created.
Key executive takeaways
- Build a unified data architecture: CIOs achieving real AI impact are investing in centralized data platforms, typically lakehouses, that support scalable, high-quality inputs. Leaders should standardize data environments across the organization to reduce risk and power effective AI.
- Prioritize data maturity to unlock AI value: Companies with mature data governance and quality controls are five times more likely to deploy generative AI successfully. Executives should accelerate metadata management, cataloging, and governance to gain a measurable edge in profit, retention, and efficiency.
- Use generative AI to strengthen internal data quality: Top firms use in-house generative models to fill data gaps, correct inconsistencies, and generate internal documentation securely. Leaders should deploy private AI tools to automate data refinement without compromising proprietary information.
- Make governance cross-functional and scalable: AI success hinges on strong yet flexible data governance that enables coordinated access without disrupting workflows. Decision-makers must align business and IT teams under transparent governance models to support innovation and regulatory compliance.
- Tailor data platforms to industry needs: Sector-specific data platforms, customized to regulatory and operational demands, are enabling real business outcomes across energy, pharma, and consumer goods. Executives should invest in data foundations purpose-built for their industry to scale AI confidently and effectively.
