Integrating AI with data: A unified strategy for business success

The era of isolated AI and data strategies is over. Today, AI doesn't operate in a vacuum; it's deeply intertwined with the data that fuels it. Rather than crafting separate AI or data initiatives, businesses must adopt a unified approach that aligns these critical components with overarching objectives. Without this integration, AI efforts risk becoming flashy but hollow—promising much but delivering little. Success demands a cohesive AI and data strategy, plain and simple.

Why this matters now

Companies that effectively integrate AI and data witness tangible business outcomes. AI on its own is intriguing, but without data, it's like icing without the cake—sweet but lacking substance.

According to a Gartner survey, 86% of CEOs expect AI to boost their revenue in 2024 and 2025. However, without high-quality data, these AI initiatives will struggle to deliver. A successful AI strategy starts with good, clean, structured data. Otherwise, your AI projects are doomed before they even begin.

Across various industries, the pattern is consistent: organizations that treat data as the foundation of their AI efforts achieve success. Those that regard AI as an isolated project tend to falter. The following three essential steps can ensure your AI and data strategies are harmonized—and yield meaningful results.

Step 1: Recognize that AI without data won't deliver results

First and foremost, understand that AI is only as effective as the data it relies on. You can have the most advanced machine learning models, but if your data is incomplete, outdated, or irrelevant, your AI won't produce meaningful outcomes. AI doesn't "make things up"; it learns, adapts, and delivers insights based on data—and not just any data, but the right data.

For example, if an organization tasks its data science team with exploring generative AI but restricts access to internal data due to governance concerns, relying solely on external data sources is unlikely to generate valuable insights. This scenario can lead to frustration, wasted resources, and a loss of momentum for future AI initiatives.

Businesses need a unified data and AI strategy aligned with their overall goals. Whether you're aiming to grow market share, improve customer service, or increase profitability, you can't achieve these objectives with AI alone—you need the data that drives your business to guide AI. This alignment transforms AI from a nice-to-have into a critical business driver.

Step 2: Prioritize data quality and architecture

The second step is getting your data in order. Simply put, data quality is everything. You might have the best intentions with your AI strategy, but if your data is poor, your outcomes will be too. Companies have invested millions in AI initiatives, only to discover that their data was too messy or incomplete to provide real value.

Data quality issues also extend to architecture. AI requires an infrastructure that can support it, especially when dealing with real-time data. Your architecture needs to be scalable, adaptable, and capable of handling the increasing demand for AI-driven insights. Whether it's upgrading your data pipelines, integrating machine learning operations (MLOps), or building processes that allow data to flow seamlessly into your AI systems, this step is crucial.

Step 3: Establish strong governance and stewardship

Governance may not be the most exciting topic, but it's the bedrock of a sustainable AI and data strategy. Governance and stewardship aren't optional—they're critical to ensuring your AI doesn't run amok. Without proper governance, you risk regulatory problems, data privacy issues, and erosion of customer trust.

Consider a hypothetical situation where a transportation company wants to implement AI-driven customer matching but fails to involve data stewards from the start. Without oversight, the project could proceed based on faulty assumptions, leading to outcomes that need to be scrapped—wasting time and resources.

Good governance doesn't just prevent disasters; it's a proactive way to ensure your AI initiatives align with business goals and comply with emerging regulations. Governance isn't solely an IT responsibility; business leaders need to be involved as well. The best outcomes occur when you pair a technical steward who understands the data with a business steward who understands the use case. This collaboration ensures your AI systems align with the real-world needs of the business.

The cultural shift: New roles and organizational changes

Integrating AI and data necessitates cultural changes within organizations. The rise of AIOps, DataOps, and roles such as Chief Data and Analytics Officer (CDAO) signals that companies recognize data and AI as strategic assets, not just technical ones. Bridging the gap between IT and the business ensures that data and AI initiatives tie into broader business objectives.

Moreover, more organizations are creating hybrid roles that combine technical expertise with business acumen—a necessity, not just a trend. You need people who understand both the data and its impact on the business. Without these roles, your AI initiatives risk being disconnected from your business goals, making it harder to drive meaningful outcomes.

The cultural shift doesn't stop there. Companies need to break down silos between IT and the business. AI initiatives often fail because they're treated as purely technical projects. In reality, AI touches every part of the organization. Business leaders need to be as invested in these projects as the IT team, and vice versa.

Gartner predicts this cultural transformation will pay off: By 2025, 25% of businesses with CDAOs whose KPIs are tied to business outcomes will see their market value grow more than companies that haven't made similar investments.

The Bottom Line: A Unified Strategy for Success

Ultimately, AI can't thrive without data. You need both working together, aligned with your business strategy, to drive real results. The future belongs to companies that understand this and are willing to put in the effort to synchronize their data and AI strategies. This means investing in data quality, building scalable architectures, and implementing strong governance.

There's no silver bullet—it's about getting the fundamentals right. But when you do, the rewards are significant. You won't just be chasing the AI trend; you'll be leading it, using data as your competitive advantage.

For those who want to succeed in this AI-driven world, the time to act is now. AI without data is like icing without cake—and no one wants that.

Dave Crolene is Vice President of Data, Analytics & AI at EXL, specializing in integrated AI and data strategies for business transformation.