Artificial intelligence is everywhere right now, in boardrooms, strategy meetings, and product roadmaps. Organisations are investing heavily in machine learning, automation, and generative AI, all with the same promise: unlock new revenue and work smarter.

In the latest episode of the Don’t Panic It’s Just Data podcast, EM360Tech’s Trisha Pillay explores this challenge with Chief Technology Officer Paul Brownell and Sergio Morales, Data and AI Engineering Leader from Growth Acceleration Partners. Their discussion unpacks why so many AI initiatives fail to translate into revenue and why the real starting point isn’t the model itself, but the data, governance, and engineering practices that make meaningful outcomes possible.

But here’s the uncomfortable truth and that is many AI strategies look powerful on paper, but the real financial impact is often unclear. This disconnect, called the revenue data gap, highlights an issue many organisations overlook. AI doesn’t create value on its own especially without strong data foundations, governance, and engineering discipline, even the most ambitious AI strategy will struggle to deliver measurable results.

The Revenue Data Gap in Enterprise AI

For many organisations, the excitement surrounding AI can create a tendency to jump straight into experimentation. Teams begin exploring tools, deploying models, or building prototypes without first defining how those initiatives will produce tangible business outcomes.

According to Brownell, this is where the first major disconnect appears. Many enterprises approach AI with what he describes as a “shiny object” mentality. They recognise that AI is powerful, but they have not yet defined where the value will actually come from. As a result, organisations may launch projects that generate interesting insights or technical demonstrations but fail to translate into revenue growth or cost reduction.

Brownell emphasises the importance of establishing a data hypothesis before pursuing any AI initiative. A data hypothesis outlines the relationship between the data an organisation holds and the business value it expects to extract from it. In practical terms, it asks a simple but critical question: If we analyse this data, what decision or action will it enable, and how will that affect revenue?

Without this hypothesis, organisations often find themselves exploring large volumes of data without a clear objective. Some companies may not even know where their most valuable data resides or whether it is reliable enough to support analytical models. Data quality, therefore becomes another major component of the revenue data gap. 

Engineering the Foundations for AI That Delivers Business Impact

While AI is often portrayed as a revolutionary technology, Morales points out that the engineering challenges behind it are not entirely new. Many of the same principles that guided earlier technology transformations such as cloud adoption or microservices architecture still apply to modern AI deployments.

In fact, Morales argues that organisations struggling with AI today are often experiencing the consequences of earlier architectural decisions. Systems built years ago were rarely designed with advanced analytics or AI in mind. As a result, critical data may be trapped inside legacy applications, scattered across departments, or stored in formats that make integration difficult. These limitations become highly visible once organisations attempt to deploy AI at scale.

Another major challenge lies in what Morales describes as the velocity mandate. Businesses increasingly expect technology teams to deliver results quickly, particularly when AI initiatives are positioned as strategic priorities. However, building the infrastructure required for reliable AI systems can take significant time and effort.

Morales explains that organisations do not necessarily need to choose between speed and stability. Instead, they can adopt a pragmatic approach that focuses on incremental progress. This strategy allows organisations to create early successes that build confidence across the business. Once stakeholders see tangible results from initial projects, it becomes easier to secure the support and investment needed for broader data transformation efforts.

Why Data Contracts and Governance Are Critical to AI Success

Are you enjoying the content so far?

One of the most practical tools discussed is the concept of data contracts. Though less flashy than AI models, they ensure data flows reliably between systems. At their core, data contracts define a dataset’s structure and expectations; schemas, formats, and validation rules. Morales describes them as a way to embed governance directly into data pipelines, automatically catching violations before they disrupt downstream processes. This prevents silent errors that can skew analytics and decisions. 

Data contracts aren’t a cure-all, though. Their effectiveness relies on clear organisational ownership and communication around each dataset. In large companies, data often comes from multiple systems managed by different teams, each with distinct priorities. Brownell explains that data contracts create a shared framework for collaboration, letting teams integrate and analyse information confidently. Implementation can be gradual: start with critical datasets for a specific use case and expand governance as needed. This iterative approach improves data reliability without requiring a full infrastructure overhaul.

What’s next for AI?

While AI tools continue to evolve, the fundamentals of data management remain unchanged. Organisations must understand their data, govern it effectively, and design infrastructure that allows information to move reliably between systems. Closing the revenue data gap, therefore, requires more than deploying new AI models. It demands a strategic approach that begins with clear business objectives, continues through data engineering practices, and is reinforced by governance frameworks such as data contracts.

If you would like to learn more visit: growthaccelerationpartners.com

Takeaways

  • The revenue data gap is a common challenge that organisations face when implementing AI.
  • It’s crucial to define a clear data hypothesis and ensure data quality to drive measurable business impact.
  • Data contracts work only if teams know who owns datasets, how to maintain them, and how changes are communicated.
  • Balancing the velocity mandate with governance is key. 
  • Engaging stakeholders and mapping the value chain ensures that AI initiatives are aligned with business needs, ultimately leading to revenue growth.