What Comes First, Vision Or Data?

Unlocking the True Potential of Artificial Intelligence

The integration of artificial intelligence (AI) into business operations has become a pressing concern for many organizations. A common dilemma that arises is whether to prioritize the development of a robust data infrastructure or to reinvent existing processes. This is a classic “chicken or egg” problem, and the answer lies in a balanced approach that considers both aspects. However, the sequence and intent behind this approach are crucial, and many companies are getting it wrong.

The temptation to start with data is understandable, given its critical role in AI functionality. Clean and accessible data is essential for AI to produce meaningful insights. As a result, many organizations invest heavily in data initiatives, including data cleanup, migration to the cloud, and the implementation of data governance frameworks. While these efforts are necessary, they are insufficient on their own to generate significant returns on investment (ROI). Data work is a foundation, not a destination, and its value lies in its ability to support a broader transformation.

The Importance of Reinventing the Operating Model

Research by Everest Group has shown that the real value of AI lies not in the technology itself, but in its ability to transform the operating model of an organization. This transformation requires a profound shift in how businesses operate, including the reorganization of teams, redefinition of roles, and reengineering of processes. Simply providing employees with AI tools without changing the underlying workflows will not yield the desired performance. The environment must be adapted to allow these tools to thrive.

To capture the full ROI of AI, companies must first design new operating models that take advantage of its capabilities. This involves defining a clear vision of what the future operating model will look like once AI is fully embedded in the business. This vision should be concrete, evidence-based, and consider the evolution of the business model, talent, governance, and value delivery. By working backward from this vision, organizations can identify the necessary data, platforms, and technical infrastructure to achieve it.

Starting with a Clear Vision

Defining a clear vision of the future operating model is not just a thought exercise; it’s a critical step in building a purposeful and targeted data strategy. This approach ensures that data initiatives are aligned with specific business outcomes and allows for the prioritization of the most critical re-platforming work. By linking each investment directly to a business outcome, organizations can evaluate ROI effectively and make informed decisions.

A future-first approach has several advantages, including ensuring that data strategies are purposeful and targeted, prioritizing critical re-platforming work, and providing a framework for evaluating ROI. This approach also enables organizations to build a “System of Execution,” a coordinated redesign of technology, data, process, and people that is necessary for successful AI implementation.

Avoiding Common Traps

Many organizations start with data because it feels tangible and easy to measure. However, this approach risks putting the cart before the horse. Without a clear picture of the AI-driven future, it’s impossible to know what data is necessary to support it. This can lead to wasted resources on data initiatives that deliver little strategic value, causing companies to become disillusioned with AI altogether.

The shift to AI-based operating models is uncharted territory, and many companies are struggling to understand what “good” looks like. As a result, they fall into predictable traps, such as assuming that AI tools will boost productivity without changing underlying workflows or treating AI as an add-on rather than a fundamental enabler of a new business model.

Bringing It All Together

The key to unlocking the true potential of AI lies in a balanced approach that considers both data and process reinvention. By starting with a clear vision of the future operating model, organizations can create purpose and direction for their AI initiatives. This vision should be used to guide data, technology, and talent strategies, ensuring that they are aligned and working in harmony to achieve the desired outcomes.

Ultimately, the integration of AI into business operations requires a thoughtful and intentional approach. By avoiding common traps and focusing on a future-first strategy, organizations can unlock the transformational value of AI and achieve significant ROI. The journey to AI-driven operations is complex, but with the right approach, the rewards can be substantial.