Business objectives, Data and AI

 

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I recently came across a thought-provoking Harvard Business Review article titled "Why Your Company Needs a Chief Data, Analytics, and AI Officer". The article emphasizes the critical role that data and AI play in shaping business strategies and decision-making processes. It makes a compelling case that organizations need a dedicated executive role focused on these areas to effectively leverage their data assets, drive innovation, and maintain competitive advantage in today's rapidly evolving technological landscape.

One question from the article particularly resonated with me: "And should overseeing AI and data be viewed as a business or a technology role?". The answer is clear—it's not merely a technology role, but a strategic business role that demands deep expertise in both the technical dimensions of data and AI, as well as their business implications and opportunities.

A persistent gap exists between technology and business, and bridging this divide is crucial for successful digital transformation. I believe that effective AI adoption requires commitment from both sides - technology and business. Technology leaders must understand data platform and AI's capabilities and, critically, its limitations, while business leaders must learn how to strategically leverage AI to drive tangible value and achieve their objectives..

Many leaders successfully articulate a compelling vision for AI adoption, yet struggle with execution. The root cause? AI (its toolings, frameworks, standardization, etc.) is evolving at breakneck speed, demanding continuous learning and adaptation. Organizations must cultivate a culture of innovation, experimentation, and ongoing learning to remain competitive in the AI landscape. While modern AI models demonstrate impressive reasoning capabilities, they still require human guidance to ensure alignment with business goals and organizational values. Data and information architecture also play a pivotal role in this context.

For instance, developing a well-defined ontology for your business domain helps structure and organize data in ways that are meaningful and relevant to your organization. This enables LLMs to access the right context and deliver more accurate, relevant, and actionable insights. Conversely, when data organization lacks proper structure, AI models struggle to understand context and generate meaningful insights. No matter how advanced the model, poorly structured data creates barriers that prevent AI from delivering expected results.

The right sequence is: start with business outcomes, then examine the data availability, and finally consider the AI capabilities that can help achieve those outcomes. This is an iterative process that requires continuously rethinking and refining business outcomes as AI capabilities evolve.

Why emphasize data first before AI? Because quality data is the foundation upon which AI and business outcomes are built. Without reliable data, AI models cannot learn effectively or generate trustworthy predictions. Organizations must invest in robust data management, governance, and quality initiatives to ensure their data is reliable, accurate, and relevant.

Furthermore, we need substantial amounts of high-quality data to train effective AI models and evaluate the performance of pre-trained models. The more diverse and representative the data, the better AI models can generalize and perform across various scenarios. In nearly all of my AI engagements, I've found that data-related challenges represent the most significant obstacles to successful AI adoption.

We're also seeing interesting developments like Microsoft's recent announcement of Fabric Data Agents. This offering is designed to simplify data management and integration across organizations, making it easier to harness data's power for AI applications. By automating data workflows and providing seamless access to diverse data sources, Fabric Data Agents help organizations build solid data foundations that support effective AI adoption.

The trend is clear: organizations that prioritize data management and governance will be far better positioned to leverage AI technologies effectively and drive meaningful business outcomes. As AI continues to evolve, the ability to manage and utilize data strategically will become an increasingly critical competitive differentiator.