To kickstart AI journeys, technopreneurs need to think beyond technology and infrastructure and focus first on developing a coherent data strategy. (We should think of a “technopreneur” as being a state of mind, and not necessarily a founder of a startup, so can equally apply to a VC or the CEO of a large enterprise.)

This article builds on our previous articles How to leverage ChatGPT in your startup and Generative AI and building competitive advantage with the focus on being data-ready.

During the span of 2022 and the early months of 2023, the world witnessed a remarkable surge in the deployment of generative AI by tech innovators. The breakneck pace at which generative AI technology is evolving, accompanied by the emergence of new use cases has left investors and business leaders scrambling to understand the intricate landscape of generative AI ecosystem. Forrester’s latest Data and Analytics Survey found that three in four enterprises have embarked on the journey of using AI to transform their business.

To kickstart AI journeys technopreneurs need to think beyond technology and infrastructure and focus first on developing a coherent data strategy. To accelerate transformation with generative AI we expect applications built from fine-tuning foundational models to stand out and offer the greatest potential for enduring value creation.

Fine-tuning foundation models involves leveraging additional relevant data and/or adjusting parameters, to deliver outputs for a particular use case. Hence, companies that use proprietary data, enhanced with in-depth knowledge of an industry or customer needs, will be able to build competitive advantage in its industry, as described in our article How to leverage ChatGPT in your startup

The AI-ready data strategy is critical for building capabilities to fine-tune foundational models. Technopreneurs need to examine and understand how data is sourced, produced and collected from daily business operations, how data is used and engineered for AI applications, and how data governance ensures trust.

Develop your data strategy to be AI ready. Traditional data domains are designed for business intelligence and reporting are narrow. AI models require a greater amount of information than traditionally captured to execute and deliver predictions and actions. More metadata is needed to describe an entity, behavior or outcomes. Data and AI scientists rely on a variety of data types beyond structured data such as documents, images, video and audio to build AI capabilities. Implement data strategy that enables producing and sourcing of metadata: seek out internal and external data that is relevant, appropriate, of high quality and permissible to use.

Forge organisational structure that ensures collaboration and alignment across departments. Generative AI will be a cross-functional value driver that will require collaboration across teams to collect data. Hence organising how your teams work to facilitate, not hinder, the flow of data in the organisation will be critical for success. Aligned teams further enable a cohesive way to evaluate what data will be of value across the business value-chain, vendors and suppliers.

Finally, AI governance is important to reduce risk, improve trust, and identify or implement best practices across your business. Traditional data strategies and analytics is developed, deployed and forgotten as the outputs don’t vary over time. But AI models learn and evolve in production applications. Change in AI output and outcomes is constant, and without strong AI governance, undesired outcomes can go unchecked. Define AI governance that extends your data governance, so the policies and standards are federated and contextual to the AI experience rather than the data source.

AI is hard and your organisation needs a solid foundation of data excellence through AI-ready data strategy to form the foundation of any AI program.