Last year, Multiverse grew our revenue per employee by 37% as a direct result of our strategic investment in AI and a parallel transformation of our company culture to enable it.
As we learned, successful AI implementation doesn't start with models—it starts with data. For any organisation aiming to harness the full power of AI, establishing the right data foundations and culture is paramount. AI is only as intelligent as the data it is trained on.
Here are three core pillars for building a robust data foundation and culture ready for the AI transformation:
The agility vs. scalability challenge
One of the biggest data challenges in an AI transformation is finding the right balance between agility and scalability. Since AI is a rapidly evolving technology, moving fast often means experimenting quickly, even if initial methods aren't scalable.
- Embrace agility for experimentation: A successful AI strategy involves enabling experimentation and allowing teams to try new things without immediately requiring a massive, fully developed data pipeline. This might mean starting with simpler, non-scalable methods to quickly prove a concept.
- Scale for sustainability: Once a concept is proven, the key is to transition to scalable solutions. An effective strategy involves doing both—experimenting quickly and building scalable structures—by knowing when to do one and when to do the other.
Building a data-first culture
Formal training is essential for building data literacy. However, training alone is not enough; success hinges on integrating data into the very DNA of the organisation.
- Consistency is key: A strong data culture means data is part of the day-to-day language and ways of working. Key metrics and definitions must be clear and consistently used across all settings, including meetings and documents.
- Empowering the whole organisation: Effective AI implementation requires giving data access to everyone, not just data scientists, and teaching them how to use it. Upskilling the entire organisation ensures the best possible use is made of the data.
- The consumption shift: As companies embrace AI, the way people and machines consume data changes. While dashboards were once common, organisations now increasingly need data formats that machines can easily read, such as APIs. Consistency in definitions is critical for trusting AI to correctly interpret the data and provide answers in natural language.
- Security and Compliance: The foundation of a trustworthy data strategy is ensuring that data is handled securely and responsibly. We demonstrate this commitment to our clients and apprentices, ensuring their sensitive information is handled with the highest level of care. Multiverse has achieved SOC 2 Type 1 certification, a result of third-party audit from the American Institute of Certified Public Accountants, confirming our internal controls exceed the necessary, rigorous standards.
Data as your competitive advantage
The most advanced AI applications are widely available to virtually everyone. What truly differentiates a company and drives value is its proprietary data, and what they build with it.
- Unlock internal value: Well-structured, proprietary data can unlock powerful AI solutions. For example, taking unstructured data, structuring it into a knowledge graph, and sharing it can provide valuable insights for customers.
- Move ahead of the pack: If a company wants to build something truly differentiated and make a real difference, it must connect AI to its unique internal data. The sooner an organisation commits to structuring and leveraging its data, the sooner it will gain a competitive edge.
By focusing on structuring data, fostering a company-wide data culture, and strategically balancing speed and sustainability, any organisation can build the solid foundations necessary to deliver powerful and effective AI transformation.
