The AI Race Is Not Decided by the Best Model, but by the Best Team

The AI Race Is Not Decided by the Best Model, but by the Best Team 

The best AI model does not necessarily determine who leads the way. Powerful AI technology is becoming increasingly widely available to organizations. The difference lies in the execution. A model must not only be able to perform a task but also function reliably with the data, systems, processes and people an organization already has. Only in daily practice does it become apparent whether AI is more than a promising experiment.

Two years after the breakthrough of generative AI, experimenting is no longer the biggest challenge for many organizations. Setting up a pilot or gaining access to a powerful model is simple. It is much more complex to reliably, securely and scalably integrate an AI application into daily business operations. This requires teams that understand what AI can do and where and how the technology can add value within an organization.

From experiment to practice

An AI demo can be impressive, but it is not yet an implementation. Companies already work with existing applications, data sources, security rules and processes. Moreover, employees have their own routines and responsibilities. AI must function within that reality. Models must be linked to the right systems and data, and the outcomes must be reliable enough for the process they support or take over. Security, governance and costs must also be taken into account from the start. Ultimately, the investments must outweigh the value the application delivers.

That is currently still a major hurdle for enterprise AI. Organizations can experiment relatively easily, but struggle more to bring AI into production at scale. To do so, models, data, infrastructure and business processes must come together in an application that works reliably. That 'last mile' requires not only technical expertise but also various disciplines that collaborate effectively.

Read the full article here. [Opens in Dutch]

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