Enterprise AI (and today’s data culture at large) has a lot of associated buzzwords and hype, but what does it actually mean for an organization in practice? Is it as grandiose as it is made out to be? Will teams fail without exceptional ambition and talent or unlimited resources?
Believe it or not, the answer is no. In order to be successful, companies need to integrate data and AI organically throughout the organization, systematizing its use at scale to unlock individual creativity. By making the use of AI an everyday behavior (elevating the people using it with the ability to make better decisions), organizations will be equipped to accomplish the complete spectrum of AI use cases, from the mundane to the moonshots.
Whether an organization is working on optimizing processes (i.e., automating monthly business reporting) or more advanced machine learning applications (i.e., complex modeling that could, if executed with the right approach, generate millions in incremental revenue), AI can’t be put on a pedestal as this flashy solution that will alter a business’s trajectory overnight. It’s not a magical fix that will change everything about a business. Rather, it’s a powerful tool that can optimize every single process, but it has to be embedded into the organization’s operating model in order to actually make an impact.
At Dataiku, we’ve seen many businesses struggle to jumpstart their enterprise AI journey over the years and we’ve also seen some that have thrived. The companies that succeed are the ones that go beyond leveraging AI for one specific project or ad-hoc use case and instead focus on scaling it out to a level that will sustain the business in the future.
Demystifying enterprise AI
As mentioned, data and AI are mostly about optimizing each individual business process that a company does, in order to make them more efficient and, ultimately, create more value. There are hundreds (or even thousands) of business processes going on at any one time, so it’s safe to say it’s not a turnkey process, but rather one that takes time and requires the careful alignment of people, processes, and technology.