Artificial intelligence is unlike previous technology innovations in one crucial way: it’s not simply another platform to be deployed, but a fundamental shift in the way data is used. As such, it requires a substantial rethinking as to the way the enterprise collects, processes, and ultimately deploys data to achieve business and operational objectives.

So while it may be tempting to push AI into legacy environments as quickly as possible, a wiser course of action would be to adopt a more careful, thoughtful approach. One thing to keep in mind is that AI is only as good as the data it can access, so shoring up both infrastructure and data management and preparation processes will play a substantial role in the success or failure of future AI-driven initiatives.

 

 

Quality and quantity

According to Open Data Science, the need to foster vast amounts of high-quality data is paramount for AI to deliver successful outcomes. In order to deliver valuable insights and enable intelligent algorithms to continuously learn, AI must connect with the right data from the start. Not only should organizations develop sources of high-quality data before investing in AI, but they should also reorient their entire cultures so that everyone from data scientists to line-of-business knowledge workers understand the data needs of AI and how results can be influenced by the type and quality of data being fed into the system.

In this way, AI is not merely a technological development but a cultural shift within the organization. By taking on many of the rote, repetitive tasks that tend to slow down processes, AI changes the nature of human labor to encompass more creative, strategic endeavors – ultimately increasing the value of data, systems, and people to the overall business model. In order to achieve this, however, AI should be deployed strategically, not haphazardly.

Before you invest in AI, then, tech consultancy New Line Info recommends a thorough analysis of all processes to see where intelligence can make the biggest impact. Part of this review should include the myriad ways in which AI may require new methods of data reporting and the development of all-new frameworks for effective modeling and forecasting. The goal here is not to produce sporadic gains or one-off initiatives, but to foster a more holistic transformation of data operations and user experiences.

By its very nature, this transformation will be evolutionary, not revolutionary. There is no hard line between today’s enterprise and a futuristic intelligent one, so each organization will have to cut its own path through the woods. On Inside Big Data recently, Provectus solution architect Rinat Gareev identified seven steps to AI adoption, beginning with figuring out exactly what you hope to do with it. AI can be tailored to almost any environment and optimized for any task, so having a way to gauge its success is crucial at the outset.

Chart a course for AI

Furthermore, organizations should identify priority use cases and establish development roadmaps for each one based on technical feasibility, ROI, and other factors. Only then should you move on to a general foundation for broad implementation and rapid scale across the organization, not to someday complete this transformation but to perpetually build a more efficient and effective data ecosystem.

However, perhaps the most important thing to keep in mind about AI is that it is not a magic bullet for everything that ails the enterprise. As CIO Dive’s Roberto Torres pointed out recently, there is currently a gap between what’s possible and what’s expected of AI, and this disconnect is hurting implementation. Sometimes, the limitations lie within the AI itself, as people come to think that an algorithmic-based intelligence is capable of far greater feats than it can actually accomplish. But problems can also arise within support infrastructure, in the data prep, as mentioned above, or sometimes in simply applying a given AI model to the wrong process.

The fact is that the enterprise has taken only the very first steps on a long journey to a new cultural paradigm, and there will undoubtedly be many missteps, wrong turns, and about-faces along the way. So while it’s important to get your hands dirty with AI sooner rather than later, you also need to pause a moment and figure out what you need to do to prepare for this change, and what you hope to get out of it.

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