It’s an understatement to note that incorporating artificial intelligence (AI) into products, services, and processes brings numerous enterprise benefits. But, as VentureBeat’s John Koetsier recently observed, “…building smart systems using machine learning is not like buying an accounting package or an enterprise resource planning system.”
Koetsier’s piece underscored that there are some common mistakes companies make when adopting AI. Drawing on a conversation with Larry Pizette, head of data science at Amazon’s Machine Learning Solutions Lab, he outlined many of these, including:
- Excluding data stakeholders. According to Pizette, data scientists and similar roles are frequently left out of AI deployment conversations. Whether intentional or unintentional, this exclusion can spell trouble down the road, leading to issues with data quality and integration, to name just a few. As Koetsier puts it, “Business owners can have a vision, but without the data to support that, any machine learning projects will be starved for input. So having your data analysts, scientists, and administrators present is essential.”
- Too much forward-looking thinking. It’s important to have an AI vision and think ahead to a certain extent, but building out a detailed multi-year strategy is a mistake and can divert resources from actually starting the project. According to Koetsier, “AI and machine learning systems are built to grow. It’s almost impossible to know upfront where that might take you over time, so spending weeks and months on long-term planning is overkill. Possibly worse, it often leads to analysis paralysis.”
- Lack of training. It’s not essential that non-technical roles know the ins and outs of algorithms and machine learning. However, in order to assess AI’s required investment and potential ROI, business leaders do need sufficient training. Koetsier quotes Pizette, “If your model is trained on some assumptions and now something changes in the future, you have to retrain your model. So training the business folks so they understand what they’re getting into… is super important.”
With the arrival of 5G, AI development is poised to drive further innovation—check out this recent APEX of Innovation post for emerging trends on the horizon. In this environment, leaders who are aware of the mistakes outlined above stand the best chance of capitalizing on AI’s massive enterprise potential.