Obstacles to Enterprise AI and How to Overcome Them
Obstacles to enterprise AI adoption include lack of internal skills and poor communications between data scientists, business staff, and AI experts.
Enterprise AI adoption obstacles can be daunting. According to an IBM survey, while 95% of companies surveyed “believe AI is the key to competitive advantage,” only 5% have “extensively implemented AI.” This large disconnect between the desire to stay on the cutting edge, and the actual adoption of AI technologies, necessitates a different approach to integrating AI into different enterprises.
The obstacles standing in the way of more widespread adoption of AI, and how to overcome them, was the topic of a talk at the recent virtual ODSC East conference by Mark Weber, Applied Research Scientist at the MIT-IBM Watson AI Lab.
The first, perhaps most obvious, obstacle is the need for data science and AI talent. And, as Weber points out, it’s not just about finding individuals with the right skills but building a team with the right “diversity of skills” to make a company’s AI efforts a success.
Once a company has recruited one or more highly-skilled data science teams, what happens next? One obstacle, which was discussed in another talk at ODSC East by Daniel Gray, VP Solutions Engineering at AtScale, is the level of stigma between existing, traditional business intelligence (BI) teams and data science / AI teams. There may be limited communication between them, due to cultural differences or even physical separation for large companies with offices in different locations and time zones.
A further obstacle to AI adoption is what appears to be a cognitive dissonance where company executives have a strong desire to build up their AI capabilities, but at the same time, they see more and more examples in the media of where AI has failed or led to unsatisfactory results.
Along these lines, Weber, from IBM, distinguishes between “Narrow AI” and “Broad AI.” He illustrates a rather benign, but instructive, example of an AI algorithm that has been trained to analyze images and identify objects, such as chairs. The algorithm works great when chairs are in their regular, upright form, but what happens when a chair has been overturned and is lying on the floor? This same algorithm no longer identifies such objects as chairs with high accuracy.
This is an example of “narrow AI.” In order for AI to be more successful and more widely trusted by organizations, AI algorithms need to become more “broad.” By this, Weber means AI that is more “robust, transferable, explainable, scalable.”
Weber points out that companies with organizational learning are much better “able to achieve significant financial benefit with AI.” This organizational learning can include workshops and “technical office hours.
