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Challenges of AI adoption in an Enterprise

By CIOReview | Tuesday, April 2, 2019

It is evident that the enterprise leaders are taking a serious interest in understanding the adoption of Artificial intelligence (AI) and Machine learning (ML) because these can provide better ROI (Return on Investment). But, the adoption of technology has been slower than expected. Yes, some famous brands across industries are leveraging it to impel significant revenue. The reasons for these circumstances are the challenges that enterprises are faced with the AI adoption curve. Here are the points to understand the challenges and the options an enterprise can follow to get past them.

Investing in AI/ML would be a big decision for most enterprises. Moreover, there is an expectation of seeing a huge ROI from it within 6 to 8 months. It is significant to choose the right business use case to optimise with AI/Ml to achieve. Many enterprises make mistakes of leveraging the technologies at the initial stage. Hence, selecting a small first project is a wise idea. ROI can showcase only if project acts as a crucial part of the core business. 

Check out: Top Artificial Intelligence Companies

It is evident that AI/Ml solutions cannot create without data. Though, while enterprises do have huge volumes of data, it falls short in term of other characteristics. The challenges include: Data is collected in vast range across different business functions (collected with different formats and stored in the separate database). This is reasonable, but the problem is with the absence of a single unified repository from the data can be accessed. Next is missing or incomplete data, which means information available for all parameters in some cases, missing specific parameters in some other cases with the same data set. This condition may result in faulty learning that can lead to a failed solution.

To solve, AI/ML solutions should explicitly be designed for analytics, decision making, and predictive maintenance, which require clean and complete datasets for active learning. Hence, it is essential to map out entire data needed to generate a solution for the business use case.