Artificial intelligence is no longer a mere tech buzzword but a driving force for many businesses. There are little to no doubts about AI’s unprecedented potential to provide value across verticals. Conversational systems, process automation tools, recognition and personalization systems, and predictive analytics are all the nascent use cases of AI actively probed right now.

Although AI is still considered to be a relatively immature technology, the business world is beginning to massively invest in custom software powered by AI. The recent study by Cognilytica called ‘Global AI Adoption Trends & Forecasts 2020’ indicates that 91% of the surveyed companies are planning to adopt AI in the next five years. According to the Global 2019 AI Survey by McKinsey, 63% of respondents report revenue increases due to AI adoption, while 44% point out cost savings driven by AI.

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The overall vector is clear – AI’s global mass adoption has already begun. Let’s look at the common hurdles of AI implementation and the steps that companies should take to mitigate the risks and successfully integrate AI in their products or operations.

AI implementation challenges and pitfalls

As with any other emerging technology, roadblocks on the way to its adoption are inevitable. Novelty often creates uncertainty, and hype around the technology may lead to impulsive, poorly strategized decisions. Based on the stories of other companies that had the bravery to implement AI in its early stages, we have the luxury to study their experience and avoid their mistakes. Although every organization has its own very specific characteristics that impact the implementation strategy, there are certain recurring issues that have been observed across many companies in a wide range of industries.

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