Three Keys to Successful AI Adoption

Source:-devops.com

Over the past several years, we have begun to see the emergence of artificial intelligence (AI) in businesses. According to a study for the AI Index 2019 Annual Report, more than half of respondents report their companies are using AI in at least one function or business unit. Thirty percent report they have AI embedded across multiple areas of their business. As businesses continue to develop their understanding of what is possible with AI, we can expect to see a continued increase in AI adoption.

People used to say “software is eating the world.” Moving forward, people will be saying “AI is eating the software world.” You can expect to see standard enterprise and cloud software programs become indistinguishable from AI. Currently, the success rates of AI projects fall well short of what we see in enterprise software success rates. A 2019 study by Pactera found that 85% of AI projects fail to deliver on their intended promises. As software and AI bind together, expect to see a dramatic increase in the success rate of these AI projects. AI adoption will begin to align more with traditional success rates associated with enterprise software as a category.

As software and AI begin to merge together and enterprises look to invest in AI, there are a few keys to success.

Increase Your Organizational IQ with AI

Human factors are public enemy number one as it relates to AI adoption. For companies wanting to invest in AI, it starts with education. Companies need to become better educated on the options available to them through AI. We need to move away from viewing AI as a black box where magic happens. Through better education, the average business consumer will be able to understand the various disciplines of AI, such as natural language processing, vision, unsupervised learning and machine learning. As companies increase their IQ relative to AI, businesses will begin to understand the difficulty of AI projects. While this will create short term challenges, it ultimately will help the success of AI adoption in the future.

Commit to a Journey with Frequent Recalibration

Enterprises must move beyond merely being educated about AI in order to remain competitive. Business leaders will have to step past the exploratory and proof of concept phases and begin delivering real value to the business. This will mean the continuous delivery of intelligent solutions across several areas of the business. AI is not like traditional software projects where you measure twice and cut once, deploy and then observe for several months. It is a continuous journey of discovery that will require consistent adjustments. AI is different and requires you to make changes to existing predictive models based on your real-time findings.

If the Insight Is Not Actionable, It Doesn’t Matter

We have already seen AI become more and more embedded into analytics solutions and this is only going to continue. The convergence of AI and analytics is crucial to the success of AI projects. Over the past 20 years, analytical reporting has been provided to all users. With the addition of AI, machine learning models become analysis engines providing prescriptive actions to business users rather than the business uses having to interpret the analytics themselves. Again, this is a huge component in the success of AI adoption. It will not be enough for businesses to gain insights from their AI investment, success will be measured on what they do with those insights. Leveraging the insights from AI starts by providing actions directly to the users that can act on them within their existing workflow.

The power of AI is still largely untapped. It is already creating a massive shift in how we work and that is only the beginning. For those who have worked in IT and software, you need to adopt a whole new approach to AI projects. In the AI world, data is the driving force rather than a necessary evil. The emerging technology will require professionals to expand their skill sets beyond the traditional technical skills and into the disciplines of math and statistics. By doing so, we can expect to see a massive increase in successful AI adoption.