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Remove the Barriers from AI Adoption

November 12, 2021
· 3 min read

Only one in ten companies reports significant financial benefits from implementing AI, according to a recent study by BCG GAMMA, the BCG Henderson Institute, and the MIT Sloan Management Review. How can that be? 

Few businesses now benefit from AI because they have not yet fully implemented it throughout their organizations. In a 2020 Capgemini Research study, only a meager 13% of businesses had successfully deployed use cases in production and continued to scale the use cases throughout multiple business teams. Yet, PwC Research estimates that AI adoption will produce nearly $16 trillion in business growth by the year 2030.  

What critical barriers prevent companies from delivering AI solutions and realizing the tremendous potential returns it can deliver? The answer lies in three critical areas: people, processes, and policy faults. 

People Shortages 

O’Reilly’s latest report, AI Adoption in the Enterprise 2021, declares that “the most significant barrier to AI adoption is the lack of skilled people and the difficulty of hiring.” Of the organizations surveyed, 52 percent were seeking machine learning modelers and data scientists, 49 percent needed employees with a better understanding of business use cases, and 42 percent lacked people with data engineering skills. 

Process Deficiencies 

“AI deployment is still largely unknown territory, dominated by homegrown ad hoc processes,” according to O’Reilly. Although teams are starting to adopt third-party tools for deployment, 46 percent of survey respondents are not using a market-tested tool for deploying AI. Instead, companies have built their own tools and pipelines for deployment and monitoring. That’s a risky business, as constructing AI models from scratch requires countless hours of time and effort, and the results may incorporate biased data and inappropriate algorithms.  

Policy Faults

AI adoption often lacks enthusiastic sponsors within an organization. Many managers are half-hearted or ambivalent about its benefits. Even if they approve of the idea, they are uncertain about how to implement it. As a result, they put off AI initiatives until they can hire a host of experienced and expensive data scientists. The longer they wait, of course, the more time their competitors have to gain a strategic advantage.  

Invest in the Team You Already Have 

Recently, in an interview to VentureBeat, DataRobot CEO Dan Wright said: “What we’re doing now is allowing our platform to be used by people who are not data scientists, as well as [by] data scientists, to create business insights and make better decisions on an ongoing basis.”  

In order to democratize AI within your company, you need to involve everybody. Your team already understands your business and your data. They want to work with AI and automation and are situated perfectly to improve your data science operations. Their success, however, requires the right tools and training. DataRobot automation and built-in best practices for machine learning efficiency can provide them with these skills. They can even attend DataRobot University, which provides the ideal combination of streamlined overview courses and specialized training needed to implement your company’s AI models. 

Don’t Reinvent the Wheel: Adopt Tested AI Methods 

It’s not necessary to create your own tools. In 2019, Beacon Street Services needed new data models to enable its marketing team to run more targeted and effective campaigns. Instead of spending hours creating its own models, the company decided to implement DataRobot’s enterprise AI platform

The results clearly justified this decision. Beacon Street experienced: 

• A 30 to 35 times ROI in revenue gains and cost decreases 

• A ten percent increase in sales and almost $15 million in directly attributable annual sales 

• A reduction of model development time from six weeks to just one 

Follow a Clear Path to AI Implementation 

What makes a company a high AI performer? According to McKinsey’s Global Survey: The State of AI in 2020, 55 percent had created a road map linking AI initiatives to business value across their organization, 43 percent had active AI partnerships with other companies and universities, and 60 percent had senior managers who were strongly committed to the company’s AI approach.  

In other words, by building corporate cultures that openly accept and encourage AI initiatives, these organizations have developed the strategies needed to achieve transformational growth and maximize success. 

Your company can do that, too. 

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About the author

Value-Driven AI

DataRobot is the leader in Value-Driven AI – a unique and collaborative approach to AI that combines our open AI platform, deep AI expertise and broad use-case implementation to improve how customers run, grow and optimize their business. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with your existing investments in data, applications and business processes, and can be deployed on-prem or in any cloud environment. DataRobot and our partners have a decade of world-class AI expertise collaborating with AI teams (data scientists, business and IT), removing common blockers and developing best practices to successfully navigate projects that result in faster time to value, increased revenue and reduced costs. DataRobot customers include 40% of the Fortune 50, 8 of top 10 US banks, 7 of the top 10 pharmaceutical companies, 7 of the top 10 telcos, 5 of top 10 global manufacturers.

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