Explore the crucial elements for adopting and implementing Artificial intelligence (AI) and machine learning (ML)

Artificial intelligence (AI) and machine learning (ML) are becoming an essential component of modern business strategy. More enterprises across almost all sectors and industries are transforming their businesses with AI/ML. The global AI market value is estimated to reach $267 billion by 2027, with the technology contributing $15.7 trillion to the global economy by 2030. Below are the top 3 take away from our customer's use case in this e-book ‘Keys to Successful Innovation through Artificial Intelligence’.

Boosting productivity by making prompt and better decisions to enhance customer experiences and satisfaction

Prior to the pandemic, the BMW Group has been efficiently deploying AI/ML, but the pandemic across the globe has created a heightened sense of urgency to take all its AI capabilities remotely. “A comprehensive application of AI/ML and allows us to gain insight from all the data we had,” Josef Viehhauser, platform lead and enterprise analytics at the BMW Group explains.

"Collaborating with a strong implementation partner is also vital as it may broaden your understanding of the possible, and create a quicker return on investment,” says Mark Maenner, BMW Group's head of data transformation.

Adopting AI/ML is never the goal; AI exists to support the overall business objective developed by considering the larger picture goal. Maenner further points out that AI and ML play an integral role in the digital transformation of the BMW Group and help the Group improve their product experience for customers, the way they develop their products, or even understanding processes.

Rethinking silos and fragmented legacy systems, adding capabilities

AI/ML are immensely valuable not only in the healthcare industry, but also paves the way to time and productivity savings for human resources management software and services. The HR technology company, according to Jack Berkowitz, chief data officer at ADP Inc., had already moved its people analytics and workforce benchmarks to the cloud, with the pandemic as a force multiplier for its use of AI/ML.

By applying AI/ML, all relevant information clients needed for their Paycheck Protection Program applications was available and accessible in the cloud within 20 days. “We were able to build new capabilities that we never thought possible,” Berkowitz remarks.

“We already had use cases running on our supply chains, most of which are quite simple events,” he continues. “The pandemic brought an understanding of the influence of catastrophes on the supply chain in specific regions. We were also able to boost our existing learnings to move us forward under the influence of catastrophes on the supply chain in specific regions and get better in those areas.”

Implementing a successful strategy for AI/ML vary depending on each business

According to Kirk Borne, Chief Science Officer at Leesburg, the chances of a successful AI/ML-powered strategy greatly improve if business leaders remember that data is a science, and that its implementation requires an interactive process of testing, validating, and refining the hypothesis, followed by testing and refining the next iteration.

Discover more important elements to successfully innovate through Artificial Intelligence here.