On June 8, 2022, Accenture presented The Art of A.I. Maturity report. The report revealed that only 12 percent of companies surveyed use A.I. at maturity level, achieving superior growth and business transformation. 63 percent of companies using A.I. are only scratching the surface.
While A.I. can provide significant benefits for Enterprise organizations across any sector, the potential of the technology is still far from reaching its peak. While multiple problems can trip up your Enterprise AI adoption, there are four key challenges that companies will face as they move into 2023. Understanding these challenges can help organizations build a road map and their A.I. strategies. They are the difference between mastering A.I. and reaping the benefits or merely playing with a tech novelty.
Creating a Business-Driven A.I. Culture
Companies excelling in machine learning and solving real business problems with A.I. will likely have a strong innovation culture across all levels. Currently, hundreds of thousands of companies are experimenting in some way or another with A.I. Within these companies, teams of data scientists or data engineers are in charge of leading the way forward by developing machine learning models that benefit the company. However, these teams are often isolated, develop models in a very ad-hoc, almost artisanal fashion and are disconnected from decision-makers, compartmentalized, and have little support from C-suite executives or other departments.
Companies that start A.I. projects as experimental and later pitch them to their organization have higher failure rates than those with initial approval for production. Data teams should get input from top decision-makers on the challenges the company faces and build machine learning models that address these real-world business problems.
Data scientists working in organizations that do not have a strong A.I. culture often conflict with the “old ways of doing things.” Blackbox A.I. projects may not get buy-in from executives because they fail to understand how the machine learning model arrived at its results.
Skilling all workers, from CEOs to IT, marketing, sales, and office workers, breaks the language and technical barrier and creates support and understanding. Data scientists within an organization can not work alone. They need to collaborate with other departments.
Continue reading: https://insights.dice.com/2022/08/19/4-key-challenges-to-mastering-a-i-heading-into-2023/
While A.I. can provide significant benefits for Enterprise organizations across any sector, the potential of the technology is still far from reaching its peak. While multiple problems can trip up your Enterprise AI adoption, there are four key challenges that companies will face as they move into 2023. Understanding these challenges can help organizations build a road map and their A.I. strategies. They are the difference between mastering A.I. and reaping the benefits or merely playing with a tech novelty.
Creating a Business-Driven A.I. Culture
Companies excelling in machine learning and solving real business problems with A.I. will likely have a strong innovation culture across all levels. Currently, hundreds of thousands of companies are experimenting in some way or another with A.I. Within these companies, teams of data scientists or data engineers are in charge of leading the way forward by developing machine learning models that benefit the company. However, these teams are often isolated, develop models in a very ad-hoc, almost artisanal fashion and are disconnected from decision-makers, compartmentalized, and have little support from C-suite executives or other departments.
Companies that start A.I. projects as experimental and later pitch them to their organization have higher failure rates than those with initial approval for production. Data teams should get input from top decision-makers on the challenges the company faces and build machine learning models that address these real-world business problems.
Data scientists working in organizations that do not have a strong A.I. culture often conflict with the “old ways of doing things.” Blackbox A.I. projects may not get buy-in from executives because they fail to understand how the machine learning model arrived at its results.
Skilling all workers, from CEOs to IT, marketing, sales, and office workers, breaks the language and technical barrier and creates support and understanding. Data scientists within an organization can not work alone. They need to collaborate with other departments.
Continue reading: https://insights.dice.com/2022/08/19/4-key-challenges-to-mastering-a-i-heading-into-2023/
