Technologists and business leaders don't see eye to eye on artificial intelligence

By Joe McKendrick for Service Oriented | July 30, 2020 -- 16:21 GMT (09:21 PDT) | Topic: Artificial Intelligence Business leaders and technologists see artificial intelligence differently -- which is not a surprise. However, they have different perspectives on the progress on AI projects, and what it takes to scale AI to meet enterprise challenges. For example, technologists are twice as likely to see lack of viable data as an issue.  Photo: Joe McKendrick "This could be attributed to business leaders misunderstanding that the data on hand is often not the data needed when it comes to deploying AI at scale," the authors of a recent survey report released by Appen, surmise. .The survey covered 374 companies. When asked about the issues encountered with AI, technologists were more likely than business leaders to cite skills issues (24% versus 21%) and lack of data (17% versus 9%). Business leaders, on the other hand, were more inclined to see budgetary issues (18% versus 13% of technologists) and the need for more management buy-in (17% versus 12%) as holding back AI initiatives,  Interestingly, technologists seem to be more aware of progress with AI, with 40% having conducted a global roll-out to their entire user bases, versus only 27% of executives. Both side agree on one important thing, however: more work needs to be done. When asked if their company was behind on adopting AI, respondents appeared to be nearly 50%-50%, regardless of if they were technologists or business leaders.  In addition, it is also clear that the Covid-19 crisis is not affecting this AI work, the survey, conducted in April and May, finds. More than two-thirds of respondents, 70%, do not expect any negative impact from the crisis on their AI strategies. One in five, 20%, report significant acceleration, while only nine percent companies say there have been significant delays to their AI strategy. There is also much more executive involvement in AI initiatives, the survey shows. In 2019, only 39% of executives "owned" AI initiatives -- a number that grew to 70% in this year's survey. With this increase in executive ownership comes money -- the number of organizations reporting AI budgets greater than $5 million also doubled. "With this level of executive involvement and increased budgets, ethics, governance, and risk management initiatives have become important topics for technologists building AI," the survey authors note. Global cloud providers gained significant traction as data science and machine learning tools from 2019, which may also be due to increased budget and executive oversight. Four times as many respondents this year over last year report using global cloud machine-learning providers. Leading the pack are Microsoft Azure (49%), Google Cloud (36%), IBM Watson (31%), AWS (25%), and Salesforce Einstein (17%). Each of these front runners saw double-digit adoption increases over the past year. There are also a number of languages used to build models. Python remains the most used language in both 2019 and 2020, SQL and R were the second and third most commonly used language in 2019. However, in 2020, Java, C/C++, and JavaScript also gained significant traction. Python, R, and SQL are often indicative of the pilot stage, while Java, C/C++, and JavaScript are more production stage languages.   Three out of four organizations report updating their AI models at least quarterly, signifying a focus on the model's life after deployment. C

Technologists and business leaders don't see eye to eye on artificial intelligence
By Joe McKendrick for Service Oriented | July 30, 2020 -- 16:21 GMT (09:21 PDT) | Topic: Artificial Intelligence Business leaders and technologists see artificial intelligence differently -- which is not a surprise. However, they have different perspectives on the progress on AI projects, and what it takes to scale AI to meet enterprise challenges. For example, technologists are twice as likely to see lack of viable data as an issue.  Photo: Joe McKendrick "This could be attributed to business leaders misunderstanding that the data on hand is often not the data needed when it comes to deploying AI at scale," the authors of a recent survey report released by Appen, surmise. .The survey covered 374 companies. When asked about the issues encountered with AI, technologists were more likely than business leaders to cite skills issues (24% versus 21%) and lack of data (17% versus 9%). Business leaders, on the other hand, were more inclined to see budgetary issues (18% versus 13% of technologists) and the need for more management buy-in (17% versus 12%) as holding back AI initiatives,  Interestingly, technologists seem to be more aware of progress with AI, with 40% having conducted a global roll-out to their entire user bases, versus only 27% of executives. Both side agree on one important thing, however: more work needs to be done. When asked if their company was behind on adopting AI, respondents appeared to be nearly 50%-50%, regardless of if they were technologists or business leaders.  In addition, it is also clear that the Covid-19 crisis is not affecting this AI work, the survey, conducted in April and May, finds. More than two-thirds of respondents, 70%, do not expect any negative impact from the crisis on their AI strategies. One in five, 20%, report significant acceleration, while only nine percent companies say there have been significant delays to their AI strategy. There is also much more executive involvement in AI initiatives, the survey shows. In 2019, only 39% of executives "owned" AI initiatives -- a number that grew to 70% in this year's survey. With this increase in executive ownership comes money -- the number of organizations reporting AI budgets greater than $5 million also doubled. "With this level of executive involvement and increased budgets, ethics, governance, and risk management initiatives have become important topics for technologists building AI," the survey authors note. Global cloud providers gained significant traction as data science and machine learning tools from 2019, which may also be due to increased budget and executive oversight. Four times as many respondents this year over last year report using global cloud machine-learning providers. Leading the pack are Microsoft Azure (49%), Google Cloud (36%), IBM Watson (31%), AWS (25%), and Salesforce Einstein (17%). Each of these front runners saw double-digit adoption increases over the past year. There are also a number of languages used to build models. Python remains the most used language in both 2019 and 2020, SQL and R were the second and third most commonly used language in 2019. However, in 2020, Java, C/C++, and JavaScript also gained significant traction. Python, R, and SQL are often indicative of the pilot stage, while Java, C/C++, and JavaScript are more production stage languages.   Three out of four organizations report updating their AI models at least quarterly, signifying a focus on the model's life after deployment. Companies are now reporting that they are updating their models more frequently.  Related Topics: Big Data Analytics Digital Transformation CXO Internet of Things Innovation Enterprise Software By Joe McKendrick for Service Oriented | July 30, 2020 -- 16:21 GMT (09:21 PDT) | Topic: Artificial Intelligence