O’Reilly: the adoption of AI across enterprise

By William Smith
O’Reilly analysts have revealed how, and how many, respondents are ramping up AI projects─and AI’s scope in their future...

This year, O’Reilly, “the premier source for insight-driven learning on technology and business”, announced the results of a survey, AI Adoption in the Enterprise 2020. The survey is entirely based on interviews and responses with 1,388 respondents, coming from 25 industries─70% of which are from technology roles, while the remaining 30% are data scientists, data engineers, AIOps engineers, or their managers. Within the report, O’Reilly has found that there has been a growing trend in the evaluation, implementation, and outcomes of AI enterprise adoption across the past year. 

Here, we take a look at some of the insights within the study. 

  1. This year, AI is being adopted evenly across enterprises, with Research & Development (R&D) taking the lead

Within O’Reilly’s survey, there are lots of little gems, but this one, in particular, captures the attention; enterprises are currently looking to stabilise their adoption patterns for AI across a number of functional areas. In fact, the report suggests that nine to twelve functional areas included in the survey already have over 10% adoption. 

AI is a transformative advancement in the tech sector, and we already know that it has the potential to redefine enterprises and organisations as we know them. AI could or even should make companies more customer-driven, adaptive, and capable of generating and sharing intelligence faster than we’ve ever seen before.   

The framework needed to lead a company through its transformation must be able to both create knowledge and remain focused on the customer or client, to ensure that the business thrives both internally and externally. It must, in other words, “provide an understanding of the entire customer journey.” 

  1. Supervised machine learning (ML) algorithms are the most popular ML method in enterprises today

It turns out, 73% of enterprises that feature advanced expertise in AI are using machine learning techniques a lot. They’re using ML to “interpret, classify, and analyse the large data sets they’ve accumulated over years of operations.” Simultaneously, O’Reilly found that enterprises who are specifically evaluating the power of AI are fond of using deep learning in their pilots. 

  1. Unexpected outcomes and predictions are the most significant risks for enterprises using AI today 

For 53% of the enterprises that are considered “advanced” in their use of AI, right now, “the greatest risk when building or deploying ML models is unexpected outcomes and predictions.” Fortunately, the more experienced you are with a system, the more likely you are to work on making processes and methods increasingly transparent so that you can identify any potential issues or negative facets.

Share

Featured Articles

Data backup gives way to data protection and cybersecurity

Data backup is no longer just about operational fallback, new research has revealed. Instead, businesses need to consider it a new kind of cybersecurity

Rich investors drawn to digital assets despite crypto crash

The world’s wealthiest people are sold on the concept of digital assets, as a new survey shows they are being used to construct more resilient portfolios

Five minutes with: Katie Nykanen, Group CTO at QA

Katie Nykanen, CTO at QA, on being a role model for girls and young women and her hopes for the future

Big business bets on real-time data and event-streaming tech

Data & Data Analytics

Mind your language: Is NLP a natural fit for the Metaverse?

AI & Machine Learning

Cyberattacks make a big difference to manufacturing profits

Cloud & Cybersecurity