Five Ways To Drive ROI With AI
datanami, September 24th, 2020
AI is often touted as the way of the future for enterprises in all industries - but ensuring that the return on investment (ROI) from an AI implementation actually comes to fruition can often be a trickier thing

"A group of AI-oriented companies - Appen, Cognizant, Cortex, Dataiku, DataRobot (which recently commissioned its own ROI study), and Deloitte - partnered to commission a study from ESI ThoughtLab that benchmarked 1,200 organizations to identify the factors that drive ROI from AI. The result: a roadmap for success in enterprise AI.

In addition to data on AI investments and returns, the cross-industry survey collected detailed data on how and why the 1,200 organizations had implemented AI. Using that data, combined with an AI maturity framework, input from an advisory board of AI experts, and in-depth interviews with AI leaders, ESI ThoughtLab arrived at a series of conclusions about the current state of AI in business..."

Pandemic Accelerates Machine Learning
InformationWeek, September 21st, 2020
COVID-19 has had profound effects on the global economy. While some industries took a hard hit, others found new opportunities -- such as machine learning

"The challenge of COVID-19 has accelerated a number of existing technology and business trends creating significant opportunities for companies that offer or deploy artificial intelligence. But it has also presented new, never-before-experienced problems for them.

Over the past six months, society has undergone a 101 course in statistics. People for whom datasets used to be a foreign language now look with interest at the latest statistical results to see whether the curve is indeed being flattened. They are following the variables and attempts to identify the key parameters and extrapolating from the information available to make predictions about prospects for the future. In short, large sections of society have become familiar with graphs, charts, databases, and even the basic principles of machine learning (ML)..."

Robotic process automation (RPA) revenues will reach $1.9 billion in 2021, up 19.5% from 2020, according to Gartner projections

The growth rate signals sustained interest in the technology, with 2020 revenue projections up 11.9% from 2019, when RPA revenues reached $1.4 billion.

Come 2024, large organizations will "triple the capacity of their existing RPA portfolios," Gartner said in its report. Most additional spend will come from enterprises turning to the vendor market for add-on capacity.

As the technology evolves and adoption expands, average RPA prices will drop 10% to 15% in 2020. Costs will fall another 5% to 10% annually in 2021 and 2022, creating what the firm says is "strong downward pricing pressure."

Top 10 Machine Learning Companies 2020
Datamation, September 22nd, 2020
These top machine learning companies leverage AI, automation and advanced analytics to provide the most sophisticated machine learning platforms available today

"Machine learning companies have emerged as key players in enterprise IT over the few years. Why? Enterprise leaders have realized the value of having software that is capable of learning on its own without human intervention. Today, machine learning (ML) capabilities are baked into many different kinds of enterprise software. This type of artificial intelligence (AI) powers everything from recommendation engines to medical diagnostic software to cybersecurity tools to self-driving cars..."

Why AI Projects Fail
ITProPortal, September 25th, 2020
Five ways a business can succeed with their AI projects.

"Seventy percent of companies have reported minimal or no impact from Artificial Intelligence projects, according to a survey by MIT and Boston Consulting Group.

There are a number of reasons for this including a lack of focus on cultural change and training within an organization as it adapts to new working practices, but the most important factor is poor data. This encompasses everything from inadequate data architecture and discovery, to modelling, quality, and governance..."

See all Archived IT - AI articles See all articles from this issue