HomeTraining ResourcesBlogDeveloper Relations Conference Evans Data Corp.Conference Presentations
Interviews 2016JobsDirectoryPost a Job
Legacy Systems and Poor Quality of Tools are Top Barriers to AI Adoption
Having to transition from existing legacy systems is the top barrier to incorporation of artificial intelligence or machine learning into organizations today, cited by 18% of developers actively working with AI or ML in Evans Data’s recently released AI, ML and Big Data Development Survey. However, the quality of existing tools was cited by almost the same number (17.4%). Other factors, such as budget or the cost of materials, regulatory or governance issues, and corporate policy restrictions were also cited by nearly as many AI developers as the top two barriers.
The June 2018 survey of active AI developers also showed that model selection is a particularly challenging aspect of AI or ML implementation along with optimizing for specific parameters and increasing algorithm accuracy. Data ingestion is the phase of AI-related development that proves most vexing to fully a third of AI developers while algorithm development is the top problem area for nearly a quarter.
“Legacy systems that are already in place and the current state of specialized tools are fairly expected issues to come up as software technology evolves to embrace artificial intelligence and machine learning,” said Janel Garvin, CEO of Evans Data Corp, “But what we also saw here was a close list of problems cited in addition to those two, and that close range is illustrative of a new but quickly maturing market.”
Additional insights from the worldwide survey of AI practitioners focus on AI in the large enterprise, hardware, parallelism, algorithms, and other focal areas crafted into data research on questions contributed by some of the largest software companies in the world.
The new Artificial Intelligence, Machine Learning and Big Data Survey provides over 130 pages of data, analysis and graphs with an industry standard margin of error of 5%. Topics covered include: Demographics and Firmographics, Perspectives on AI, Enterprise AI, Parallelism, AI Concepts and Approaches, Tools and Processes, Security Concerns, Conversational Systems, Blockchain, Infrastructure Optimization, and more.
See the complete Table of Contents and Methodology here: Table of Contents