Artificial Intelligence continues to be one of the hottest and most controversial topics covered by technology analysts and researchers in 2018. By the end of the year, most of us use some form of AI in our individual lives everyday. Everytime we ask Alexa how many tablespoons are in a cup or follow a recommendation made by Netflix or Amazon, we are interacting with and using AI. in fact, everytime you follow a recommendation you are training that AI, ensuring it makes better and better recommendations for you over time.
Analysts and researchers who have been following the emergence of AI continue to witness it’s growth. More projects, more companies, more departments from those companies, are discovering ways AI can improve efficiencies and customer experiences. It’s still early days, keep your eyes on this space.
We scoured the internet and put together a list of available public reports on the topic, pulled some highlights and linked and listed them below.
(image by Hacker Noon)
Gartner AI Survey
- The survey shows that a lack of quality training data (29%) and in-house skills (27%) are the top challenges in deploying AI in digital commerce. AI skills are scarce and many organizations don’t have such skills in-house and will have to hire from outside or seek help from external partners.
- 43% of respondents chose to custom-build the solutions developed in-house or by a service provider. In comparison, 63% of the more successful organizations are leveraging a commercial AI solution.
PWC Survey
- Of the 1,001 corporate presidents, C-suite executives and other company officials at large U.S. firms surveyed by PwC, roughly 25% said they had delegated responsibility for overseeing AI projects to managers in individual business units or outside providers.
- Only 15% had appointed a single enterprise-wide AI leader -- though not necessarily the CIO – and 3% said they were not sure who was in charge of AI, the survey found. Not one respondent identified a single corporate official as “owning” AI at their firms – let alone a CIO.
- Respondents were from businesses in a range of industries, most with more than $1 billion in annual revenue.
- Among the officials surveyed by PwC, 27% said their firms have implemented AI in multiple areas of the business, while 20% said they are working on deploying AI across the enterprise.
- Another 16% were implementing pilot projects within discrete areas of the business, and 22% were at the earliest stages of looking into AI.
IBM Survey
- In a recent global survey of 5,000 executives, IBM finds skills deficiencies to be the number-one pressing challenge to moving forward with AI.
- 63% of executives in the IBM survey cited skills as their greatest challenge in implementing AI, making this the leading issue on their minds.
McKinsey survey
- Nearly half of the respondents in a 2018 McKinsey survey on AI adoption say their companies have embedded at least one AI capability in their business processes, and another 30% are piloting AI. Still, only 21% say their organizations have embedded AI in several parts of the business, and barely 3% of large firms have integrated AI across their full enterprise workflows.
DZone 2018 AI Survey
- Around 2,869 developers and tech professional across the world responded.
- 46% respondents think they don't know enough about applying machine learning and artificial intelligence to real-world problems.
- 46% respondents don't know which frameworks and tools they should learn and use.
State of Enterprise Machine Learning
- 38% of respondents reported difficulty in deploying models to the needed scale. Anecdotally, the reasons include: DevOps and IT teams not having sufficient resources; data scientists being expected to build the infrastructure to put their models into production; and a lack of existing infrastructure within the organization to support the needs of running ML models at scale.
- 30% of respondents reported challenges in supporting different programming languages and training frameworks.
- 30% reported challenges in model management tasks such as versioning and reproducibility.
Machine Learning and Artificial Intelligence Adoption
- According to the survey of over 1,600 respondents, 61%, regardless of company size, indicated ML and AI as their companies’ most significant data initiative for next year
- The majority of respondents (88%) indicated that their company already has, or has plans to, implement AI and ML technologies within their organization
- Of those planning to implement ML/AI, the respondents appear eager for these implementations, and 95% indicated it would either complement or make it easier to do their job rather than reducing or making their role harder.
- 65% of respondents using, and planning to use, ML/AI cited that a key aspect of adopting ML and AI was to enable more informed business decision making, underscoring the importance of these technologies for analytics.
- 74% of all respondents consider ML and AI to be a game changer, indicating it had the potential to transform their job and industry.
- Of those indicating they actively use ML and AI, 58% indicated they ran models in production.
- The findings also suggest that uses of such technologies are evolving rapidly, with 77% of respondents actively using ML/AI indicating that creating new models was part of their short-term goals.
Do you agree with this research? Email us and let us know.