What does the future of artificial intelligence bring and how are companies currently dealing with and planning with the new developments of topics like machine learning and generative AI?
According to a global survey from Accenture from 2022, the mere mention of AI in earning calls can impact share prices by up to 40%.
40% of all respondents of a McKinsey survey will increase their AI investment because generative AI has made such big leaps (source: McKinsey, 2023).
Generative AI describes AI that generates text, images, and media. It does so by learning patterns and structures from input data and is then able to create similar results with some variations.
22% respondents of the McKinsey survey already use generative AI regularly in their work (McKinsey, 2023).
According to a Gartner webinar poll of more than 2,500 executives, the main purposes for the use of generative AI are:
(Source: Gartner poll, 2023)
AI can support sustainable measures but is also a big reason why carbon emissions have increased in the last years since many AI systems use up a lot of power to process the massive amount of data (source: Stanford, 2023, PDF).
Despite AI also causing possible sustainability issues, many adopters of AI report that their sustainability initiatives have been improved by 33% thanks to the use of AI (source: data iku, 2023).
Additionally, early adopters saw improvement rates of 35% for innovation and of 32% of customer and employee retention.
According to the AI Index Report, most machine learning models nowadays are being developed by the industry instead of academia. The study mainly cites resources (budget, computer power, data) that are challenging for nonprofits and academia to obtain (source: Stanford, 2023, PDF).
Compared to the results from four years ago, IDG found that in their current survey, 20% more German companies are considering or already using machine learning. Especially bigger companies are more involved with machine learning technologies (source: IDG, 2023).
Machine Learning includes technologies that allow machines to analyze their own data and algorithms to "learn" and solve problems without any additional human input.
According to the data iku report (2023), some of the biggest reasons for failure of AI projects are:
According to the Accenture survey, most companies (over 60%) are still in the experimenting phase while only 12% of surveyed companies are using it at a maturity level that offers a "strong competitive advantage".
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