![]() ![]() This technology has reached 1% to 5% of the target audience. Governments around the world are setting regulations for AI with some voluntary guidance and some binding. AIOAP has reached 1% to 5% of the target audience.ĪI governance - AI governance establishes accountability around the use of AI. Companies are combining DevOps and CI/CD elements into the AI model lifecycle to standardize DataOps, MLOps, and deployment pipelines. ![]() According to the report, composite AI has penetrated 5 to 20% of the target market.ĪI orchestration and automation platform (AIOAP) - This technology unifies development, delivery, operation, and model performance. The right choice of AI techniques depends on the business problem and the data sets available. The report also identifies six key technologies to watch out for in the innovation trigger phase and expected to hit the plateau of productivity (the end of the hype cycle) within two to five years:Ĭomposite AI - This approach combines various AI techniques to solve business problems more efficiently. Gartner expects that by 2025, 70% of businesses will shift their focus to “small and wide data” which will enable more robust analytics and AI. Such changes have resulted in a shift from traditional big data to what is known as “small and wide data”. Changes in regulations or global events like the Covid-19 crisis change the conditions in which businesses are conducted and completely alter consumer behavior. This includes explainable AI, risk management, and AI ethics for increased trust, transparency, fairness, and auditability.Īs user behavior and patterns change over time, AI and ML models must be continuously updated to avoid becoming obsolete. The AI landscape is moving towards developing a fair and responsible AI. While some results can be misleading and biased, others can lack transparency and auditability, seriously affecting an organization’s reputation and customer base. For instance, composite AI combines elements for deep learning, graph analysis, and other techniques to solve a broader range of business problems in a more efficient manner.Īs with most technologies, there is the positive and negative impact of AI. Multi-experience AI, composite AI, generative AI, and transformers are examples of this trend. ModelOps reduces the integration cycle into production, optimizes operations, and achieves a higher degree of success.Įfficient use of data, models, and computeĮfficient use of data, models, compute power, and other resources lead to innovations in AI. AI orchestration and automation platforms and model operationalization reflect this trend. Companies are looking to operationalize AI platforms to enable scalability and accelerate AI adoption and growth. Hence, the following four megatrends dominate this year’s AI landscape:īy 2025, 70% of businesses are expected to have operationalized AI architectures. ![]() But their focus continues to be on improving speed to market, productionizing AI, and going beyond POCs. ![]() Most organizations have adopted proven AI technologies like NLP and Computer Vision to drive growth, improve customer experience and create new products and services. 34 AI technologies are described in this year’s report, with an above-average number of innovations reaching mainstream adoption within two to five years. The Hype Cycle for Artificial Intelligence 2021 presents some new trends that will dominate the AI landscape. Several AI technologies like computer vision, NLP, chatbots, and edge AI have been driving enterprise adoption over the years. Gartner recently updated its Hype Cycle for Artificial Intelligence 2021 report by Gartner analysts Shubhangi Vashisth and Svetlana Sicular. ![]()
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