Source: AIGC Open Community

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On October 12, the globally renowned information consulting and research firm Gartner announced a survey on its official website, revealing that by 2026, over 80% of enterprises will be using generative AI APIs or deploying applications with generative AI, a significant increase from the less than 5% projected for 2023.

Gartner Vice President and Senior Analyst Arun Chandrasekaran stated that generative AI has become a top priority for enterprise senior management, sparking a wave of significant innovation beyond foundational models.

The demand for generative AI is continuously increasing in sectors such as healthcare, life sciences, law, and financial services.

In Gartner's "2023 Generative AI Technology Maturity Curve," more and more enterprises are beginning to embed generative AI into their actual business operations to achieve cost reduction and efficiency improvement.

Over the next decade, three innovations expected to have a significant impact on organizations include: applications supporting generative AI, foundational large models, and AI trust, risk, and security management.

Applications Supporting Generative AI

Enhancing user experience (UX) and task augmentation through the use of generative AI applications to accelerate and assist users in completing various tasks. As enterprises start using generative AI, this will permeate a wide range of skills within the workforce.

Chandrasekaran noted that the most common mode of embedding generative AI functionality currently is "text-to-X" (text, code, images, videos, etc.), primarily through the use of natural language prompts to generate various content, democratizing work processes.

However, these applications still face some obstacles, such as hallucinations and inaccuracies, which may limit their widespread impact and adoption.

Foundational Large Models

Foundational large models represent a significant advancement in AI as they undergo extensive pre-training and have broad applicability across various use cases.

Chandrasekaran stated that foundational large models will drive the internal digital transformation of enterprises by enhancing employee productivity, automating and improving customer experiences, and creating cost-effective new products and services.

Foundational large models are at the peak of expected expansion on the technology maturity curve. Gartner predicts that by 2027, foundational large models will support 60% of natural language processing (NLP) use cases, a substantial increase from the less than 5% in 2021.

AI Trust, Risk, and Security Management (AI TRiSM)

AI TRiSM ensures the governance, credibility, fairness, reliability, robustness, effectiveness, and data protection of AI models. AI TRiSM includes solutions and technologies for model interpretability, anomaly detection in data and content, AI data protection, model operations, and resistance to adversarial attacks.

AI TRiSM is a crucial framework for achieving responsible AI and is expected to become mainstream within 2-5 years. By 2026, organizations implementing AI transparency, trust, and security are projected to achieve a 50% improvement in their AI models' application, business objectives, and user acceptance.

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