Generative Artificial Intelligence (GenAI) is the leading type of AI solution being implemented in organizations, according to a recent study.
In the fourth quarter of 2023, management consulting firm Gartner, Inc. conducted a new survey involving 644 participants from the United States (U.S.), Germany, and the United Kingdom (U.K.).
The report revealed that 29 percent of the participants have deployed and are actively using GenAI. This makes GenAI the most widely deployed AI solution, surpassing other techniques such as graph techniques, optimization algorithms, rule-based systems, natural language processing, and other forms of machine learning.
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The survey also discovered that the most prevalent method of utilizing GenAI for specific use cases is by embedding it within existing applications, such as Microsoft’s Copilot for 365 or Adobe Firefly. This approach was cited as the primary method by 34 percent of the respondents. Customizing GenAI models with prompt engineering was the second most common method (25 percent), followed by training or fine-tuning bespoke GenAI models (21 percent), and using standalone GenAI tools like ChatGPT or Gemini (19 percent).
Among the challenges faced by organizations in adopting AI, the survey revealed that 49 percent of the participants reported difficulty in estimating and demonstrating the value of AI projects. This obstacle outweighed other barriers such as talent shortages, technical difficulties, data-related issues, lack of business alignment, and trust in AI.
Foundational capabilities for AI-mature organizations
The survey also identified that only 9 percent of organizations can be classified as AI-mature. These organizations stand out because they focus on four foundational capabilities:
- A scalable AI operating model that effectively balances centralized and distributed capabilities.
- A strong emphasis on AI engineering, involving a systematic approach to building and deploying AI projects in production.
- Investments in upskilling and change management across the entire organization.
- Prioritizing trust, risk, and security management (TRiSM) capabilities to mitigate risks associated with AI implementations and drive better business outcomes.
The report further indicated that by concentrating on these foundational capabilities, organizations can advance their AI maturity and address the current challenge of successfully deploying AI projects. The survey also noted that, on average, only 48 percent of AI projects reach the production stage, and it takes approximately 8 months to transition from an AI prototype to production.
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