AI Knowledge Base Statistics To Drive Success: Complete AI Knowledge Management Stats

These AI knowledge base statistics demonstrate the power of AI knowledge management to enable organizations to effectively transform and become leaders in their respective fields. 

These AI knowledge base statistics touch on digitization, Industry 4.0, AI and specifically GenAI, smart factories, and more. 

For example, in an industrial context, effective GenAI is only possible if the right technology is used to access the full body of internal knowledge repositories, and run a private and secure GPT to return accurate results. 

See a comprehensive list of sources below the statistics.

AI Knowledge Base Statistics

1. Adoption and Market Growth

• By 2026, 80% of enterprises will be using GenAI APIs, applications, and models in production, compared to under 5% in 2023.
• The AI market is projected to grow from approximately $200 billion in 2023 to over $1.8 trillion by 2030, highlighting a massive expansion in AI applications, including knowledge management.
• The potential value of AI and analytics across industries is between $9.5 trillion and $15.4 trillion.
• 65% of organizations are regularly using generative AI, showcasing its growing adoption.
• Roughly 80% of enterprises are using retrieval-augmented generation (RAG) for AI model development, compared to 20% using fine-tuning.
• Over 75% of workers globally use AI in their workplace, with many not disclosing this to employers.

“Generative AI will drive a democratized workplace, empowering employees with knowledge and skills to achieve their potential. IT leaders must harness its value to increase productivity, cut costs and create growth opportunities, while also mitigating its significant risks.”
– Arun Chandrasekaran, Gartner Distinguished VP Analyst

2. Productivity and Efficiency Gains

• GenAI drives productivity gains of up to 30% in tasks such as code generation, documentation, and testing.
• In a Python coding contest, non-technical participants using GenAI achieved a score of 86% of the benchmark set by data scientists, and 49% better results than participants not using generative AI.
• In the same contest, participants using GenAI finished 10% faster than others.
• GenAI-using coders were proven to be 15% more likely to choose and apply the appropriate machine learning methodologies.
• The approximate proportion of labor time that can be automated using GenAI includes:
  • Manufacturing and engineering: 35%
  • Construction: 30%
  • Mining, oil, and gas: 31%
  • Finance: 36%
  • Agriculture: 39%
  • Information (media and telecoms): 40%
  • Professional services: 41%
• 42% of organizations primarily seek efficiency, productivity, and cost reduction when implementing GenAI.

3. Challenges and Barriers

• 55% of organizations reported avoiding certain generative AI use cases due to data-related issues, with concerns around sensitive data, privacy, and security.
• Only 23% of organizations rated themselves as highly prepared for the challenges generative AI poses to risk management and governance.
• Risk, regulation (e.g., the European Union’s AI Act), and governance issues are among the top barriers to developing and deploying generative AI tools.
• More than 40% of respondents said their companies struggle to define and measure the exact impacts of their generative AI initiatives.
• 70% of manufacturers still enter data manually, while over 50% aim to standardize data formats by 2030.
• 85% of learning and development leaders anticipate a surge in skills development needs over the next three years due to AI and digital transformation.

“The perceived value of information is higher where information is easy to access”

Deloitte

4. Knowledge Management Impact

• Knowledge management ranks among the top three issues influencing company success, yet only 9% of organizations feel ready to address it.
• 29% of respondents find it difficult or nearly impossible to extract knowledge for daily work from repositories, a figure 50% higher than the 19% who find it difficult or impossible to retrieve information from colleagues.
• 71% of individuals who found information easy to access perceived its value as above average, emphasizing the link between accessibility and perceived value.
• In companies prioritizing knowledge transfer, 80% of workers found it easy or very easy to access repository information, compared to 51% in companies that do not prioritize it.
• Over 50% of surveyed workers find it difficult to locate required information, and 80% reported needing to recreate documents because they couldn’t locate them in their company’s network.
• Korra enables organizations to locate knowledge five times faster and reduce open ticket rates by 30%.

AI Knowledge Management Sources

The following publications and articles were used in compiling these AI knowledge base statistics:

Here is the list of all the URLs from the content provided:

1. Gartner: Generative AI Can Democratize Access to Knowledge and Skills
2. McKinsey: The Executive’s AI Playbook
3. Boston Consulting Group: Generative AI and Knowledge Workers
4. Bain: What Every Executive Needs to Know About AI
5. Bain: How Generative AI Changes the Game in Tech Services
6. Deloitte: State of Generative AI, Q3
7. Deloitte: The New Knowledge Management
8. LUT University: The Impact of Artificial Intelligence on Knowledge Management Practices
9. Harvard Business Review: How to Train Generative AI Using Your Company’s Data
10. McKinsey: The State of AI
11. Wall Street Journal: How a Decades-Old Technology and a Paper from Meta Created an AI Industry Standard
12. LinkedIn: AI and Manufacturing Transformation