Introduction
As organizations evolve from the AI concept to the macro-scale, new challenges emerge regarding the organizational requirements needed for success. A recent study reveals that organizations implementing industry-specific AI literacy can achieve 3.8 times higher success rates than competitors. This represents a groundbreaking opportunity for data-driven innovators, and an imminent threat for organizations now nearly 2 years behind the curve. In this article, we dive into the important factors that dictate how organizations should invest in AI based on their maturity stage.
Implementation Challenges Today
One of the most common business challenges with AI is recognizing value across the technical enterprise atmosphere. Despite vast investments into artificial technology, the widening gap in adoption presents a strong opportunity for management evolution:
- 76% of organizations report significant gaps between AI investment and realized ROI. *
- Only 23% of AI initiatives deliver on their expected performance within the project lifecycle. **
- 68% of business executives blame inadequate workforce capability as a primary barrier to successful implementation. ***
The gap between people, AI, technology, and data is clear. If your team hasn’t started using AI nearly every day, the risk of becoming competitively obsolete is knocking at the door. Organizations have now started to invest in tailored AI strategies based on their current stage of maturity, a complete game changer in terms of predicting success:
- Early-stage AI implementations saw a 2.1x success differential between generic training and industry-specific literacy.
- Mid-stage implementations demonstrated a 4.7x success differential.
- Mature implementations maintain a 3.4x success differential with personalized training.****
Next steps to prioritize:
- Audit current AI investments, training programs, and products. Understand core adoption rates across specific internal teams to determine AI maturity across relevant organizational goals.
- Invest in developing your people behind industry-specific use cases, best practices, and measurement frameworks. Execute“off-the-shelf” use cases that can immediately add value across current technology products and data warehouses.
- Build GenAI, AI Tools, and Use Cases built specifically for your company, your data, and your existing operations and processes. Before investing in external tools or partners, consider truly maximizing the usage of the tools, the AI literacy of your people, and data systems, to establish a culture prepared for success.
AI Acceleration
Strong evidence clearly shows that the format of AI literacy and education has a massive impact on the organizational value derived from artificial intelligence. From the fundamentals of data and analytics to the complexities of LLMs and machine learning, organizations have a tremendous amount of growth potential with the right strategic partner. Having trained over 1000 enterprise professionals around driving internal value from artificial intelligence, AI Prophets is passionate about co-crafting AI solutions to enable human and business growth. Reach out to learn more.
Academic References:
*Society for Information Management. (2024). Enterprise Technology Survey: AI Implementation Challenges and Success Factors. SIM.
**Deloitte. (2024). Digital Transformation Index: AI Implementation Benchmarks. Deloitte Insights.
***Harvard Business Review Analytics. (2023). The Enterprise AI Value Gap: Capability Development and Implementation Success. Harvard Business Review Press.
****Williams, J., & Thompson, K. (2024). Industry-Specific Patterns in Enterprise AI Implementation. Journal of Applied AI, 12(2), 87-103.
