The Implications of “Smaller” AI Solutions

Across industries and their shared functions, the quest for “everything AI” continues to expand fueled by firms with trillion-dollar market valuations and startups showcasing seemingly improbable use cases.  The chaos and uncertainty of AI is altering the corporate 2025-2026 planning and budgeting cycles as 2023-2024 solutions are already undergoing material alterations and sunsetting discussions. 

As performance measures (e.g., customer, efficiency, differentiation) generally show positive results, the AI pace of change and technological advancements threaten early solution obsolescence.  Whereas AI increasingly is the “answer” for corporate challenges and impacts, business leaders are now understanding that AI technology is not the end-state—it’s just the enabler. 

Moreover, while Gen AI dominates the discussions and solutions, enterprise implementations are also understanding that large solutions (i.e., LLM’s) are expensive to develop and maintain requiring vast skills, data sources, and organizational change, which aren’t analogous to prior application frameworks.  Finally, data relevancy and resiliency are foundational building blocks for intelligent decisioning that should not be scraped from outside the enterprise due to privacy, security, ethics, and accuracy.

So, what should business leaders be considering when reviewing the growing, complex capabilities and transitory forms which comprise AI?  As AI commercial solutions are measured in weeks and months, what steady-state, foundational functions provide sustainable returns during AI’s iterations?   What is missing and what will aid leaders with their AI implementation requirements? 

For the complete article please visit the Thomson Reuters Institute page at https://www.thomsonreuters.com/en-us/posts/technology/smaller-ai-solutions/. For the AI avatar short video, please see https://mpdangelo.com/wp-content/uploads/2024/08/The-Implications-of-Smaller-AI.mp4.