Introduction
The MIT NANDA research report titled ‘The GenAI Divide – State of AI in Business 2025’ revealed that most Generative Artificial Intelligence (GenAI) pilots are failing to progress to full deployment, with only 5% of projects achieving success. 95% of pilots are failing sounds like a major deviation from what we read about AI takeover in various mediums therefore the MIT NANDA (https://nanda.media.mit.edu) research has made a lot of noise lately. Research provides valuable insights for crossing the “GenAI Divide” to be one of the successful organizations in GenAI deployments. (Thanks to Aditya Challapally, Chris Pease, Ramesh Raskar, Pradyumna Chari for this insightful report.)
Agentic AI Features for Business to Succeed – Persistent Memory, Feedback and Refinement Loops
An interesting inference from the research is that personal use of GPT-like LLMs has increased in recent years meanwhile Business fails 95% in GenAI pilots. I see this gap exists because of repetitive tasks like document processing or consulting to an AI expert without any human supervision and judgement fuel the use of GenAI by workforce.
In order to Business to increase deployment of pilots, Agentic AI systems built with adaptive agents are needed which should promptly be integrated into business workflows and handle new situations and tackle errors (I touched on this with a previous post here, under the section “Ending the Era of Spreadsheets“). Adaptive autonomous agents can process workforce feedback through LLM-driven iterative refinement loops. Persistent memory is also a key ingredient to track long-term goals and contextual awareness for user productivity. Further, fine-tuning the model will help to shape it with the domain and organization specific feedback but most of the time Feedback and Refinement Loops would be sufficient for most cases.
Preliminary AI Skills for Everyone – Familiarity with AI tools and Prompt Engineering
Considering
the research, most successful integrations came from front lines in contrast
with centralized automation departments. This is a signal of job interview
questions to ask about Preliminary AI Skills. Familiarity with AI tools is
becoming workplace staples. Therefore, be ready for your interviews in advance
to explain which toolset you can work with comfortably and better take a course
on Prompt Engineering before that.
Referrals and Peer Trust to Bridge the “Divide”? – No. Value must be the essential decision factor for an AI Product
The interesting part of the research is that interview questions are answered by leaders those failed AI Transformation in their Business. In that case, I expect the survey results may not reflect all the right actions for the future. Based on that, the method of selection of partners via Referrals and Peer Trust is one of them in my opinion. If “Limited disruption” is one of the patterns that define the GenAI Divide, then referral opinion led product selection might not make your company a disruptive one.
Conclusion
Agentic AI systems will be the key player in the future of AI adoption by Business. Nowadays Businesses are making new plans to increase their AI project deployments where teams to be expected to carry responsibilities as the frontiers. By considering this, it is going to be inevitable to learn Preliminary AI Skills by everyone and even go beyond that.
It is best to evaluate the report’s outputs cautiously as onboarding decision of an AI Product must not heavily depend on peer trust and recommendation. The value should be the key point where the pilot project target objectives are fulfilled by observing robust workflow loops not broken by edge cases.
I highly recommend you read the research report
with many learnings and insights you can find in a single reading (I added the pdf file as
document to my LinkedIn post).
Founder, Vubion.ai
https://www.linkedin.com/in/leventsertac/