Industrial AI. Infra for AI. Integration of AI

September was another busy month. I met 100+ potential clients, partners, and ecosystem players across power, utilities, datacenter, electrical engineering, property assessment, and industrial domains.

While my real eyes are astigmatic, in the world of AI, I am going towards 20/20. Each time I hit the road, I come back with a new understanding. Let me share them all with you:

1. Infra spend is opening up I used to think that the GenAI wave has legitimized spending on machine learning and statistical modeling. That is not true. That is a second order effect. GenAI has created an urgency in enterprises to understand their own data. Harmonization, Context, and Orchestration spends are getting legitimized.

2. Industrial AI is a whitespace With due respect to the massive businesses industrial systems companies like Siemens and ABB have created, they are not quite there when it comes to data applications. They push boxes and reluctantly, some APIs and digital twins. Unlocking the value of that data is still a greenfield endeavor.

3. Midmarket Industrial needs new solutions 'Make in America' is a wave. The mid-market manufacturing company has not even started logging data, let alone use generative AI. This is an unspoken opportunity.

4. Ignore the MIT report but pay attention to 'why' failures happen Pilots are not failing. The MIT report is wrong. Their own data shows that 30% of pilots go to production. But, product vendors are overselling their place in the enterprise. Pilots fail not because LLMs fail. The surrounding engineering that ensures consistent tool calling, evals that set a robust guardrail system, data cleanliness, accessibility, structured cataloging, and modelling of enterprise data is a very manual process and the failure of it shows when you do pilots. That most organizations (including the AI app layer vendors) don't understand how to build data foundations is a double-whammy. 

5. Speed and success comes from orchestration Enterprises are realizing that apps and general tooling provided through captive contracts by hyperscalers are limiting them. But only some realize that point solutions may also fail for the reasons in Point 4. Very few vendors bring together context layers, pre-built LLM-based applications, and custom workflows, and orchestrate all of them together. 4 and 5 are the reasons Moative exists.

6. Professional Services go beyond custom build Enterprises have had a bad experience with professional services. In the past, professional services meant custom software and they were fleeced to death by 'change requests.' But professional services in AI transformation are about data engineering, tool calling, evals, and workflow orchestration. They are the true system integration work.

If you are thinking of an enterprise AI pilot, your checklist should be based on your data, context readiness and your vendor set should be a mix of point solutions, infra tools, and orchestrators/integrators.

Enjoy your Sunday!