AI adoption starts with a vocabulary problem

In the last month alone, I’ve read executive reports from multiple sources saying essentially the same thing, and I wouldn’t be shocked if some of it was written by ChatGPT itself. Here’s my attempt to help the leaders who still believe strategy should come before action.

First, a language fix that changes the CxO conversation more than it should have to. Stop calling it ‘artificial’ intelligence and start calling it ‘augmented’ intelligence. Once the finance team sees this as workforce augmentation rather than workforce replacement, the question shifts from ‘how do we replace people with AI’ to the far more productive ‘how do we supercharge our workforce with AI.’

Technology adoption isn’t a race to a finish line, it’s the construction of a sustainable, long-term strategic advantage. Generic use cases that improve day-to-day operations will mostly be served by your existing SaaS providers. The real impact comes from use cases that cut across the enterprise. Private, RAG-augmented LLMs fine-tuned to your own data. AI-as-co-pilot will also dramatically improve communication quality across a workforce, especially for non-native English speakers, but the risk runs the other way too. Once everyone leans on the same model to wordsmith everything, output starts to look and sound the same. Call that the ‘Blah Factor’, and it’s a real cost to brand and communication quality that rarely makes it into the ROI model.

In my book on Cloud computing, I had posited that as enterprises adopt various cloud solutions, the burden of effort to collate and make sense of their data will increase as the data will be fragmented across multiple systems. The situation gets compounded because now, not only your data but predictions will also be siloed and fragmented.

My recommendation is to use this year and next quarter to experiment and build a real data strategy and a shortlist of use cases with genuine strategic advantage, then move to tech-stack selection and scalable POCs the following quarter.

One last caution before you offload decision-making to AI. Read the case study of Volvo, which ended up with more green cars in inventory than it started with, because no human was left in the loop to catch the error.

If your organization is still debating the word ‘artificial’ versus ‘augmented,’ that’s not a semantic detail. It’s the difference between a workforce that adopts AI and one that resists it, and it’s worth getting right before the rollout, not after.