AI adoption’s sticking point may be how companies work, not which tool they buy

Companies can buy AI tools quickly; changing the way people work with them is the harder part. Leonardo Thielo de La Vega argues that disconnected systems and unchanged workflows can leave firms with more sophisticated inefficiencies rather than better results.

Watch Desk analysis

What happened

In an expert-view article for RFID Journal, de La Vega makes the case that AI adoption is as much an organisational challenge as a technical one. He points to spreadsheets and work outside formal systems as signs that some companies have yet to make full use of the technology they already own.

The article cites the 2026 Stanford AI Index for figures that AI has been adopted by 88% of surveyed organisations, while generative AI is used in at least one business function by 70%. It also says deployment of AI agents remains in the single digits across nearly all business functions. Those figures frame the gap between access to AI and putting it to work at scale.

Why it matters

A new model or agent cannot, by itself, repair a broken hand-off, make staff trust a system or ensure its output reaches someone able to act on it. De La Vega’s argument is that companies need training, workflow redesign and measures that reflect outcomes such as decision speed and the quality of collaboration between people and AI.

That shifts the useful question from “Which tool should we buy?” to “What changes in the work would make this tool valuable?” It is a less glamorous question, but it may be the one that separates a live business improvement from another pilot with a polished dashboard.

Our read

The culture argument is persuasive when it points to concrete things companies can change: how work moves, whether people use the systems they have, and how success is measured. It is not a claim that technology does not matter. It is a reminder that buying the tool is the beginning of implementation, not its triumphant little finish line.

What to watch

• Whether organisations redesign workflows around AI rather than adding it on top.
• Whether training and staff involvement feature in deployment plans.
• Whether success is measured through useful outcomes, not adoption figures alone.

Discussion spark: When an AI project stalls, should leaders first change the technology, or the workflows and incentives around it?

Watch Desk is operated by WittyWires as an independent cross-cutting AI news tracker. It does not speak for the organisations or people it covers.