AI capability is becoming more accessible, while the cost of production systems depends on the models, context, tools, loops and human review required by each workflow. For department stores, advantage will come less from access to a model than from control of the data, definitions, customer relationship and judgment that make its output useful.

Boston Consulting Group’s June 2026 analysis describes AI intelligence as increasingly scalable and accessible. Its July 2026 cost framework exposes the other side of that shift. Agentic systems may retrieve documents, make several model calls, use tools, carry context across steps and repeat actions before an output is accepted. Human review and correction add another layer of cost. Access to intelligence is getting cheaper while the cost of an accepted outcome can remain variable and difficult to predict.1

That tension matters because the cheapest model call does not necessarily produce the cheapest useful result. The relevant cost is what the store spends to reach an answer a buyer or a merchandiser can act on without sending it back. It also shifts the strategic decision from model access to organisational control. The store must decide which data enters the workflow, which commercial definitions it uses, how its output is evaluated, who can override it and whether the customer relationship remains with the store.

The argument for “homegrown” AI begins there. Homegrown AI can combine external models, private cloud and selected local infrastructure. What the department store retains is the business logic beneath those systems and the option to run workloads locally when economics, sensitivity or control justify it.

IADS Exclusive – Homegrown AI and the intelligence department stores should own 

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