German DIY giant Obi has introduced a “hey Obi” service on its app that analyzes voiced requests to route the customer to the most suitable product expert on its help desk.
Construction supply dealers have reason to be both intrigued and confused by the hubbub over Artificial Intelligence (AI). Whether LBM dealers should care depends both on what you’re doing now and whether AI’s biggest recent advances will be worth the cost.
Here’s my take:
- AI’s innovations in marketing and data analysis hold the greatest short-term promise for construction supply.
- Big dealers will benefit by using AI to achieve more consistency across far-flung operations.
- Most small dealers should continue improving their key processes—particularly dispatch/delivery, inventory management, e-commerce and ERP utilization—before spending any time thinking about higher-level AI innovations. Smaller dealers also will have to move their data to the cloud.
- Except for better pricing, AI’s benefits are more likely to show up on the expense rather than the revenue line.
“I think it’s starting to become more important,” said John Carrico, a vice president at Epicor specializing in product management, distribution and building supply. Earlier in the interview, he stressed: “We by no means are saying AI is the end-all and be-all. But we see the use of AI, and it comes down to AI helping a user make the best decision and make an informed decision—and sometimes make a decision for them.”
Sodimac, Latin America’s biggest DIY chain, uses robots and AI to spot which of its branches are having the biggest operational problems.
A Fuzzy Definition
Within the context of the hardware industry, what exactly is AI? Arthur Duffy, founder and managing director of Excenta Ltd., a U.K.-based IT consultant, cautions that lots of processes cited as examples of AI strike him as just data processing. You could argue that a cash register that tells how much change to give on a purchase is “intelligent.” But to Duffy, that’s not AI.
As he defines it: “AI is a computer system that is able to perform tasks that ordinarily require human intelligence. These artificial intelligence systems are powered by machine learning.” Another way to put it is that AI’s value arises when the problem comes too frequently, with too many variables, and from too many sources for humans to solve the task at a profitable speed.
Consider direction-finding software like Uber and Waze. A lumberyard’s dispatcher can manually direct deliveries as well as the apps if only a few trucks are involved. But if a dispatcher had to handle Uber’s 15 million daily rides, that person would be overwhelmed. Beyond that, these route-finding apps are constantly “learning” which routes work best by collecting data from lots of resources, such as the crowdsourcing contributions that help power Waze.