9 AI Innovations That'll Be Standard in LBM by 2030, and 4 Challenges to Overcome

By Craig Webb

Remember when Internet service at a hotel was so new that a hookup cost several dollars? Now Wifi regularly is free, and customers expect to have it. You can see the same trend with artificial intelligence today. Webb Analytics attended several events this fall that highlighted AI advances. Here are nine new, game-changing features--mostly technological, sometimes philosophical--that we believe are likely to become common at your location in the next four yours, along with four challenges you most likely will have to overcome.

1. Hablamos su idioma

Voice recognition and computerized conversation systems will expand beyond English to deal with your customers in Spanish, Russian, Korean, Vietnamese, and scores of other languages. Yes, talking to a human is preferable, but having multilingual backup might make the difference on an order.

2. Take a picture, discover a prospect

Imagine your delivery driver is going through a neighborhood and spots a sign for a contractor in front of a house. Rather than just scribble notes and pass them along to a sales rep, the driver takes a picture of the yard sign. AI bots then "read" the sign and begin researching the contractor's company. Where is it located? How many permits has it pulled? Where are those other houses? What's the company's credit rating? And if that contractor already is a customer, how come we were unaware of this job and the materials it was purchasing--presumably from a competitor? By the time the driver returns to the yard, the sales team already has a full report that it can use to pursue the business.

3. The Big Brain

Cory Gundberg, Chief Technology Officer at SRS Distribution, said he believes AI excels when it collects information from across the company and puts it into a central database that serves everyone just the way they need it. He laid out that vision in the slide above. The information could come from e-mails, invoices, credit checks, phone calls, weather reports, texts, security cameras, traffic counts, road conditions, historical ERP data, and more. The goal is to maximize the company's productivity (and profits) while eliminating situations in which an error occurs because one hand doesn't know what the other is doing.

4. Automatically anticipating needs

Imagine that Saturday's forecast of a hailstorm came true on Sunday afternoon. The roofing store is closed and you, the manager, are at home. By the time you arrive Monday morning, you'll have several hours of work awaiting you. Was the hail big enough to trigger insurance claims? What's the extent of the damage? Have my customers worked in neighborhoods that got hit? What brands and colors of shingles are these contractors most likely to need to repair the damage? And are those contractors' credit limits big enough?

Christopher Brox of Google DeepMind (Google’s research model pushing AI frontiers), says AI can be set up to go to work as soon as the first forecasts arrive. Then, as more information arrives via the weather service, satellite photography, and customers' calls, photos, and texts, the AI system can adjust your response. For example, when it becomes clear that there aren't enough of a certain kind of shingles in stock, the AI system can poll nearby yards in the company's network and arrange to get supplies from them. It also can start purchase orders and even adjust a contractor's credit limit, all while the manager is getting his first cup of coffee.

5. Marketing in a minute

ERP systems like Rundoo now can identify slow-moving products and organize a multi-media marketing campaign in the amount of time it normally takes a person to spot an issue.

6. Contractor-driven AI advances

Consultant Chris Parsons notes that contractors will be using more AI, too. "A contractor could tell their AI agent, 'I need these materials delivered to this job on Tuesday. Check my preferred dealers, use my account pricing and make sure the order meets the specifications,'" Parsons says. "The agent could compare stock, delivery windows and pricing, then prepare or complete a purchase within approved limits. If one item is unavailable, it could flag the problem and bring forward alternatives for review."

7. OCR and much more

We're increasingly seeing ads for products that can read a sketch on a napkin and turn it into a drawing. Optical character recognition definitely is coming, but Parsons sees it in a bigger context. "A contractor walking a job with a homeowner is already gathering information needed for a scope of work, an estimate and a material order," Parsons says. "Today, much of that information has to be written up and entered elsewhere afterward. With everyone’s knowledge and permission, AI could begin organizing it during the conversation. As dimensions, materials, finishes and timing are discussed, it could build a draft scope, identify unanswered questions and prepare a preliminary material list. When the contractor speaks with the dealer, that same information could support stock checks, account pricing, a draft quote, and delivery options."

8 Personalized sales news and advice

SRS sales reps are beginning to get weekly emails from Cosailor, an AI-powered platform that gives advice to each rep on what it should focus on during the coming week: Customers who did something notable recently, contractors that deserve a nudge, dormant accounts most worth calling back, and so on. Each message is tailored to the particular rep's wants and needs (including coaching). While sales reps are Cosailor's focus, it's easy to see how this concept will spread to create individualized messages to sales, branch, procurement, and credit managers.

9. Everyone can spot inconsistencies

Big, complicated companies make regular use today of programs that scan the enterprise to find inconsistencies: A branch in Oklahoma City that spends far more than Tulsa does for the same product, for instance, or people with company credit or gas cards that don't buy from particular companies that have a specially negotiated price. The scans are particularly popular at roll-up companies with lots of branches and/or ERP systems. Now businesses like SpendAI.com are offering these reviews on a software as a service basis.

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Four Challenges to Overcome

1) Data hygiene. AI needs clean, consistently entered data in order to excel. Customer and product data should be standardized as much as possible, and then employed as new data gets poured in.

2) Willingness to change. AI can enable you to do bad things faster, but your real goal should be to do things better. This is likely to require examining your current processes and exploring how AI can be employed to create newer, more efficient processes that generate less friction and bigger profits.

3) Fear of betting on the wrong horse. AI systems are evolving rapidly, and in slightly different ways. That can lead to fear you'll tie yourself to a company that will go out of business. But AI isn't like ERP programs, where the coding is so customized that switching systems is painful. It's relatively easy to employ multiple AI systems for one task, or to split your jobs between systems based on which does that task better today.

4) Expecting perfection. Your customer accounts may well be a mess now, and odds are they will still be unruly even after you've worked hard on them. Other datasets, such as products and inventory and revenues and credit lines, all are more likely to approach being pristine. Just work regularly toward getting better and better.