David Boardman, founder and CEO of EveryPoint® and Stockpile Reports®, joined Stuart Burgess on Episode 49 of Mine EX-Plorers, the podcast-style show Stuart co-hosts with Crystal Burgess. Stuart is co-founder and chairman of Burgex Mining Consultants, and the two had shared a panel on AI in aggregates at AggNexus. This conversation went further: why bulk material stockpiles matter more than most people think, how to tell AI hype from AI value, and why accurate inventory data has to come before any AI project in the aggregates industry.
Key moments
- 2:53 Digitizing every point on the planet
- 6:35 Surviving the AI hype cycle
- 10:56 From blast analysis to stockpiles
- 16:31 Why stockpile names are not standardized
- 26:08 Who goes all in on technology first
- 30:34 How AI investment diverted capital from aggregates
- 40:26 It is never who is first, it is who is last
- 45:08 Connecting every pile in the supply chain
- 46:39 How far off inventory really is
- 48:43 Garbage in, garbage out
Quotes below are lightly edited for readability.
From blast analysis to stockpiles
David started the company with a goal of digitizing the physical world from imagery, and spent its first year and a half looking for a problem valuable enough to measure over and over. In mining, he first worked on blast analysis: modeling a bench before the shot and the muck pile after it. Then the president of an aggregates company asked a simpler question. Could he take pictures of a stockpile and say how much was in it?
The story that came with the question changed David's mind. Before drones, producers could afford a full count maybe twice a year, and public companies brought in a third party to count inventory at year end. Most years the overs and unders at different sites roughly cancelled out. Every seven to ten years, they did not, and a company could face a year-end adjustment the executive put in the hundreds of millions of dollars.
"When I learned about that, I was like, well, now measuring that pile over there seems a lot more important."
The aggregates associations he called told him it was a problem every company faced. At the time of the episode, David said, people in 130 countries were measuring piles with their phones.
Why bad inventory data breaks AI
The center of the conversation is a problem aggregates producers will recognize. Based on years of working with the industry and conversations with customers, David estimates that a typical aggregates inventory at a large producer is off by 12 to 18 percent, more at smaller operations, and around 40 percent at DOTs.
"Can you imagine running any other type of business where your key asset that you're selling, consuming, or using is off 40 percent? Imagine trying to run a grocery store."
That error shows up the moment a producer tries AI on top of it. David's example: a company rolls out AI truck optimization, a truck arrives at a pile, and there is nothing to load, because the inventory system still showed 10,000 tons. Some of the error is unavoidable, because the material itself changes with moisture and weather. The answer is to measure often enough that the number stays current.
"Five years from now, we're going to be getting the call: we're trying to use AI to optimize our production or our sales forecasting, and it doesn't work, because our data is wrong. Garbage in, garbage out."
For what that looks like in practice, see Your AI Mandate Starts With Your Stockpile Numbers.
How to navigate the AI hype cycle
David has worked through the early web, mobile, and now AI, and he walked through the hype cycle that each one followed: a peak of inflated expectations, a crash into the trough of disillusionment, and then the slow climb where the tools that create real value emerge. His advice to mining and aggregates leaders is to respect the hype without being driven by it.
"Just don't be sold a bill of goods by a bunch of people in Patagonia jackets. Always know that we're in a hype cycle, and behave appropriately. Do your experimentation, do your pilot projects, but know that you're going to be going all in probably in another five to seven years."
He added a lesson from earlier technology waves: "It's never who's first, it's who's last." Nokia and BlackBerry once looked unbeatable. The iPhone's breakthrough, David argued, depended as much on Apple's carrier deal as on the device itself.
Crews measure piles, not products
Stuart raised a problem he is working on with AI: product names are not consistent across the aggregates industry. A #57 stone in one market goes by another name somewhere else. David compared it to the electronic data interchange (EDI) standards that made grocery supply chains possible to automate, and pointed out a second layer. When someone walks into a yard with a phone, they measure a pile, not a product, and a site can hold three piles of the same product with different purposes. Pile names are often whatever the site manager chose, like "North Pile" or "West Pile."
Who goes all in on technology first?
Because aggregates are hyper-local and the cost to start a pit is low, technology alone has rarely been a differentiator. David expects that to change when one producer, likely not the largest, commits fully to technology and automation, the way the Oakland A's did in Moneyball. What holds companies back, in his view, is experience: for many, the last large technology rollout was an ERP system, and the scars remain.
He also offered a theory on timing. Before the AI boom, private equity was hunting for the last under-digitized industries, and construction and bulk materials were in its sights. Then investment capital flowed to AI. David believes that delayed the digitization of aggregates by about a decade, and that the opportunity is still waiting.
The end game: connected piles from pit to job site
Measuring piles is the starting point, not the goal. Once every pile in the supply chain is digital, from the pit and plant to the distribution yard, the rail yard and the job site, the data can start working together.
"That pile is being consumed at the construction site, and now that data talks to all the other data: at the rail distribution yard that fills that construction site, we're going to need seven rail cars loaded tomorrow."
That is the shift David describes from running the industry on gut feel to running it on automation and optimization. As he put it near the end of the episode, "Piles are boring, but what you can do with the pile of data is going to be exciting."
About Stockpile Reports
Customers have been measuring piles with Stockpile Reports since 2012.
Thanks to Stuart and Crystal Burgess for having David on the show. You can find more episodes on the Mine EX-Plorers YouTube channel.