Maximizing the Value of Your Manufacturing Data with AI

Artificial intelligence promises huge gains in insights and productivity. But your manufacturing data is your company’s gold, and you may be skeptical about trusting AI with this valuable asset. To move forward, you need a data platform that ensures you get all the value from your data while keeping your competitive advantage secure.

In this episode of the podcast, a veteran from the AI software industry talks about a new Data Platform Initiative and what excites him about its potential for manufacturers.

Chris Herron is aPriori’s new Chief Technology Officer. He’s spent the majority of his career building products that leverage data in a significant way, including platforms that combine data with AI in powerful ways. In this interview, he speaks about the Data Platform Initiative and what it will mean for aPriori’s customers.

Chris Herron: My name is Chris Herron. I’m the CTO at aPriori, which means I lead software engineering and support. These are the teams building the product, operating the product, and supporting it. A lot of my past work has been in B2B SaaS, focusing on data technology, traditional AI, and generative AI. Those experiences are very applicable here. I saw an opportunity to invest in leveraging data in new ways, combining it with AI. That was one of the reasons I was excited to take this role.

How does the work of engineering fit into aPriori’s overall strategy?

Chris Herron: As leader of the engineering function, it’s my responsibility to deliver on the product roadmap. That’s how we achieve the outcomes that matter. And I emphasize that word—outcomes. Marty Cagan, in his book Empowered, champions the idea of empowered teams and the mantra “outcomes, not output.” I like to run engineering with that mindset. It’s not just about completing a project and shipping it. The real measure of success is achieving the desired outcome.

What is the Data Platform Initiative?

Chris Herron: The purpose of the Data Platform Initiative is to leverage the inherent value in the large amounts of data we already generate. For example, in our core cost engineering use cases, we generate a tremendous amount of data. That data supports our existing capabilities, but it can also be used in ways we haven’t fully taken advantage of yet.

We see opportunities to combine that data with AI to address use cases across the entire product development lifecycle for our customers.

An example of maximizing the value of manufacturing data with AI

Chris Herron: You’ve asked for an example of how we can leverage data in new ways. Cost engineering is a use case we’re best known for. Right next to that is sourcing. A cost engineer produces an output, which is then handed over to a sourcing professional. When that professional negotiates with a supplier, they have a solid idea of what something should cost. If the supplier comes back with a much higher number, the sourcing professional can challenge it, negotiate, or suggest a different manufacturing process that brings the cost more in line with expectations.

We already have professional services that support this. Our experts—highly experienced practitioners—help customers identify opportunities for cost savings and advise on negotiation tactics. The challenge is that it’s hard to scale human expertise.

With the right data and AI, we can scale this knowledge. We know the heuristics and insights our experts look for. We know the playbook for negotiations and the best practices. An AI version can highlight impactful insights, identify opportunities for savings, and provide recommended negotiation tactics—helping sourcing professionals achieve better outcomes with their suppliers.

That’s just one example. We’re exploring many use cases across the entire product development and manufacturing lifecycle. It’s one of the most exciting initiatives we have right now.