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AI that actually works: built for chemical procurement

AI that actually works - built for chemical procurement

Finding chemical and raw material suppliers is tougher than ever. Use supplier scouting technology to quickly uncover relevant, qualified alternatives

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Vera Barile

<h1>A standard approach for a non-standard category</h1>
We sat down with Arturo Pizano, VP of Customer Success at Valdera, to talk about what raw material buyers are dealing with right now: portfolios too large for one person to cover, decades of supply base knowledge approaching retirement, and a market full of AI tools built for a different kind of spend.
Arturo and his team speak to raw material buyers daily, and they have a short list of reasons why they get in touch. Risk is top of mind, often surfacing well before any actual disruption occurs.
&#8220;They&#8217;re looking at the raw materials and they&#8217;re saying, ‘Hey, we&#8217;ve got all of these single-source materials coming from all over the world. We know that eventually we&#8217;re going to need to replace one of those sources, and we want to do it before it becomes an urgent need.’&#8221;
Increasingly, companies under pressure to improve profit margins are tasking their procurement teams with finding those cost savings in their raw material spend.
While these are standard procurement objectives, achieving them in raw materials introduces an added layer of complexity. Arturo explains, “They have things that work on a lot of categories, but it&#8217;s that level of specificity that introduces complexity. They don&#8217;t have the data, or the right technology on hand to find suppliers that can meet those raw material-specific needs.”
Then there&#8217;s the size of the portfolio one category manager is responsible for. A single category manager might cover a hundred or more materials across 20-40 supplier relationships. Their biggest categories get real attention, and as Arturo puts it, “They can&#8217;t apply that level of focus on all of their materials.” There is only so much planning time to distribute, and responsiveness suffers too, because no one can watch every category at once. When major disruptions occur, issues often remain hidden until they cause active damage, forcing teams into reactive crisis management.
<h2>The talent gap is widening</h2>
That coverage problem is about to get harder. The talent pool currently absorbing it is on its way out of the industry. These are the category managers holding twenty or thirty years of supply base knowledge. It’s unrealistic to provide six to twelve months of unhurried onboarding anymore, and a new hire won’t know the supply base from day one.

“The expertise gap is huge. To be fluent with a particular supplier, you need to understand the way that market works. Historically that's taken a long time to ramp up.”


Ramp-up time is only part of it. Raw material specifications are complicated, which narrows the supplier pool and multiplies the vetting work. Three things have to be understood at once:
<ol>
<li>The technical requirements, which means working closely with R&amp;D</li>
<li>What suppliers are truly capable of, which is how new viable sources get identified</li>
<li>What the market is doing to pricing and availability over time</li>
</ol>
Bringing those together is the hard part, and Arturo&#8217;s conclusion is that no single tool has historically done it. “It&#8217;s not just enough to go out there and source the materials. You really need to have that end-to-end solution that is specific to raw materials.”
Behind his solution to onboarding lies an overarching theme: having access to data is distinct from operational capability.
“First it&#8217;s about getting the information, then it&#8217;s about understanding what that means. Only then can they focus on what they are going to do with their suppliers.”
Compressing that path is what lets a newcomer to the role focus on what they need to learn rather than trying to boil the ocean.
Closing that gap is where AI should help, and most companies are only now looking to it. The complication is that most of the AI being sold to procurement teams sits on top of tools built for indirect spend, so it inherits data that was never designed to describe a raw material. Sorting the real claims from the rest is now part of the category manager&#8217;s job.

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<h2>Debunking three common misconceptions in raw material procurement</h2>
As AI tools spread across procurement, category managers have to separate what&#8217;s being marketed from what will really help them. Unpacking where generic tools fall short usually comes down to three recurring assumptions Arturo encounters in the market:
<h4>1. “I can already do this with ChatGPT or Copilot”</h4>
When asked where AI is being oversold in raw materials, Arturo went straight to the assumption that a general-purpose model is enough on its own. &#8220;We&#8217;ll hear, &#8216;How is this different from what I can already do with ChatGPT or Copilot? I&#8217;m already doing this analysis.&#8217; And it&#8217;s just not true. You get incomplete and misleading answers without the right data.&#8221;
Of those two vulnerabilities, the misleading answer carries more risk, especially in a category where a single number can anchor a negotiation.
“There&#8217;s a place for using generic LLMs, but it&#8217;s not enough when it comes down to raw materials. I&#8217;ve seen an LLM giving advice on pricing that was six months old. In this environment, that is simply not enough; it&#8217;s too risky.”
<h4>2. “We already have a sourcing platform, and it works well”</h4>
The second misconception Arturo runs into is that the existing procurement stack has this covered, usually said in the same breath as the AI point.
&#8220;I hear, &#8216;We already have a sourcing platform and it works well. We&#8217;re already using AI in everything we&#8217;re doing.&#8217; That&#8217;s a huge misconception.&#8221;
There&#8217;s a reason for that. A specific raw material isn&#8217;t bought by everybody, so it takes an enormous amount of coverage before the data is useful to anyone on the direct side. Unlike indirect categories, each buyer&#8217;s context makes a huge difference: their industry, requirements, and customers&#8217; needs matter. Arturo went looking for a vendor who had taken that complexity on.
“They&#8217;re not out there. I looked for them for years when I was a consultant. And frankly, that&#8217;s why Valdera was built.”
Where that gap shows up most often is geographic. Buyers know their European and North American supply base well and have far less visibility everywhere else.
“Historically there&#8217;s a lot of hesitation there. There&#8217;s a sense of risk, there&#8217;s a lack of familiarity, and they don&#8217;t know whether it&#8217;s going to be credible or not,” Arturo explains. 
That hesitation clears once the vetting is built into the process. With Valdera, suppliers are vetted as the quotes come in, so a buyer sees the certifications before deciding whether to take a conversation further.
<h4>3. “Savings are only in the new suppliers”</h4>
The last assumption is about where savings come from. Buyers expect them to sit in supplier discovery, so they expect to qualify a new source before realizing savings.
That&#8217;s a reasonable place to look, and often it is where the value sits. Valdera surfaces suppliers who match the spec and the standards from a proprietary dataset, the largest compilation of raw material suppliers and specifications. However, discovery isn&#8217;t the only thing that moves price, and customers a few weeks into using Valdera don&#8217;t always see the second lever coming.
Arturo explains: “We sometimes hear from our new customers, &#8216;I know a lot of these suppliers, there are a few new ones, but honestly, I&#8217;m not sure we&#8217;re going to be able to get savings.&#8217; That&#8217;s when they start to have the best conversations. Just running the process, getting those extra quotes, and showing the suppliers that they&#8217;re serious about looking for new sources will often trigger savings from their incumbents right away.”
Incumbents price differently once they know a buyer has credible alternatives and is willing to use them. “There&#8217;s this perception that AI can solve it and that it can tell you the answer. But at the end of the day, it&#8217;s the execution that gets you to the value. When you bring them together, that&#8217;s where things get really powerful.”
The same logic applies to where a second source turns out to be. Arturo tells the story of a pharma customer who had a single-source excipient feeding many different products. The alternative Valdera surfaced was a supplier they already bought from, so qualification moved much faster because the relationship was already in place.
“Supplier discovery doesn&#8217;t necessarily have to be a new supplier. It can be a new way of sourcing a material from an existing supplier.”
<h2>Building an early partnership between R&amp;D and procurement</h2>
Buyers call about risk and cost. The value Arturo and his team uncover often needs to be unlocked one step upstream, at the spec level, working directly with R&amp;D.
Category managers already work with R&amp;D, but usually as a dependency: waiting on technical input passed back and forth. What changes here is that both sides work from the same market data in real time, so the specification and the sourcing conversation happen together rather than in sequence.
Spec harmonization is the first version of it. A team can end up buying twenty different materials where three or four would do, not through any failure of judgment but by accumulation, each new product selecting whatever suited it best at the time. Consolidation exercises are not new. What changes is what comes back: instead of a proposal suggesting three materials could probably be one, the answer names specific products from specific suppliers at a competitive cost that would support the consolidation. It brings a clear technical basis into a conversation that has usually been commercial.
Specifications can also work forward, not just backward. Arturo recalls a category manager with a technical background who recently drew a molecule on a piece of paper and asked the market whether anyone could supply it.
“It&#8217;s amazing what we can do now. You don&#8217;t even really need to go out there and find a material that you&#8217;re looking to source. You can just put an idea into the market and allow suppliers to be responsive to that.”
That collapses a handoff that used to cost weeks, where an R&amp;D engineer translated a requirement to a sourcing colleague who then took it to market. Now it happens in one step, directly from the expert to the suppliers. For companies whose growth depends on speed to market, sourcing and validation is often the binding constraint, and this is where procurement stops defending margin and starts contributing to top-line revenue. 
“If you can reduce that cycle, then inevitably you&#8217;re going to grow revenue faster.”
<h2>Giving category managers time back</h2>
Faster cycles are one outcome. Freeing up a category manager&#8217;s time is the bigger one, and that has meant building a platform well beyond sourcing.
“Valdera has gone from being a sourcing platform to being that end-to-end cockpit for category managers to do all of the work that they do, from a category management suite that gives them visibility into all of their spend and recommends things that they could be doing, to market intelligence, and then to sourcing.”
Sourcing ultimately delivers the value, but category management and market intelligence pave the way. Knowing where prices are moving, which regions are worth a look, and whether supply is tightening is what turns a list of suppliers into a negotiating position. This is exactly the knowledge that used to take years on the job to accumulate.
Valdera started as a data science company, so that data layer has been core to the platform from day one. What keeps evolving is how much of the legwork the AI carries, which is what gives a category manager room for the strategic parts of the role.
The Valdera Agent is where that shows up most clearly. As Arturo describes it, the agent, “Actively makes recommendations on what should be a buyer&#8217;s priorities, what kinds of conversations they could be having, and where they have risks and opportunities.&#8221;
For Arturo, that is the whole point.
“The highlight for me is seeing the shift from category managers who are stuck in the weeds because they don&#8217;t have the information or they don&#8217;t understand the market, to getting to do the things that add value:  relationship building, and making strategic decisions. The way that we&#8217;re unblocking people to do the hard but important part, is super exciting.”
Curious to see the platform in action? <a href="https://prod.valdera.com/signup?utm_source=website&amp;utm_medium=blog&amp;utm_campaign=ai-misconceptions-raw-material-procurement">Start your trial today</a>, no credit card required.

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