Next Generation Strategic Sourcing: Meet aiSource

April Guenet: 

I’m April Guenet, a Senior Product Marketing Manager at aPriori, and my job is to understand the problems our customers are trying to solve, particularly in sourcing, and translate what our product does into something that actually maps to those problems. 

Today, buyers are often at an information disadvantage, not because suppliers are trying to pull a fast one over on you, but because a quote usually reflects how that supplier knows how to make the part, and that might not necessarily be the most efficient or cost-optimal way to make it.  There might be a better material stock or a process change that cuts the manual work and reduces the labor cost.  But the buyer just doesn’t have that visibility or the technical background to go toe-to-toe with the supplier and get that information from them.  Teams try to close that gap in a number of ways, but sometimes there’s a problem. First, they negotiate without that cost intelligence.  Negotiation is the number one savings lever that procurement leaders point to today. Hackett puts it at 48 percent.  But most buyers walk in with one move, which has been mentioned earlier in the sessions today: asking for a percentage off the top. 

“Give me 3 to 5 percent off all of the parts that I source with you.”  That’s not really a negotiation with the supplier. It’s more of a request. 

The cost of that shows up downstream.  McKinsey finds that nearly half of projected savings never reach the P&L.  A third is lost in planning to overly optimistic assumptions and siloed teams. Another 20 percent is lost in execution due to slow pace and skills gaps.  It’s the same root cause.  Buyers don’t have the data to defend the numbers they’re asking for. 

Second, they invest in tools that require specialists to use.  Should-cost modeling and clean-sheet analysis are very powerful tools, but they can require a technical expert or someone in cost engineering in the room to interpret that information for a more commercially savvy buyer.  Typically, those tools stay with the specialists, and everyone else is negotiating blindly or with only half of the information they need to successfully procure those savings. 

Third, and this is the one everyone is looking at right now, they turn to AI, but without a true outcome in mind for how to use it.  Workload is climbing, headcount isn’t, and most of the technology spend is going into AI tools that really focus on productivity improvement but not cost intelligence.  Deloitte found the same gap from another angle.  Most people are using AI weekly, but very few call their own AI literacy advanced, and most say the training hasn’t really helped their role.  There’s a large investment in AI. It’s real.  But there’s still a data gap that remains. 

Negotiation without cost intelligence, tools without accessibility for everyone to use and leverage, AI without an outcome.  That’s the gap we’re trying to build aiSource to close. 

AI is clearly the investment everyone is making.  But there’s a question worth sitting with: What is the AI actually doing for you?  Deloitte’s 2025 CPO survey asked exactly that.  Sixty-eight percent of CPOs rank enhanced decision-making as GenAI’s top benefit.  But cost savings, the thing procurement is ultimately measured on, comes in well behind at just 29 percent. 

Procurement leaders have embraced AI, but the value is landing in the wrong place.  McKinsey’s October 2025 research backs this up from the productivity side as well.  AI copilots are delivering 25 to 40 percent productivity improvement, and that’s real.  But that number assumes that the person using the copilot knows how to prompt it well and knows how to judge whether the answer they get back is actually correct. 

It all comes back to that trust factor.  Not everyone on a sourcing team has built up that skill yet.  When they haven’t, a generic AI tool doesn’t just fail to help. It can actively work against you.  So that 25 to 40 percent productivity number isn’t a guarantee.  That’s really the gap. 

When we look at AI tools today in the procurement space, a lot of it is focused on this left side of the slide.  Faster RFQ generation, automated spend reporting, quicker contract summarization.  All useful when the person using it has the expertise to prompt it well and to sanity-check whether the answer is correct.  But that’s exactly the burden most buyers don’t need: expertise required from the user rather than built into the tool. 

aiSource, on the right-hand side here, is really outcome by design.  It’s purpose-built to provide that information, grounded in manufacturing cost data, and delivered to you right away without having to prompt it.  There’s not a lot of prompt engineering required, and it surfaces what matters most and what buyers need to know without having to ask.  It also guides responses so any non-expert can start using it immediately.  No AI literacy required, because the expertise is built into the product and not demanded from the user. 

So, a quick slide on what aiSource is.  It’s aPriori’s first AI-powered sourcing tool, really purpose-built to give every buyer that technical expertise and intelligence at the moment they need it most.  That shows up in a couple of ways. Bringing the cost, design, and sourcing teams together to turn alignment into leverage. Being able to coordinate those teams, break the silos that typically exist between different systems and data points, and have everyone working from the same cost truth. Making every cost negotiator perform like an expert.  Regardless of expertise, whether you’re a first-time buyer or a buyer with 10-plus years of experience, you’re all working from that same data set, and you have a standard approach to speaking with your supplier. Then bringing clarity to every supplier conversation.  When you’re getting feedback from your supplier, you have a structured response based on data to respond faster and go toe-to-toe with that supplier with the technical expertise that’s required. 

Ultimately, the goal is to capture more of that identified savings and have it make it to the P&L.  You’re capturing more because you’re getting to that root cause faster.  I want to spend a minute here because this is the piece that actually sets aiSource apart from other AI tools you might be seeing on the market. 

Most AI procurement tools reason from whatever you feed them: market data, commodity indices, public benchmarks, historical pricing.  The problem is, your supplier already knows all of that too.  They built their quotes against those same benchmarks, and that same history you’re using as analysis is based on supplier input. 

When a generic AI tool helps you challenge a quote, you’re often fighting with the same ammunition they used to build it. 

aiSource reasons from manufacturing reality instead, and it rests on two different layers.  The first is the physics-based foundation, which is rooted in manufacturing reality.  Should-cost: the true cost to make the part, plus the cost drivers underneath that cost and an understanding of what’s actually driving it.  Process: how the part is made.  Cycle times: how long it takes to produce.  Carbon and environmental impact information.  And then benchmarking that across industry and different regions.  This is the manufacturing intelligence layer. 

The second is the AI guidance layer that sits on top of it, turning all of that complexity into action, something you can take and use with you.  Recommended talk tracks and negotiation guidance.  Pushback coaching when you get those responses back from your supplier, so you can anticipate and respond with facts.  And real-time Q&A that gives instant answers grounded in data, not guesswork. 

One thing to know and to be really clear about: It’s not another AI chatbot.  It doesn’t just guess, and it doesn’t just pull from market benchmarks that your supplier has already priced against.  It reasons from how the part is actually made.   That’s the difference, and that’s the foundation for aiSource and what we’re about to show you. 

This is the framework I’ve been using to talk about how you can actually use aiSource, and it comes down to five different moves.  One is comparing the quoted cost against the should-cost.  Preparing and building confidence in your negotiation strategy from data grounded in reality, and making sure you’re confident and comfortable with the assumptions behind the estimate before going toe-to-toe with the supplier.  Negotiation: feeding that supplier pushback in and getting guided, fact-based responses back out.  Collaboration: because sourcing shouldn’t be in a silo, and we need to get everyone on the same page.  Aligning internally on design changes and supplier feedback, and making those decisions together.  Then closing out: reporting what those savings are and being able to calculate, “Okay, I’ve done this negotiation. Here’s my result. How can I feed this information back into my organization to show the value that we’re providing?” 

Each move builds on the last, and together they make up the supplier negotiation. 

Let’s walk through each one of these, starting with compare.  Compare sounds very simple, but it’s often the most time-consuming part of the negotiation process, and it can be where a negotiation is won or lost before it even begins.  With aiSource, you can place the supplier’s quote right beside the aPriori objective estimate, and you immediately start to see where the gap lives before the conversation even starts.  That’s what’s really going to change that conversation.  It’s not just the total delta. It’s a side-by-side breakdown of the cost categories.  When you know the number, you know the opportunity, and you know how to start approaching the supplier, you start to own that conversation. 

So let’s move into negotiation preparation and strategy.  With aiSource’s conversational AI, you can interrogate the assumptions behind the cost estimate, like cycle times, material specifications, and labor rates, before you ever sit down with a supplier.  You’re not walking in hoping to ask the right question.  You walk in already knowing what the numbers are, the questions to ask, and why you should be asking them.  You can ask the AI why a cost line is the way it is, identify the highest-leverage points of the quote, and generate targeted negotiation questions straight from the cost delta. 

Then, when we get into the actual negotiation, there’s always a moment when the supplier pushes back.  “Our costs are justified because of X.”  Most buyers either accept that, or they apply pressure without a real counterargument.  aiSource gives you a third option.  Feed that argument directly into aiSource, and the AI returns structured, fact-based responses drawn from that manufacturing reality.  The goal is to turn what could feel like an adversarial attack on margin into a shared conversation about cost truth, both sides working from the same data and getting to the same result.  Every response is grounded and defensible.  But as I mentioned before, sourcing doesn’t happen in a vacuum, especially in manufacturing where a supplier might come back with a design change suggestion and suddenly you need the engineering team in the room too.  That handoff is where a lot of deals can slow down.  Three email threads, two weeks pass, and your supplier is wondering where the heck you went.  aiSource closes that gap through internal collaboration by bringing sourcing, cost engineering, design, and manufacturing into a shared workspace to review supplier feedback, validate design trade-off decisions, and confirm that final negotiation position together. 

The last use case I’d like to highlight is close, and it’s one that connects everything back to where leadership actually cares: realizing the savings.  You’ve done the negotiation. You’ve challenged the right cost drivers. You’ve reached an outcome.  Now you need to show what you’ve achieved.  You can easily ask aiSource to generate a complete negotiation summary.  What savings were captured? Which cost drivers were challenged? What was the outcome versus the target?  It’s structured so any buyer can paste it directly into a management report and show exactly how the team is hitting company savings goals. 

I want to connect this back to where we started.  Remember that number from the beginning.  Nearly half of projected savings never reach the P&L.  A lot of it isn’t lost because the work wasn’t done. It’s lost because nobody could tell the story of the work well enough or consistently enough for leadership to see it.  Procurement and supply chain have always driven real value, but this function has had to fight for a seat at the table.  Part of that fight is being able to walk in and say, “Here’s what’s going to hit your P&L, and here’s exactly the strategy I took to get us there.”  This gives every buyer, not just the best negotiator, a fast, scalable way to make that case.  That’s not just good reporting.  Over time, that’s the kind of proof point that justifies growing that team.  

Everything you’ve just seen is available to use in aiSource today.