How Walmart Automated Supplier Negotiations

It’s an age-old problem in procurement: Corporate buyers lack the time to negotiate fully with all suppliers. Historically this has left untapped value on the table for both buyers and suppliers. To address this challenge, Walmart deployed AI-powered negotiations software with a text-based interface (ie, a chatbot) to connect with suppliers. So far, the chatbot is negotiating and closing agreements with 68% of suppliers approached, with each side gaining something it values. This article offers four lessons to deliver results from automated procurement negotiations: move quickly to a production pilot, start with indirect spend categories with pre-approved suppliers, decide on acceptable negotiation trade-offs, and scale by extending geographies, categories, and use cases .

Walmart, like most organizations with large procurement operations, can’t possibly conduct focused negotiations with all of its 100,000-plus suppliers. As a result, around 20% of its suppliers have signed agreements with cookie-cutter terms that are often not negotiated. It’s not the optimal way to engage with these “tail-end suppliers.” But the cost of hiring more human buyers to negotiate with them would exceed any additional value.

Walmart solved the problem with artificial intelligence–powered software that includes a text-based interface (or chatbot) that negotiates with human suppliers on behalf of Walmart. Walmart Canada piloted the solution in January 2021 and used supplier feedback to hone the system. Walmart has since deployed the solution in three other countries, and Walmart operations in more countries plan to implement the technology soon.

This article shares four lessons on how to use automated procurement negotiations in ways that benefit both buyers and suppliers. Such systems can generate savings, improve the terms for both parties, and increase the flexibility and resilience of a supply chain.

The Pilot

With advances in artificial intelligence (AI), Walmart began exploring the possibility of automating procurement negotiations for tail-end suppliers and licensed a software product called Pactum AI in 2019. The deployment was postponed because of Covid-19, but one of us (Michael DeWitt) resurrected the initiative a year later, in January 2021, for his organization, Walmart International.

Since Walmart had already experimented with the software in a sandbox environment, Walmart International moved directly to a small pilot in the company’s Canadian business. The pilot, which lasted three months, included a variety of stakeholders — 89 suppliers, five buyers, and representatives from Walmart Canada’s finance, treasury, and legal departments — and Pactum, the company that had created the underlying AI technology.

At the outset, Walmart International estimated that the system would yield a positive return on investment if the chatbot could close deals with 20% of the suppliers involved in the pilot. The retailer selected “goods not for resale” — categories such as fleet services, carts, and other equipment used in retail stores — and not products sold to Walmart customers. It decided to focus on suppliers for whom there was accurate data on payment terms and where there was a clear opportunity to improve payment terms and secure additional discounts.


Walmart International targeted payment schedules, hoping to negotiate early payment discounts or extended payment terms without discounts. In exchange, Walmart would offer suppliers the option to change Walmart’s right to terminate contracts immediately without cause (known as “termination for convenience”) to providing a 30-, 60-, or 90-day written termination notice. Walmart would also selectively offer suppliers opportunities for growth in assortment and sales volume in exchange for price discounts.

Internal buyers selected the suppliers to target and created training scenarios for Pactum AI’s machine learning algorithm. The scenarios were used to create structured scripts to guide suppliers through negotiations. Suppliers could respond to scenarios at their own pace.

Walmart International invited around 100 tail-end suppliers to try the solution. Eighty-nine agreed to participate. The chatbot was successful in reaching an agreement with 64% of them — well above the 20% target — and with an average negotiation turnaround of 11 days. Walmart gained, on average, 1.5% in savings on the spent negotiated and an extension of payment terms to an average of 35 days.

In post-pilot interviews with suppliers that engaged in successful negotiations, 83% of them described the system as easy to use and liked the ability to make a counteroffer and the time the system gave them to think about the negotiation at their own pace. For example, Ben Garisto, president of MIWE, a bakery-equipment manufacturer, said, “During in-person negotiations, you don’t always have the questions in advance, and you are responding in real time. Other types of automated requests for proposals sometimes feel a bit like a template with little room to tell your story.”

Several suppliers, however, still would have liked to negotiate face to face. Other suppliers wanted a less verbose, more fluid script instead of prohibiting suppliers from backtracking to early steps in the negotiation.

After the production pilot, Walmart improved the scenarios and scripts and extended the solution to suppliers in the United States, Chile, and South Africa. So far, the chatbot has closed deals with 68% of the suppliers approached and generated an average savings of 3%.

Other companies interested in automating procurement can apply these lessons on how to develop and introduce such a system:

1. Move to a production pilot quickly.

The AI ​​journey for many companies languishes in the proof-of-concept phase — fewer than half make it into production, according to Gartner. That’s because proof-of-concept phases focus on technical capabilities instead of business goals. Walmart decided to skip a proof-of-concept phase and to go straight to a production pilot focused on business goals.

Walmart’s “business owners” — people in charge of budgets and responsible for spending with suppliers (for example, operations for store supplies and IT for hardware and software) — helped to create negotiation use cases and scenarios. Walmart’s buyers provided crucial subject-matter expertise on the negotiation scenarios needed to train the chatbot and nominated suppliers to participate in the pilot (based on which suppliers conduct enough business with Walmart to warrant a negotiation and which would welcome a chance to negotiate). The legal team made sure the chatbot’s script and resulting contract conformed to Walmart contracting standards and policies.

2. Start with indirect-spend categories and pre-approved suppliers.

Walmart began with goods not for resale (ie, not sold to its retail customers) to minimize the risks to the business posed by the testing of a new procurement practice. Walmart also focused on pre-approved suppliers so the need to validate new suppliers wouldn’t delay the start of the pilot.

3. Decide on acceptable trade-offs.

Automated procurement requires precisely defining the boundaries of what the buyer is willing to concede in exchange for what he wants. For example, the AI ​​chatbot needs to know the specific trade-offs the buyer is willing to give for, say, moving from full payments in 10 days after receipt of the invoice to receiving payment in 15, 20, 30, 45, or 60 days after receipt of invoice in exchange for improved termination terms and the opportunities for suppliers to expand their business with Walmart.

4. Scale by expanding geographies, categories, and use cases.

Walmart’s motto for this project was to “nail it and scale it.” Successful production pilots helped Walmart sell the solution to other parts of the business. After the pilot in Canada, the United States, Chile, and South Africa, deployments in Mexico, Central America, and China are imminent. The categories have also expanded to include route rate negotiations for transportation and some goods for resale. Some mid-tier suppliers now use the system, and the chatbot is multilingual.

Scaling has increased productivity because the software learns from each negotiation, reducing the setup time for new categories. Additionally, the chatbot can run 2,000 negotiations simultaneously — something no human buyer can do.

One can see the trajectory: As terms and conditions become more algorithmic, fewer suppliers and parts of spending pools will go unmanaged. Procurement professionals will focus less on negotiating agreements and more on strategic relationships, exceptions, and continuous improvement.

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