.svg)
The age-old “build vs. buy” debate presents a familiar crossroads for procurement professionals. This fundamental question arises for many products when developing a category strategy, but it is particularly relevant for software. As AI agents become more commonplace in the procurement world, this debate has intensified as many organizations consider building their own AI agents and use cases. But is this the right decision?
This blog explains the nuances of the build vs. buy decision, outlines procurement AI-specific considerations, and explains how buying existing solutions can help procurement teams expedite their path to value.
In the build vs. buy debate context, “build” refers to developing a custom solution in-house, whereas “buy” means purchasing or subscribing to an existing solution from a third-party vendor. The build vs. buy question frequently recurs, as each option has unique benefits and trade-offs concerning cost, speed to market, flexibility, and risk.
Organizations often believe that building their own software in-house would be more beneficial than buying from an external vendor. This belief is rooted in a few key assumptions and expected benefits:
Sounds too good to be true, no? If this were the case, wouldn’t every enterprise build its own software stack instead of buying generic solutions?
AI use cases and agents are emerging as powerful tools, capable of automating routine tasks, extracting insights from large volumes of data, and even driving autonomous decision-making. In Procurement, our overview of AI use cases and AI agents spans possible and available applications across the end-to-end procurement process, covering everything from spend analytics to category management, sourcing and negotiations, contract/supplier/risk management, intake, and purchasing.
As these capabilities become more accessible, many organizations are exploring how to adopt them, and the build vs. buy debate resurfaces. Many organizations believe they can (and should) build their own AI applications using internal resources to achieve the potential benefits outlined above. But there is a caveat, and this may not be the best path to value. Like any other Total Cost of Ownership (TCO) consideration, developing software has many potential downsides and hidden costs that need to be considered. And purpose-built, domain-specific AI use cases and agents have their own complexity.
When deciding whether to build or buy AI applications for procurement, teams must consider the following key factors:
Unless your organization’s primary focus is software development, creating procurement software or AI applications in-house is likely a distraction from your core business. Diverting valuable resources toward developing tools can stretch internal IT and Procurement teams thin, delay time-to-value, and ultimately diminish focus on achieving core business objectives.
Initial estimates often underestimate the true cost of building software. According to Gartner, developing a single AI agent can cost between $750,000 and $1.5 million, factoring in talent, infrastructure, and time. These costs can escalate quickly if the project scope expands or if multiple agents are needed. When making the build vs. buy decision, procurement must consider the TCO across the life span of the solution, which includes development, testing, project management, ongoing operational and maintenance costs, and unforeseen expenses.
IT resources are scarce and expensive. Should procurement decide to build their own software or AI agents, they will compete with other business functions that drive innovation and revenue for the organization for access to IT teams. The way in which the SAP S/4HANA migration is already pushing organizations’ IT teams is a valuable reminder of the fragile nature of available resources.
Developing AI tools is a complex process beyond most IT teams' typical skill set. Successful AI projects require deep subject matter expertise in data science, machine learning, software engineering, and—critically—procurement and supply chain.
While it is already extremely challenging for organizations that are not software companies to attract and retain exceptional technical talent in the AI field, it is unlikely to find individuals who also possess strong product and subject-matter expertise in procurement.
Software development for Procurement requires a combination of advanced technical skills and deep procurement knowledge. Defining use cases, processes, (data) requirements, and the interconnectivity with other systems cannot be done on the side by your process lead.
Procurement software and AI applications are not “set and forget”. They require ongoing monitoring, retraining, and updates to remain effective as data and business needs evolve. Maintenance demands can quickly outstrip initial development efforts and drain internal resources. While technical documentation can help, changing product owners or restructuring a business can quickly compromise the consistency of the people working on the project.
Commercial software platforms are designed to scale across multiple use cases and agents, making it easy to add more use cases and AI capabilities as and when needed. Conversely, in-house solutions usually target very specific, isolated use cases. As a result, they may struggle to keep pace with evolving business needs, regulatory requirements, or time-sensitive opportunities.
Building AI agents from scratch can take months or even years, delaying the realization of the key benefits organizations seek. In contrast, buying accelerates deployment, allowing organizations to capitalize on AI capabilities much sooner. It is easy to have an idea, but the first six months will most likely have passed before the project team even starts working on it.
The pace of innovation in AI is accelerating rapidly, with fundamental updates and new capabilities emerging nearly every week, often supported by a vast and active community. Unless your organization is dedicated to continuously tracking and integrating these developments, in-house solutions will struggle to keep pace with the evolving possibilities. This technology gap will only widen over time, making it increasingly challenging to maximize value and remain competitive with companies that focus on adopting best-in-class AI.
Procurement software and AI applications represent classic build vs. buy decisions. While building your own software and AI applications sounds exciting and logical, many considerations around the practicability, scalability, and durability should be factored into the TCO decision. Procure Ai provides an advanced AI automation platform specifically designed for procurement. It combines a powerful AI data foundation with a suite of ready-to-use and highly configurable agents that can scale with your requirements and ambition.
The Procure Ai data foundation manages the underlying technology required to run AI agents effectively, including secure access to proprietary enterprise data, the enrichment and structuring of that data, and integrating advanced components such as vector embeddings. The platform leverages a wide range of foundational AI models to access, process, and utilize the available data and build out-of-the-box agents and use cases on top of it.
The Procure Ai agents are ready to operate immediately, allowing teams to configure and specify precisely how each agent should function, set boundaries, and ensure compliance with security and trust requirements. But we don’t only enable our customers to use and configure existing agents, but also build their own agents as co-innovation partners based on their specific challenges. Think of it as AI as a Service. Or the platform advantage.
The build vs. buy decision remains alive for procurement teams, especially as AI agents become central to driving efficiency and value. While building seems to offer benefits such as increased control and better customization, the TCO, complexity, and risks for developing for AI agents are often underestimated. Buying a dedicated AI platform for procurement can expedite time-to-value, reduce risk, and limit the burden on organizations’ IT teams.
One of Procure Ai's major strengths is our unique talent pool and subject matter expertise. We invest significantly in developing deep procurement and supply chain knowledge across our entire team, from technical experts to customer success specialists, to provide our customers with a level of subject-matter understanding and technical know-how that is extremely hard to find in the market. It is this expertise that allows us to offer a best-in-class platform and to be a dedicated partner for co-innovation projects.
We have dedicated the last five years to building a connected and secure automation platform with a strong data ontology and foundation, and various AI applications and agents purposely built for Procurement. It would require a massive investment of time and expertise for an organization to build similar capabilities internally and keep pace with technological advancements. We therefore firmly believe that implementing a data foundation like Procure Ai’s, equipped with our established agents can accelerate the deployment of AI in Procurement by a factor of six or more compared to in-house development, shortening timelines from up to two years to just a few months, and providing tangible value from the outset.
Contact us to learn more about how Procure Ai’s Procurement Automation Platform can help you realize the benefits of AI agents and help you scale in line with your ambitions.
%201.svg)
.svg)
