In the rapidly evolving landscape of enterprise technology, the traditional debate of “build vs. buy” is undergoing a seismic shift, largely driven by advancements in artificial intelligence (AI). For decades, organizations have grappled with the question of whether to develop software solutions in-house or purchase them from external vendors. The conventional wisdom was clear: build if the solution is core to the business and buy if it isn’t. However, this framework is becoming increasingly obsolete as AI democratizes software development, enabling even non-technical employees to create functional prototypes in a fraction of the time and cost previously required.
Imagine a scenario where a company is on the verge of finalizing a substantial vendor contract for a software solution. The pitch has gone smoothly; the demo was impressive, the pricing aligns with the budget, and the timeline appears feasible. Just as the decision-makers are about to give the green light, a member of the finance team enters the room. They casually mention that they built a working prototype of the proposed solution in just two hours using an AI tool like Cursor. This prototype, while not perfect, closely mirrors the vendor’s offering and comes at a fraction of the cost—essentially just the time it took to create it.
This scenario encapsulates the profound implications of AI on the software acquisition process. It challenges long-held assumptions about who can create software, how it is developed, and the decision-making processes surrounding it. The emergence of AI tools has made software development accessible to a broader range of employees, effectively blurring the lines between technical and non-technical roles. This shift is not merely a trend; it represents a fundamental change in how organizations approach problem-solving and innovation.
The Old Framework
Historically, the decision to build or buy software was straightforward. Companies would assess their needs and determine whether developing a custom solution was justified based on factors such as strategic importance, resource availability, and potential return on investment. Building software often involved significant investments of time and money, requiring skilled engineers to write code, manage infrastructure, and maintain the system over time. Conversely, purchasing software was seen as a quicker, safer option, providing immediate access to support and expertise.
However, the landscape has changed dramatically. AI has transformed the software development process, reducing the time and complexity associated with building applications. Tasks that once took weeks or months can now be accomplished in hours, and the need for deep programming knowledge has diminished. Instead, fluency in plain English is often sufficient to instruct AI tools to generate code, allowing employees from various departments to contribute to software development.
When the Cost of Building Collapses
As the barriers to entry for software development continue to fall, the traditional dichotomy of build versus buy is being replaced by a more nuanced approach. Organizations are beginning to recognize that they can leverage AI to build lightweight prototypes that help clarify their actual needs before making significant financial commitments to external vendors. This new paradigm shifts the focus from simply deciding whether to build or buy to understanding what to build in the first place.
For instance, consider a company that identifies a need for a specific tool to streamline its operations. In the past, the process might have involved lengthy discussions, extensive research, and multiple vendor demos. Now, teams can quickly create a basic version of the tool using AI, allowing them to experiment and iterate based on real-world feedback. This rapid prototyping enables organizations to gain insights into their requirements, identify which features deliver value, and determine whether a purchased solution would genuinely enhance their operations.
The Role of Non-Technical Employees
One of the most significant changes brought about by AI is the empowerment of non-technical employees to engage in software development. In many organizations, engineering teams have historically been the gatekeepers of technology, controlling the development process and determining which projects receive resources. However, with AI tools, employees from various backgrounds can take initiative and address issues directly.
For example, a customer experience representative may notice a bug in the company’s communication platform. Instead of submitting a support ticket and waiting for the engineering team to address the issue, they can use an AI tool to describe the problem and generate a fix. Within minutes, the solution is live in production, demonstrating how AI can enable rapid responses to customer feedback without relying solely on technical staff.
This shift not only accelerates problem-solving but also fosters a culture of innovation within organizations. Employees who may not have considered themselves capable of contributing to software development can now actively participate in creating solutions that improve their work processes. As a result, organizations can tap into the collective intelligence of their workforce, leading to more effective and relevant solutions.
Reversing the Build vs. Buy Decision-Making Process
The traditional model of defining needs before deciding whether to build or buy is being inverted. Instead of spending months identifying requirements and evaluating vendor solutions, organizations can now build lightweight prototypes using AI to explore their needs. This approach allows teams to conduct controlled experiments, testing whether a particular problem is worth solving and which features are essential.
By building first, organizations can avoid the costly mistakes associated with purchasing software that ultimately does not meet their needs. They can determine whether a problem is significant enough to warrant a solution and, if so, what that solution should look like. When it comes time to evaluate vendor offerings, decision-makers will be armed with firsthand knowledge of their requirements, enabling them to ask informed questions and negotiate from a position of strength.
The Trap of Cargo Cult AI
While the rise of AI presents exciting opportunities, organizations must also be cautious of falling into the trap of “cargo cult AI.” This term, coined by physicist Richard Feynman, refers to the phenomenon where individuals mimic the superficial aspects of a successful practice without understanding its underlying principles. In the context of AI, many companies are rushing to adopt AI-powered tools without critically assessing whether these solutions genuinely enhance their operations.
As AI becomes a buzzword, vendors are eager to label their products as “AI-driven,” often adding superficial features like chatbots or auto-complete functions to existing systems. However, these enhancements may not provide meaningful value to customers. Organizations must resist the temptation to invest in AI tools simply because they are trendy and instead focus on whether these tools will genuinely transform how work gets done.
To avoid this pitfall, companies should prioritize understanding their unique needs and challenges before seeking out AI solutions. By leveraging AI to prototype and test ideas internally, organizations can ensure that any tools they ultimately purchase are aligned with their specific requirements and will deliver tangible benefits.
Empowering Finance Teams
One of the most exciting developments in this new paradigm is the empowerment of finance teams. Traditionally, finance departments have played a supporting role in technology decisions, often relying on IT or engineering teams to define requirements and evaluate vendor options. However, with the ability to prototype solutions using AI, finance teams can take a more proactive approach.
Finance professionals can now test various workflows and processes before committing to expensive software purchases. For example, when evaluating vendor management software, they can create a basic version of the desired functionality using AI tools. This hands-on experience allows them to determine whether the problem lies in the tooling itself or in the underlying processes. By gaining clarity on their needs, finance teams can make more informed decisions and negotiate better terms with vendors.
Moreover, this newfound capability enables finance teams to understand what constitutes a “good” solution. They can enter vendor demos with a clear sense of their requirements and edge cases, allowing them to assess whether a vendor’s offering genuinely addresses their needs. This shift not only streamlines the decision-making process but also reduces the risk of investing in solutions that do not deliver value.
The New Paradigm: Build to Learn What to Buy
As organizations embrace this shift, the mantra is evolving from “build or buy” to “build to learn what to buy.” This new approach encourages experimentation and learning, enabling teams to iterate quickly and refine their understanding of what they truly need. It recognizes that the process of acquiring software is no longer linear; instead, it is a dynamic cycle of building, testing, and refining.
Companies that adopt this mindset will be better positioned to navigate the complexities of the modern software landscape. They will be able to move faster, spend smarter, and make more informed decisions about the tools they choose to implement. By leveraging AI to prototype solutions, organizations can gain deeper insights into their operations and identify opportunities for improvement.
The Future of Software Acquisition
The transformation of the build vs. buy debate is not a distant future; it is happening now. Across industries, organizations are recognizing the potential of AI to revolutionize how they approach software development and acquisition. Customer representatives are using AI to address product issues in real-time, finance teams are prototyping analytical tools, and cross-functional collaboration is becoming the norm.
As this trend continues to evolve, companies that embrace the shift toward AI-driven software development will gain a competitive advantage. They will be able to innovate more rapidly, respond to customer needs more effectively, and make smarter investments in technology. Conversely, organizations that cling to outdated practices will find themselves at a disadvantage, struggling to keep pace with their more agile counterparts.
In conclusion, the rise of AI is fundamentally reshaping the landscape of enterprise software acquisition. The traditional dichotomy of build versus buy is giving way to a more nuanced approach that emphasizes rapid prototyping, collaboration, and informed decision-making. As organizations harness the power of AI to empower their employees and streamline their processes, they will unlock new levels of efficiency and innovation, ultimately transforming how they operate in an increasingly digital world.