Imagine the scene: you’re deeply engrossed in a vendor presentation, the kind where every slide promises efficiency and innovation. The software demo looks slick, the pricing seems surprisingly agreeable, and the implementation timeline feels almost too good to be true. Around the table, heads are nodding in agreement, and you’re just moments away from giving the green light.
Then, an unexpected guest from your finance department steps in. Their gaze falls on the presentation deck, and a slight frown creases their brow. A few minutes later, your Slack pings with a message: “You know, I actually whipped up a version of this last week. It took me about two hours using Cursor. Want to take a look?”
A moment of stunned silence.
This individual, you know for a fact, has never written a single line of code in their life. Their domain is spreadsheets, budgets, and financial modeling, not JavaScript or Python. Yet, there they are, displaying a functional prototype on their laptop that accomplishes… almost exactly what the vendor just spent an hour pitching. It’s certainly not polished, a bit rough around the edges perhaps, but it works. And it didn’t come with a six-figure price tag; just two hours of focused effort. This scenario vividly illustrates how AI changes build vs buy, radically shifting the landscape of software procurement and development in enterprises today.
AI Changes Build vs Buy: The New Paradigm for Software Decisions
The traditional dichotomy of “build versus buy” has long been a cornerstone of strategic software decisions within organizations. For decades, the path was clear: either invest significant internal resources into developing a bespoke solution, or acquire an off-the-shelf product from a vendor. This fundamental choice was guided by a seemingly immutable set of principles, often hinging on whether the software represented a core competency or a peripheral utility. However, the relentless acceleration of artificial intelligence capabilities has not merely tweaked this framework; it has fundamentally re-engineered it, presenting a new paradigm where the lines between internal creation and external acquisition are increasingly blurred, demanding a fresh perspective on enterprise technology strategy.
The Obsolete Framework: A Look Back at Traditional Software Decisions
For a substantial period, the software decision-making process was a relatively straightforward, albeit often arduous, exercise. The guiding principle was elegantly simple: “Build if it’s core to your business; buy if it isn’t.” This logic held immense sway because “building” entailed substantial investment and significant organizational overhead. It meant diverting highly skilled and often scarce engineering talent from other critical projects, drafting intricate specifications, planning exhaustive development sprints, managing complex infrastructure, and preparing for the inevitable long tail of maintenance, updates, and bug fixes. The capital expenditure and operational costs associated with bespoke software development were, and still can be, formidable.
Conversely, “buying” offered an enticing alternative: speed, predictability, and a perceived reduction in risk. Companies would pay a premium for a pre-packaged solution, along with the promise of vendor support, regular updates, and the peace of mind that comes from offloading maintenance responsibilities. The decision was often a pragmatic balance of cost, time-to-market, and the strategic importance of the software in question. This established framework, while effective for its era, was rooted in a world where software creation was the exclusive domain of specialized programmers, and the barrier to entry for development was exceptionally high. The very foundation of this build vs. buy dichotomy rested on the high cost and complexity of building, a foundation that AI is now systematically eroding.
AI’s Transformative Power: Democratizing Software Development
Something truly fundamental has shifted. Artificial intelligence has emerged not just as a feature within software, but as a catalyst for software creation itself. What once required weeks, if not months, of highly specialized coding can now be prototyped in hours. The need for fluency in complex programming languages is increasingly being supplanted by the ability to articulate needs in plain English. This revolutionary shift underscores how AI changes build vs buy strategy, making the act of building software accessible to an unprecedented segment of the workforce.
The rise of advanced large language models (LLMs) and sophisticated no-code/low-code AI platforms has dramatically lowered the entry barrier. These tools act as intelligent co-pilots and automated development engines, capable of generating code snippets, entire functions, and even complete application prototypes based on natural language prompts. This capability directly addresses how AI impacts software decision making by empowering individuals who are experts in their business domains but not in coding. Recent data from industry analysts suggests that developers using AI-powered coding assistants can experience a productivity boost of 30-50% for certain tasks, illustrating the profound impact of this new tooling.
This paradigm shift is particularly significant in the context of
Democratizing software development with AI tools
. No longer is innovation confined to the IT department. Business analysts, marketing specialists, and operations managers can now actively participate in the creation of tools tailored to their immediate needs. This distributed innovation model accelerates problem-solving and fosters a culture of agile development, aligning perfectly with the modern demand for rapid iteration and responsiveness.
Building to Learn: Uncovering True Needs with AI
Many organizations, perhaps without explicitly planning to, find themselves building an increasing number of internal tools not because market offerings are absent, but because the precise solutions they need simply do not exist in a packaged form—yet. Through this process of agile, AI-assisted development, companies cultivate a profound, almost visceral understanding of what truly works, what delivers tangible value, and what is merely theoretical. This invaluable clarity is not derived from glossy vendor presentations or abstract analyst reports, but from direct, hands-on engagement with the problem and its potential AI-powered solutions. This approach epitomizes the “Build to learn what to buy AI approach.”
By engaging in preliminary, lightweight building with AI, teams can quickly validate assumptions, identify critical pain points, and precisely map out desired functionalities. This exploration enables them to discern where AI creates genuine leverage versus where it merely adds complexity or noise. Only after gaining this hard-earned insight and practical experience do they transition to a buying phase. At this point, they are exceptionally well-informed, equipped with an exact understanding of their requirements, and capable of distinguishing genuine substance from marketing hype. This strategic shift is crucial for effective
Strategic AI software adoption insights
. They can ask pointed questions that challenge vendors, often because they’ve already constructed a rudimentary version of the very solution being pitched, revealing the depth of their understanding in the process.
The concept of
AI driven rapid software prototyping techniques
empowers teams to create functional mock-ups and MVPs (Minimum Viable Products) with unprecedented speed. This allows for immediate user feedback, iterative refinement, and a clear understanding of problem-solution fit before any significant financial commitment is made. This process drastically reduces the risk of investing in software that doesn’t align with actual operational needs, a common pitfall in the traditional build-or-buy model.
When Anyone Can Build: The Rise of the Citizen Developer
Consider a recent scenario: a customer experience (CX) team member identifies a minor bug reported by a customer through a Slack channel. In a conventional setup, this would typically trigger a support ticket, followed by a wait for an overloaded engineering team to address it. However, in an AI-empowered environment, the narrative changes dramatically. The CX agent, with no formal coding background, opens an AI coding assistant like Cursor, describes the desired fix in natural language, and allows the AI to generate the necessary code. After a quick review, they submit a pull request, which engineering swiftly reviews and merges. Within minutes of the customer complaint, the fix is live in production.
This incident vividly illustrates the power of
Non-technical employees building software with AI
. The individual behind this rapid fix might not discern the fundamental differences between Python and JavaScript, yet they were able to resolve a technical issue effectively and efficiently. This highlights a crucial point: AI is now proficient enough to handle approximately 80% of the routine coding tasks that historically consumed significant engineering time and necessitated elaborate sprint planning meetings. This capability is effectively
Empowering teams with AI development tools
across all departments.
AI is systematically dissolving the long-standing boundary between technical and non-technical roles. Work that was once a bottleneck, exclusively dependent on engineering capacity, is now being performed by the individuals closest to the problem—those who possess the most direct contextual understanding. This transformation is not a future prospect; it is actively unfolding in forward-thinking organizations across the USA, representing a significant evolution in
AI in enterprise software strategy USA
.
Furthermore, this development fosters innovation in areas often overlooked by core engineering teams. For example, a marketing team might quickly prototype an internal tool for content tagging and analysis using
Best no-code AI platforms for enterprises
, bypassing lengthy development cycles. Similarly, operations teams can leverage these platforms to create
AI for business process automation examples
, streamlining repetitive tasks without deep technical expertise. This ability to rapidly create
Custom business solutions with AI cost
effectively moves the power of software creation into the hands of those who stand to gain the most from its immediate application.
The Strategic Inversion: Rethinking the Decision-Making Sequence
For finance leaders, the implications of this shift are particularly profound, as AI has fundamentally inverted the strategic logic underpinning the build versus buy decision. The traditional model followed a linear path:
- Define the need.
- Decide whether to build or buy.
This sequence was fraught with challenges. Defining the need alone could consume months, often requiring deep technical expertise or leading to costly trial-and-error implementations with vendors. Teams would endure countless demos, struggling to envision whether a proposed solution truly addressed their specific problems. Then came the protracted negotiations, complex implementations, data migrations, and workflow adjustments. Six months and often six figures later, the organization would finally discover whether their initial assumptions were correct. The inherent risk and cost of failure were substantial, often leading to
Understanding AI software investment decisions
that were, in hindsight, suboptimal.
With AI, this entire sequence is dramatically reordered, presenting a new model focused on learning and validation:
- Build something lightweight with AI.
- Use it to deeply understand what you actually need.
- Then decide whether to buy (and you’ll know precisely why).
This iterative approach facilitates controlled experiments. Organizations can quickly determine if a perceived problem genuinely warrants a software solution. They can discover which features deliver tangible value and which are merely impressive during sales demonstrations. Only after this rigorous internal validation do they engage with the market. Instead of relying on external vendors to define their needs, companies can now proactively identify and articulate their requirements from a position of informed strength. This is the essence of
Why AI is redefining build vs buy logic
.
Consider the numerous software purchases that, in retrospect, addressed problems that never truly existed within the organization. How often have companies found themselves several months into an implementation, questioning whether the investment was genuinely beneficial or simply an effort to justify a previous expenditure? This new AI-driven approach reframes the procurement question entirely: “Does this commercial solution solve the problem significantly better than what we have already proven we can build internally?” This single reframe transforms the entire conversation. Teams approach vendor discussions with genuine insights, ask sharper, more incisive questions, and negotiate from a position of informed choice rather than desperate need. Crucially, they mitigate the most expensive mistake in enterprise software: investing in a solution for a problem they never truly had. This shift also enables significant
AI impact on IT spending and budgets
by reducing wasted investments and optimizing resource allocation.
Avoiding the “Cargo Cult” Trap: True AI Transformation
As these new capabilities emerge, a concerning trend is the rush by some companies to adopt AI superficially. Driven by the imperative to be “AI native,” they embark on a procurement spree, acquiring tools merely because they boast GPT integrations, chatbot interfaces, or “AI” prominently displayed on their marketing materials. They believe they are undergoing a profound transformation, but in reality, they are often missing the point.
This phenomenon echoes what physicist Richard Feynman termed “cargo cult science.” After World War II, isolated island communities in the South Pacific constructed imitation airstrips and control towers, meticulously mimicking the external forms they had observed during the war. They hoped these gestures would entice planes laden with cargo to return. They had all the outward appearances of an airport: towers, headsets, even individuals miming flight controllers. Yet, no planes ever landed, because the form was entirely divorced from the underlying function. This analogy perfectly describes the current state of
AI innovation in company operations
in many boardrooms across the corporate landscape.
Leaders are acquiring AI-branded tools without critically assessing whether these products meaningfully alter work processes, empower employees, or unlock novel operational efficiencies. They are building the airstrip, but the planes—the genuine transformative benefits—are failing to materialize. The market itself is often complicit in this trap. Nearly every SaaS product now features a bolted-on chatbot or an auto-complete function, slapped with an “AI” label that has largely lost its meaning. It has become a mere checkbox for vendors, irrespective of whether it generates actual value for customers or contributes to genuine
AI’s role in modern software strategy
. A critical understanding of
Understanding AI software investment decisions
is vital to avoid these pitfalls.
The Finance Team’s New Superpower: Data-Driven Procurement
Perhaps one of the most exciting aspects of this evolving landscape is the newfound power it grants to finance teams. No longer are they relegated to merely approving budgets based on speculative vendor claims. They don’t have to wager six figures on the promise of a sales deck. Instead, they can now test, learn, and validate before committing significant capital. This capability directly enhances the
Cost efficiency of AI software development
and procurement processes.
For instance, if a finance team is evaluating new vendor management software, they can leverage AI tools to rapidly prototype the core workflows. This hands-on experience allows them to quickly ascertain whether they are primarily addressing a tooling deficiency or a more fundamental process problem. Crucially, they can even determine if software is truly necessary at all. This is where
Finance team AI tool prototyping
becomes a game-changer, moving beyond theoretical discussions to practical, empirical validation.
This approach doesn’t imply that organizations will, or should, build everything internally. Commercial enterprise tools exist for compelling reasons: scalability, comprehensive support, robust security, and ongoing maintenance. In most scenarios, buying will still be the ultimate decision. However, the crucial difference is that now, procurement is undertaken with “eyes wide open.” They will approach the market with a deep, practical understanding of their specific needs, recognizing what “good” looks like in practice.
When engaging with vendors, they will arrive with an acute awareness of edge cases and specific operational nuances, enabling them to quickly assess whether a vendor truly grasps their unique challenges. Implementations will be faster and more efficient. Negotiations will be conducted from a position of strength, as the organization is no longer entirely dependent on a single vendor’s solution. Ultimately, the chosen software will be adopted not out of necessity, but because it genuinely outperforms the validated internal alternatives. This leads to more informed choices and prevents
AI disruption in corporate software procurement
from being a haphazard process.
The finance team, empowered by AI, will have already precisely mapped out the contours of their true needs, and their role transforms into seeking the most effective and efficient external solution to fulfill those well-defined requirements. This intelligent, data-driven approach to procurement is a testament to the
Low-code AI development benefits for business
.
The New Paradigm: Build to Learn What to Buy
For years, the strategic imperative was a binary choice: “Build or buy.” Now, the mantra has evolved into something far more sophisticated and intelligent: “Build to learn what to buy.” This isn’t a futuristic concept; it is the current reality actively shaping enterprise operations globally. This truly highlights how
AI changes build vs buy
dynamics are evolving.
Right now, in companies worldwide, a customer representative is leveraging AI to swiftly resolve a product issue they identified moments ago. Elsewhere, a finance team is actively prototyping bespoke analytical tools, realizing they can iterate and refine solutions far more rapidly than they can articulate complex requirements for an engineering team. Across various departments, teams are recognizing that the perceived boundary between “technical” and “non-technical” roles has always been more cultural than fundamental. This new paradigm is driving the
Future trends in enterprise software with AI
.
Organizations that embrace this transformative shift will operate with unprecedented agility and financial prudence. They will cultivate a deeper, more intimate understanding of their internal operations than any external vendor ever could. They will make fewer expensive, misguided software investments and acquire superior tools because their purchasing decisions are grounded in practical, validated experience. Furthermore, this approach naturally integrates with modern software development methodologies, fostering
AI and agile software development integration
across the enterprise.
Conversely, companies that cling to the outdated playbook will continue to sit through repetitive vendor pitches, nodding along to budget-friendly proposals that may not align with their true needs. They will debate extended timelines and persistently mistake professionally designed decks for actual, functional solutions. Until, that is, someone within their own team opens a laptop, casually remarks, “I built a version of this last night. Want to check it out?” and demonstrates a solution created in two hours that delivers 80% of the value they were about to pay six figures for. In that moment, the rules of software procurement, driven by the profound capabilities of AI changes build vs buy, will change for good.
Siqi Chen is co-founder and CEO of Runway.
This article reflects independent insights into the evolving software procurement landscape.