AI has rendered the traditional build-versus-buy framework obsolete
The old debate, whether to build software in-house or to buy it off the shelf, no longer serves today’s world. This framework was built on the assumption that developing software was expensive, slow, and required teams of engineers. For decades, that was true. You had to scope everything out in detail. Engineering teams would run sprints. Then weeks or months later, you might get something stable enough to use.
That assumption is no longer accurate.
AI has made it possible for anyone, literally anyone in your organization, to build functioning software in a fraction of the time and at nearly zero cost. What once took skilled developers weeks to code can now be prototyped by someone with no technical background, in a tool like Cursor, using natural language instructions. You write what you want, AI generates the code, and suddenly you’ve got a working version of the thing you were going to spend six figures on.
This shift collapses traditional cost and time dynamics. And once those barriers fall, the logic for buying versus building falls with them.
Your teams no longer need to wait for engineering resources to free up or go through a lengthy procurement cycle. If someone close to the problem can test an idea and generate a working solution in a few hours, it changes the entire decision-making process. The assumption that “buy is faster and safer” simply doesn’t hold anymore. It’s not even about speed, it’s about leverage. AI shifts power to the edge of your organization. The people closest to the problem now have the tools to create solutions themselves.
This isn’t about removing engineering, it’s about removing unnecessary bottlenecks. And most of the old framework was built around bottlenecks that now don’t exist.
Rapid AI-enabled prototyping informs precise buying decisions
You don’t need to commit to a vendor without knowing if the solution actually works. That’s where things go wrong. Teams push forward with enterprise tools based on vague ideas of what they think they need, then spend months in implementation before realizing it’s not the right fit.
With AI, you can prototype quickly, test assumptions fast, and actually see whether the problem is real and whether the solution adds value. Once you’ve prototyped, even in a lightweight way, you know what your team needs. You’ve already confirmed what works and what doesn’t. Then when you go to a vendor, you’re not guessing. You’re comparing their solution to something your team already built. If their tool is better, you buy it. If not, you don’t.
This eliminates waste. It turns the buying process into a data-informed decision. You build first, not because you plan to scale that solution internally forever, but because building clarifies what you’re looking for. You understand what matters and what doesn’t, and then you choose a vendor based on real-world results, not a polished sales deck.
For leadership teams, this should be a core shift in mindset. You want your people quickly testing things before they commit. It takes days, not weeks. What this unlocks is huge: fewer failed software implementations, better vendor negotiations, and tighter alignment with how your business actually works.
In this model, buying becomes the final step, not the first one. And you buy stronger, because you already know what “good” looks like. That alone saves time. It saves budget. And most of all, it speeds up execution. Which is the point.
The boundary between technical and non-technical roles is diminishing
The old way of thinking, where building software was something only engineers could do, is fading. AI has changed the rules. It’s made it possible for non-technical team members to contribute directly to product iterations, technical fixes, and internal tools, without needing to know how to code. And we’re not talking about theoretical possibilities. This is already happening across real teams, in real companies.
One example: A customer experience team member noticed a user-reported bug in Slack. Instead of routing the problem through traditional engineering queues, they opened an AI-assisted code tool, described the issue in plain English, and let the system write the fix. Moments later, a pull request was created, reviewed, and merged. The bug was fixed in production within 15 minutes. That team member had no formal technical background. They didn’t need one.
This kind of direct action used to be impossible. Today, AI tools handle the heavy lifting, writing syntactically sound code, testing it, and even suggesting implementation paths. That enables people who really understand the customer or the problem to go ahead and implement an effective solution, immediately. No waiting. No bottlenecks. No miscommunication between departments.
Which raises the bar.
It means organizations that get this right, who allow their teams to take more ownership earlier in the solution cycle, will move significantly faster than those still gated by outdated workflows. Leadership teams should see this not as a threat to engineering but as a new dynamic where engineering shifts to guiding, reviewing, and scaling high-impact work rather than creating every solution from the ground up.
This is a structural shift. Not cultural or aspirational. Set the right permissions, implement proper review mechanisms, and this becomes a repeatable system. It raises productivity across every function, not just in engineering.
Superficial adoption of AI is a risky pitfall
A big mistake companies are making right now is mistaking AI consolidation for AI transformation. Just adopting AI-branded tools and throwing money at products labeled “GPT-enabled” or “machine powered” doesn’t mean anything unless it changes how your teams actually work or what they’re capable of doing. Integrating a chatbot into the interface or adding autocomplete to a workflow doesn’t add value by default.
Many companies are buying tools that say “AI” on the surface but offer limited operational gain. They chase perceived innovation without verifying whether any real transformation is taking place at the systems or process level. That way of spending creates the illusion of progress but delivers no edge.
What matters is not whether the tool uses AI. What matters is whether it unlocks something difficult, expensive, or previously impossible for your team. Does it automate a process that used to take hours? Does it enable someone junior to solve a problem they used to escalate? Does it bring ground-level action closer to the people who see the problem in real time?
If the answer is no, you’re not transforming. You’re just layering complexity.
You need to ask harder questions before you integrate any AI-labeled product into your stack: What does this tool solve that we couldn’t solve before? Who in the organization becomes more empowered by using it? What process improves, and by how much?
Most importantly, don’t confuse brand positioning with product capability. Every vendor will claim AI leadership now, whether or not their tools meaningfully leverage it. Your job is to cut through that noise and look at results. If it doesn’t shift how your company operates, it’s not real AI transformation. It’s reaction.
That distinction defines whether you lead the shift, or get left behind reacting to it.
Empowering finance teams through AI-driven experiments redefines procurement
Finance teams don’t need to rely on pitch decks and vendor projections to make smart software investment decisions anymore. AI has changed how procurement works, dramatically. Now, teams can build internal prototypes to pressure-test whether a proposed solution actually solves the problem before they spend a single dollar on implementation. That gives finance a real edge: less guesswork, fewer sunk costs, and far greater control over outcomes.
Let’s say your team is evaluating software for vendor management. Previously, you’d set up calls, collect quotes, and compare demos. Then you’d commit based on limited information and hope everything worked well once implemented. But with AI tools, this workflow shifts. Instead of speculating, your team can now create a working version of the core flow, and then learn from it. You’ll see if the bottleneck is really about software, or whether it’s upstream inefficiency, a broken process, or unclear ownership.
From there, decision-making becomes grounded. If the prototype works well enough, maybe you just improve on it. If it falls short, you at least have a clear sense of what the limitations are, and what the paid tool needs to outperform. Either way, you walk into vendor conversations with a clear agenda, better technical awareness, and stronger negotiation leverage.
This process doesn’t mean you’ll stop buying third-party tools. You’ll still buy. But now, you’ll buy with firsthand insight. You’ll have stronger input into what “good” looks like and a better understanding of whether the tool integrates cleanly into your environment. That can accelerate time-to-value after the purchase and reduce the risk of misalignment during rollout.
Finance departments traditionally focused on cost control and reporting. Now they’re positioned to co-own innovation, leveraging data and tooling insights to shape better system-level outcomes. This combination of experimentation and financial oversight leads to smarter, faster, and more sustainable decisions.
The new paradigm: “Build to learn what to buy”
The old software strategy was linear. You defined a problem, reviewed vendors, picked one, then implemented. That sequence made sense when building was expensive and slow. But that’s not the case anymore. Today, you can build in minutes, test questions directly, and learn fast. That doesn’t mean you replace third-party tools, it just means you use internal builds to understand what problems are worth solving, and what kind of solutions you actually need.
This updated paradigm is more efficient. You test before buying. You get direct data. You understand what matters and filter out what doesn’t. By the time you meet vendors, you’re armed with insights. You’ve already tested edge cases. You’ve watched real users interact with early builds. As a result, you avoid paying for features that look good in demos but don’t translate into value in practice.
When buying becomes the second step, not the first, you naturally avoid the most expensive mistake in enterprise procurement: buying tools to solve the wrong problems. Instead of reacting to market noise, you align spend with verified need. You don’t just save money, you buy better.
For executive teams, this is the shift to internal clarity. You’re no longer relying solely on vendor claims. Your teams aren’t wasting months writing specs or evaluating products that might not be necessary. And when you do deploy vendor tools, implementation time shrinks, because your requirements are already fully understood.
This model scales. It leads to faster execution, because you’ve skipped the initial uncertainty. It creates a smarter procurement cycle, because you’ve already ruled out bad fits. And more importantly, it strengthens how you invest, by making every decision based on what your team has already seen in action.
This isn’t a trend or a future prediction. It’s already happening. And the companies that adopt this now will gain speed, alignment, and resilience their competitors won’t be able to match.
Key takeaways for decision-makers
- AI redefines build vs buy decisions: Leaders should view AI-driven prototyping as a fundamental shift, not a feature upgrade. Fast, low-cost builds now make traditional buy-versus-build analysis outdated.
- Build first to clarify needs: Organizations should use AI to build lightweight internal solutions before buying. This shortens decision cycles, reduces waste, and exposes unclear pain points early.
- Non-technical teams can now drive execution: Executives should empower cross-functional teams by enabling AI-assisted creation tools. This speeds up issue resolution and reduces dependency on overextended engineering teams.
- Avoid surface-level AI adoption: Leaders must evaluate tools by their operational impact, not their marketing claims. If an AI tool doesn’t change how work happens, it adds no real value.
- Finance can now lead smarter procurement: CFOs and finance leaders should drive tech validation by building and testing critical workflows pre-purchase. This reduces procurement risk and sharpens vendor negotiations.
- Shift to “build to learn what to buy”: Embrace a new procurement mindset where internal builds guide external purchases. This approach leads to faster implementations, fewer blind bets, and better-fit software investments.
