Imagine this scenario: You’re midway through a vendor presentation in a meeting room. The demonstration looks promising, the pricing fits comfortably within your budget, and the proposed timeline seems achievable. Everyone appears convinced.
You’re just moments away from giving your approval.
Suddenly, a member of your finance team enters, glances at the presentation, and frowns. Shortly after, they message you on Slack: “I actually put together a similar version last week. It took me just two hours using Cursor. Want to see it?”
Wait, what?
This is surprising because you know they’ve never written a single line of code before. Yet, here they are, showing a functional prototype on their laptop that replicates nearly everything the vendor promised. It might be a bit rough around the edges, but it works-and it didn’t cost a fortune, just a couple of hours of their time.
Suddenly, the assumptions you had about software development-who creates it, how decisions are made, and the costs involved-begin to unravel.
Rethinking the Build vs. Buy Dilemma
For many years, companies faced a straightforward question: Should we develop this solution internally or purchase it from an external provider?
The conventional wisdom was clear: Build if the software is central to your business; buy if it’s not.
This made sense because building software was costly and time-consuming, requiring engineers to draft specifications, plan development cycles, manage infrastructure, and maintain the product long-term. Buying software was faster, less risky, and came with vendor support and reliability.
However, the landscape has shifted dramatically with the rise of AI. Tasks that once took weeks can now be accomplished in hours, and coding no longer demands fluency in programming languages-plain English commands often suffice.
As the barriers to building software fall, the traditional build-versus-buy framework is becoming obsolete. We’re entering a new era where the decision-making process is more nuanced and less defined by old rules.
When the Market Lacks Tailored Solutions
Our company never intended to create so many custom tools. We only did so because the exact solutions we needed weren’t available on the market. Through this hands-on development, we gained a deep understanding of what truly mattered-what features genuinely impacted our business versus what vendor presentations or analyst reports suggested.
This experience helped us identify which challenges were worth addressing and which were distractions. Only after achieving this clarity did we begin to explore purchasing options.
By then, we knew precisely what to look for and could quickly distinguish between genuine value and marketing fluff. We asked vendors tough questions, often making them uneasy, because we had already built basic versions of their offerings ourselves.
Empowering Non-Technical Teams to Build Solutions
Recently, a member of our customer experience team spotted a minor bug reported in Slack. In many organizations, this would trigger a support ticket and a waiting period for engineering to intervene. Instead, they opened Cursor, described the fix, and let AI generate the necessary code. They then submitted a pull request, which engineering reviewed and merged.
Within just 15 minutes of the complaint surfacing, the fix was live in production.
This team member isn’t a developer and likely couldn’t differentiate between Python and JavaScript, yet they resolved the issue effectively.
This example highlights a broader trend: AI can now handle approximately 80% of coding tasks that previously required lengthy engineering sprints. The divide between technical and non-technical roles is blurring, enabling those closest to the problem to implement solutions directly.
Forward-thinking companies are already leveraging this shift to accelerate problem-solving.
Flipping the Build-Buy Strategy
For finance leaders, this evolution is particularly intriguing because AI is reversing the traditional logic behind build-versus-buy decisions.
The old approach followed these steps:
- Identify the business need.
- Decide whether to build or buy a solution.
However, defining needs was often a slow, technically demanding process prone to costly trial and error. Companies would endure numerous demos, negotiate contracts, implement new systems, and only after months and significant investment discover if the solution truly fit.
Today, the sequence is inverted:
- Create a lightweight prototype using AI tools.
- Use this prototype to clarify the actual requirements.
- Decide whether to purchase a solution, armed with precise knowledge.
This method allows for rapid experimentation, helping organizations determine if a problem is worth solving and which features add real value versus those that are merely superficial. Only then do they engage vendors, ensuring they understand their needs before shopping.
Consider how many software purchases you’ve made that, in retrospect, addressed problems that weren’t truly pressing. How often have you been months into a rollout wondering if the investment was justified?
Now, the question becomes: “Does this vendor’s product outperform the solution we’ve already proven we can build?”
This shift transforms vendor interactions. You approach demos informed, ask incisive questions, negotiate from a position of strength, and avoid the costly mistake of buying solutions to non-existent problems.
Beware the AI Hype Trap
Despite these advances, many companies rush headlong into AI adoption without a clear strategy. They accumulate AI-labeled tools-chatbots, GPT integrations, and auto-complete features-believing this equates to transformation.
This phenomenon resembles what physicist Richard Feynman termed cargo cult science. After World War II, some South Pacific islanders built mock airstrips and control towers, imitating what they had seen, hoping planes would land. They replicated the appearance but not the function, so no cargo arrived.
Similarly, organizations are assembling AI “airstrips” without ensuring these tools genuinely change workflows, empower employees, or unlock new capabilities.
The market exacerbates this issue by branding nearly every product as AI-powered, regardless of actual impact. Vendors add superficial AI features to check a box, diluting the term’s meaning and leaving customers with tools that don’t deliver real value.
Finance Teams: Harnessing AI as a Strategic Advantage
This new reality offers finance teams unprecedented power. Instead of relying on guesswork or expensive vendor pitches, they can experiment and learn before committing funds.
For example, when evaluating vendor management software, finance professionals can prototype key workflows using AI tools. This helps determine whether the challenge lies in tooling or processes, or if software is necessary at all.
Of course, this doesn’t mean every solution should be built internally. Enterprise software often provides essential benefits like scalability, security, and ongoing support. But now, purchases are made with full awareness.
Finance teams will know what “good” looks like, attend demos with a clear understanding of their needs, and quickly assess whether a product addresses their unique challenges. Implementation speeds up, negotiations improve, and decisions are based on genuine value rather than vendor persuasion.
They’ll have already sketched the blueprint of their requirements and will seek the best available solution.
Embracing the Future: Build to Discover, Then Buy
The old mantra of “build or buy” is evolving into a smarter, more dynamic approach: build to learn what to buy.
This isn’t a distant vision-it’s happening now. Somewhere, a customer service rep is using AI to fix a product glitch moments after it’s reported. Elsewhere, a finance team is rapidly prototyping analytics tools, iterating faster than traditional engineering cycles allow. Across organizations, the line between technical and non-technical roles is proving to be more cultural than practical.
Companies that adopt this mindset will accelerate innovation, optimize spending, and gain deeper operational insights than any vendor could provide. They’ll avoid costly missteps and select tools that truly enhance their business.
Meanwhile, those clinging to outdated methods will continue enduring vendor pitches, nodding at budget-friendly proposals, and mistaking polished presentations for effective solutions-until someone on their team quietly builds a working prototype in a few hours that accomplishes most of what they were about to pay six figures for.
And just like that, the rules of the game change forever.
