The long-standing consensus within enterprise information technology—that purchasing off-the-shelf software is inherently superior to building custom applications—is facing a significant structural challenge. For decades, the "buy over build" mantra dominated corporate boardrooms, driven by the high costs, development delays, and maintenance burdens associated with bespoke software. However, the emergence of sophisticated low-code platforms and the recent surge in generative AI "coding agents" are fundamentally altering the economics of software development. This shift, often referred to as the "democratization of coding," is prompting a re-evaluation of how organizations acquire and manage their digital infrastructure.
The Decline of the Buy-First Orthodoxy
The historical preference for Software-as-a-Service (SaaS) was rooted in a desire for predictability. Organizations preferred to adapt their internal business processes to fit the constraints of a purchased application rather than risk the "interminable delays" of internal development. This led to an explosion in SaaS adoption; according to industry data from 2023, the average enterprise now utilizes upwards of 130 different SaaS applications, often resulting in significant "SaaS sprawl" and redundant licensing costs.
However, the tide is turning as a new generation of "vibe coders"—individuals utilizing AI prompts and visual development tools—challenge the necessity of expensive subscriptions. This movement is not merely a trend among hobbyists but is gaining traction within major corporate IT departments. The central question has shifted from "What solution shall we buy?" to a more provocative "Why don’t we just build it?"
Case Study: The Transformation at FranklinCovey
Blaine Carter, Chief Information Officer at the global executive training provider FranklinCovey, has emerged as a leading proponent of this "build-first" heresy. Over the past decade, Carter has transitioned his department from a traditional procurement-heavy model to one centered on internal creation using the low-code platform Make.
The transition was not a sudden pivot but an evolution driven by the realization that modern tools have effectively lowered the barrier to entry for professional-grade software development. By utilizing visual orchestration and automated integration, Carter’s team has demonstrated that bespoke solutions can be delivered faster and more cost-effectively than many standardized SaaS offerings.
Chronology of a Shift: The Fiscal Binder Project
The catalyst for FranklinCovey’s strategic shift was a manual, labor-intensive process known as the "fiscal binder." Each month, a 10-person finance team was required to compile a three-inch physical binder containing evidence of financial metrics. This process involved printing documents, manual verification, and physical hand-offs, taking approximately 30 days to complete.
When the finance department requested a $50,000-per-year software package to digitize this process, Carter identified an opportunity to test the build-first hypothesis. Using the Make platform, the IT team integrated reports directly from their Enterprise Resource Planning (ERP) system. They developed an automated workflow that:
- Generated digital templates for monthly evidence.
- Automatically populated data where possible.
- Notified relevant personnel to upload and digitally sign off on information.
The results were immediate. The 30-day manual process was reduced to a few days. Beyond the $50,000 saved in annual licensing fees, the project yielded an unexpected financial benefit: the company’s external auditors reduced their annual fees by $10,000, citing the increased transparency and speed of the new digital system.
The AI Inflection Point: Reimagining the RFP Process
While low-code tools provided the foundation for FranklinCovey’s transition, the arrival of generative AI expanded the scope of what could be built internally. Carter turned his attention to the Request for Proposal (RFP) process—a critical but document-heavy function for a company dealing with large-scale government and corporate contracts.
Traditionally, FranklinCovey relied on specialized RFP software to manage these bids. However, Carter’s team built a custom knowledge base using historical contract data and integrated a Large Language Model (LLM) to automate the triage and drafting process.
Strategic Triage and "Golden Questions"
The AI-driven system was designed to distinguish between "boilerplate" content—such as corporate governance and insurance details—and the "golden questions" that determine the success of a bid. By automating 90% of the repetitive documentation, the system allowed subject matter experts to focus exclusively on the high-value, verbose sections of the response.
The implementation resulted in a 30% to 40% reduction in software licensing costs and a staggering productivity gain: RFPs that previously took weeks were completed in 10% to 15% of the original time. This move underscored a broader realization in the industry: AI coding agents are not just for writing software; they are for reshaping the human work that the software was designed to support.
Technical Analysis: Configuration vs. Customization
A primary criticism of the "build" approach is the risk of creating "technical debt"—complex, unmanageable code that becomes a liability over time. Carter addresses this by adhering to a strict mantra: "Configuration over customization."
By using a standardized low-code platform like Make, the IT team operates within a controlled architectural framework. Instead of writing unique lines of code that require specialized knowledge to maintain, they configure visual models. This approach provides several key advantages:
- Visibility: Workflows are represented visually, making them easier for non-developers to understand.
- Sustainability: The underlying platform handles security updates and API maintenance, reducing the long-term burden on the internal team.
- Agility: Solutions can be tweaked in real-time as business requirements change, without the need for a full development cycle.
This methodology bridges the gap between the rigidity of SaaS and the chaos of traditional bespoke coding. It allows the organization to retain the structural discipline of a packaged application while enjoying the specificity of a custom-built tool.
Supporting Data and Market Context
The shift observed at FranklinCovey aligns with broader market trends. Gartner predicts that by 2026, low-code development tools will account for 75% of new application development. Furthermore, the rise of "shadow IT"—where business units purchase their own software without IT oversight—has cost companies an estimated $1.35 trillion globally in 2023. By adopting a build-first posture, IT departments can regain control of the technology stack, ensuring better security and data integration while satisfying the business’s demand for speed.
| Metric | Traditional SaaS Approach | Low-Code/AI Build Approach |
|---|---|---|
| Annual Cost | $30k – $100k+ per app | Platform subscription + internal time |
| Development Speed | Immediate (but rigid) | Days to weeks (flexible) |
| Process Fit | 70-80% (requires compromise) | 100% (built to purpose) |
| Audit/Transparency | Dependent on vendor logs | Full internal data control |
Broader Implications for the IT Operating Model
The transition from "buy" to "build" necessitates a fundamental change in how IT departments interact with the rest of the organization. At FranklinCovey, this involved moving from an "arm’s-length service provider" model to a "partnership" model.
Carter’s team engaged with every department to identify specific use cases where automation could move measurable Key Performance Indicators (KPIs). This grassroots approach ensured that the solutions built were not just technically sound but were deeply integrated into the operational reality of the business.
This shift suggests that the future of the CIO role may be less about vendor management and more about internal product management. As AI continues to lower the cost of code generation, the value of an IT department will increasingly be measured by its ability to translate business problems into automated workflows.
Conclusion: The Path Toward Sustainable Advantage
The current discourse surrounding the "SaaS-pocalypse"—the idea that generative AI will lead to the mass cancellation of enterprise software subscriptions—may be hyperbolic, but the underlying shift in power is undeniable. The experience of FranklinCovey demonstrates that for many common enterprise functions, the "build" option is now faster, cheaper, and more effective than the "buy" option.
However, the success of this strategy depends on discipline. The value is not derived from the act of coding itself, but from the architectural constraints and business partnerships that ensure the resulting systems are sustainable. Whether through low-code platforms or AI coding agents, the organizations that thrive in this new era will be those that treat software development as a core competency rather than an outsourced necessity. The "unevenly distributed" future of IT is becoming more uniform, and it is built on the foundation of internal innovation.