The perennial debate within the technology industry, a strategic crossroads faced by every organization since the dawn of computing, centers on whether to "build" custom solutions or "buy" commercial off-the-shelf (COTS) products to address specific business problems. This fundamental question, which has long guided decisions for traditional IT departments and modern product teams alike, is undergoing a profound transformation, driven by the democratization of software development through user programming and the advent of Generative Artificial Intelligence (Gen AI). Far from simplifying the choice, these advancements are ushering in a more nuanced, hybrid era where the answer is increasingly "yes to both."
A Historical Arc: From IT Gatekeepers to Empowered Users
For decades, the landscape of enterprise software was largely bifurcated. Product teams, often focused on external customer offerings, frequently possessed the resources and expertise to build bespoke solutions tailored to their specific needs. Conversely, "the business" — the various internal departments like HR, finance, and operations — typically relied heavily on internal IT departments to develop or customize applications. This often resulted in extensive backlogs of requests for IT, which acted as the gatekeeper for new software capabilities. The "buy" option, while available, frequently came with functional limitations and significant customization costs, especially for large enterprises with highly specialized requirements. If a problem touched upon a company’s core competency, building was the default; for non-core functions, buying was preferred, but often complicated by the need for extensive tailoring.
The first significant shift in this dynamic began not in a corporate IT department, but on the burgeoning personal computing frontier. In 1979, the introduction of VisiCalc for the Apple II marked a watershed moment. VisiCalc, the first electronic spreadsheet program, was a pioneering "user-programming" tool. It empowered non-technical individuals to create complex financial models and calculations without writing traditional code. This was an enormously liberating development, moving rudimentary programming capabilities out of the exclusive domain of professional developers and into the hands of business users.
The legacy of VisiCalc continued through applications like Microsoft Excel, which today hosts countless millions of user-created programs—primarily formulas and macros—that drive critical business processes across virtually every industry worldwide. This era saw a gradual expansion of user-driven development. The release of Visual Basic in 1991 further accelerated this trend, offering a graphical interface and simplified coding environment that enabled millions more to create desktop applications. It was, arguably, one of the earliest widespread "low-code" options, predating the modern wave of dedicated low-code and no-code platforms that have emerged over the past decade.
This progression laid the groundwork for the current paradigm shift. According to a 2023 report by MarketsandMarkets, the global low-code development platform market size is projected to grow from USD 27.5 billion in 2023 to USD 71.3 billion by 2028, at a Compound Annual Growth Rate (CAGR) of 20.9%. This substantial growth underscores the increasing demand for tools that allow non-technical users, often dubbed "citizen developers," to contribute to software development. These platforms abstract away much of the underlying coding complexity, enabling users to build applications through visual interfaces, drag-and-drop functionality, and pre-built components.
Generative AI: A New Frontier in Accessibility
Now, the landscape is undergoing an even more profound transformation with the rise of Generative AI. This new generation of user-programming tools, exemplified by products like Lovable and Bolt, takes the concept of accessibility to an unprecedented level. The programming language is, quite literally, natural language—English, in many cases. This capability, often referred to as "vibe coding," allows nearly anyone with a problem to solve to articulate their needs in plain text, and the AI generates the necessary code, workflows, or application components. This eliminates the need for even the simplified visual programming skills required by earlier low-code platforms, effectively opening up application development to an even broader audience.
The implications are far-reaching. Imagine a marketing manager needing a custom dashboard to track campaign performance across disparate platforms, or a human resources specialist requiring a workflow to automate employee onboarding. Historically, these requests would languish in IT backlogs or necessitate the purchase of an expensive, often oversized, commercial solution. With Gen AI tools, these individuals can describe their desired functionality, and the AI assists in building it, accelerating innovation and responsiveness within departments.
The Enduring Complexity: Why SaaS is Not Doomed
This rapid democratization of software creation has led some to speculate that the days of traditional SaaS (Software-as-a-Service) providers are numbered. The argument posits that if anyone can "program" an application using natural language, why would companies continue to pay for expensive, standardized commercial solutions? However, this perspective, while superficially appealing, overlooks a critical and often underestimated layer of complexity: business rules and business logic.
The notion that new user-programming tools will unilaterally replace sophisticated business software—such as procurement, invoicing, payments, budgeting, forecasting, payroll, staffing, sales force automation, customer relationship management (CRM), or customer service systems—is almost certainly an oversimplification. Behind virtually every enterprise business solution lie literally thousands of often intricate business rules and millions of lines of associated business logic. These rules encapsulate vital constraints and processes related to policy, regulatory compliance, data security, legal mandates, financial governance, pricing strategies, and countless other operational necessities.
Consider the complexity inherent in a payroll system. It must adhere to ever-changing tax laws, union contracts, employee benefits structures, regional minimum wage requirements, overtime calculations, holiday pay rules, and various deduction policies. Each of these represents a business rule, often with specific nuances and exceptions. Similarly, a procurement system must enforce spending limits, approval hierarchies, vendor compliance checks, contractual terms, and inventory management protocols. These are not simple functionalities; they are deeply embedded, often interdependent, and critical for operational integrity and legal adherence.
The challenge is that the vast majority of non-technical people attempting to create business applications have little to no awareness of these deeply embedded business rules. Even seasoned technical developers often struggle to unearth and fully comprehend them, as they are frequently hard-coded into legacy systems. The institutional knowledge surrounding their rationale and nuances often resides with individuals who have long since moved on, and documentation, when it exists, is rarely comprehensive or up-to-date. This "tribal knowledge" about why specific rules exist and how they are applied is precisely what product managers and, historically, business analysts, painstakingly acquire to define viable and compliant solutions. It is also why addressing technical debt in legacy systems is so difficult; one must first painstakingly reverse-engineer the embedded business rules to determine their ongoing relevance.
Therefore, building a system that can accurately capture, manage, and enforce thousands of essential business rules, ensuring that transactions are handled precisely as required by policy, law, and operational necessity, remains an undertaking of considerable effort and expertise. This robust, rule-driven foundation is precisely the value proposition offered by strong SaaS vendors. They invest heavily in developing, maintaining, and updating these complex systems, ensuring compliance, security, and scalability across diverse regulatory and operational environments.
The Future Convergence: ‘Yes to Both’ – A Hybrid Ecosystem
While strong SaaS vendors are not poised to disappear, their role and the nature of their offerings are undeniably evolving. The future of the "build vs. buy" paradigm will be characterized by a sophisticated hybrid approach: "yes to both."
The next generation of business solutions will transcend being solely human-centric. They will be designed not just for human users but also for interaction with AI agents and custom solutions built on top of them. This necessitates a fundamental shift in how software services are designed and exposed. SaaS applications will increasingly become modular "component services," offering robust APIs and machine-readable descriptions of their capabilities.
A major enabler for this transformation is the emergence of widely accepted protocols for describing business services in a way that computers, not just people, can understand. A significant development in this area is Anthropic’s proposal of The Model Context Protocol (MCP). Introduced approximately a year ago, MCP has rapidly gained traction because it addresses a long-standing architectural challenge that has been apparent since the early days of the internet: the need for a standardized, machine-readable way for software systems, especially AI, to comprehend and interact with complex business services programmatically.
The MCP aims to provide a structured method for describing the inputs, outputs, constraints, and functionalities of various business services. This allows AI agents to understand how to invoke specific services, what data they require, and what results they will produce, effectively enabling AI to "reason" about and orchestrate complex business processes.
In this future, companies will continue to procure robust, complex, and valuable component services from SaaS vendors for critical parts of their operations. However, these purchased components will be designed with interoperability in mind, accessible and controllable by both human interfaces and intelligent software agents. Some of these AI agents will be developed by the SaaS vendors themselves, offering enhanced automation within their ecosystems. Others will be crafted by system integrators, who will build industry-specific layers of AI intelligence that orchestrate multiple purchased components. Crucially, end-customers, empowered by Gen AI tools, will also develop their own custom workflows and AI agents, "vibe-coding" solutions that sit atop and integrate these purchased component services.
For instance, an enterprise might subscribe to a leading CRM SaaS platform for managing customer data and interactions. Simultaneously, an internal team, using Gen AI tools, could develop a custom AI agent that automatically analyzes customer sentiment from various sources, then triggers specific follow-up actions within the CRM (e.g., creating a support ticket, scheduling a sales call) based on predefined business rules. This agent, in turn, could interact with a separate marketing automation SaaS component to tailor personalized communication. This creates a highly customized, agile, and automated ecosystem built upon a foundation of robust, bought components.
Broader Implications and Strategic Imperatives
The implications of this shift are profound for all stakeholders in the technology ecosystem.
For SaaS Vendors: The imperative is clear: evolve from offering monolithic applications to providing modular, API-first component services. Robust API documentation, adherence to protocols like MCP, and a focus on seamless integration will become competitive differentiators. Vendors may also find new revenue streams in offering AI agent development kits or pre-built, domain-specific AI agents that extend the functionality of their core offerings. Those who fail to adapt risk being bypassed by a new generation of agile integrators and empowered end-users.
For Enterprise IT and Product Teams: The role of IT will transform from a primary builder and maintainer to that of an orchestrator, curator, and governor of a hybrid software landscape. Expertise in integration, data governance, security, and compliance will become paramount. IT will be responsible for ensuring that custom-built AI agents and workflows adhere to organizational standards and regulatory requirements. Product managers, too, will need to broaden their scope, understanding not just how humans interact with their products, but also how AI agents and custom layers will consume and extend their functionalities. The rise of "citizen developers" also necessitates new frameworks for oversight, training, and support to ensure solutions are viable, secure, and maintainable.
For System Integrators: This era presents immense opportunities. SIs are uniquely positioned to bridge the gap between purchased component services and bespoke AI-driven solutions. Their expertise in complex integrations, industry-specific knowledge, and now, AI agent development and MCP implementation, will be highly sought after as companies navigate this hybrid future.
The Democratization of Solution Building: User programming, once largely confined to the margins of enterprise software, is now moving decisively into the mainstream. This is overwhelmingly a positive development, empowering countless individuals and departments who have long grappled with constrained IT resources and slow development cycles. It promises unprecedented levels of innovation, agility, and responsiveness within organizations. However, as more non-technical people aspire to create solutions beyond simple personal time-savers, a critical lesson from the product development world becomes increasingly relevant: the hardest part is rarely building and delivering the solution; the truly challenging and valuable endeavor is discovering the right solution to build. This demands deep business understanding, critical thinking, and a rigorous process of problem definition and validation, skills that transcend mere technical proficiency.
Conclusion: Navigating the Hybrid Future
The "build vs. buy" dilemma, a cornerstone of technology strategy, is not disappearing but rather evolving into a more sophisticated, collaborative dynamic. The future of business software solutions lies in a powerful interplay between robust, commercially acquired component services and highly customized, AI-driven solutions generated by users, system integrators, and vendors themselves. This hybrid ecosystem, enabled by advancements in user programming and crucial protocols like MCP, promises a future of unprecedented agility, innovation, and empowerment. Success in this new paradigm will hinge not on an exclusive choice between building or buying, but on the strategic orchestration of both, underpinned by a profound understanding of business logic and an unwavering focus on discovering truly viable solutions.
