The fundamental question of whether to "build" custom solutions or "buy" commercial off-the-shelf software has been a cornerstone of technology strategy since the dawn of the digital age, a dilemma that continues to evolve with each new technological wave. This query is not confined to traditional IT departments wrestling with infrastructure and legacy systems; it permeates every product team, from agile startups to multinational corporations, as they strive to deliver value and solve complex business problems. While the allure of bespoke solutions promises perfect fit and competitive differentiation, it comes laden with significant costs in development time, resources, and perpetual maintenance. Conversely, commercial alternatives, particularly Software-as-a-Service (SaaS) offerings, provide speed, proven functionality, and shared maintenance burdens, yet often entail functional limitations, integration challenges, and the inherent compromise of adapting business processes to predefined software structures.
Historically, the decision has largely hinged on whether the problem being addressed represents a "core competency" of the organization. If a function is central to a company’s unique value proposition or competitive edge, building a proprietary solution often makes strategic sense. However, for functions outside this core — areas like human resources, finance, or customer relationship management — buying a specialized solution has typically been the preferred path, leveraging the expertise and economies of scale offered by dedicated software vendors. This dichotomy, however, has always been an oversimplification. The reality for many enterprises, especially large ones, frequently involves a hybrid approach: purchasing a solution and then undertaking extensive customization to align it precisely with their unique operational requirements, a process that can blur the lines between buying and building.
For decades, the luxury of building custom applications was largely reserved for specialized product development teams or the beleaguered IT departments, who often faced an "endless list of requests" from business units clamoring for tailored solutions. This created a bottleneck, as IT struggled to keep pace with evolving business demands, leading to frustration and often, the proliferation of "shadow IT" — unapproved, departmental solutions built by non-technical staff out of necessity.
The Genesis of User Programming: Empowering the Non-Technical
The seeds of democratized software creation were sown long before the current AI revolution. The concept of "user programming," defined as the ability of non-technical individuals to create software solutions, began to gain traction in the late 1970s. This movement aimed to empower business users to directly address their immediate operational needs without relying solely on professional developers.
A pivotal moment arrived in 1979 with the introduction of VisiCalc, the first electronic spreadsheet program, designed for the Apple II personal computer. VisiCalc was nothing short of revolutionary. It transformed the personal computer from a hobbyist’s toy into an indispensable business tool, allowing accountants, financial analysts, and managers to manipulate data, perform complex calculations, and model scenarios with unprecedented ease. Its grid-based interface, where formulas could be entered into cells to perform calculations, was intuitively understandable to anyone familiar with a ledger. This seemingly simple innovation empowered millions to "program" without writing a single line of traditional code, effectively giving birth to the citizen developer. The impact was profound, with VisiCalc often credited for driving early personal computer sales in the business sector.
Following VisiCalc’s success, the spreadsheet paradigm matured with applications like Lotus 1-2-3 and, most notably, Microsoft Excel. Today, countless millions of user-created programs, predominantly in the form of complex formulas and macros, underpin critical operations across virtually every company worldwide. The sheer volume of business logic embedded within Excel spreadsheets is staggering, reflecting its enduring utility as a flexible, user-programmable environment.
The evolution continued with the advent of Visual Basic (VB), first released by Microsoft in 1991. VB offered a more structured, yet still highly accessible, environment for building graphical user interface (GUI) applications. It abstracted away much of the complexity of traditional programming languages, allowing users to drag-and-drop visual components and write event-driven code in a relatively straightforward manner. Visual Basic was arguably the first widely adopted "low-code" option, enabling millions of non-professional developers and power users to create custom applications, ranging from departmental tools to more sophisticated business systems. Its influence on empowering "citizen developers" to build functional software solutions was immense, bridging the gap between spreadsheet-based formulas and full-stack application development.
Even before the current generative AI boom, the industry witnessed a significant "low-code" and "no-code" wave throughout the 2010s. Platforms from vendors like OutSystems, Mendix, Appian, and later, Microsoft Power Apps, emerged, promising rapid application development through visual interfaces, pre-built components, and minimal coding. This movement further solidified the idea that non-technical business users could contribute significantly to an organization’s software development efforts, addressing specific departmental needs with greater agility than traditional IT backlogs often allowed. The global low-code development platform market size, valued at approximately $20 billion in 2022, is projected to reach over $187 billion by 2032, underscoring the growing demand for accessible development tools.
Generative AI: The New Frontier of User Programming
The landscape of user programming is now undergoing its most significant transformation yet, propelled by the advent of Generative AI (GenAI). This new generation of tools is fundamentally changing the skill set required for software creation, making it accessible to an even wider audience. Products like Lovable and Bolt exemplify this shift, where the primary "programming language" is no longer a structured syntax or a visual drag-and-drop interface, but natural language – specifically, English.
This breakthrough democratizes software creation to an unprecedented degree. If the main skill required is the ability to articulate a problem or a desired outcome in plain language, then virtually anyone with a business challenge can potentially become a solution creator. This concept, sometimes referred to as "vibe coding," allows users to describe an application’s functionality, data flows, and user interface elements, with the GenAI system translating these natural language prompts into functional code or application components. The types of applications that can be built are also becoming significantly less constrained, extending beyond simple data manipulation to more complex workflows and interactive interfaces.
The Enduring Role of Enterprise SaaS: The Unseen Complexity of Business Rules
Despite the undeniable power and accessibility offered by this new generation of user programming tools, a critical analysis reveals that predictions of the imminent demise of traditional SaaS providers are almost certainly exaggerated. While GenAI will profoundly reshape how businesses interact with and customize software, it is unlikely to fully replace the core enterprise business solutions that underpin modern operations.
The reason lies in the intricate web of business rules and business logic that form the backbone of robust enterprise software. Consider typical business software domains such as procurement, invoicing, payments, budgeting, forecasting, payroll, staffing, sales force automation, customer relationship management (CRM), and customer service. Behind each of these seemingly straightforward functions lie literally thousands of often complex business rules, translated into millions of lines of business logic. These rules encapsulate critical constraints and processes related to:
- Policy: Internal company policies governing approvals, spending limits, data access.
- Compliance: Adherence to regulatory frameworks (e.g., GDPR, HIPAA, Sarbanes-Oxley), industry standards, and government mandates.
- Security: Data protection, access control, fraud prevention mechanisms.
- Legal: Contractual obligations, intellectual property rights, legal reporting requirements.
- Financial: Accounting standards (GAAP, IFRS), tax regulations, revenue recognition rules, expense categories.
- Pricing: Dynamic pricing models, discount structures, regional variations, promotional rules.
These rules are not static; they evolve constantly due to legislative changes, market shifts, and internal policy updates. Moreover, the process of discerning and codifying these rules is a monumental undertaking. It requires deep institutional knowledge, often acquired over years, and a precise understanding of the operational nuances of a specific business.
The vast majority of non-technical people, even those adept at using GenAI tools, have little to no awareness of the full scope and implications of these embedded business rules. Even technical professionals frequently struggle, as these rules are often deeply embedded within legacy codebases, with the original architects and implementers having long since departed. Documentation, if it exists, is often outdated, incomplete, or fails to capture the subtle rationale and exceptions behind each rule. This critical knowledge gap is precisely what product managers and business analysts have traditionally bridged, working tirelessly to define viable solutions that adhere to all necessary constraints. It’s also why addressing technical debt is so challenging; disentangling and re-validating these embedded rules is often a prerequisite for modernization.
A robust enterprise system, therefore, is not merely a collection of features; it is a meticulously engineered framework that captures, manages, and enforces these thousands of essential business rules, ensuring transactions are handled correctly, compliantly, and securely. Building such a system from scratch, even with advanced AI assistance, requires an understanding of regulatory environments, industry best practices, and organizational specificities that goes far beyond what a typical user programming interface can elicit or manage. The global SaaS market, projected to reach nearly $1 trillion by 2030, continues its robust growth precisely because it delivers these complex, pre-packaged, and continually updated solutions, taking the burden of compliance, scalability, and maintenance off individual enterprises.
The Hybrid Future: "Yes to Both" with Intelligent Orchestration
While strong SaaS vendors are not facing an existential threat, significant changes are indeed on the horizon. The future of the "build vs. buy" debate will increasingly lean towards "yes to both" – a symbiotic relationship where organizations continue to acquire powerful, specialized component services from SaaS providers, but these components are designed for deep integration and intelligent orchestration by both humans and sophisticated software agents.
Today’s business solutions are primarily designed for human users. However, the next generation of enterprise software will be architected with dual interfaces: one for human interaction and another, equally critical, for interaction with AI agents and custom solutions. This paradigm shift will allow enterprises to leverage the robust core functionality, compliance, and scalability of commercial SaaS products while simultaneously building highly customized, agile layers on top using user programming tools, low-code platforms, and AI agents.
A major enabler for this sophisticated interoperability is the emergence of widely accepted protocols that allow computers, not just people, to understand and interact with business services. For years, the industry has needed a standardized way to describe the capabilities and constraints of business services in a machine-readable format. This critical architectural gap is now being addressed by initiatives such as The Model Context Protocol (MCP), proposed by Anthropic.
The MCP Protocol is designed to provide a structured, machine-interpretable description of an application’s functionalities, data models, and business rules. By establishing a common language for AI models to comprehend the context and capabilities of various software services, MCP facilitates seamless integration and automated interaction. Instead of AI agents needing to "learn" how to use a human-designed interface, they can directly interpret the service’s API and logic through its MCP description. This protocol is rapidly gaining traction because it solves a long-standing challenge in enterprise architecture: how to enable intelligent systems to autonomously and reliably interact with complex business applications.
In this future, companies will continue to invest in complex and valuable component services for critical business functions – their ERPs, CRMs, HRIS, and financial systems will remain SaaS-based. However, these systems will be intrinsically designed to be accessed and controlled not only by human operators but also by sophisticated AI agents. Some of these agents will be developed by the SaaS vendors themselves, enhancing their product offerings with intelligent automation. Others will be crafted by systems integrators, building industry-specific or enterprise-specific extensions. Crucially, a growing number will be developed by end customers using generative AI tools, creating highly tailored workflows and intelligent automations that sit on top of, and interact with, the core SaaS components. This blend allows for the best of both worlds: the reliability and compliance of bought solutions combined with the flexibility and customization of built components.
Broader Implications and the Evolving Role of "Discovery"
The rise of mainstream user programming, particularly with GenAI, is overwhelmingly a positive development. It promises to liberate countless individuals and departments from the constraints of limited IT resources, empowering them to rapidly develop solutions for their specific pain points. The agility gained can be a significant competitive advantage, accelerating innovation and responsiveness within organizations.
However, as more non-technical individuals venture beyond simple personal time-savers to create impactful business applications, they will inevitably confront the same challenges that professional product teams have navigated for decades. The most crucial lesson, often learned through costly trial and error, is that the hard part is rarely the "building" or "delivering" of a solution. With modern tools, building can be incredibly fast. The true difficulty lies in discovering the right solution to build.
This "discovery" phase encompasses understanding the real problem, identifying the target users, validating assumptions, discerning underlying business needs, assessing technical feasibility, and ensuring alignment with strategic objectives and, critically, existing business rules and compliance requirements. Without a rigorous discovery process, even the most elegantly coded or AI-generated solution risks being irrelevant, unusable, or, worse, detrimental to the business due to misaligned logic or compliance failures.
For enterprises, this means a shift in focus from simply approving or denying IT requests to fostering a culture of informed citizen development, providing guardrails, best practices, and access to critical business knowledge. For SaaS providers, it necessitates designing products with robust APIs, clear Model Context Protocol descriptions, and an "AI-first" mindset, anticipating interaction not just from humans, but from intelligent agents. For product managers and IT professionals, their role evolves from sole builders to architects, governors, mentors, and strategic orchestrators, guiding citizen developers and focusing their expertise on the most complex, core systems and strategic integrations. The age of user programming is here, not as a replacement, but as a powerful augmentation, transforming how enterprises innovate and solve problems, while simultaneously reinforcing the enduring value of well-defined business logic and the art of true solution discovery.
