The fundamental question of whether to "build" a proprietary solution or "buy" an off-the-shelf product has been a cornerstone of strategic decision-making in the technology industry since its inception. This debate is not confined to traditional IT departments but permeates every product team, business unit, and entrepreneurial venture. While seemingly straightforward, the choice is fraught with complexities, balancing immediate needs against long-term strategic goals, cost implications, and functional limitations. The advent of Generative Artificial Intelligence (AI) is now dramatically altering the landscape of this perennial dilemma, promising an era where the answer is increasingly "yes to both."
Historically, the "build" option offers the promise of bespoke functionality, tailored precisely to an organization’s unique requirements, and often represents a core competency. However, it comes with significant challenges: substantial upfront investment in development time and cost, the ongoing burden of maintenance, and the need for specialized technical talent. Conversely, the "buy" option, typically involving commercial off-the-shelf (COTS) software or Software-as-a-Service (SaaS) solutions, offers rapid deployment, shared maintenance costs, and access to industry best practices. Yet, these solutions often entail functional limitations, vendor lock-in, and the necessity for extensive customization to align with specific business processes, especially within large enterprises. The conventional wisdom has long dictated that if a problem touches a core competency, an organization builds; if it lies outside, it buys. This simplification, however, has always overlooked the nuanced reality where many problems are so specialized that no viable "buy" option exists, or where "buying" merely serves as a starting point for significant "building" through customization.
The Evolution of User Programming: A Chronological Overview
For decades, the luxury of building custom solutions was largely reserved for dedicated product teams or central IT departments. Business units, often referred to as "the business," would present an endless list of requests to "IT," leading to backlogs and a perception of IT as a bottleneck rather than an enabler. This dynamic began to shift with the emergence of user programming, a concept that empowers non-technical individuals to create functional applications or automate tasks without needing formal coding expertise.
The genesis of user programming can be traced back to 1979 with the groundbreaking introduction of VisiCalc, the first spreadsheet program designed for personal computers, specifically the Apple II. VisiCalc was revolutionary, allowing financial analysts and business professionals to create complex models and perform calculations without writing traditional code. This marked a profound shift, democratizing computational power and proving that "programming" could be accessible to non-technical users through intuitive interfaces. It was enormously empowering, enabling individuals to solve their own problems directly.
Following VisiCalc’s success, the spreadsheet paradigm matured, culminating in the ubiquity of Microsoft Excel and similar platforms. Today, countless millions of user-created programs, predominantly in the form of formulas and macros, run daily across virtually every company worldwide. This era solidified the concept of user-driven automation, albeit often within the confines of data manipulation and basic logic.
The next significant leap occurred in 1991 with the release of Visual Basic (VB). VB offered a graphical user interface (GUI) development environment that allowed users to drag-and-drop elements and write event-driven code in a relatively straightforward language. It quickly became a cornerstone of citizen development, enabling millions of non-technical users and domain experts to build standalone applications and custom solutions. Visual Basic is widely considered one of the earliest mainstream "low-code" options, bridging the gap between spreadsheet formulas and traditional software development.
In the decades leading up to the current AI revolution, the low-code and no-code movements gained substantial traction. Platforms like Appian, Mendix, OutSystems, and Airtable provided visual development environments, pre-built components, and abstraction layers that significantly reduced the need for deep coding knowledge. These tools empowered a new generation of "citizen developers" to build web and mobile applications, automate workflows, and create custom databases, addressing specific departmental needs and reducing reliance on overburdened IT resources. The market for low-code/no-code platforms experienced robust growth, with Gartner forecasting the global low-code development technologies market to reach $30.2 billion in 2024, a 20% increase from 2023, underscoring its established value.
Generative AI: The New Frontier of User Programming
The current era marks an unprecedented acceleration in user programming capabilities, driven by the transformative power of Generative AI. Products such as Lovable and Bolt exemplify this new generation of tools, where the traditional programming language is effectively replaced by natural language, primarily English. This paradigm shift means that nearly anyone with a problem to solve and the ability to articulate it can now "program."
What distinguishes this new wave from its predecessors is the sheer breadth and depth of application types that can be created, coupled with the dramatically lowered skill barrier. Previous user programming tools, while powerful, often constrained users to specific domains (e.g., spreadsheets for data, low-code for business apps with defined components). Generative AI, however, leverages large language models (LLMs) to understand intent, generate code, and even design interfaces based on conversational prompts. This "vibe coding" approach allows for the creation of far more diverse and complex applications, from custom data analytics tools and interactive dashboards to specialized workflow automation and even basic web applications, all articulated through everyday language.
Industry analysts predict a massive surge in AI-driven development. A report by McKinsey & Company in 2023 highlighted that Generative AI could automate up to 70% of coding tasks, significantly lowering the entry barrier for software creation. This empowerment promises to unlock immense latent innovation within organizations, enabling business users to rapidly prototype and deploy solutions that directly address their operational pain points, without waiting for IT cycles.
The Enduring Challenge of Business Rules and Logic
Despite the revolutionary potential of Generative AI-powered user programming, the notion that it will render existing SaaS providers obsolete and lead to a universal "build" philosophy is, by most expert analyses, a profound oversimplification. The core reason lies in the inherent complexity of business rules and the business logic that underpins robust enterprise software solutions.
Consider the intricate systems governing critical business functions like procurement, invoicing, payments, budgeting, forecasting, payroll, staffing, sales force automation, customer relationship management (CRM), and customer service. Behind these seemingly straightforward processes lie literally thousands of often complex business rules and millions of lines of intricate business logic. These rules are not arbitrary; they meticulously capture and enforce constraints and processes related to:
- Policy: Internal company guidelines and standard operating procedures.
- Compliance: Adherence to industry regulations (e.g., HIPAA, GDPR), government mandates, and legal frameworks.
- Security: Data access controls, authentication protocols, and cyber security measures.
- Legal: Contractual obligations, data privacy, and intellectual property rights.
- Financial: Accounting standards (GAAP, IFRS), tax regulations, and internal financial controls.
- Pricing: Complex algorithms, discount structures, and regional variations.
Many of these rules have evolved over years, even decades, and represent a collective institutional knowledge. The effort required to discern, codify, and maintain these rules is immense. Most non-technical individuals attempting to create business applications via user programming tools have little to no awareness of this deep layer of underlying business rules. Even experienced technical professionals often struggle, as these rules are frequently embedded deep within legacy code, and the original architects or definers may have long since departed. Documentation, if it exists, rarely captures the nuanced rationale or historical context behind each rule.
This critical knowledge—understanding the "why" behind the "what"—is precisely what product managers, and previously business analysts, dedicate significant effort to acquire. It is the foundation for defining truly viable and compliant solutions. Furthermore, this entanglement of business rules within code is a primary reason why addressing technical debt is so challenging; one must painstakingly extract these rules and determine their continued relevance. A system that effectively captures, manages, and enforces thousands of essential business rules, ensuring transactions are processed accurately and compliantly, represents a monumental effort and inherent value that cannot be trivially replicated by individual user-programmed solutions.
The Future of Business Software: A Hybrid Ecosystem
While strong SaaS vendors are not poised for obsolescence, a significant transformation is undoubtedly underway. The traditional model, where business solutions were designed almost exclusively for human users interacting directly with interfaces, is evolving. The future will see these sophisticated solutions utilized not only by humans but also by AI agents and custom solutions crafted (whether "vibe-coded" or traditionally "hand-coded") on top of these foundational component services.
This shift is being facilitated by advancements in interoperability and communication protocols. A major enabler, long needed since the advent of the internet, is only now becoming a widespread reality: a universally accepted protocol for describing business services in a way that can be read and understood by computers, not just people. This critical architectural problem found a promising solution approximately a year ago with Anthropic’s proposal of The Model Context Protocol (MCP). The MCP has rapidly gained traction because it provides a standardized framework for AI models to understand the capabilities, constraints, and operational context of external tools and services. It acts as a universal translator, allowing AI agents to intelligently interact with and orchestrate complex enterprise systems.
The implication of MCP and similar protocols is profound: the future of "build vs. buy" will be an emphatic "yes to both." Companies will continue to procure complex, high-value component services for critical parts of their operations. However, these services will be explicitly designed with API-first principles, making them accessible and controllable by both human users and sophisticated software agents. Some of these software agents will be developed by the SaaS vendors themselves, offering enhanced automation and intelligent features. Others will be created by systems integrators, building bespoke workflows and integrations for specific enterprise needs. Crucially, a growing segment will be defined and generated by end customers themselves, leveraging Generative AI tools to create custom workflows and mini-applications that orchestrate these bought services in novel ways.
This paradigm represents a significant shift for enterprise IT. Instead of solely focusing on building everything from scratch or merely integrating disparate systems, IT departments will increasingly become architects of an intelligent ecosystem. Their role will evolve towards selecting, governing, and orchestrating a blend of robust bought services, internal core competency builds, and a myriad of agile, user-programmed AI agents and workflows.
Empowerment and the New Imperative for Product Thinking
The rise of user programming, amplified by Generative AI, is overwhelmingly a positive development. It promises to empower countless individuals and departments that have historically contended with constrained IT resources and long development queues. The ability to rapidly prototype, iterate, and deploy solutions to immediate problems can significantly boost productivity and foster innovation from within.
However, as more non-technical individuals venture beyond simple personal time-savers to create more complex business applications, they will inevitably confront many of the same challenges that professional product teams have learned to navigate. The most crucial lesson, often learned through costly experience, is that the hard part is rarely building and delivering the solution; the hard part is discovering the right solution to build.
Effective product development requires a deep understanding of user needs, market viability, technical feasibility, and business value. User programmers will need to cultivate "product thinking" skills:
- Problem Identification: Clearly defining the actual problem, not just the perceived symptom.
- User Empathy: Understanding the needs, pain points, and workflows of the intended users.
- Business Context: Appreciating the broader organizational goals, constraints, and the impact of the solution.
- Iterative Design: Understanding that initial solutions are rarely perfect and require continuous refinement.
- Governance and Security: Even in a low-code/no-code environment, ensuring solutions adhere to organizational standards for data integrity, security, and compliance.
Without these foundational principles, even the most advanced AI-powered tools risk generating solutions that are technically functional but ultimately ineffective, unsustainable, or even detrimental to the business. The new age of user programming demands not just technological literacy but a more widespread adoption of strategic problem-solving and product management disciplines.
In conclusion, Generative AI is not merely an incremental improvement; it is a catalyst for a fundamental re-evaluation of how organizations acquire, develop, and deploy software solutions. The "build vs. buy" debate is transforming into a sophisticated integration challenge, where robust, compliant, and AI-enabled commercial services form the backbone, augmented by agile, custom-built AI agents and workflows created by a newly empowered generation of user programmers. This hybrid future promises unprecedented flexibility, efficiency, and innovation, contingent on a nuanced understanding of technology, business rules, and the enduring principles of effective solution design.
