The fundamental question of whether an organization should build custom software or buy off-the-shelf solutions to address its operational challenges has been a constant companion since the dawn of the computing era. This strategic decision, deeply embedded in the fabric of technological advancement, impacts not only traditional IT departments but also modern product teams across all industries. While the core dilemma remains, the advent of sophisticated user programming tools, particularly those powered by Generative Artificial Intelligence (AI), is profoundly reshaping the landscape, prompting a re-evaluation of established paradigms.
Historically, the build-or-buy choice was largely dictated by a company’s core competencies and the availability of commercial alternatives. If a problem resided at the heart of an organization’s competitive advantage—think proprietary algorithms for a trading firm or unique manufacturing process controls—building a bespoke solution was often the default. Conversely, for non-core functions like payroll, accounting, or standard HR processes, purchasing a commercial off-the-shelf (COTS) product, typically from a Software-as-a-Service (SaaS) provider today, became the preferred route. This approach balanced the high initial investment and ongoing maintenance of custom development against the often considerable costs and functional limitations of pre-built systems. The reality, however, was rarely binary; large enterprises frequently found themselves in a hybrid model, acquiring solutions and then undertaking extensive customization to align them with specific business requirements, blurring the lines between pure "buy" and pure "build."
The Evolution of User Empowerment: From VisiCalc to Vibe Coding
The ability for non-technical users to "program" solutions to their own problems has a longer history than many realize, tracing back to the earliest days of personal computing. This evolution marks a significant shift in who can create technological solutions and how.
The watershed moment arrived in 1979 with the introduction of VisiCalc, the first electronic spreadsheet program, designed for the Apple II. VisiCalc empowered individuals, typically business analysts and accountants, to create complex financial models and calculations without writing traditional code. This was, in essence, the genesis of "user programming" – enabling non-technical professionals to define logic and automate tasks, albeit within a constrained environment. The impact was revolutionary, democratizing data analysis and giving rise to an entirely new category of software. Millions of "user-created programs," primarily in the form of formulas, continue to run daily in spreadsheet applications like Microsoft Excel across virtually every enterprise worldwide.
The trajectory of user programming continued with tools like Visual Basic, first released by Microsoft in 1991. Visual Basic provided a graphical development environment that allowed users to build desktop applications with relative ease, using a more accessible programming language. This marked an early foray into what would later be termed "low-code" development, enabling a broader segment of technically-inclined but not professional developers to create functional applications.
The 21st century witnessed a surge in dedicated low-code and no-code platforms. These tools, such as Appian, OutSystems, and Salesforce’s Lightning Platform, abstract away much of the underlying code, allowing users to build applications through visual interfaces, drag-and-drop functionality, and pre-built components. This wave further broadened the accessibility of application development, enabling business users, often referred to as "citizen developers," to rapidly prototype and deploy solutions for departmental or specific business needs, thereby reducing reliance on central IT and alleviating the perennial backlog of development requests. The market for low-code development platforms alone is projected to reach over $65 billion by 2027, underscoring its growing importance in enterprise IT strategies.
Generative AI: Ushering in a New Era of User Programming
Today, the landscape is undergoing its most profound transformation yet with the rise of Generative AI. This new generation of user programming tools, exemplified by platforms like Lovable and Bolt, is pushing the boundaries of accessibility by making natural language – specifically English – the primary interface for software creation. Essentially, the "programming language" is now conversational, allowing users to describe their desired application or automation in plain text, and the AI generates the underlying code or workflow.
This shift, often referred to as "vibe coding," drastically lowers the barrier to entry for application development. The skill set required moves away from syntax and logic structures towards clear articulation of intent and problem definition. This opens up development capabilities to virtually anyone with a problem to solve, promising unprecedented levels of individual and departmental autonomy in creating digital solutions. The implications for productivity and innovation within organizations are immense, as bottlenecks in traditional IT departments could be significantly reduced, and creative solutions can emerge from unexpected corners of the business.
The Nuance of Business Logic: Why SaaS Solutions Endure
Despite the revolutionary potential of AI-powered user programming, the assertion that "everyone will build, and nobody will buy," leading to the demise of SaaS vendors, is a significant oversimplification. While the new tools empower individuals to create sophisticated applications, they largely sidestep the most critical and complex challenge in enterprise software: the intricate web of business rules and logic.
Enterprise business software—spanning procurement, invoicing, payments, budgeting, forecasting, payroll, staffing, sales force automation, customer relationship management (CRM), and customer service—is not merely a collection of features. Behind every transaction, every workflow, and every data point lie thousands of often-complex business rules and millions of lines of associated business logic. These rules codify an organization’s policies, ensure compliance with regulatory frameworks (e.g., GDPR, HIPAA, Sarbanes-Oxley), enforce security protocols, manage legal obligations, define financial accounting principles, and dictate pricing structures.
Consider a seemingly straightforward task like processing an invoice. This involves rules for vendor validation, purchase order matching, approval hierarchies based on value or department, tax calculations, payment terms, currency conversions, fraud detection, and integration with general ledger systems. Each of these steps is governed by specific, often undocumented, rules that have evolved over years, sometimes decades, to reflect internal policies, industry best practices, and legal mandates.
The challenge lies in the fact that most non-technical individuals, and even many technical developers, embarking on creating business applications have little to no awareness of the full breadth and depth of these embedded business rules. These rules are frequently hard-coded into existing systems, and the institutional knowledge held by the original developers or business analysts who defined them has often departed with those individuals. Documentation, where it exists, is often incomplete, outdated, or lacks the crucial context and nuance behind each rule. This complexity is a primary reason why addressing technical debt in legacy systems is so arduous; it requires meticulously extracting and re-validating these business rules to determine their ongoing applicability.
Product managers and business analysts traditionally dedicate significant effort to uncovering, documenting, and codifying these rules to ensure any new solution is viable and compliant. SaaS vendors, particularly those specializing in specific domains, invest heavily in embedding these complex rules into their platforms. They offer pre-built solutions that encapsulate best practices, regulatory compliance, and robust security frameworks, making them immensely valuable to businesses that would otherwise have to invest enormous resources in replicating this intricate logic from scratch.
The Model Context Protocol: Bridging the Divide
However, the enduring value of robust SaaS solutions does not mean the status quo will remain unchallenged. Significant changes are on the horizon, driven by the need for greater interoperability and the rise of AI agents. The future of enterprise software points towards a "yes to both" scenario, where companies continue to buy sophisticated component services but these services are designed for seamless interaction with both human users and intelligent software agents.
A critical enabler for this hybrid future is the development of standardized protocols that allow computers, not just people, to understand and interact with business services. For years, the industry has needed a widely accepted method to describe the capabilities and constraints of business services in a machine-readable format. Anthropic’s proposal of "The Model Context Protocol (MCP)" approximately a year ago has rapidly gained traction because it addresses this long-standing architectural gap.
The MCP aims to provide a structured way for AI models and other software systems to comprehend the context, functionality, and constraints of external tools and APIs. By offering a standardized "instruction set" or "schema" for how to interact with a service, MCP allows AI agents to intelligently utilize and orchestrate various enterprise applications. This means that instead of a human user directly navigating a SaaS interface, an AI agent, given a high-level instruction, could interact with multiple bought services (e.g., a CRM, an ERP, and a payment gateway) to complete a complex workflow autonomously.
In this evolving ecosystem, strong SaaS vendors will adapt by ensuring their platforms are not just user-friendly for humans but also "AI-agent-friendly" through robust APIs and adherence to protocols like MCP. Their value proposition will shift from merely providing a functional application to offering a highly reliable, compliant, and intelligent component service that can be seamlessly integrated into broader, AI-orchestrated workflows.
Implications for the Enterprise and the Future of Work
This paradigm shift has profound implications for various stakeholders:
- For Enterprises: The "yes to both" future offers unprecedented agility and customization. Businesses can leverage the deep functionality and compliance built into commercial SaaS solutions while simultaneously building bespoke, AI-driven orchestrations and custom interfaces on top of these components. This allows for tailoring solutions to precise operational needs without reinventing the wheel on core business logic.
- For IT Departments: The role of IT will evolve from solely building and maintaining systems to curating, integrating, and governing a complex ecosystem of bought services, custom-built solutions, and AI agents. IT professionals will become orchestrators and architects, ensuring security, scalability, and compliance across hybrid environments. They will also play a crucial role in educating business users on the responsible and effective use of user programming tools.
- For SaaS Vendors: Competition will intensify, not just on features, but on the "AI-readiness" of their platforms. Vendors that embrace open standards, provide robust APIs, and design their services to be consumable by AI agents will thrive. Those that remain closed and human-centric risk being bypassed or becoming mere data repositories.
- For Systems Integrators: Their expertise in connecting disparate systems will be more critical than ever. Integrators will specialize in designing and implementing complex workflows involving multiple SaaS components, custom-built AI solutions, and various AI agents. They will become crucial partners in translating business requirements into effective AI-orchestrated architectures.
- For Business Users: The age of user programming empowers a new wave of citizen developers. They will be able to automate personal tasks, create departmental dashboards, and even build custom front-ends for complex backend services. However, this empowerment comes with the responsibility to understand the underlying business rules and implications, echoing the crucial lesson from the product world: the challenge is not just building a solution, but discovering the right solution to build.
In conclusion, the rise of Generative AI and advanced user programming tools is not a death knell for the SaaS industry but rather a catalyst for its evolution. The future of business software will be a sophisticated tapestry woven from robust, bought component services, integrated and enhanced by custom, AI-driven solutions and intelligent agents. This hybrid model promises unprecedented levels of flexibility, innovation, and efficiency, fundamentally reshaping how organizations address their technological needs and ushering in a new era of collaborative development between humans and machines.