The long-standing debate within the technology industry, whether to "build" custom solutions or "buy" off-the-shelf products, is undergoing a profound transformation driven by the advent of Generative Artificial Intelligence (AI) and a new generation of user programming tools. This fundamental question, which has historically governed decisions for both traditional IT departments and agile product teams, now faces unprecedented shifts in capabilities and economic models, challenging conventional wisdom and reshaping the future of enterprise software.
The Enduring Dilemma: Build vs. Buy
For decades, organizations have grappled with the build-versus-buy decision. The choice typically hinged on several critical factors: cost, development time, functional limitations, and, crucially, whether the problem to be solved represented a "core competency" of the business. If a function was central to a company’s competitive advantage—like a proprietary trading algorithm for a financial firm or a unique supply chain optimization system for a logistics giant—building was almost always the preferred route, despite its inherent challenges of high upfront investment, prolonged development cycles, and ongoing maintenance burdens. Conversely, for non-core functions such as standard HR, accounting, or CRM systems, buying a commercial off-the-shelf (COTS) solution or Software-as-a-Service (SaaS) offering was often deemed more efficient.
However, this binary choice has long been an oversimplification. The reality for many enterprises, particularly large ones, frequently involved a hybrid approach: buying a foundational solution and then undertaking extensive customization to tailor it to specific business needs. This "buy and customize" model attempted to balance the speed and cost-efficiency of commercial products with the necessity for unique operational alignment, often leading to complex integration projects and specialized skill requirements. Historically, product teams within tech companies enjoyed the flexibility to build, while the broader "business" segments relied heavily on their internal IT departments, leading to lengthy request queues and perceived IT bottlenecks.
According to a 2023 report by Gartner, global IT spending on enterprise software is projected to reach $898 billion in 2024, an increase of 13.9% from 2023. This significant investment underscores the continuous demand for software solutions across all business functions, highlighting the scale of the build-versus-buy decisions organizations face annually. Despite the rise of readily available SaaS solutions, the complexity of enterprise operations often necessitates bespoke integrations or highly specialized functionality that commercial products cannot fully address out-of-the-box.
A Chronology of User Empowerment: From Spreadsheets to Vibe Coding
The seeds of user programming—the ability for non-technical individuals to create functional applications or automations—were sown decades ago, long before the current AI revolution. This evolution marks a significant chronology of democratizing technology:
- 1979: The Dawn of VisiCalc: The invention of VisiCalc for the Apple II personal computer marked a watershed moment. As the first spreadsheet program, it empowered business users to perform complex calculations and financial modeling without writing traditional code. This tool revolutionized financial planning and introduced the concept of "programming" through formulas, making sophisticated computational power accessible to millions. Its impact was profound, driving the adoption of personal computers in the business world.
- 1980s-Present: The Excel Era: Following VisiCalc, Microsoft Excel emerged as the dominant spreadsheet application, becoming ubiquitous across virtually every industry. Today, countless millions of user-created programs, predominantly in the form of complex formulas and macros, underpin critical business processes worldwide. This extensive reliance on user-generated solutions highlights a pre-existing demand for accessible tools to solve specific, often niche, business problems.
- 1991: Visual Basic and Early Low-Code: Microsoft’s release of Visual Basic (VB) further expanded user programming capabilities. VB provided a graphical development environment that allowed users to build Windows applications with significantly less coding than traditional languages. It’s widely considered one of the earliest forms of "low-code" development, enabling a new generation of business power users and departmental IT specialists to create custom tools.
- 2010s: The Low-Code/No-Code Wave: The 2010s saw a surge in specialized low-code and no-code platforms. Tools from companies like Salesforce (with its Lightning Platform), Microsoft (Power Apps), OutSystems, and Mendix aimed to accelerate application development by providing visual interfaces, drag-and-drop functionality, and pre-built components. These platforms sought to bridge the gap between business needs and IT capacity, allowing citizen developers to build departmental applications, automate workflows, and create custom interfaces. According to a forecast by Statista, the low-code development platform market is projected to grow from $23.3 billion in 2023 to $65.1 billion by 2027, indicating strong continued interest in empowering non-technical builders.
Generative AI: Ushering in the "Vibe Coding" Era
The latest and most disruptive phase in user programming is now being driven by Generative AI. Tools such as Lovable and Bolt are pioneering a new paradigm where the programming language is effectively natural language—English, or any human language. This development, sometimes referred to as "vibe coding," dramatically lowers the barrier to entry, making sophisticated application development accessible to almost anyone who can articulate a problem.
"The shift to natural language as a programming interface is monumental," states Dr. Anya Sharma, lead analyst at TechInsights Global. "It moves the focus from syntax and logic to intent and outcome. For the first time, millions of business users who previously relied on IT or specialized low-code developers can directly translate their needs into functional software, promising an unprecedented acceleration in problem-solving."
This new generation of tools transcends the limitations of earlier user programming methods. While VisiCalc and Excel focused on data manipulation and calculations, and Visual Basic on desktop applications, GenAI-powered platforms can generate diverse applications, from workflow automations and data integrations to custom interfaces and analytical dashboards, with significantly less constraint on the type of output. The primary skill required is clear communication, not technical expertise, effectively democratizing solution creation on an unimaginable scale.
The Resilience of SaaS: Beyond the "Doomsday" Scenario
With the rise of user programming and GenAI, a common speculation has emerged: that SaaS vendors are doomed, destined to be replaced by custom, AI-generated solutions. However, this perspective, while understandable, largely overlooks the profound complexity inherent in enterprise-grade business software.
The core reason why robust business solutions—spanning procurement, invoicing, payments, budgeting, forecasting, payroll, staffing, sales force automation, customer relationship management, and customer service—are unlikely to be fully supplanted by user-programmed alternatives lies in the intricate web of business rules and associated business logic.
Most enterprise solutions are built upon literally thousands of often complex business rules and millions of lines of underlying code. These rules are not arbitrary; they meticulously capture and enforce critical constraints and processes related to:
- Policy: Internal operational guidelines and best practices.
- Compliance: Adherence to regulatory frameworks (e.g., GDPR, HIPAA, Sarbanes-Oxley).
- Security: Data protection, access controls, and threat mitigation.
- Legal: Contractual obligations, intellectual property, and jurisdictional requirements.
- Financial: Accounting standards, tax regulations, and audit trails.
- Pricing: Complex tiered pricing models, discounts, promotions, and regional variations.
Discovering, codifying, and maintaining these rules is an immense undertaking. Product managers and business analysts dedicate significant effort to understanding these nuances to define viable and compliant solutions. Furthermore, these rules often evolve, necessitating continuous updates to the underlying software. A 2022 survey by McKinsey found that managing complex business logic and technical debt consumes up to 40% of IT budgets in large enterprises, underscoring the challenge.
The vast majority of non-technical individuals attempting to create business applications lack a comprehensive understanding of these embedded business rules. Even experienced technical teams struggle, as these rules are often deeply embedded in legacy code, and the original architects may have long departed. Documentation, if it exists, rarely captures the full rationale and subtle implications behind each rule. Therefore, while a user might quickly "vibe-code" an app to track sales leads, ensuring it adheres to stringent data privacy regulations, integrates seamlessly with existing financial systems, and complies with internal sales policies requires a level of institutional knowledge and system integration that is far beyond the scope of ad-hoc user programming.
"The idea that GenAI will instantly replace mature SaaS platforms ignores the sheer depth of codified business knowledge and regulatory compliance embedded within them," observes Sarah Chen, CEO of InnovateCorp SaaS. "We see GenAI as a powerful accelerator for our platform, allowing customers to unlock even more value through tailored extensions and integrations, rather than a threat to our foundational offerings."
The Model Context Protocol (MCP): Bridging the Gap
Despite the enduring complexity of core business rules, significant changes are indeed on the horizon for how enterprises interact with their software. The future envisions a symbiotic relationship where commercial SaaS solutions are not just used by humans, but also by AI agents and new custom solutions crafted through "vibe coding" or traditional development.
A major enabler for this hybrid future is the emergence of a widely accepted protocol designed to describe business services in a manner comprehensible to computers, not just people. Anthropic’s proposal of The Model Context Protocol (MCP) about a year ago has rapidly gained traction because it addresses a crucial, long-standing architectural challenge.
The MCP aims to standardize how AI models can understand, interact with, and orchestrate various software services and APIs. It provides a structured way to convey context, capabilities, and constraints of a service, allowing AI agents to intelligently utilize and integrate with existing enterprise systems. This protocol is vital because it moves beyond simple API calls, enabling a deeper, more semantic understanding of what a service does and how it should be used in complex workflows.
For complex enterprises, the MCP represents an extraordinarily powerful tool. It means that purchased SaaS components, rich with embedded business rules and logic, can now be exposed and controlled by sophisticated AI agents. These agents could automate complex multi-step processes, analyze data across disparate systems, or dynamically adapt workflows based on real-time conditions, all while respecting the underlying business rules enforced by the core SaaS platform.
The Future: A Hybrid Ecosystem of "Yes to Both"
The trajectory suggests a future where the build-versus-buy dichotomy evolves into a dynamic "yes to both" ecosystem. Companies will continue to procure robust, complex, and valuable component services from strong SaaS vendors for critical business functions. However, these services will increasingly be designed not just for human users but also for seamless access and control by intelligent software agents.
This new landscape will see:
- AI Agents from Vendors: SaaS providers themselves will develop and offer AI agents that can extend the functionality of their platforms, automate routine tasks, and provide intelligent insights, operating within the defined parameters of their core product.
- System Integrator-Defined Workflows: Consulting firms and system integrators will leverage GenAI tools and MCP to create highly customized workflows and integrations that connect various SaaS components, orchestrate complex business processes, and build bespoke solutions tailored to specific client needs, acting as a sophisticated layer above the purchased software.
- Customer-Defined Solutions: End customers, empowered by natural language user programming tools, will be able to create their own AI agents or custom applications that interact with core SaaS platforms. These solutions might address highly specific departmental needs, automate personal productivity tasks, or generate unique analytical views, operating in conjunction with the larger enterprise systems.
"This is not just about automation; it’s about intelligent augmentation," explains Dr. Sharma. "The MCP, combined with Generative AI, allows enterprises to unlock the full potential of their existing SaaS investments while simultaneously fostering a culture of innovation and agile solution development at the edge."
Challenges and the Enduring Lesson of Product Discovery
While the age of user programming promises unprecedented empowerment and agility, it also introduces new challenges. As more non-technical individuals venture beyond simple personal time-savers to create business-critical solutions, they will inevitably encounter the complexities that have long defined professional software development.
The most important lesson for this new generation of user programmers, a lesson hard-won by the product world, is that the hardest part is rarely building and delivering a solution. The truly difficult and often overlooked challenge is discovering the right solution to build. This involves:
- Problem Definition: Clearly understanding the root cause of a problem, not just its symptoms.
- User Empathy: Designing solutions that genuinely meet user needs and workflows.
- Viability: Ensuring the solution is technically feasible, economically sustainable, and aligns with business objectives.
- Feasibility: Assessing if the solution can be built and maintained with available resources.
- Compliance and Governance: Adhering to all relevant business rules, policies, security standards, and regulatory requirements.
Without a rigorous approach to product discovery, the proliferation of user-generated solutions, while initially empowering, could lead to new forms of technical debt, fragmented data, security vulnerabilities, and operational inconsistencies. Enterprises will need to establish new governance frameworks, training programs, and collaboration models to harness the power of user programming responsibly, ensuring that agility does not come at the cost of stability, security, or compliance. The future of enterprise software is not a simple choice between building or buying, but rather a sophisticated orchestration of both, intelligently guided by human insight and empowered by artificial intelligence.
