For as long as the technology industry has existed, organizations have grappled with a fundamental strategic question: whether to build custom software solutions to address specific problems or to acquire commercial off-the-shelf (COTS) products. This enduring "build vs. buy" dilemma permeates every level of technological decision-making, from traditional IT departments managing vast infrastructures to agile product teams developing cutting-edge applications. While seemingly straightforward, the choice is laden with complexities, balancing immediate costs against long-term flexibility, maintenance, and strategic alignment.
The Enduring Dilemma: Cost, Customization, and Core Competency
At its core, the build vs. buy decision hinges on several critical factors. Building a custom solution offers unparalleled control, allowing an organization to tailor functionality precisely to its unique operational needs and competitive differentiators. However, this bespoke approach comes with significant challenges: substantial upfront investment in development time and resources, the ongoing burden of maintenance, bug fixes, and future upgrades, and the inherent risks associated with project delays and scope creep. Gartner, for instance, has consistently highlighted that custom development projects often exceed initial budget and timeline estimates, with a significant percentage failing to meet all original requirements.
Conversely, buying a solution, typically in the form of Software-as-a-Service (SaaS) or packaged software, offers rapid deployment, shared maintenance costs across a vendor’s customer base, and access to industry best practices embedded within the product. Yet, purchased solutions inevitably come with limitations, requiring compromises on specific functionalities or workflows. Each buy alternative carries an associated cost, not just for licenses but also for implementation, training, and potential integration challenges.
For most companies, the decision often aligns with core competencies. If a problem directly relates to a company’s unique value proposition or competitive advantage, building a proprietary solution is usually preferred to maintain differentiation. However, for functions deemed non-core—such as standard human resources, financial accounting, or customer relationship management—buying a proven, scalable, and secure commercial product is often the more pragmatic and cost-effective approach.
However, the reality in many large enterprises often transcends this binary choice. The concept of "buy vs. build" has long been an oversimplification, as it’s exceedingly common to acquire a commercial product and then undertake significant customization efforts to tailor it to the specific intricacies of a particular business. This "buy and configure/customize" hybrid approach attempts to leverage the stability of a COTS solution while still achieving a degree of bespoke functionality, though it introduces its own complexities in terms of upgrades and vendor support.
Historically, product development teams within technology companies enjoyed the luxury and expertise to build solutions from the ground up. In contrast, other departments, often referred to as "the business," were largely dependent on a centralized IT department to fulfill their software requirements. This often led to extensive backlogs, slow delivery cycles, and a perception of IT as a bottleneck rather than an enabler, fueling an endless list of requests for custom applications or modifications.
A Historical Arc: The Evolution of User Empowerment in Technology
The landscape of software development and problem-solving began to democratize long before the advent of modern AI. The seeds of user programming—the ability for non-technical individuals to create functional software—were sown decades ago, fundamentally shifting the dynamics of technology creation.
The Dawn of User Programming:
The first significant milestone in user empowerment arrived in 1979 with the invention of VisiCalc, the inaugural spreadsheet program designed for the Apple II personal computer. VisiCalc was revolutionary. It allowed non-programmers, particularly business professionals, to manipulate data, perform complex calculations, and model financial scenarios without writing a single line of traditional code. Users could define relationships between cells using simple formulas, effectively "programming" the software to meet their specific analytical needs. This was an enormously empowering development, moving computational power and solution creation out of the exclusive realm of IT specialists and into the hands of domain experts.
Following VisiCalc’s groundbreaking success, the concept evolved. Microsoft Excel, introduced in 1985, built upon this foundation, becoming the ubiquitous tool for data analysis and countless user-created applications. Today, millions of user-created programs, primarily in the form of formulas and macros, run daily across businesses worldwide within spreadsheet applications.
From Macros to Low-Code/No-Code:
The trajectory continued with the release of Visual Basic (VB) in 1991. VB was arguably the first widely accessible low-code option, offering a graphical user interface (GUI) builder and an event-driven programming model that significantly simplified the creation of Windows applications. It empowered a new generation of "citizen developers" to build desktop applications with relative ease, without needing deep expertise in C++ or other complex languages of the era.
This momentum led to a more concerted movement in the 21st century: the rise of low-code and no-code (LCNC) platforms. These platforms abstract away coding complexities even further, offering visual development environments with drag-and-drop interfaces, pre-built components, and intuitive logic builders. Companies like OutSystems, Mendix, Appian, and Microsoft Power Apps emerged as leaders, enabling business analysts and power users to rapidly develop web and mobile applications, automate workflows, and integrate systems without writing extensive code.
The impact of LCNC has been substantial. According to Gartner, the worldwide low-code development technologies market is projected to reach $30.7 billion in 2023, an increase of 19.1% from 2022. Furthermore, Gartner predicts that by 2026, developers outside of IT departments will account for at least 80% of the total low-code developer population, up from 60% in 2021. This growth underscores the widespread adoption and the increasing demand for tools that democratize application development. These platforms have empowered businesses to accelerate digital transformation, respond more quickly to market demands, and reduce the burden on professional IT teams.
Generative AI’s Transformative Leap:
Now, the technology landscape is experiencing another seismic shift with the advent of Generative AI (GenAI). This new generation of user-programming tools, exemplified by platforms like Lovable and Bolt, takes the concept of accessibility to an unprecedented level. The programming language is, essentially, natural language—English, or other human languages. Users can describe the application they want to build, the data they need to process, or the workflow they wish to automate, and the GenAI model translates these instructions into functional code or application logic.
This development dramatically lowers the barrier to entry, opening sophisticated application development capabilities to nearly anyone with a problem to solve and the ability to articulate it. The primary skill required is no longer coding syntax or even understanding visual programming paradigms, but rather clear communication and logical thinking. This "vibe coding" approach promises to make software creation as intuitive as describing a desired outcome, potentially bringing millions more "citizen developers" into the fold.
Beyond Hype: Why SaaS Remains Indispensable Despite User Programming Advances
The rapid emergence of GenAI-powered user programming tools has led some to speculate that the days of commercial SaaS providers are numbered. The argument posits that if anyone can "program" their own solutions using natural language, why would businesses continue to pay for expensive, often rigid, commercial software? This narrative, however, is almost certainly an oversimplification that fails to account for the profound complexities inherent in enterprise-grade business solutions.
The Unseen Labyrinth of Business Rules:
The primary reason why core business software—such as procurement, invoicing, payments, budgeting, forecasting, payroll, staffing, sales force automation, customer relationship management, or customer service systems—is unlikely to be entirely replaced by user-programmed solutions lies in the intricate web of business rules and the associated business logic.
What many non-technical users, and even some technical ones, fail to fully appreciate is that behind most enterprise business solutions lie literally thousands of often complex business rules and millions of lines of underlying business logic. These rules are not arbitrary; they are the codified embodiment of an organization’s policies, industry regulations, compliance mandates, security protocols, legal requirements, financial standards, pricing strategies, and operational processes. For instance, a payroll system must adhere to myriad tax laws, labor regulations, and company-specific compensation policies that vary by jurisdiction, employee type, and tenure. An invoicing system must follow specific financial reporting standards, payment terms, and legal disclosure requirements.
Many of these rules are not immediately obvious and take considerable time, expertise, and effort to discern, document, and codify accurately. They are often the product of years of operational experience, legal precedents, and evolving regulatory environments.
The vast majority of non-technical individuals aspiring to create business applications using new user programming tools have little to no awareness of these deeply embedded business rules. Even professional developers often struggle with them, as these rules are frequently buried within legacy codebases as undocumented business logic. The original architects and implementers of these rules may have long since moved on, and while static documentation might occasionally exist, it rarely captures the nuanced reasoning or historical context behind each rule. This lack of institutional knowledge makes it incredibly challenging to replicate or even fully understand the existing functionality.
This critical knowledge gap is precisely what product managers and, before them, business analysts, are trained to bridge. Their role involves deep engagement with stakeholders, meticulous discovery processes, and comprehensive analysis to uncover, validate, and define viable solutions that comply with all pertinent business rules and constraints. This arduous process of "teasing out" business rules is also why addressing technical debt—modernizing or refactoring legacy systems—is so difficult; it requires not just understanding old code but re-evaluating which rules still apply and how they should be re-implemented.
Therefore, building a system from scratch that accurately captures, manages, and enforces thousands of essential business rules, ensuring transactions are handled correctly and compliantly, represents a monumental undertaking. This intrinsic value, complexity, and specialized knowledge are precisely what strong SaaS vendors bring to the table. They invest heavily in understanding, codifying, and maintaining these rule sets across diverse industries and geographies, offering solutions that are not just functional but also compliant and secure out-of-the-box.
Data & Market Reality:
The market trends affirm the continued indispensability of SaaS. The global SaaS market size was valued at approximately $272.49 billion in 2023 and is projected to grow at a compound annual growth rate (CAGR) of over 18% from 2024 to 2030, according to Grand View Research. This robust growth trajectory directly contradicts the notion of its impending demise. Businesses continue to invest in SaaS because it provides proven solutions for complex, non-differentiating functions, allowing them to focus their internal resources on areas that provide true competitive advantage. SaaS vendors offer not just software, but expertise, continuous updates, security patches, scalability, and compliance assurance—all critical elements that are incredibly difficult and expensive for individual companies to replicate through custom development.
The Emergence of a Hybrid Future: "Yes to Both" with Intelligent Integration
While the narrative of SaaS’s downfall is misguided, the advent of sophisticated user programming tools and AI agents does signal significant changes ahead for enterprise software. The future of the "build vs. buy" dilemma is not an either/or proposition, but rather a sophisticated "yes to both" approach, where commercial solutions and custom-built components coexist and interact seamlessly.
Today’s business solutions are primarily designed for human users, often with varying degrees of success in user experience. However, the next generation of these solutions will evolve to serve not only humans but also intelligent AI agents and new custom applications crafted either through vibe-coding or traditional hand-coding. This marks a profound shift in how software components will be consumed and orchestrated within an enterprise.
The Model Context Protocol (MCP): A Key Enabler:
A major enabler for this sophisticated hybrid future is the development of robust, machine-readable protocols for describing business services. For years, with the growth of the internet and distributed systems, the industry has needed a widely accepted standard that allows computers, not just people, to understand and interact with business services programmatically. This architectural gap has been a significant hurdle in achieving true interoperability and intelligent automation.
About a year ago, Anthropic, a leading AI safety and research company, proposed "The Model Context Protocol" (MCP). This protocol has rapidly gained traction because it addresses this critical, long-standing architectural problem. The MCP aims to provide a structured, standardized way for AI models and other software agents to comprehend the capabilities, inputs, outputs, and constraints of various business services. By offering a common language for describing APIs and service semantics, MCP allows AI agents to intelligently discover, invoke, and chain together complex business operations, turning abstract goals into concrete actions across disparate systems.
The New Ecosystem:
With protocols like MCP facilitating intelligent communication, the future enterprise IT landscape will feature:
- SaaS Components as Intelligent Services: Strong SaaS vendors will continue to provide complex, valuable component services for critical business functions. However, these services will be designed from the ground up to be accessed and controlled seamlessly by both human users and autonomous software agents. This implies more robust, well-documented, and machine-readable APIs that align with protocols like MCP.
- AI Agents as Orchestrators: AI agents, developed by the SaaS vendors themselves, by systems integrators, or by end customers, will act as intelligent intermediaries. These agents will interpret human commands (often in natural language), understand business goals, interact with various SaaS components via their machine-readable interfaces, and orchestrate complex workflows across the enterprise. For instance, an AI agent could process a customer complaint by accessing CRM data, initiating a refund in the financial system, and updating inventory—all by interacting with separate SaaS applications.
- Customer-Defined Workflows and Applications: Leveraging GenAI tools, low-code platforms, and traditional development, organizations will build custom workflows and lightweight applications on top of these robust commercial component services. These custom solutions will address highly specific, differentiating needs or integrate unique internal processes, without having to rebuild the underlying core functionalities provided by SaaS. This means a company might "vibe-code" a custom front-end interface or a specialized reporting tool that pulls data from and interacts with their purchased CRM, ERP, and HR platforms.
This hybrid model empowers organizations to maintain the benefits of best-of-breed commercial software—security, compliance, scalability, vendor expertise—while simultaneously gaining unparalleled agility and customization through internal development efforts. The focus shifts from merely acquiring software to intelligently integrating and orchestrating services across a heterogeneous landscape.
Navigating the New Landscape: Challenges and Opportunities for User Programmers
The age of user programming, now significantly amplified by generative AI, is bringing powerful capabilities to the mainstream. This is, unequivocally, a positive development for countless individuals and departments that have long struggled with constrained IT resources and slow development cycles. It democratizes technology creation, fostering innovation and responsiveness across the organization.
However, as more non-technical people aspire to create solutions beyond simple personal time-savers, they will encounter the same fundamental challenges that professional product teams have long faced. The most important lesson, often learned through costly experience, is that the hardest part of software development is rarely the actual building and delivering of the solution. With modern tools, coding is becoming increasingly accessible. The enduring difficulty lies in discovering the right solution to build in the first place.
This means that new user programmers, even with sophisticated AI tools, will need to cultivate a deeper understanding of problem definition, requirements gathering, user empathy, and the aforementioned business rules. They will need to ask: What problem are we truly trying to solve? Who is the user? What are the constraints (technical, legal, compliance)? Is this solution viable and desirable? Without this critical product thinking, even the most advanced user programming tools risk generating elegant solutions to the wrong problems.
Furthermore, the proliferation of user-generated applications introduces new challenges around governance, security, data integrity, and system sprawl. Organizations will need to establish clear frameworks for managing, securing, and supporting these citizen-developed solutions to avoid creating new forms of "shadow IT" or technical debt. This will require collaboration between IT, business units, and legal/compliance teams to ensure that agility does not come at the expense of control and resilience.
In conclusion, the strategic build vs. buy decision is evolving into a more nuanced, sophisticated "build AND buy" paradigm. Companies will continue to procure robust, complex component services from specialized SaaS vendors for their foundational business operations. These commercial solutions, however, will increasingly be designed to be consumed and controlled not just by humans, but by intelligent AI agents and custom-crafted workflows. Enabled by transformative technologies like Generative AI and vital integration protocols such as the Model Context Protocol, enterprises will gain unprecedented flexibility to differentiate and innovate. The era of user programming is here to stay, but it serves as an augmentation to the established software ecosystem, not an outright replacement, with the enduring challenge remaining the intelligent discovery and validation of solutions that truly meet business needs.
