Building Software Is Easy. Running It Is Hard.
A GW Apps Perspective
For decades, the build-versus-buy debate revolved around one simple question: Is it faster and less expensive to build an application ourselves, or should we buy software?
AI has fundamentally changed that equation. Today, a business analyst or developer can describe an application in plain English and generate a working prototype in a matter of hours – something that would have required weeks of development only a few years ago. It’s an extraordinary advancement that will allow organizations to innovate faster and experiment with new ideas at a fraction of the traditional cost.
At first glance, it might even appear that the build-versus-buy debate has finally been settled. If AI can generate software so quickly, why wouldn’t organizations simply build every application they need?
The answer is that creating software is only one step in a much longer journey. While AI has dramatically reduced the effort required to build the first version of an application, it has done very little to reduce the work required to make that application ready for production or to operate it over the years that follow. Those responsibilities haven’t disappeared—they’ve simply become the next bottleneck.
That’s why we believe the build-versus-buy discussion is evolving. The real question is no longer “Can AI build this application?” It’s “How do we safely deploy, govern and maintain the growing number of applications AI now makes possible?“
AI Changes Application Creation—Not the Entire Lifecycle
One of the biggest misconceptions surrounding AI-generated software is that a working application is the same as a production-ready application. In reality, AI has transformed only one phase of the software lifecycle: application creation.
Generating forms, workflows, business logic and user interfaces has become dramatically faster, allowing organizations to move from an idea to a working prototype in hours instead of weeks. That’s a genuine breakthrough, and one that should be embraced.
Before that application is deployed into production, however, a completely different set of activities still needs to take place. Security reviews, authentication, role-based permissions, integration with corporate identity systems, hosting, backups, monitoring, compliance requirements and operational support all need to be addressed. Depending on the organization, performance testing, disaster recovery and integration with existing business systems may also be required before IT is comfortable approving the application for production.
Only after those steps have been completed does the application enter the longest phase of its lifecycle: day-to-day operation. Business processes evolve, regulations change, security vulnerabilities are discovered, and new requirements continue to emerge. The application must be maintained, enhanced and supported for as long as the business depends on it – regardless of whether the original version was written by a developer, assembled in a no-code platform or generated by AI.
AI has dramatically shortened the journey to a working prototype. It has had far less impact on the work required to prepare an application for production and to keep it running successfully for years to come.
The Cost of Software Didn't Disappear. It Shifted.
Because AI has reduced development effort so dramatically, many organizations assume software itself has become inexpensive. In reality, only the cost of creating the initial application has fallen. The cost of deploying, governing and supporting production software remains largely unchanged.
Every production application still requires user administration, security management, monitoring, auditing, backups, upgrades and ongoing support. Someone must respond when business requirements change, integrations fail or security vulnerabilities are identified. These responsibilities exist regardless of how the application was originally created.
In fact, AI may increase these operational demands. As organizations generate more applications than ever before, IT inherits a growing portfolio that must be secured, governed and maintained. The challenge is no longer building one application. It’s managing dozens, or even hundreds of them responsibly.
The economics of software creation have changed dramatically. The economics of software ownership have changed far less.
Buying Software Still Makes Sense
None of this suggests organizations should stop purchasing commercial software. Enterprise applications such as ERP, CRM, accounting, payroll and human resources systems continue to deliver enormous value because they encapsulate decades of engineering, regulatory knowledge and product investment.
When purchasing commercial software, organizations also benefit from a vendor that assumes responsibility for maintaining the platform, delivering updates, addressing security vulnerabilities and evolving the product over time. Those responsibilities are shared across thousands of customers instead of being carried by a single IT department.
The tradeoff, of course, is flexibility. Organizations often adapt their processes to fit the software rather than tailoring the software to fit their business.
For standardized business functions, that compromise usually makes sense.
If you’re seeking to create a paperless office solution, the following points will help you get started.
Where AI Changes the Equation
The opportunity created by AI lies elsewhere.
Most organizations operate dozens of internal workflows that are unique to their business: purchase approvals, vendor onboarding, employee requests, inspections, project governance, capital expenditure approvals and countless other operational processes. These applications frequently evolve, differ across departments and rarely justify the cost of purchasing specialized packaged software.
Historically, many of these applications were never built because development resources were scarce. Others remained trapped in spreadsheets, email threads or manual processes because IT simply had higher priorities.
AI changes that equation. Organizations can now create these applications faster, validate ideas earlier and automate many processes that previously remained manual. That’s a significant competitive advantage.
The New Build-vs-Buy Decision
AI hasn’t eliminated the build-versus-buy decision. It has simply changed the criteria.
Organizations should continue purchasing mature enterprise applications for standardized business functions while using AI to build the operational applications that commercial software rarely addresses well.
The critical question is no longer whether an application can be built. In most cases, AI has already answered that question. The more important questions are whether the application can be deployed securely, governed consistently and supported throughout its lifecycle.
For many organizations, that also means rethinking where these applications should be built. Rather than creating standalone solutions that each require their own infrastructure and operational support, there can be significant advantages to building them on a platform that already provides the production capabilities every enterprise requires—security, governance, identity management, monitoring, auditing, integration and long-term maintainability.
Organizations that shift their focus from application creation to application lifecycle management—and choose an approach that simplifies both—will be able to innovate more quickly without creating tomorrow’s operational and governance challenges.
AI has transformed how software is created. It has not changed what it takes to run software successfully.
If you have any questions or would like to schedule a demo/meeting, you can reach out to us at sales@gwapps.com or request your meeting here: Request a Meeting. You can also use this link to start your free trial: 30-day GW Apps Free Trial.
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