AI is changing the economics of software development. But is it changing the economics of software ownership?
For years, organizations have debated whether to build their own software solutions or purchase established applications.
The traditional considerations were relatively straightforward: cost, functionality, implementation time, customization, security, and long-term support.
Artificial intelligence is changing that conversation.
With AI-assisted development, low-code platforms, and increasingly capable coding agents, organizations can prototype applications in days that previously might have taken months.
That creates an interesting question for business and technology leaders.
If AI makes software easier and cheaper to build, why should organizations continue buying it?
The answer requires looking beyond the initial development effort.
Because while AI is making it easier to create software, it doesn't necessarily make that software easier to operate, maintain, secure, scale, or continuously improve.
And those responsibilities are where the real economics of build versus buy begin to emerge.
AI Has Lowered the Barrier to Building
There is no denying the impact AI is having on software development.
Developers can generate code, create interfaces, automate testing, troubleshoot issues, and accelerate integrations with increasingly sophisticated AI tools.
Business users can also participate more directly in application development through natural-language interfaces and low-code environments.
What once required a dedicated development team may now begin with a small team and a well-defined set of requirements.
This is a meaningful shift.
Organizations can experiment faster, validate ideas with less investment, and develop applications tailored to their specific business processes.
For certain use cases, building internally has never been more attractive.
But there is an important distinction between creating an application and delivering a sustainable enterprise solution.
AI can dramatically reduce the effort required to write code. It doesn't eliminate the responsibilities that come with owning that code.
The Prototype Is Not the Product
One of the biggest risks in the current AI development environment is confusing a successful prototype with a production-ready application.
A prototype might demonstrate functionality, connect to existing data, and provide an impressive user experience.
But what happens next?
What happens when the business introduces new requirements? When underlying platforms release updates? When security policies change? When users encounter unexpected scenarios? When the original developer moves to another project?
Enterprise software must account for far more than its initial functionality.
It requires architecture, testing, permissions, documentation, deployment processes, monitoring, support, and ongoing enhancements.
AI can assist with many of these responsibilities, but someone still needs to own the outcomes.
A working prototype may prove that something can be built.
It doesn't necessarily prove that it should be built internally.
The Hidden Costs of Building
Organizations frequently underestimate the total cost of internally developed software because the initial development expense receives most of the attention.
In reality, the financial commitment extends well beyond the first release.
Consider an internally developed application supporting account planning, relationship management, or sales execution within Salesforce.
The first version might provide the core functionality the business needs.
But over time, users will expect improvements, administrators will require configuration options, and leadership will request additional capabilities.
The application may need to support new Salesforce releases, evolving data models, permission structures, reporting requirements, integrations, and changes in business processes.
Someone must maintain it.
Someone must support it.
Someone must prioritize enhancements.
And someone must be accountable when it stops working.
These costs are often absorbed into existing IT budgets, making them less visible than a software subscription.
But less visible doesn't mean less expensive.
The cost of building software is only part of the equation. The cost of owning it is what determines its long-term value.
The Opportunity Cost Nobody Talks About
There is another consideration that deserves greater attention: what your technical resources could be doing instead.
Every hour spent developing and maintaining an internal application is an hour unavailable for other initiatives.
For many organizations, technology teams already face substantial backlogs involving integrations, data quality, security, automation, AI adoption, and operational improvements.
Is recreating functionality already available in the market the best use of those resources?
Sometimes the answer is yes.
An internally developed solution may address a highly specialized process that creates a meaningful competitive advantage.
But if the functionality is relatively standard, building it internally may divert resources away from more differentiated initiatives.
This becomes particularly important in the age of AI.
Organizations have an opportunity to use AI to transform how they operate, engage customers, and make decisions.
Should their technical teams spend that opportunity recreating existing software capabilities?
Or should they focus on applying AI where it can create unique business value?
Buying Software Is Changing Too
The build-versus-buy discussion often assumes that commercial software is static, expensive, and difficult to customize.
That assumption is increasingly outdated.
Modern enterprise applications can offer extensive configuration, automation, APIs, integration capabilities, and AI-enabled functionality.
For organizations operating within Salesforce, native applications can provide additional advantages by working directly with existing data, security controls, and business processes.
Rather than developing an entirely new system, organizations can adopt established capabilities and configure them around their operating model.
The right application can provide a foundation that continues evolving without requiring the customer to own every aspect of its development.
Of course, buying is not automatically the better decision.
Organizations must evaluate vendor stability, product quality, security, roadmap alignment, implementation requirements, integration complexity, and long-term pricing.
A poorly selected application can introduce just as much complexity as an internally developed solution.
The objective isn't to buy more software.
It's to invest in capabilities that deliver the greatest business value with the least unnecessary complexity.
AI Creates a New Risk: Building Because You Can
Perhaps the most interesting change AI introduces is psychological.
When building software becomes easier, the temptation to build increases.
A business leader sees a demonstration of an AI-generated application and begins questioning why the organization should pay for an existing product.
A developer produces a compelling prototype in a weekend.
A team concludes that a commercial solution can be replaced with a relatively small internal project.
Sometimes they're right.
But the ability to build something quickly can create false confidence about the effort required to sustain it.
An application that takes two weeks to create may still require years of maintenance, enhancement, and operational support.
And when software becomes deeply embedded in business processes, replacing or retiring it can become increasingly difficult.
AI reduces some of the friction associated with development.
It doesn't eliminate the consequences of architectural decisions, technical debt, or operational dependencies.
The real question is no longer simply, "Can we build this?"
It's "Should we own this?"
Build What Differentiates You. Buy What Accelerates You.
A practical approach to build versus buy starts by distinguishing between capabilities that create competitive differentiation and capabilities that enable the business to operate effectively.
For example, a proprietary pricing model, specialized customer experience, or unique operational process might justify internal development.
These capabilities may represent intellectual property that directly contributes to an organization's competitive advantage.
On the other hand, capabilities such as relationship mapping, account planning, workflow management, reporting, and standard business process automation may already be available through established platforms.
In those situations, purchasing an existing solution can allow organizations to focus on adoption, execution, and measurable outcomes rather than rebuilding foundational functionality.
This doesn't mean organizations should abandon customization.
In fact, the most effective approach may combine commercial applications with internally developed AI capabilities, integrations, and specialized workflows.
Buy the foundation.
Configure it to support the business.
Build the capabilities that make the organization unique.
And use AI to connect, extend, and improve the overall experience.
That is often a more sustainable strategy than attempting to build everything from scratch.
The Salesforce Example
Consider the growing demand for more sophisticated revenue execution capabilities inside Salesforce.
Organizations want to understand customer relationships, visualize buying committees, develop account plans, identify expansion opportunities, track strategic initiatives, and connect those activities to revenue performance.
With modern AI development tools, building individual components of these experiences is becoming easier.
An internal team might create a relationship visualization, a custom planning interface, or a dashboard.
But connecting these components into a cohesive enterprise experience introduces additional considerations.
How will the application support Salesforce's security and permission model? How will it work with custom objects and changing data structures? How will it scale across teams? How will users navigate between relationships, strategy, execution, and analytics?
And how will it evolve as Salesforce, AI capabilities, and customer expectations continue changing?
These are not insurmountable challenges.
But they are responsibilities that need to be considered before committing to internal development.
Solutions such as Squivr's Salesforce-native Revenue Strategy & Execution Platform offer an alternative by bringing relationship intelligence, account planning, analytics, and execution capabilities together within the Salesforce environment.
Organizations can then focus more of their resources on how those capabilities are used to improve revenue performance rather than developing and maintaining each component independently.
The value isn't simply in having the functionality.
It's in accelerating the organization's ability to put that functionality to work.
A Better Framework for Build vs. Buy
Before deciding to build or buy an application, organizations should consider several fundamental questions:
Competitive differentiation: Does owning this software create a unique advantage, or does it replicate capabilities already available?
Total cost of ownership: What will the solution cost to develop, secure, operate, support, and enhance over the next three to five years?
Time to value: How quickly can users begin realizing meaningful business benefits?
Technical resources: What other priorities will be delayed or deprioritized if the organization builds internally?
Long-term flexibility: Can the solution adapt to new requirements, AI capabilities, and platform changes?
Operational accountability: Who will own the application when issues arise, requirements change, or key personnel leave?
Business outcomes: Which approach is more likely to improve productivity, adoption, decision-making, and financial performance?
The answers will vary by organization and use case.
But these questions move the discussion beyond development cost and toward the strategic value of the investment.
The Future Is Probably Build and Buy
AI is unlikely to eliminate the need for commercial software.
Instead, it may fundamentally change how organizations evaluate, extend, and consume it.
Software vendors will face greater pressure to demonstrate differentiated value, faster innovation, better experiences, and measurable business outcomes.
At the same time, internal development teams will have more opportunities to create specialized capabilities that would previously have been too expensive or time-consuming.
The distinction between building and buying may become less rigid.
Organizations will increasingly combine established platforms, commercial applications, internally developed workflows, and AI-driven experiences.
The winners won't necessarily be the companies that build the most software or purchase the fewest applications.
They will be the companies that make the smartest decisions about where to invest their resources.
The Squivr Advantage: Accelerating Value Without Reinventing the Wheel
At Squivr, we believe organizations should spend less time building and maintaining software and more time using technology to drive meaningful business outcomes.
That's why we've built a comprehensive Revenue Strategy & Execution Platform, 100% native to Salesforce, bringing relationship intelligence, account planning, analytics, and revenue execution together in one unified experience.
Rather than investing internal resources in developing and maintaining separate applications for organizational charts, relationship mapping, account planning, whitespace analysis, playbooks, workflows, and analytics, organizations can leverage Squivr's established capabilities directly within their Salesforce environment.
And because Squivr is native to Salesforce, customer data remains within their existing Salesforce organization, leveraging the platform's security, permissions, and infrastructure.
As AI continues to evolve, our focus is on helping organizations take advantage of these advancements while maintaining the flexibility to extend and adapt their revenue processes.
The value of Squivr isn't simply replacing the need to build software. It's accelerating time to value, reducing ongoing development and maintenance demands, and enabling revenue teams to focus on what matters most: building stronger relationships, executing better account strategies, and driving revenue growth.
The Bottom Line
AI is making software development faster, more accessible, and potentially less expensive.
But faster development doesn't automatically translate into lower total ownership costs or better business outcomes.
The build-versus-buy decision still requires a clear understanding of strategy, economics, technical responsibility, and long-term value.
Just because AI makes it possible to build something doesn't mean building it is the smartest investment.
The question organizations should be asking isn't whether they can replace a software subscription with AI-generated code.
It's whether owning that software will create more value than the alternatives.
At Squivr, we're focused on helping organizations accelerate their revenue strategy and execution without having to reinvent the wheel.
Build what differentiates your business. Buy what accelerates it. Use AI to make both more valuable.
