Software development costs are dropping rapidly, thanks to a powerful combination of technologies that are stacking on each other. AI-assisted development (like GitHub Copilot), reusable open-source libraries, cloud-native development, and low-code/no-code platforms are accelerating this trend in unprecedented ways.
The implications of this trend go beyond budget savings—it’s redefining the competitive landscape and reshaping what businesses need to stay ahead. I’ll save my broader thoughts on business strategy for another time; today, I’m focusing on some practical ideas specifically for fellow enterprise software development leaders and consultants working with business applications.
Below, I share a few strategies I believe can help us make the most of these reduced development costs. I’d love to know if you agree with these approaches—and what other strategies you think we should consider.
1. Lower the Cost of Change Management and Requirement Gathering
Requirement gathering, idea exploration, and change management are often some of the priciest phases of development, and they can be major blockers for impactful projects. Many applications and features never make it past this stage, ending up in what I call the "feature graveyard"—a space filled with great ideas that were requested but went unused. To address this, one strategy I’ve found effective is empowering business teams with low-code and no-code platforms. These tools allow them to create solutions that directly address their everyday challenges, leveraging their firsthand insights to develop truly relevant solutions.
One example illustrates this perfectly. A recent college graduate joined our business team and brought a refreshing mix of enthusiasm, skill, and a fresh perspective. Using a low-code platform, he developed an application specifically tailored to his department’s needs. Since he had firsthand experience with the challenges, he had an intuitive sense of what the application needed to accomplish. Not only did he define the requirements effectively, but he also championed the solution within his department, gathering feedback and refining his vision. His prototype laid a strong foundation for our development team, who were able to build upon it and transform it into a fully-featured application with additional capabilities. This kind of groundwork enabled us to reach outcomes that, in my opinion, would have been difficult to achieve otherwise.
2. Focus on Data Consistency and Integration
As software development costs decrease, organizations are deploying more applications than ever. Back in my consulting days, we used to count the number of applications as a measure of efficiency; today, however, the conversation has shifted. The issue is less about how many applications there are and more about the data each one produces. When applications proliferate without a cohesive plan, data can become fragmented, inconsistent, and siloed, making it nearly impossible to harness its full value.
The solution? Establish standardized and integrated data fields across all applications, even lightweight or disposable ones. Core fields—such as “People,” “Place,” and “Product”—are essential to building a meaningful, holistic understanding of the business. As software deployment accelerates, so does data generation. Organizations with a deep understanding of their customers, including what they buy, how they engage, and when they interact, will be at a distinct advantage.
I also believe knowledge graphs will be crucial in organizing and leveraging this data, especially as application ecosystems expand. They can provide context, link related information, and deliver insights that would otherwise be hidden within data silos. I’m curious to hear from anyone currently working with knowledge graphs—how are they helping your organization tackle data complexity?
Reducing Data Entry Burdens: Another essential piece of this strategy is leveraging technology to automate data entry. Microsoft’s recent announcement about their Gen AI feature, which can read unstructured text and generate structured data, is a promising development. Check out an example of this application here. This type of AI-driven automation has the potential to minimize manual data entry, improve data quality, and allow employees to focus on more value-added work.Takeaway: Make data standardization and architecture a priority across applications to reduce fragmentation and unlock the full potential of your organization’s data.
3. Develop Reusable Applications for Common Processes
Many applications across an organization share similar functionalities. Approval workflows, for instance, are necessary but often replicated in multiple applications. Instead of continually building approval functions from scratch, why not develop a reusable approval application that can be integrated wherever it’s needed?
This “approval app” idea could address needs across departments and serve as a common resource, saving time and costs on redundant functionality. The same approach could apply to various processes: notifications, reporting, task management—the possibilities are endless. Building these modular, reusable solutions allows for more efficient development and promotes consistency across the organization.
