The way U.S. companies buy software development services is changing.
From my perspective as COO of a global software engineering organization, the biggest change I have seen in 2026 is not simply that companies are investing more in AI. It is that business leaders are becoming much more deliberate about where technology investment creates measurable business value.
Customers are coming into conversations better informed. They have researched AI development tools, software development costs, development team structures, automation, SaaS platforms and modernization options before they ever speak with a development partner.
That is changing the conversation.
The question is increasingly less about “Can you build this?” and more about:
“How quickly can we create business value, what should we build versus buy, how should AI change the solution, and what is the right engineering model to get us there?”
That shift is affecting everything from software development budgets and legacy modernization to development partner selection.
1. Software development spending is becoming more focused on business outcomes
We are seeing continued investment in technology, but customers are being much more selective about what receives funding.
The projects getting attention are generally tied to a clear business objective: legacy system modernization, digital transformation, AI automation, ERP modernization, data and analytics, workflow automation, customer experience, operational efficiency and revenue growth.
This is consistent with the broader market. Gartner’s July 2026 forecast projects worldwide IT spending of approximately $6.37 trillion in 2026, a 14.2% increase from 2025. Software spending is projected to grow 15.5%, while IT services spending is projected to grow 5.3%. At the same time, Gartner notes that technology budgets are under pressure from competing investments and shifting priorities.
So I would not describe the market as companies simply “cutting technology spending.”
I would describe it as technology spending being held to a higher standard.
We are seeing customers ask:
- What business problem are we solving?
- What will improve for our customers or employees?
- How much manual work can we eliminate?
- Can AI make the process materially better?
- Can we modernize the existing system instead of replacing everything?
- What is the expected ROI?
- How quickly can we get something into production?
- Can the architecture support the next three to five years of growth?
2. AI is changing expectations about software development cost and speed
AI has unquestionably changed the conversation around software development.
Customers are doing their homework before they contact us. Many have experimented with ChatGPT, Claude, Gemini, Perplexity, GitHub Copilot, AI coding tools, low-code platforms or other AI development tools.
As a result, they understand that certain parts of software development can now be completed much faster.
And they are asking legitimate questions:
“If AI can write the code, why do I need as many developers?”
It is a fair question.
My answer is that AI changes the economics of software development, but it does not eliminate the need for engineering expertise.
AI can accelerate coding, testing, documentation, analysis and other development activities. But building production software still requires architecture, business understanding, data modeling, security, integration, testing, deployment, governance and long-term maintainability.
In fact, the faster code can be produced, the more important some of those disciplines become.
DORA’s Impact of Generative AI in Software Development report, based on its 2024 research, is a good example of why this distinction matters. Its research linked greater AI adoption to improved individual productivity and job satisfaction, but also to lower software delivery throughput and stability, emphasizing the importance of the broader software delivery system.
That is what we see operationally.
“AI can make an individual developer dramatically more productive. It does not automatically make an entire software organization more productive.”
You still need experienced engineers who know what should be built, what should not be built, how systems should interact, how data should be protected and how the solution will operate after launch.
The opportunity is therefore not simply to use AI to write more code.
It is to use AI to deliver better software faster while maintaining quality, security and business alignment.
3. Build vs. buy is becoming a much more sophisticated decision
The traditional build-versus-buy discussion is also changing.
Companies have more options than ever.
We are seeing customers come to us after trying some of these approaches themselves.
A common pattern is that an organization can get surprisingly far using AI-assisted development.
Then they reach a point where the application needs to become a real business system.
That is where the complexity increases.
The questions become:
- Is the architecture scalable?
- How will the application integrate with existing systems?
- How will data be structured?
- How will permissions and security work?
- What happens when requirements change?
- How will the application be tested?
- Who will maintain it?
- Can the AI-generated code be trusted?
- How will the system perform under real production workloads?
This is where experienced software engineering becomes important.
AI has lowered the barrier to creating software. It has not eliminated the complexity of creating business-critical software.
The most effective model we are seeing is often a combination of the two: business teams use AI to explore and accelerate ideas, while experienced engineers provide the architecture, integration, security, scalability and production discipline necessary to turn those ideas into dependable systems.
4. Legacy systems are becoming more—not less—important
One of the most interesting developments in 2026 is the changing conversation around legacy technology.
For years, the assumption was that modernization meant replacing older systems.
That is increasingly being challenged.
Recent Ensono research found that 78% of IT decision-makers surveyed in the U.S. and UK said legacy systems play a more important role in their organizations today than they did two years ago. The research also found that 52% of organizations are optimizing and extending existing systems while modernizing applications around them.
That makes sense.
A legacy system often contains decades of business rules, customer information, operational knowledge and integrations.
Replacing it simply because it is old can create enormous risk.
The better question is:
“What part of this system needs to change, what should remain, and where can modern technology, including AI, create new value?”
We are increasingly helping customers think about modernization in terms of business continuity, incremental transformation and AI readiness.
That can include:
- Modernizing legacy applications
- ERP modernization
- CRM modernization
- API and system integration
- Cloud migration
- Data modernization
- Workflow automation
- AI integration
- Reporting and analytics
- Predictive analytics
- Application re-platforming
- Modern user experiences around existing systems
The goal is not to modernize technology for the sake of modernization.
The goal is to modernize the parts of the business that are limiting growth, efficiency, customer experience or future innovation.
5. Customers are changing how they evaluate software development partners
AI has also changed the buying process itself.
Customers can now research technology options, compare development approaches and estimate project costs before speaking with a software development company.
That means the traditional sales conversation has changed.
A development partner can no longer differentiate simply by saying:
“We have good developers.”
Customers want evidence.
They want to understand:
- Do you understand our industry?
- Do you understand our business problem?
- Can you work with our existing technology?
- How do you use AI during development?
- How do you protect our intellectual property and data?
- How do you approach software architecture?
- How do you manage quality?
- How stable is the development team?
- Can the team scale?
- How transparent is communication?
- What happens after launch?
- Can you help us make better technology decisions, not simply execute requirements?
This is a significant change.
The development partner increasingly needs to function as a technology advisor and engineering partner, not simply a source of developers.
At Shinetech, we see this particularly in conversations involving AI development, legacy modernization, ERP customization, digital transformation, custom software development and dedicated software development teams.
Clients are looking for people who can understand the business problem and then translate it into the right technology approach.
That requires engineering experience and business judgment.
The bigger shift: software development is becoming a business decision
I believe this is the most important change we are seeing in the second half of 2026.
Software development is moving further away from being an isolated IT function and closer to being a business transformation capability.
AI is accelerating that shift.
Companies can now explore ideas faster, build prototypes faster and develop software faster. But speed alone does not create business value.
The companies that benefit most will be the ones that combine AI-assisted development with strong engineering fundamentals, business understanding, modern architecture, secure data practices and disciplined execution.
For technology leaders, that means the question is no longer simply:
“How much does it cost to build this software?”
It is:
“What is the fastest, safest and most economically responsible way to create the business capability we need?”
That is a much better conversation for both the client and the technology partner.
And, from my perspective as COO, it is the conversation we should be having before writing the first line of code.
Discuss your next software investment
Talk with Shinetech about your business priorities, existing systems and the engineering work needed to move forward.
Sources and Further Reading
Sources reviewed September 23, 2026.
Referenced research
- Gartner, July 2026 worldwide IT spending forecast. Global, full-year market context; not a measure of U.S. custom-development purchases.
- DORA, Impact of Generative AI in Software Development. Version 2025.2, based on the 2024 DORA research. See pages 6–7 and 13–14 for individual and delivery outcomes.
- Ensono, 2026 State of IT Modernization research. September 15, 2026. The legacy-system findings cited in this article concern IT decision-makers surveyed across the U.S. and UK.
Further reading
Selected by the Shinetech editorial team.
- Shinetech Software, How to Choose a Software Development Partner That Will Still Deliver Years From Now.
Illustration created with AI for this article. It depicts a conceptual scenario, not a real client project.