How AI Is Changing Enterprise Expectations Around Software Delivery 

AI is changing what businesses expect from software delivery. As development teams adopt AI-assisted tools, business leaders reasonably expect some work to happen faster, resources to be used more efficiently, and ideas to move from concept to working software sooner. 

But faster development is not the same as faster business value. The real opportunity with enterprise AI solutions is to reduce time spent on routine work while preserving the judgment, quality, security, and business alignment that enterprise software requires. For leaders, that creates a new standard for evaluating software partners: not simply whether they use AI, but whether they use it to improve delivery without losing sight of what the software needs to accomplish. 

AI Is Raising Expectations for Software Delivery 

AI has moved beyond isolated experimentation in many organizations. Stanford’s 2025 AI Index, drawing on McKinsey survey data, reported that 78% of respondents said their organizations used AI in at least one business function in 2024, compared with 55% the previous year. 

As AI becomes more familiar across the business, expectations naturally extend to technology teams. AI-assisted development tools can help with tasks such as writing and explaining code, generating tests, debugging, documentation, code review, refactoring, and navigating an unfamiliar codebase. 

Those capabilities create legitimate opportunities for efficiency, but they can also create unrealistic expectations. A team can produce software quickly and still solve the wrong problem, overlook an important integration, or build functionality users do not need. Rather than asking only, “How much faster can AI make development?” I think business leaders should ask a more useful question: Where can AI shorten the path from a business need to a reliable business outcome? 

Where Can AI Actually Make Software Delivery Faster? 

AI can improve delivery speed by reducing time spent on repeatable, research-intensive, or code-intensive work. But writing code is only one part of creating enterprise software. 

Faster Coding Does Not Eliminate Other Constraints 

Discovery, requirements, architecture, data decisions, integrations, testing, security, deployment, stakeholder feedback, and internal decision-making all influence how quickly an organization reaches a usable result. 

If AI helps a development team complete features faster, but stakeholders still disagree about a workflow; the overall initiative may not move much faster. The same is true when teams are waiting on data, an integration decision, or clarification about business priorities. 

Research from Google’s DevOps Research and Assessment program (DORA) into AI-assisted software development reinforces this distinction. Its findings indicate that AI can improve individual productivity, but broader results depend heavily on the development environment and practices surrounding it. 

For leaders evaluating enterprise web app development, that means looking beyond whether a partner uses AI. AI can improve a strong delivery process, but it does not remove the need for clear priorities, sound technical practices, and timely business decisions. 

Why Does Quality Matter More When Development Accelerates? 

When teams can produce and modify software faster, quality controls become more important. Every change still needs to work within a larger system, interact correctly with existing applications, protect data, perform under real-world conditions, and remain maintainable. 

Moving faster early in development only to spend additional time correcting problems later is not meaningful efficiency. AI-assisted work should meet the same engineering standards as other software development, with controls appropriate to the application and the consequences of failure. 

Enterprise leaders do not need to dictate which AI tools developers use. They should understand how their software partner manages them. Useful questions include: 

• Where is AI being used in the development lifecycle? 
• What still requires human review and approval? 
• How is AI-assisted work tested for correctness, security, and maintainability? 
• What company data or source code can AI tools access? 
• How does the team prevent greater development speed from creating technical debt? 
• How are security and compliance requirements incorporated into the process? 

How Can AI Teams Use Expertise More Effectively? 

One of AI’s more valuable effects may be changing where experienced developers spend their time. If AI handles portions of routine work, developers can devote more attention to architecture, integrations, business rules, edge cases, risk, and other decisions that benefit from experience and context. 

It also challenges the assumption that faster software delivery should always result in more software. Sometimes the right answer is a new custom application. In other situations, an existing platform can be configured, systems can be integrated, or an application can be modernized incrementally. When building becomes easier, deciding what is worth building becomes even more important. 

Measure Progress by the Outcome, Not the Output 

Lines of code, prompts submitted, and features generated with AI measure activity, not necessarily business value. Depending on the need, meaningful progress might look like shortening an important workflow, eliminating manual steps, improving adoption, reducing defects, lowering operating costs, or enabling a new service. 

Before asking how much faster AI can make the work, I encourage business leaders to define what improvement they expect the software to create. That gives the organization a better way to determine whether greater development speed is producing a better result. 

What Does AI-Enabled Enterprise Delivery Look Like in Practice? 

A recent SOLTECH engagement with Mölnlycke Health Care provides a useful example of balancing acceleration with longer-term needs. 

Mölnlycke had developed an early AI prototype and wanted to move it toward a scalable enterprise solution. The work required more than adding functionality. SOLTECH assessed the existing technology, helped strengthen the architecture, and established a development roadmap that could evolve with the organization’s business priorities. 

The important distinction is between moving quickly and creating momentum. Focusing only on completing the next set of features could have deferred larger architectural questions. Instead, acceleration had to be considered alongside what the organization would need from the solution over time. 

That is a useful expectation for enterprise AI development more broadly. Speed has value when the decisions made along the way create a strong foundation for what comes next. 

 What Are Best Practices for Architecting AI in Enterprises? 

 Architecting enterprise AI solutions should begin with the business use case, available data, level of risk, and technology already in place. 

Not every enterprise application needs AI, and adding a valuable AI capability does not necessarily require rebuilding an application around it. An organization might integrate an AI service into an existing workflow, add functionality to an enterprise web application, configure capabilities already available within a current platform, or develop something more specialized. 

Start With the Business Problem 

“We need AI” is not a useful requirement. A better starting point is a specific problem: employees spend too much time finding information, customers face a repetitive process, teams need to review large volumes of documents, or users need an easier way to work with complex information. 

Once the problem is clear, the organization can determine whether AI is the right approach and define what a successful result should look like. 

Design Around Data, Access, and Risk 

Leaders need to understand what information the AI will use, where it comes from, how reliable it is, who should have access to it, and what the system should be permitted to do. 

Privacy, security, access controls, monitoring, integrations, and human approval requirements should be considered as part of the architecture. These boundaries become particularly important as AI systems move from generating information toward taking actions within business systems. 

Leave Room for Change 

Enterprise AI adoption is accelerating, but organizations are still at very different stages of implementation maturity. That pace of change makes flexibility important. 

Today’s preferred model, tool, or approach may not remain the best choice throughout the life of an application. 

Organizations do not need to design for every possible future scenario, but leaders should understand where they are creating dependencies and whether those dependencies are justified.  

How Is Software Transforming Different Industries Today? 

Software continues to change how organizations operate, serve customers, use information, and introduce new products and services. AI expands those possibilities, but the opportunities vary considerably by industry. 

In healthcare, AI-enabled software may support information-intensive workflows or help professionals work with complex data. Manufacturers may combine software, analytics, and automation to improve operations. Other organizations may use AI-enabled applications to improve customer experiences, internal processes, planning, or decision-making. 

A successful use case elsewhere can provide inspiration, but it does not establish that the same investment makes sense for another organization. Value depends on the business’s customers, processes, data, existing technology, risks, and constraints. The requirements surrounding an AI solution can also vary significantly. Organizations working with sensitive or regulated data, for example, may need to place greater emphasis on security, privacy, access controls, compliance, and human oversight. 

For leaders, the better question is not simply, “What can AI do in our industry?” It is, “Where do we have a business problem that AI is particularly well suited to solve, and what requirements need to shape the solution?” 

What Should You Expect From a Software Partner Now? 

As AI-assisted development matures, enterprise buyers should reasonably expect software partners to become more efficient. They should also expect transparency about where AI creates that efficiency and where experienced human judgment remains necessary. 

A good conversation should go beyond which AI tools a development team uses. A partner should be able to explain how it understands the business problem, where AI can realistically improve delivery, how quality is protected, what risks need to be considered, and what alternatives exist. 

Be cautious when a partner: 

• Promises dramatic cost or timeline reductions simply because it uses AI 
• Recommends AI before understanding the underlying business problem 
• Accepts requirements without challenging important assumptions 
• Cannot explain how AI-assisted work is reviewed and tested 
• Focuses on speed without discussing maintainability or architecture 
• Has no clear approach to data access, security, or governance 

Organizations should also expect their technology partners to be evaluating where AI can genuinely improve their work. AI should make a strong software delivery process better, not compensate for a weak one. 

Faster Delivery Should Lead to Better Business Decisions 

AI can reduce routine effort, help development teams explore solutions faster, and shorten certain feedback cycles. But efficiency should ultimately show up in business results, not just development activity. 

For leaders evaluating a software initiative, the goal is not to find the partner that can generate the most code with AI. It is to find one that understands where AI can remove unnecessary effort, where human judgment remains essential, and how both can work together to solve the right problem. 

Start with the result the business needs, then determine where AI can shorten the path to that result without introducing unnecessary risk. That is a more useful standard for what faster software delivery should mean. 

FAQs

What Tools Are Most Commonly Used for AI-Augmented Software Development? 

AI tools now support many parts of software delivery, not just coding. Development teams may use AI-assisted tools for writing and explaining code, debugging, testing, code review, documentation, and navigating existing codebases. Other tools can support areas such as UX and design, requirements and research, or content and documentation. The specific products will continue to change. For enterprise organizations, the more important questions are where AI is being used, what company data or source code those tools can access, how their output is reviewed, and what security and governance controls apply. 

Will AI Make Enterprise Software Development Cheaper? 

AI can reduce the cost of software development when it allows teams to complete certain work in less time. We are already seeing opportunities for AI-assisted development to reduce effort around tasks such as coding, testing, debugging, documentation, and research. However, those efficiencies do not reduce every part of an enterprise software initiative equally. Architecture, integrations, security, business requirements, stakeholder decisions, and other complexities still require time and expertise. The result may be a lower overall development cost, a shorter timeline, or the ability to accomplish more within the same budget. 

Does AI Replace the Need for Experienced Software Developers? 

Experienced software engineers and architects are still needed to understand the business problem, determine the right technical approach, and make decisions about architecture, security, integrations, testing, maintainability, and tradeoffs. AI can reduce the time they spend on certain routine or code-intensive tasks, but it does not replace the judgment required to determine what should be built or how it should work within the larger business and technology environment. Ideally, AI allows experienced engineers to spend more of their time on the decisions where their expertise creates the most value. 

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Ann Mooney

Director of Business Development

Ann Mooney is the Director of Business Development at SOLTECH, and has over 30 years in Sales and Account Management in the Technology, Telecommunications, and Medical Industries. Ann’s key specialties are building long-term business relationships, results-driven sales, and account management.

Ann joined SOLTECH in 2016, she works directly with SOLTECH’s clients to help find them the best technology solutions for their business. Ann utilizes her strategic leadership and proactive problem-solving skills to continually grow SOLTECH’s business and ensure excellent customer service.

With her years of experience in the technology industry, Ann likes to share her expertise to educate her audience on the enhancement of workplace productivity and growth through software solutions in her articles. Her insights offer advice on important considerations for creating custom software, including initial steps, development costs, and timelines, as well as the advantages of collaborating with a skilled software development team.