AI Is Changing How Software Gets Built
Article
Over the past several years, conversations about artificial intelligence in software tended to focus on what AI might eventually make possible. That conversation has changed quickly. Across the software industry, AI is becoming part of how software is routinely developed, changing not only the speed at which developers work, but increasingly the very nature of the work itself.
Recent industry research suggests AI-assisted development is rapidly becoming standard practice. Major developer surveys cited in recent research by Digital Applied place adoption of AI coding tools between 84% and 91%, while the 2025 Stack Overflow Developer Survey reported that 51% of professional developers were using AI tools every day. Among developers entering the profession, AI-assisted development is quickly becoming the default rather than something learned later.
But adoption numbers tell only part of the story. The more significant change is what developers are beginning to ask AI to do, and how naturally that fits into the broader evolution of software development.
The Next Step in Software Development
Software development has steadily moved toward greater automation, integration, and efficiency. Cloud computing changed where applications could be built and deployed. APIs made it easier for separate systems to communicate and “play nice” with each other. DevOps, together with continuous integration and delivery, helped teams test, improve, and release software more efficiently.
AI represents the next natural stage of that progression. Instead of automating only predetermined processes, it can assist with work requiring context, pattern recognition, and increasingly sophisticated reasoning. For Ryan, AI is not a sudden change in direction in the Firm’s software development efforts, but the next step in an ongoing effort to use technology to reduce friction and allow people to focus their expertise where it creates the greatest value.
From Writing Code to Directing the Work
The first wave of AI development tools largely functioned as assistants. Developers could use them to generate code, troubleshoot problems, explain unfamiliar code, or accelerate repetitive tasks. That model is already evolving as development teams begin experimenting with AI agents capable of handling more complex, multistep activities.
Rather than simply suggesting a line of code, an agent might investigate a software issue, identify the appropriate service, develop a proposed fix, and run tests before presenting the work for human review. The distinction may sound subtle, but its implications are significant. Software development is beginning to move from “help me do this” toward “do this for me.”
Obviously, there are implications for efficiency here. For developers, that can mean spending less time producing every individual element themselves and more time defining problems, reviewing outputs, validating solutions, and deciding what happens next. Ryan’s Software Development team is incorporating these emerging practices into its own environment, using AI to help engineers work more efficiently, expand development capacity, and accelerate product fixes and updates.
From Better Development to Smarter Software
While the same technologies are changing how software gets built, they are also changing expectations about what that software should do once it reaches the customer.
Across the industry, the focus is increasingly shifting toward agentic software, and AI is moving beyond assisting with individual actions and beginning to perform multistep tasks on behalf of users. Ryan’s development strategy is evolving along the same path, with an increasing focus on what might be characterized as “do it for you” software. Instead of simply giving users better tools to complete a task themselves, software starts taking responsibility for portions of that task.
That is particularly relevant in Ryan’s area of expertise: tax. Documents must be collected, data entered or extracted, information checked, and forms and filings prepared. Numerous administrative steps may need to occur before a tax professional can actually begin applying the expertise that creates the greatest value.
AI is already being incorporated into Ryan’s solutions for functions such as intelligent document processing and text extraction. Looking ahead, agentic capabilities create opportunities to address increasingly complex tasks that have traditionally depended upon manual input. The longer-term opportunity is not simply making an existing task faster. It is reconsidering which parts of the process need to be performed manually at all.
Innovation with Guardrails
As AI becomes more capable, questions about autonomy and control are understandably becoming part of the broader conversation. Discussions about agentic AI can sometimes create the impression of systems operating independently or making decisions without human oversight.
Enterprise software development provides a more practical picture. AI can perform increasingly sophisticated tasks while still operating within defined permissions, testing processes, security requirements, and approval gates. An agent might investigate an issue, generate code, or propose a change without having unrestricted authority to move that change into production.
That balance between innovation and oversight is particularly important in tax, where accuracy, compliance, and accountability matter. Ryan’s objective isn’t simply to incorporate the latest AI capabilities, but to apply them in the places where they solve real problems while maintaining the human expertise and controls required to use them responsibly.
The meaningful measure is not whether a product can be described as AI-powered. It is whether the technology works and creates tangible customer value through time saved, effort reduced, greater accuracy, or work performed more effectively.
A Natural Evolution
AI-assisted software development is rapidly becoming part of how modern software is built, while the industry is already moving toward increasingly sophisticated agentic workflows. Ryan is evolving alongside those changes, using AI to improve how software is developed while expanding what that software can ultimately do for customers.
Seen in that context, AI is not a sudden change in direction, but the latest stage in a continuing progression toward greater automation, integration, intelligence, and scalability. Being at the forefront of that evolution means understanding where the technology can create meaningful value while applying it with the discipline and oversight enterprise software demands.
For tax professionals, that raises a much more interesting question than whether AI is coming to tax: What happens when tax software moves from helping you do the work to increasingly doing parts of the work for you?
In the next installment, we’ll look more closely at the emergence of agentic software and what the shift from “help me do it” to “do it for me” could mean for the future of tax technology.
