Why AI-first development matters — and how to get there

Development teams on the fast track are using AI throughout the software development life cycle, and building applications with AI components and for AI users. For a growing number of organizations, AI has become not only a key component of the software development process, but the centerpiece. These enterprises have shifted toward an AI-first development strategy, where artificial intelligence is integrated into all phases of the software development life cycle, rather than being a feature to use selectively during the process. In most cases, this means a move toward agentic workflows, with developers acting as architects responsible for overseeing AI agents. But in a larger sense, AI-first development is a transformation of how developers explore and understand code, with a focus on designing and producing more and more applications with AI in mind. Why AI-first development? For what is surely a rising number of software development teams and organizations, the AI-first approach is becoming more necessary than optional. “AI-first development matters because the alternative is increasingly untenable,” says Mona Rajhans, senior software engineering manager at cybersecurity products provider Palo Alto Networks, who leads a 14-person AI engineering team. Simply put, development teams that use AI to build applications can accomplish far more in less time. By the same token, it is increasingly important to design applications with AI components and agentic users in mind. “Bolting AI onto an existing architecture after the fact is like adding a second floor to a house with no load-bearing walls,” Rajhans says. “It works until it doesn’t, and when it breaks it breaks badly.” One of the biggest advantages of AI-first development is the speed with which teams can complete projects and get products into development. This includes identifying and addressing issues much earlier than would be otherwise possible. “Work that used to stall in the backlog for quarters, like platform migrations and updating aging libraries, now clears in days,” says Collin Hogue-Spears, senior director, DTE, at application security software company Black Duck Software. “Defects surface at design time rather than in production, where a catch costs a code review instead of an incident.” When you apply AI from the onset of your software project, “you’ll typically learn about issues with your concept in hours rather than weeks,” says Bob Hutchins, CEO at consulting and advisory firm Human Voice Media. “I developed full production applications where the first working prototype was completed prior to the kickoff meeting for a traditional shop.” AI-first development also helps to keep unnecessary costs down. “Retro-fitting AI into projects is often costly and difficult,” Hutchins says. “Project designs that include AI from day one result in cleaner data flow patterns, much clearer permission schemes, and user interface elements that understand how agents will interact with these systems.” Implementing a chatbot to an existing application produces a chatbot, Hutchins says. “Designing for AI provides an entirely new type of product,” he says. Perhaps most important, using AI-first development causes teams to improve their documentation and overall clarity in their thought processes, Hutchins says. “Since an AI cannot read minds, teams which follow this approach will provide better documentation specifications, because the specification itself will serve as the actual instruction set.” How to succeed at AI-first development Not all development teams are ready for this sea change in the development process. Many struggle to adopt AI-first development for any number of reasons. Maybe they have underlying engineering systems that rely on undocumented knowledge, implicit rules, or manual oversight. Or they don’t have the right quality assurance, security, and testing processes and skills in place. AI requires precise context in order to function correctly, and teams that rely on undocumented legacy codebases and obscure architectures might find that AI generates inaccurate information that ends up requiring significant rework by humans. By following a few good practices, developers and teams can prepare for an AI-first environment and excel at this new paradigm for development. Add new skills and roles One of the most important steps is to add new skill sets and roles that fit into the AI-first strategy, even if that means uncomfortable changes for some. Without the needed skills in place, nothing else in the AI-first approach will work well. “The scarce ability is no longer writing code, but reading it quickly and assessing it accurately,” Hogue-Spears says. “The strongest developers will work as architects: they frame the problem, direct the agent, and verify the result. That is how one engineer safely oversees the work of several.” Organizations need to focus staffing on the senior architecture roles and cut or redesign the entry-level junior coding roles, says William Flaiz, founder of CleanSmartLabs, a provider of data cleansing products. “Tools like Claude Code can write good clean code fast so that is no longer the bottleneck,” Flaiz says. “The judgment and experience of what to build, how to architect it, and what is going to break comes from the judgment of senior architects. Skip this and you will ship product fast, but it will break under user load.” Consider training programs to ease the transition Much of AI-first is relatively new for a lot of organizations, and many developers and managers will need to learn what it means to be architects in this new world. The architect role “is changing in a way most organizations have not caught up to yet,” Rajhans says. “The job is less about writing code and more about designing the boundaries, where the agent acts, where the human decides, and what happens when the agent is wrong. That last question is the one most teams skip until it is too late.” Development teams also need to shift the developer role to an orchestrator role for agentic development, Flaiz says. Training will be needed in a lot of cases. “They have enough product context and user understanding to where agents hand off, conflict, and which agent wins when in conflict,” Flaiz says. “This is not a programmer role in the traditional sense, but a new role that is a good transition for junior and entry-level developers.” In addition, Flaiz recommends integrating user experience expertise into the development process, “not as a starting point only or as a gate check at the end,” he says. “AI can write requirements, specs, and assist with architecture, but it doesn’t understand your users. It doesn’t know the habits of how real users interact with software. That comes from watching [user experience] tests, analyzing behavior, and being human.” Shift toward agentic workflows A key part of AI-first development is shifting toward agentic workflows, where autonomous agents deploy reasoning, planning, and external tools to achieve complex goals. This is the scenario in which developers will act as architects who oversee AI agents as they work to solve problems and build applications. “The fundamental transition here is from typing—writing code—to specifying what you want done,” Hutchins says. “People who thrive within agentic workflows are individuals who can clearly define systems, think through potential [uncommon scenarios], and assess others’ work objectively.” While keying in code might seem to matter at times, the skills required for agentic workflows “represent true architecture and editorial judgment,” Hutchins says. “As such, senior engineers’ values increase rather than decrease.” With the new workflows, “developers will spend less time writing every line of code manually and more time defining goals, considering product constraints, iterating through architecture, designing intuitive user interfaces, envisioning data flows, and calculating expected outcomes,” says Bob Brauer, founder and CEO of Interzoid, a consultancy and provider of data enrichment and structured intelligence services. “AI tools and agents can then help generate code, test plans, documentation, deployment scripts, and monitor ongoing maintenance requirements,” Brauer says. Create new testing and review processes Another good practice is to create a new review process prior to building agent pipelines. “AI will generate ‘plausible’ garbage without hesitation,” Hutchins says. “Organizations that are currently suffering damage due to [poor] generation are the same ones that grew their generation capabilities faster than their ability to verify generated output. We have already seen consultants return client monies paid for fake citations. This represents a process failure, not a technology failure.” Reviews need to be ongoing. “Design for the human checkpoint,” Hutchins says. “For every workflow the questions should be ‘where does a person with judgment need to review before we send this?’ If you cannot answer this question for your current workflow, then you are not implementing AI-first development. You are simply allowing unsupervised delegation.” As for testing, “developers have to get comfortable with non-determinism,” Rajhans says. “AI-first systems do not behave the same way twice. Testing, observability, and failure handling all have to be redesigned from scratch around that reality. Teams that treat AI outputs like function returns get burned fast.” Start small and proceed with caution Despite the urgency among many business leaders to move aggressively toward AI, a somewhat cautious approach might be best for both near-term and long-term goals. “Begin small and track everything,” Hutchins says. “Allow developers access to approved tools and clearly defined guidelines, then determine where [and] when AI reduces developer time verses when AI generates additional rework.” AI-first development will become increasingly critical as competition intensifies and software teams face growing pressure to deliver faster, more sophisticated, and easier-to-use applications, Brauer says. “By leveraging AI across requirements gathering, architecture/design, coding, testing, deployment, and ongoing maintenance, development teams can significantly increase their effectiveness and create greater organizational value,” Brauer says. “Organizations that embrace AI-first development will be better positioned to deliver higher-quality software faster and gain a meaningful competitive advantage.”