Non-developer teams like support and HR are adopting technical tools because their workflows now involve AI agents. Since building and maintaining these agents requires engineering input, the engineers' preferred tools get pulled into these other departments, blurring organizational lines.
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Generative AI and low-code tools empower individuals to perform tasks previously owned by specialized roles, like a PM creating a functional prototype. This blurs traditional job descriptions. The critical skill shifts from mere tool proficiency to learning how to collaborate effectively in new, blended team structures.
Top-performing engineering teams are evolving from hands-on coding to a managerial role. Their primary job is to define tasks, kick off multiple AI agents in parallel, review plans, and approve the final output, rather than implementing the details themselves.
AI tooling is creating a 'fluid model' where any employee, regardless of role, can potentially ship code. This dramatically expands the design system team's responsibility, which must now create tooling and guardrails to support a much broader and less technical user base across the entire organization.
Experienced engineers using tools like Claude Code are no longer writing significant amounts of code. Their primary role shifts to designing systems, defining tasks, and managing a team of AI agents that perform the actual implementation, fundamentally changing the software development workflow.
Traditional SaaS was built for siloed human departments (e.g., sales, marketing, support). AI enables a single agent to manage the entire customer journey, forcing these distinct software categories to converge into unified platforms.
As AI tools empower individuals to handle tasks across the entire product development lifecycle, traditional, siloed roles are merging. This fundamental shift challenges how tech professionals define their value and contribution, causing significant professional anxiety.
At Block, the most surprising impact of AI hasn't been on engineers, but on non-technical staff. Teams like enterprise risk management now use AI agents to build their own software tools, compressing weeks of work into hours and bypassing the need to wait for internal engineering teams.
Visual AI tools like Agent Builder empower non-technical teams (e.g., support, sales) to build, modify, and instantly publish agent workflows. This removes the dependency on engineering for deployment, allowing business teams to iterate on AI logic and customer-facing interactions much faster.
Historically, developer tools adapted to a company's codebase. The productivity gains from AI agents are so significant that the dynamic has flipped: for the first time, companies are proactively changing their code, logging, and tooling to be more 'agent-friendly,' rather than the other way around.
Contrary to the belief that PMs are the earliest tech adopters, go-to-market functions (sales, marketing, support) are leading agent adoption. Their work involves frequently recurring, pattern-based tasks that are a perfect fit for automation, putting them ahead of the curve.
