The most significant productivity gains come from applying AI to every stage of development, including research, planning, product marketing, and status updates. Limiting AI to just code generation misses the larger opportunity to automate the entire engineering process.

Waiting for perfectly clean data stalls AI adoption. Instead, deploy AI agents to execute tasks. Their diligence and consistency in handling information will progressively clean underlying systems of record as a byproduct of their work.

At OpenAI, teams of just one or two engineers leverage AI agents to own entire product lines. This model reduces human collaboration overhead and empowers engineers to make most micro-decisions autonomously, increasing speed and ownership.

A major hurdle for enterprise AI is messy, siloed data. A synergistic solution is emerging where AI software agents are used for the data engineering tasks of cleansing, normalization, and linking. This creates a powerful feedback loop where AI helps prepare the very data it needs to function effectively.

Run HR, finance, and legal using AI agents that operate based on codified rules. This creates an autonomous back office where human intervention is only required for exceptions, not routine patterns. The mantra is: "patterns deserve code, exceptions deserve people."

Artemis automates the analysis of product usage data by deploying AI agents instead of relying on manual session reviews. These agents identify points of customer friction and can even suggest new features to streamline workflows, turning a time-consuming process into a scalable, automated one.

The true power of an AI agent is its capacity to handle the mundane, repetitive work that humans—both internal teams and external agencies—often neglect or de-prioritize. SaaStr couldn't find people willing to consistently manage hundreds of follow-ups, a task their AI now handles flawlessly.

While product teams are a natural fit for AI coding tools, Replit's CEO identifies Operations teams as a surprisingly high-ROI customer segment. Ops teams are often stuck with inadequate SaaS tools and manual workflows, and AI agents can deliver massive efficiency gains by automating tasks like deal desk and support operations.

A killer app for AI in IT is automating tedious but critical tasks. For example, investigating why daily cloud spend deviates by more than 5%. This simple-sounding query requires complex data analysis across multiple services—a perfect, high-value problem for an AI agent to solve.