Instead of traditional IT roles focused on software, an AI Ops person focuses on identifying and automating workflows. They work with teams to eliminate busy work and return hundreds of hours, shifting employees from performing tasks to directing AI.
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To successfully automate complex workflows with AI, product teams must go beyond traditional discovery. A "forward-deployed PM" works on-site with customers, directly observing workflows and tweaking AI parameters like context windows and embeddings in real-time to achieve flawless automation.
The common fear of AI eliminating jobs is misguided. In practice, AI automates specific, often administrative, tasks within a role. This allows human workers to offload minutiae and focus on uniquely human skills like relationship building and strategic thinking, ultimately increasing their leverage and value.
To overcome employee fear of AI, don't provide a general-purpose tool. Instead, identify the tasks your team dislikes most—like writing performance reviews—and demonstrate a specific AI workflow to solve that pain point. This approach frames AI as a helpful assistant rather than a replacement.
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."
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.
Instead of focusing on foundational models, software engineers should target the creation of AI "agents." These are automated workflows designed to handle specific, repetitive business chores within departments like customer support, sales, or HR. This is where companies see immediate value and are willing to invest.
To maximize AI's impact, don't just find isolated use cases for content or demand gen teams. Instead, map a core process like a campaign workflow and apply AI to augment each stage, from strategy and creation to localization and measurement. AI is workflow-native, not function-native.
Adopt a 'more intelligent, more human' framework. For every process made more intelligent through AI automation, strategically reinvest the freed-up human capacity into higher-touch, more personalized customer activities. This creates a balanced system that enhances both efficiency and relationships.
Shift from departments staffed with people to a single owner who directs AI agents, automations, and robotics to achieve outcomes. This structure maximizes leverage and efficiency, replacing the old model of "throwing bodies" at problems.
Powerful AI assistants are shifting hiring calculus. Rather than building large, specialized departments, some leaders are considering hiring small teams of experienced, curious generalists. These individuals can leverage AI to solve problems across functions like sales, HR, and operations, creating a leaner, more agile organization.
