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.
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Instead of hiring a 'Chief AI Officer' or an agency, the most successful GTM AI deployments empower existing top performers. Pair your best SDR, marketer, or RevOps person with AI tools, and let them learn and innovate together. This internal expertise is more valuable than any external consultant.
AI agents will automate PM tasks like competitive analysis, user feedback synthesis, and PRD writing. This efficiency gain could shift the standard PM-to-developer ratio from 1:6-10 to 1:20-30, allowing PMs to cover a much broader product surface area and focus on higher-level strategy.
AI automates tactical tasks, shifting the PM's role from process management to de-risking delivery by developing deep customer insights. This allows PMs to spend more time confirming their instincts about customer needs, which engineering teams now demand.
The next frontier for AI in product is automating time-consuming but cognitively simple tasks. An AI agent can connect CRM data, customer feedback, and product specs to instantly generate a qualified list of beta testers, compressing a multi-week process into days.
C-suites are more motivated to adopt AI for revenue-generating "front office" activities (like investment analysis) than for cost-saving "back office" automation. The direct, tangible impact on making more money overcomes the organizational inertia that often stalls efficiency-focused technology deployments.
Unlike older sales tools, AI agents shouldn't be handed to individual SDRs to manage. This approach leads to failure. Instead, centralize the strategy: a core team must own agent training, contact routing, and performance tuning to ensure a consistent and effective GTM motion across the entire organization.
Stop thinking of sales, marketing, and support as separate functions with separate tools. AI agents are blurring these lines. A support interaction becomes a lead gen opportunity, and a marketing email can be sent by a 'sales' tool. Prepare for a unified go-to-market operational model.
In the AI era, marketing and growth roles are splitting into two distinct archetypes: the 'tastemaker' who has exceptional creative taste and intuition, and the 'engineer' who can technically analyze and orchestrate complex systems. Being average at both is no longer a viable path to success.
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.
This emerging role applies engineering and AI to GTM functions, building agents to automate tasks like lead qualification and personalized outreach. This dramatically increases efficiency, allowing one person, with an AI agent, to do the work of ten.
