the companies actually making money with AI aren't using it the way this sub thinks they are

2026-04-09 · Source: Artificial Intelligence · Field: Business & Management — Operations & Process Management, Corporate Strategy & Leadership, E-commerce & Digital Commerce · Depth: Novice, long

Summary

The prevailing discourse in AI communities, often centered on frontier models, AGI timelines, and theoretical capabilities, significantly diverges from how businesses are actually generating substantial ROI with AI. Instead of pursuing "moonshot" applications or attempting to replace humans with autonomous AI agents, profitable companies are implementing AI to make existing, often "boring," processes incrementally faster and more efficient. Examples include logistics firms using AI to categorize customer emails, recruiting firms enriching candidate profiles, B2B companies personalizing outbound emails for 3x reply rates, and insurance brokers pre-validating claim forms. These unglamorous applications, while not making headlines, quietly compound productivity gains, allowing existing human teams to become 2-3x more productive and freeing up hundreds of annual hours for strategic work.

Key takeaway

For AI Product Managers evaluating strategic investments, prioritize solutions that enhance existing workflows and boost human productivity rather than chasing headline-grabbing, unproven AGI applications. Your focus should be on identifying and automating "boring" but time-consuming tasks to deliver clear, compounding ROI, such as improving email processing or data validation, which will yield tangible business value faster than moonshot projects.

Key insights

Real AI value stems from incremental automation of mundane tasks, not revolutionary AGI or human replacement.

Principles

• Focus AI on existing process optimization.
• Small, compounding efficiency gains drive ROI.
• AI augments human productivity, it doesn't replace it.

Method

Identify specific workflow pain points, then apply minimal AI to smooth them out, prioritizing efficiency improvements over paradigm shifts.

In practice

• Automate email categorization and routing.
• Enrich data for candidate profiles.
• Pre-validate forms for error checking.

Topics

• Enterprise AI Strategy
• Workflow Automation
• Business Process Optimization
• Productivity Enhancement
• Return on Investment

Best for: Executive, AI Product Manager, Product Manager, Director of AI/ML, Consultant, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.