How Do We Fix This?

2021-11-04 · Source: High ROI AI · Field: Business & Management — Corporate Strategy & Leadership, Project & Product Management, Consulting & Professional Services · Depth: Intermediate, quick

Summary

The article argues that current AI investment, projected to reach $650 billion by Meta, Microsoft, Alphabet, and Amazon in 2026, is largely unprofitable because AI's cost structure scales with usage, complexity, and poor data quality, unlike prior technology waves. It proposes a "Framework Certainty" approach, emphasizing that AI should only be applied to use cases where returns scale faster than costs. This strategy, exemplified by companies like Walmart, Johnson & Johnson, and Deere, focuses on identifying novel customer value, assessing monetization and execution feasibility, and prioritizing the most lucrative opportunities. The author contends that traditional companies are better positioned to monetize AI than tech incumbents, whose existing SaaS and cloud business models are not suited for AI monetization and would require disruptive changes to their core revenue streams.

Key takeaway

For CTOs and VPs of Engineering navigating AI investments, your teams should rigorously evaluate AI use cases based on a "returns scale faster than costs" framework. Prioritize opportunities that deliver novel customer value and align with your business model and data advantages, rather than pursuing "cool" tech for its own sake. This approach will prevent costly PoCs and ensure your AI initiatives contribute directly to profitability and competitive advantage.

Key insights

Focus AI investments on use cases where returns outpace costs to achieve economic viability and avoid negative unit economics.

Principles

• AI costs scale with usage and complexity.
• Not every AI workload is economically viable.
• Prioritize novel customer value.

Method

Start with novel customer value, assess monetization and execution feasibility, then prioritize the top 10 most lucrative opportunities ensuring returns scale faster than costs, supported by iterative delivery and feedback.

In practice

• Identify use cases with positive unit economics.
• Implement quarterly delivery for feedback.
• Shelve unprofitable initiatives promptly.

Topics

• AI Monetization
• AI Use Case Selection
• AI Cost Structure
• AI Product Strategy
• Business Model Transformation

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Product Manager, Executive, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by High ROI AI.