Why It’s Hard to Redesign Work Processes with AI

2026-07-07 · Source: Tom’s Substack · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management · Depth: Intermediate, quick

Summary

There is a strong consensus that realizing significant value from AI requires redesigning business processes, rather than merely integrating AI into existing workflows. A McKinsey 2025 survey and a 2026 MIT paper both highlight the correlation between workflow redesign and AI value, with Jacob Nielsen's field experiment showing startups redesigning end-to-end workflows around AI generated 90% more revenue. Despite this clear benefit, widespread AI-driven process reengineering faces significant obstacles. Many companies, particularly in the US, lack a clear understanding or orientation towards their existing processes. Furthermore, while process design requires effort to map current states and technologies, the implementation of new process designs is considerably more difficult and time-consuming. This involves new systems, skills, and behaviors, often stretching over months and costing millions, with risks of failure due to stakeholder issues or technical problems. There's also a risk of "AI washing" where AI is used as a pretext for layoffs instead of genuine reengineering.

Key takeaway

For Directors of AI/ML or Operations Professionals considering AI integration, recognize that true value comes from deep process reengineering, not superficial additions. Your efforts must extend beyond design to the challenging implementation of new systems, skills, and behaviors across end-to-end processes. Be prepared for significant investment in time and resources, and proactively address organizational clarity on processes to mitigate risks of failure or "AI washing."

Key insights

Realizing AI's full value demands fundamental business process redesign, not just incremental integration.

Principles

• AI value correlates with workflow redesign.
• Process implementation is harder than design.
• Lack of process clarity hinders AI adoption.

Method

Process mining can help understand existing process performance, bottlenecks, and foster process orientation before redesign.

In practice

• Map end-to-end processes like order management.
• Use process mining to identify bottlenecks.
• Focus on new systems, skills, and behaviors.

Topics

• AI Integration
• Business Process Redesign
• Workflow Automation
• Process Mining
• Organizational Change Management
• AI Adoption Barriers

Best for: Entrepreneur, Executive, AI Product Manager, Director of AI/ML, Consultant, Operations Professional

Related on AIssential

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• Cognizant's Ollie O'Donoghue on AI's Value Creation Struggle — AI value creation struggles because organizational adaptation lags rapid technological capability advancements.
• Congratulations. You’ve Hired An AI Assistant. Now What? — Effective AI adoption hinges on identifying specific business problems it can solve, not merely acquiring tools or chasing hype.
• Redesign Work Before You Add More AI Agents — AI value is realized by redesigning workflows to integrate human-agent systems, not merely by adding more AI tools.
• Adoption Is Not Absorption — AI adoption metrics create false confidence; true value requires deep absorption into redesigned workflows and operating models.
• Stop Just Automating: Why Redesigning Work is the Secret to AI ROI — Redesigning work with AI yields significantly higher ROI than merely automating existing processes.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Tom’s Substack.