Kurt Petersen discusses how past automation mistakes such as silos and lack of orchestration are resurfacing as enterprises adopt AI, and outlines key strategies to avoid repeating these errors.

Learning From the Past: What Automation Mistakes Can Teach Us About AI

By Kurt Petersen

Introduction

Organizations across industries are rapidly embedding AI into their operations, with 84% planning to expand AI capabilities in the coming years. From customer service bots to AI copilots, the adoption is accelerating. However, this rush brings risk: many organizations are repeating the mistakes of past automation projects—namely, implementing isolated solutions without an overarching strategy or orchestration framework.

Three Core Automation Mistakes and Their Lessons

1. Chasing Quick Wins

Automation technologies like RPA were often seen as a shortcut to efficiency, but these efforts frequently struggled to scale. Siloed bots and lack of centralized control led to brittle, fragile solutions that became maintenance burdens rather than lasting assets.

Lesson: Prioritize long-term scalability and governance over rapid, tactical automations.

2. Automating in Silos

Different departments historically adopted separate automation tools (e.g., RPA for operations, iPaaS for support, legacy BPM for finance), leading to complex webs of disconnected systems. Without unified process architecture or oversight, these tools couldn’t work together and required repetitive, manual reconfiguration during major changes.

Lesson: Enterprise-wide process orchestration is essential for robust automation and AI adoption.

3. Starting with Rigid Foundations

First-generation BPM tools forced organizations into inflexible business models, which didn’t adapt well to new products, regulations, or strategy shifts. Similarly, hardcoded AI solutions that can’t evolve with the business become a liability.

Lesson: Adopt dynamic, composable architectures. AI agents should exist within adaptable process modeling frameworks that support flexibility, transparency, and continuous improvement.

Best Practices for AI-powered Automation

  • Treat AI as an integral part of broader business processes, not as isolated components.
  • Build orchestrated, governable processes that connect AI with human tasks and traditional systems.
  • Focus on adaptability and continuous improvement to ensure solutions remain relevant and scalable as business and regulatory requirements evolve.

Orchestration enables the visibility and guardrails that high-risk, complex AI initiatives demand. Without it, organizations risk creating the same technical debt and inefficiencies that have historically plagued automation efforts.

Conclusion

Success with AI in automation hinges on learning from past experiences: avoid isolated deployments, invest in orchestration, and embrace flexibility. By doing so, AI initiatives can deliver scalable, sustainable value across the enterprise.

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