Across industries, organizations are rushing to embed AI into their operations. In fact, 84% of organizations are looking to add more AI capabilities within the next three years. From customer service bots to AI copilots, the future is arriving faster than many expected. But in the rush for quick wins, many executives are overlooking a hard truth: the pattern is a familiar one. The same pitfalls that plagued past automation efforts are re-emerging, and we’re at risk of repeating the same costly mistakes.

From rigid first-generation Business Process Management (BPM) tools to Robotic Process Automation (RPA), the root problem was the same: automation in isolation. Siloed tools, brittle logic, and a lack of orchestration turned many automation solutions into long-term liabilities and sources of technical debt.

If we’re not careful, AI threatens to repeat those mistakes—at scale, in real time, and under increasing regulatory and customer scrutiny. Here are three of the biggest automation mistakes, and how to avoid them in the future with AI.

1. Chasing Quick Wins

Any automation technology pitched as a fast track to results should come with a word of caution. For example, in the past, RPA promised to automate repetitive tasks with minimal IT involvement. While some use cases delivered short-term time savings, many programs failed to scale. All too often, RPA bots ran in departmental silos, with no end-to-end visibility or centralized control.

The problem? Technologies like RPA need orchestration. Without it, they become tactical—brittle, reactive, and hard to govern. As a result, many IT teams struggle to maintain the very systems that claim to be hands-off.

2. Automating in Silos

Automation silos remain a common problem across many organizations. In the early days of automation, different teams deployed different tools for different tasks—RPA in operations, iPaaS in customer support, legacy BPM in finance—each addressing local inefficiencies. But without a shared business process architecture or governance, these initiatives rarely worked together.

The result was operational inefficiency or processes that broke down altogether. When change inevitably hit—like a regulatory update or a system migration—every “island” had to be reconfigured independently.

The lesson? Automation that doesn’t scale across the enterprise ultimately won’t work. AI is no different. Without a central orchestration layer connecting people, systems, devices, and emerging technology like AI agents, automation remains a fragile patchwork.

3. Starting with Rigid Foundations

During the BPM wave of the early 2000s, many organizations focused on documenting how work *should *happen. However, the BPM tools these organizations adopted locked them into rigid business processes, which ultimately couldn’t adapt to changing needs.

That rigidity became a barrier. When teams needed to launch a new product, shift a customer journey, or respond to new regulations, they often had to rework entire business processes from scratch. Similarly, if AI tools can’t evolve with your business, they become liabilities—failing to scale, breaking under change, or introducing risk.

The better path forward is composable and dynamic. Processes should be modeled, but also executable. AI agents should operate inside adaptable business process modeling and design frameworks that support governance, transparency, flexibility for AI-based decision-making, and continuous improvement.

Avoiding Past Automation Mistakes in the AI Era

The pattern in all of these examples is clear: automation in isolation is fragile. AI is no exception.

Avoiding these historical issues means integrating AI into orchestrated, observable, and governable business processes from the start. We’ve seen what happens when automation is deployed without coordination—systems become harder to maintain, outcomes less predictable, and change more expensive. These failures were architectural, not due to a lack of ambition or investment.

To ensure AI delivers on its promise, we need to apply those hard-earned lessons proactively. That starts with treating AI as a participant in a broader business process architecture—one that’s modeled, orchestrated, adaptable, scalable, and governed.

Orchestration brings order to the complexity of AI-enabled operations. It connects human tasks, technologies, and AI into an end-to-end business process. It allows teams to observe what’s happening across the process, understand why AI made a particular decision, and step in when needed. Most importantly, it provides the guardrails that fast-moving, high-risk technologies like AI demand.

Without orchestration, AI is just another isolated tool that can be prone to failure, difficult to manage, and disconnected from the outcomes that matter. With orchestration, it becomes something else entirely: an intelligent, integrated part of how work gets done.

If we want AI to scale beyond prototypes and pilots, to operate safely and strategically across the enterprise, we can’t afford to make the same mistakes. We need to learn from them and make orchestration the default, rather than an afterthought.