The “Hourglass” Narrative Overstates the Decline of Control

KEYWORDS: Bain & Company, Industrial AI, Open Process Automation (OPA), Control Systems, SDA, OT, IoT

Overview

If you manage automation at an operating site, or if you build the platforms those sites buy, you are likely hearing the same message. AI and data platforms will become the primary profit pool, field devices will get smarter, and the control layer will be compressed in the middle. This narrative is already influencing product roadmaps, capital plans, and architecture decisions. The risk is that it encourages organizations to optimize for the wrong part of the stack and overlook where industrial value is actually created, protected, and scaled.

For end users, the question is not whether AI matters. The question is how to translate analytics into repeatable operational improvement without compromising safety cases, uptime commitments, or regulatory accountability. In brownfield environments, model governance, cybersecurity, change management, and lifecycle support determine what can be trusted in production. This context helps explain why control systems remain the execution backbone for Industrial AI, and why software-defined control, virtualization, and open architectures are changing what control is and how it delivers value.

For automation suppliers, the implication is equally direct. Winning strategies are unlikely to come from abandoning control. They will come from integrating AI-enabled decision support with trusted execution, and from delivering portability, tools, and services that reduce integration risk over decades. In this Insight, ARC summarizes the hourglass thesis, highlights where it matches market reality, and explains where it overreaches. The intent is to support clearer product, partnering, and modernization decisions grounded in how industrial operations run.

Industrial AI does not eliminate control. It increases the importance of trusted, software-defined execution. ARC expects AI adoption to remain uneven and use case specific through the remainder of this decade, with measurable value delivered incrementally rather than through broad, synchronized shifts in spending away from control systems.
 

AI, IoT, and the Shifting Economics of Industrial Automation

A recent Bain & Company analysis, summarized in this report, argues that the economic center of gravity in industrial automation is shifting as AI and IoT adoption accelerate. The core premise is that traditional industrial control systems (i.e., PLCs, DCSs, SCADA, and associated control hardware) are no longer the primary locus of value creation. Instead, value is migrating upward toward AI‑enabled software and data platforms and downward toward increasingly intelligent and connected field devices, creating what Bain characterizes as an hourglass shaped automation stack.

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