Bain & Company warns that traditional industrial control systems are losing their central role as AI and smart devices redefine the economic landscape. By 2030, software, AI workflows, and intelligent field devices will capture the bulk of industry profit.
In sum – what to know:
Hourglass figures – Economic value is migrating away from middle-layer control hardware to top-layer AI software and bottom-layer IoT devices.
AI sense-making – By 2030, nearly half of industry revenues will rely on AI solutions, which could unlock up to $70 billion in new market value.
Hybrid verticals – hybrid sectors like pharma already show the shift, with discrete and process-heavy industries set to also accelerate AI adoption.
Bain & Company has issued a fairly meaty report ahead of Hannover Messe this month (April 20-24) about the state of Industry 4.0 in the AI era, and declared that industrial control systems, where enterprises have traditionally sunk most of their innovation budgets, have reached their limits, and that their economic priorities have also changed. AI has upped the ante, the company says, and made the kind of incremental production gains traditionally achieved with upgraded control systems appear limiting – versus the new value creation that industrial AI promises.
Industrial enterprises are all-in on AI, the report says. Take note: they are also all-in on IoT, in a broad fashion – in the form of ‘smart field devices’ (“sensors, actuators, drives, robots, conveyors, and machine vision”); as they have been for years. The shift away from control hardware – programmable logic controllers (PLCs), distributed control systems (DCSs), input/output modules (I/O), supervisory control and data acquisition (SCADA), proprietary software – will see the old profit ‘pyramid’ morph into an ‘hourglass’ shape, with the middle control layer squeezed.
Meanwhile, the top software layer, most notably, will be expanded over the next few years to reflect the new AI money-flows. By 2030, more than 80 percent of industry “profit pools” (enterprise investments / vendor profits) will sit at the two ends of this hourglass-shaped value map, showing investments in different levels of the industrial tech stack. More than half of spending will go on top-layer data management software, to orchestrate and optimize data in AI-related workflows. Such software scales fast, and affords high margins, it says.
An additional 25-30 percent will go on field devices, mostly designated as ‘smart’ and invariably connected. It writes: “Value is concentrating in software, data platforms, and AI-enabled workflows. These [top] layers scale faster, carry higher margins, and compound in value as data and use cases accumulate… At the bottom, value is reemerging in smart field devices… [which] are no longer passive endpoints. With embedded intelligence, connectivity, and edge computing, they generate data, execute decisions, and continuously improve performance.
“The traditional control layer in the middle… remains essential but is becoming harder to scale and differentiate. New entrants are compressing margins by shifting value away from these core controls… Most industry profit pools will flow to the two ends of this hourglass, away from the center.” In terms of new revenue value for tech vendors, the (blog of the) report has a couple of weird graphs, a little hard to make out, suggesting the “profit pool” for industrial tech will go from $30 billion in 2025 to $52 billion in 2030, while the market goes from $250 billion to $400 billion.
Notes: Market and profit pool size are estimates; field categories include sensors, actuators, drives, industrial robots, conveyors, and machine vision; smart field includes equipment with Internet of Things (IoT)–enabled intelligence, compute, and native connectivity; other software includes historian software, advanced process control (APC), simulation software, and digital twins; I/O is input/output; PLC is programmable logic controller; DCS is distributed control system; SCADA is supervisory control and data acquisition; HMI is human-machine interface; MC is motion control; MOM is manufacturing operations management; MES is manufacturing execution system. Sources: Bain analysis; ARC Advisory Group Automation Report
These are estimates, it notes. It offers a percentage breakdown of all the elements in the stack, which appears to be rough percentage-share estimates, and don’t clearly add up (to 100 percent). But the message is the same: spiralling shares either side of a collapsing forecast in the middle, related to industrial control hardware. “Control still matters, but it is no longer the profitable core of the industrial automation industry,” it writes. It has another revenue forecast, besides (see below): that “AI-enabled solutions alone could unlock up to” $70 billion in “new market value” by 2030.
But “AI-enabled solutions” apply to every part of the stack, anyway – to bring intelligent orchestration of control hardware. The fastest-growing sub-segment for new AI-related tech is ‘manufacturing control’ software, worth $31 billion by 2030, up about 10 percent (CAGR) in the period. Tech (of whatever sort) related to the ‘physical manufacturing process’, now with AI, will still contribute about a quarter of this ‘new’ market value ($88 billion), even if the CAGR jump is lower (two percent), and its contribution-share is down ($81 billion out of $293 billion in 2025).
Regardless, the pattern is still clear: all the boats rise, and AI sensing and AI sense-making capture and expand the traditional investment flow in a pincer movement. Bain says the shift is visible most clearly already in “hybrid industry verticals”, which mix elements of process manufacturing (continuous or batch production, often liquids, chemicals, or ingredients) and discrete manufacturing (assembly of distinct physical units) – notably pharmaceuticals and food and beverage, which will allocate a higher share of spending to automation (34 percent and 31 percent, versus 2025).
But discrete verticals and process verticals will quickly come online, with the aerospace and electronics sectors pumping in about 45/44 percent more, and oil and gas going 26 percent higher. Bain also observes a trend towards vertical integration. “The industry shifts toward software, smart devices, and vertical depth,” it says. “As margins tighten, value accrues to those who own the decision layer – not just the systems that execute instructions.” This marks a “clear break from the past”, it says – process intelligence versus just process automation.
Interestingly, it also suggests a timeline for when AI will be swapped-into different industrial use cases, effectively. “AI’s first wave of impact will also be far more concentrated – and time-bound – than many leaders expect.” While a small number of use cases account for a “disproportionate share of AI’s upside” (adaptive robotics, predictive maintenance), nearly half (47 percent) of industry revenues will rely on “AI-enabled offerings” by 2030, with “substitution pressure” above 50 percent in several core use cases.
“AI is no longer a differentiator – it is a prerequisite for market access… Much of the value will materialize in the next one to five years, leaving little room for incremental or experimental approaches,” it says. It lists them, as below: “peak substitution” in mid/late-2028 (consulting and integration, maintenance and support, monitoring and supervision), mid-2029 (manufacturing operations), and mid/late-2030 (physical manufacturing, manufacturing control, business optimization), with a mid-2031 schedule for IoT-related sensors and components.
