The numbers are arresting in their scale and in their contradiction; artificial intelligence (AI), both generative and analytical, is projected to unlock up to $70 billion in new market value for the industrial automation sector alone by 2030, whilst the oilfield services and equipment (OFSE) industry stands to capture an additional $12 billion to $20 billion per year in EBITDA gains through diligent AI deployment.
Yet across both sectors, the gap between strategic intent and operational reality has never been wider.
In the OFSE industry, fewer than one in four companies have progressed beyond the pilot phase, and only 1 per cent of respondents to a major industry survey conducted in May 2025 reported that their organisations had achieved significant gen AI scale.
Industrial automation incumbents, meanwhile, face the prospect of gradual irrelevance as the architecture of their industry undergoes a structural transformation more profound than anything seen in a generation.
The urgency is not hypothetical; it is financial, competitive, and immediate.
STRUCTURAL SHIFT FROM CONTROL TO INTELLIGENCE
The industrial automation sector is undergoing what Bain & Company describes as a fundamental reordering of its profit pools, a transformation from what analysts characterise as a “pyramid” model, in which value was concentrated in the control layer, to an “hourglass” architecture in which economic power accumulates at both ends of the technology stack.
By 2030, more than 80 per cent of industry profit pools are expected to sit at the two extremities of this hourglass.
Software, data platforms, and AI-enabled layers are projected to account for more than half of the total profit pool, whilst smart field devices are forecast to capture an additional 25 to 30 per cent.
The traditional control layer, historically the profitable core of industrial automation, faces intensifying margin pressure from both directions simultaneously.
The macroeconomic case for urgency is reinforced by Bain’s finding that AI-enabled solutions alone could unlock up to $70 billion, representing a 22 per cent expansion in new market value by 2030.
A small number of use cases, such as adaptive robotics, predictive maintenance, and knowledge-based systems, are expected to account for a disproportionate share of that upside, with the majority of that value projected to materialise within the next one to five years.
Companies that have already moved to orchestrate data, software, and smart devices at scale are realising productivity gains of 30 to 50 per cent, maintenance cost reductions of up to 35 per cent, and measurably longer asset lifetimes.
In these application domains, AI is no longer a competitive differentiator, it has become a prerequisite for market access.
The competitive dynamics facing automation incumbents are equally consequential. Legacy advantages are eroding faster than many established players anticipate, with hyperscalers and AI-native firms expanding aggressively into industrial software and data platforms from above, whilst hardware competitors compress margins in core automation components from below.
Compounding this pressure, switching costs are falling as software progressively decouples from hardware and interoperability standards improve across the industry.
Nearly 60 per cent of incremental industry growth toward 2030 is expected to derive from vertical-specific offerings that embed process knowledge, data semantics, and regulatory requirements, signalling a decisive shift away from horizontal scale toward deep domain expertise.
OFSE SECTOR’S ADOPTION GAP
In the OFSE sector, the gap between expectation and reality is equally stark, and the financial consequences of inaction are now clearly quantified.
McKinsey’s 2025 OFSE Leaders Gen AI Survey, which draws on responses from 164 executives representing integrated service providers, engineering, procurement and construction companies, equipment manufacturers, and services firms across global markets, found that gen AI adoption has fallen dramatically short of targets set just two years earlier.
In the 2023 iteration of the same survey, respondents anticipated that over 70 per cent of companies would be piloting and scaling gen AI by 2025.
The actual figure for those showing any use at all reached less than 25 per cent, with a mere one per cent of respondents reporting significant scale.
McKinsey’s analysis estimates that scaling both generative and analytical AI across current OFSE operations could translate into an EBITDA gain of between $12 billion and $20 billion annually, equivalent to a four to six percentage point improvement in EBITDA margin against the 2024 baseline of 18 per cent.
Over three-quarters of survey respondents believed that the technology would deliver operational efficiencies, and 80 per cent of OFSE leaders attributed the anticipated value uplift to streamlined execution, maintenance optimisation, and competitive differentiation through personalised service delivery.
When asked to allocate a hypothetical $1 billion investment to strengthen their businesses, 65 per cent of leaders said they would direct the capital toward digital infrastructure and AI capabilities, a figure that speaks to the gap between investment intent and actual deployment progress.
Executives across the sector characterise gen AI as a tool for cost reduction capable of enabling leaner operations and faster decision-making, whilst simultaneously viewing it as a catalyst for differentiation through enhanced service quality, accelerated delivery, and more tailored, data-driven customer solutions.
The emergence of agentic AI adds a further dimension to this outlook, offering the prospect of movement from incremental productivity improvements to transformational operational performance.
BARRIERS THAT HAVE PRODUCED PILOT PARALYSIS
The disconnect between ambition and execution across both sectors is not attributable to a single cause but to a convergence of structural, organisational, and cultural barriers that have proved more resistant than industry leaders initially anticipated.
In the OFSE sector, more than 50 per cent of McKinsey survey respondents identified data fragmentation and legacy system integration as the primary barriers to scaling.
Successful pilots routinely failed to extend across geographies or business functions because of the complexity of existing process architectures and technology estates.
Less than 30 per cent of OFSE leaders reported having a clear strategy or value-backed roadmap for AI deployment.
A second and equally significant barrier concerns organisational readiness, and it is characterised by a striking perceptual asymmetry.
In OFSE companies, 80 per cent of executives and 60 per cent of business and functional leaders described themselves as mostly to fully ready to adopt gen AI.
Yet these same leaders assessed 90 per cent of their operational and functional frontline employees as being only slightly ready or not ready at all.
McKinsey’s broader research directly contradicts this perception. The actual number of employees using gen AI for a third or more of their work is three times greater than their leaders perceive, and 70 per cent of employees surveyed believe that within two years gen AI will change 30 per cent or more of their work.
It is, according to the same research, 2.4 times more likely for C-suite leaders to cite employee readiness as a barrier to gen AI adoption than to acknowledge their own deficiencies in leadership alignment.
The third structural barrier is economic. The cost of scaling from pilot to production consistently causes initiatives to stall, whilst the difficulty of quantifying gen AI’s return on investment generates hesitation around further capital commitment.
For industrial automation incumbents, the barriers are structural rather than perceptual, manifesting in the erosion of switching costs as software decouples from hardware, and in the speed with which AI-native competitors are building positions in the data and intelligence layers that will define future value capture.
Both sectors, however, face a common imperative. McKinsey’s framework for OFSE leaders identifies three pillars: Strategy, capabilities, and change management, as the essential scaffolding for scaling AI from experimentation to enterprise impact.
That framework calls for every gen AI initiative to be tied to measurable outcomes, for operating models to evolve toward support of autonomous, agent-enabled workflows, and for change management to embed AI into daily operations through visible leadership, responsible governance, and systematic upskilling.
Bain’s parallel assessment for industrial automation emphasises the criticality of connecting software, data, and smart field devices into integrated solutions, and of building recurring engagement models that make companies accountable for outcomes over time rather than at the point of commissioning.
The window for decisive action remains open, but it is narrowing with each quarter that passes in pilot paralysis.