The global energy and industrial artificial intelligence segment is seeing accelerated levels of consolidation and strategic partnerships pointing to a survival of the cleverest.

The last five years offer ample evidence of this unsubtle shift and how traditional industrial equipment and software vendors view the approaching horizon.

They include moves the leading six on the Forbes Global 2000 list - Siemens (FRA: SIE), Schneider Electric (EPA: SU), Honeywell (NASDAQ: HON), ABB (SWX: ABBN), Emerson (NYSE: EMR) and Rockwell Automation (NYSE: ROK).

Industrial AI Deals Galore

In July, Schneider Electric announced a deal to acquire AiDASH, a software company that provides AI-driven vegetation, asset and climate risk intelligence to utilities and other critical infrastructure operators, in an all-cash offer of $350 million.

The announcement came barely a month on from a big-ticket confirmation by the French multinational vendor to acquire industrial AI and data firm Cognite for $3.1 billion. These latest acquisitions will be integrated under AVEVA - a subsidiary of Schneider Electric’s Industrial Automation business unit, which it took full control of in January 2023, ultimately giving the company an enterprise value of $11.6 billion.

In between those two announcements by Schneider Electric, Siemens announced a strategic partnership and exchange with industrial AI solutions provider IFS to "connect design, production, and asset performance in one continuous loop—from engineering intent to operational outcome."

And ABB said it has another $14 billion for strategic acquisitions, after spending $5.5 billion on automation company Rotork. The Swiss multinational firm has also sold its robotics division to Softbank for $5.375 billion and is busy boosting capabilities via research and development focused buyouts like Meshmind and partnerships with LandingAI.

Rockwell Automation is deploying a similar strategy through acquisitions like Knowledge Lens and and Clearpath Robotics. Honeywell’s industrial automation unit is said to be targeting acquisitions in the $2 billion to $4 billion range, and has already spent $14 billion on ten acquisitions in past years, according to Reuters.

The biggest of the big moves came in March last year, when Emerson agreed to buy the shares it didn't already own in AspenTech completing a takeover deal valued at $7.2 billion.

Under the terms of the agreement, Emerson - which already held a majority 57% stake in AspenTech - bought the remaining stake at $265 per share in an all-cash tender offer. That gave AspenTech an implied market valuation of $16.8 billion.

It is the vendor’s concerted push to unlock value for end-users of its industrial AI, said Claudio Fayad, chief technology officer of what is now Emerson’s Aspen Technology business.

"We believe that depending on how the industry creates, deploys and orchestrates industrial AI - over $1 trillion of value may be unlocked by 2036."

Building Industrial AI-Native Capabilities

These moves appear to be about building AI-native capabilities by the industry equipment and software majors. But they are also potential bets on winning the industrial AI race at the data layer.

Furthermore, many believe that the industrial AI business is more grounded on the expectations front, as is the R&D and startup ecosystem that feeds it.

As such, it does not suffer from as much hype, volatile news headlines and anxieties about market bubbles as consumer AI, even though no business can ever be fully recession proof.

Industrial AI startups - often targets of the incumbents - share this viewpoint of solving complex industrial problems in touch with operational realities and constraints, said Dan Jeavons, a former Shell executive, and president of Applied Computing - an AI startup that is building physics-grounded foundation models for the energy sector.

"If you look at the industry’s natural progression and the last few waves forward - we saw the creation of startups that came out of nowhere and then became vital pieces of the process optimization puzzle," Jeavons added.

"That journey is ongoing especially as many in the industry haven’t quite figured out what they want to do with AI or remain wary of it. Yet, they still don’t want to miss out on the very obvious operational advantages industrial AI brings to the table - a sort of a commercial FOMO ["Fear of missing out"] moment if you wish, for good reasons."

Tie-ups, partnerships and scale-up investments are as much a part of the picture in an arena where scale matters.

Applied Computing has its very own with global engineering services provider KBR (NYSE:KBR), which saw the latter invest $20 million in the former via the startup’s last raise in March, alongside Databricks Ventures.

“Investing Applied Computing is our first ever investment in an AI company - one that wants to achieve scale. It entails a multi-year agreement to co-create industrial AI tools that combine KBR’s engineering expertise with Applied Computing’s models. This isn’t AI for AI's sake, but a collaboration on redefining how AI powers the critical systems," said Andy Webster, senior director at KBR.

Some Lofty Projections

Market projections being floated around appear to be pretty lofty, and particularly so for agentic AI.

If various industry assessments (e.g. Capgemini, Dimension Market Research, EY, Fortune Business Insights, GVR) are aggregated and squared against anecdotal evidence, the global industrial AI and automation market is expected to grow at a compound annual growth rate of around 12% to $570 billion by 2034, up from $200 billion in 2025.

But the agentic AI market could be worth anywhere between $150 billion and $200 billion by then, with a CAGR of over 40% from a current market valuation in the range of $7 billion to $10 billion, according to some projections. Of course, healthy skepticism is warranted as projection methodologies and parameters vary. However, the potential opportunities ahead look promising.

Unsurprisingly, the deal signals and rising numbers of strategic partnerships indicate that industrial AI consolidation is rapidly accelerating. Incumbents are buying AI stacks as well as building it, and putting much of the ecosystem they dominate on notice.