Close to $3 trillion is making its way into American industries, but how much of it will be wasted by inefficient operations? As more companies invest in U.S. manufacturing, companies will need to build new value chains that factor in short-term supply chain risks while delivering long-term economic transformation.
But business as usual isn’t – and can’t be – the way forward. Nearly every aspect of industrial operations is being transformed by artificial intelligence (AI), but its impact depends on integrated value chains. Or as I like to call it, radical collaboration.
This is Management 101: decisions depend on information. Without a clear picture of end-to-end business data, you’ll make poorer decisions.
AI’s strength is its ability to crunch large data volumes at scale and in near real time. But its output is limited by the data available.
That’s why companies are breaking conventional business boundaries to share what is recognized as one of the industry’s most valuable resources, using open and agnostic platforms. And unlike more traditional resources (such as oil), data becomes more valuable the more it’s shared.
Rather than hoarding it, collating and making data available to business partners, together with the resulting AI-infused insights, fosters benefits for every player along the value chain.
The cost of going it alone
Instead, American industrial companies are leaving millions on the table through disconnected operations. The majority of operations and supply chain officers – an astonishing 69% – report that technology investments haven’t fully delivered expected results, according to PWC data.
Although the average manufacturer spends 6.6% of annual revenue on IT, industrial data remains largely siloed and underutilized.
The greatest risk in data-sharing is resisting change and technology. But why is it so sharing data important?
The financial and operational case
First, there are the financial incentives. Modern businesses often function within ecosystems—communities of diverse participants who generate value through collaborative and competitive models. Recent academic research confirms these “synergetic effects”. With new collaborative arrangements come new technological solutions, revenue improvements, and cleaner, digitalized, more efficient production processes. Joint innovations in response to evolving market conditions becomes easier.
In numerical terms, sharing data with partners—such as suppliers and distributors—can increase revenue by 9% and reduce costs by 11% annually, as noted by Capgemini. McKinsey confirms other benefits: successfully implementing AI-enabled supply chain management delivers 15% lower logistics costs while improving service levels by up to 65%, compared with slower-moving competitors.
Secondly, there’s network resilience. Companies with mature industrial data ecosystems reduce risk exposure by enabling multiple redundant communication paths and real‑time monitoring across interconnected systems. Sharing operational data enables companies to anticipate and detect potential issues, thereby minimizing downtime, so if one part of the system is disrupted, alternate routes sustain critical operations.
While only 16% of producers have achieved real-time monitoring across their entire manufacturing process, those that do have significantly reduced risk exposure. This is Industry 4.0 at work.
Beyond financial and operational benefits, data ecosystems also contribute to sustainability initiatives. Without data-sharing partnerships, firms risk supply chain blind spots, especially when it comes to tracking emissions from suppliers.
The future’s smarter new workforce
The fourth—and perhaps most important—set of benefits comes by way of workforce improvements. The integration of automation, AI, and platform technologies offers connected users access to end-to-end insights from across the value chain, enabling better, quicker decisions that accelerate sustainable growth. This is particularly useful in critical industries, where even small delays can have significant consequences.
Research by the Tony Blair Institute for Global Change estimates that adopting AI effectively could save UK firms almost a quarter of private-sector workforce time or equivalent to the annual output of 6 million workers.
Unlocking these benefits is not without its challenges. Concerns about data security, privacy and interoperability can deter companies from engaging in data sharing. It is therefore vital to develop a clear value proposition, establishing trust among partners and implement secure technologies and common standards to overcome these barriers.
At the same time, companies must balance technology implementation with meaningful workforce engagement and training.
A data-driven roadmap to capture the ecosystem advantage
Stanford professor Erik Brynjolfsson calls it the Second Machine Age, a new era characterized by exponential growth in digital innovation, which is transforming industries and reshaping the way we live and work.
Every time a new technology comes along, you need to rethink how the economy is run. Data-driven technologies such as AI will be central to our future.
But reshoring without radical collaboration is just a relocation of old problems. Simply bringing back production won’t fix business inefficiencies or reduce operating costs, but AI-driven industrial ecosystems will.
Start by quantifying inefficiencies and benchmarking savings from supply chain integration. Reduce compliance costs through ecosystem-driven frameworks and unlock new revenue streams via secure data-sharing. Then, establish governance models and scalable collaboration protocols to ensure long-term success.
Start by quantifying your ecosystem’s value. Assess inefficiencies and compliance burdens and map new revenue opportunities. Next, build a governance structure that ensures trust, security, and mutually beneficial collaboration. Then, move quickly: by launching pilot programs and scaling collaboration efforts. First movers who build connected AI-powered ecosystems will shape the rules of the game.
For decades, industrial leaders have defined success as a zero-sum game: protecting data, defending silos, shutting out competitors. But that thinking is now a liability. Without AI-powered supply chain intelligence and radical data-sharing, reshoring will just replicate old inefficiencies on new soil.
The greatest new industrial titans won’t be the loudest, they’ll be the most connected.
