PROGRESS THROUGH PARTNERSHIP
From insights to impact: Building a shared, digital path for higher productivity and efficiency
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Cross-industry collaboration and connected digital ecosystems are becoming competitive necessities.
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Industry’s next transformation, defined by software
When industrial teams connect trusted, actionable insights with human ingenuity, they can drive smarter decisions, greater efficiency, and sustainable growth. Data scientists, freed from reconciling spreadsheets, can focus on uncovering efficiency breakthroughs that cut costs and emissions. Engineers, able to make judgements using AI-infused analysis, can anticipate equipment failures, cut waste, and minimize downtime. And on the front lines, operators, equipped with real-time insights, can fine-tune processes, boosting productivity while reducing carbon output.
This is industrial intelligence in action—not just collecting data but turning it into powerful insights that amplify human expertise. Yet, for many companies, this future remains out of reach. Despite major investments in AI and digital transformation, initiatives often stall after promising pilot phases. And when predictive maintenance solutions prove successful, they aren’t implemented across the enterprise.
The barriers are rarely technical—they’re often organizational: Risk-averse cultures hesitate to scale unproven innovations, departments impose overly strict data controls, workers lack the training or authority to act on AI recommendations, AI itself isn’t always fully trained or trusted, and fragmented systems only add complexity, feeding tools incomplete information and eroding trust.
“Currently many organizations struggle to use AI effectively because they often don’t have a clear, aligned strategy on how AI fits their core business goals,” says Arti Garg, chief technologist at industrial software firm AVEVA. “They want to use AI but don’t always have the right processes or frameworks in place to start.”
But it doesn’t have to be this way. Forward-thinking industrial leaders are dismantling these barriers, rethinking how teams collaborate and how technology can elevate expertise in a data-driven era. “Leading companies are building centralized platforms that enable collaboration across all disciplines in the value chain,” explains Rob McGreevy, chief product officer, AVEVA. “They’re able to securely share this information so partners in an ecosystem can drive optimizations both internally and externally.” The businesses achieving this level of integration are finding that unified operations deliver what disjointed tools struggle to achieve: simultaneous progress on productivity, carbon reduction, and efficiency.
But it doesn’t have to be this way. Forward-thinking industrial leaders are dismantling these barriers, rethinking how teams collaborate and how technology can elevate expertise in a data-driven era. “Leading companies are building centralized platforms that enable collaboration across all disciplines in the value chain,” explains Rob McGreevy, chief product officer, AVEVA. “They’re able to securely share this information so partners in an ecosystem can drive optimizations both internally and externally.” The businesses achieving this level of integration are finding that unified operations deliver what disjointed tools struggle to achieve: simultaneous progress on productivity, carbon reduction, and efficiency.
Over the past decade, industrial leaders have poured billions of dollars into digital transformation—from digital twins (virtual replicas of physical assets) and engineering design to operational analytics and process optimization. While valuable individually, fragmented implementation of these systems has created information silos, preventing workers from seeing the complete picture needed for effective decision-making. This digital disunity represents a major drain on resources. Countless employee hours are spent reconciling conflicting data sets, pulling information from systems that don’t communicate, and translating among platforms that speak different languages. A more coherent, collaborative approach is crucial, one that brings operations, engineering, and business data together in a virtual space—founded on a digital twin and enhanced with industrial AI—that allows teams to see and enhance all workflows at once. “When you create a digital representation of a business process and all your assets, you’re able to drive interconnected optimizations across productivity, efficiency, planning, scheduling, and sustainability,” says McGreevy.
Bridging organizational divides
The challenge of driving efficiency and reducing emissions in a complex industrial organization illustrates this perfectly. With a splintered digital setup, compliance teams may track productivity and emissions through specialized software, while facilities managers monitor equipment efficiency and energy consumption on entirely different platforms. At the same time, production uses its own systems for scheduling and output metrics. These departments might sit in the same building, but their technology stacks function separately or in parallel, making it nearly impossible to coordinate meaningful carbon reduction strategies.
When organizations unify these disparate systems through connected platforms, coordination—real industrial intelligence—becomes possible. In Lexington, Ky., Schneider Electric worked with AVEVA to deploy advanced automation technologies which have digitalized operations, cut paperwork by more than 95% and reduced overall energy usage by 6%. The site has been recognized by the World Economic Forum as an Advanced Global Lighthouse for sustainable digital manufacturing, and these advantages have now been replicated across Schneider Electric’s network of smart factories worldwide.
Maximizing AI’s potential
Connected systems become even more powerful when AI enters the equation. Using integrated data from digital twins, AI algorithms can analyze thousands of variables simultaneously, running scenarios, predicting outcomes, and recommending optimizations that are not visible to fragmented programs. As Garg explains, “By pairing advanced AI capabilities with traditional analytics, we help reduce workers’ cognitive load, making their work much easier, much safer, and much more productive.”
However, many industrial AI initiatives disappoint because they lack adequate data preparation and miss critical context. “Once you’ve identified a problem to solve, the data that you need may not have all of the necessary information, or it may not be of sufficient volume and quality,” says Garg. This creates a trust problem—machine learning models working with incomplete information generate recommendations that operations teams view with skepticism. The difference when AI operates on a truly connected infrastructure is substantial. With near-complete operational visibility, algorithms have the right context to deliver accurate, actionable insights—the essence of industrial intelligence. At Maple Leaf Foods, one of Canada’s largest food processors, connecting production data from manufacturing systems to AI-powered analytics enabled employees to monitor and adjust processes with extreme precision. These changes dramatically reduced material waste, boosting gross profits by more than 10% within just three months of implementation while also advancing corporate sustainability goals, according to internal data. But tech and digital integration alone, however effective, won’t deliver the full progress industries need. As Garg notes, success “requires not just connecting systems but connecting people—breaking down the cultural barriers that keep departments from sharing information.”
Cloud-based collaboration
A combined digital approach transforms how people function together. When floor staff, engineers, executives, and data scientists operate within the same virtual space, they stop working at cross-purposes and start building on each other’s expertise to solve problems that once seemed intractable. But connected systems do more than enable real-time collaboration—they preserve knowledge for the future. Digital twins are becoming repositories of institutional memory, capturing decades of operational know-how from experienced workers before they retire. The problem-solving approaches of veteran engineers, the intuitive adjustments operators have perfected over years—all this expertise gets preserved and passed on to new teams entering the workforce, as well as being hardwired into AI processes throughout the organization. In this way, AI really can be an “angel on your shoulder” for team members, delivering insights and supporting intuitive decision-making that improves the entire operational cycle. The benefits multiply further when these connections extend beyond company walls to create entire digital ecosystems. Cloud-based platforms enable secure, selective data sharing across entire value chains—suppliers providing real-time inventory updates to manufacturers, customers contributing demand signals that optimize production, and partners collaborating on efficiency improvements. Indeed, even competitors can find value in participating in these networks, sharing nonproprietary operational insights on things such as water consumption or energy optimization that apply across sectors. Noting the importance of staying curious and open-minded in the pursuit of industrial success, McGreevy emphasizes that “A willingness to collaborate and learn from others, both within and outside one’s industry, is vital.” Shared systems provide the foundation for this knowledge exchange, enabling organizations to draw on and unite the best ideas and most innovative solutions, regardless of where they originate. When present at scale, this deeper form of collaboration—sometimes called radical collaboration—can materially change how industrial companies operate.
The pressures confronting today’s global industrial players are deeply entwined. Productivity gaps affect competitiveness, emissions targets drive operational changes, and workforce transitions threaten to erode institutional knowledge.
Building a digital ecosystem approach
“When you create a digital representation of a business process and all your assets, you’re able to drive interconnected optimizations across productivity, efficiency, planning, scheduling, and sustainability.”
—Rob McGreevy, Chief Product Officer, AVEVA
Faced with these interconnected challenges, businesses can’t afford to work in silos—whether it be data silos that hide critical patterns, cultural silos that prevent departments from collaborating, or industry silos that force companies to repeatedly solve problems others have already cracked. “Industrial leaders play a critical role in building shared data ecosystems by championing collaboration, setting standards, and fostering trust across stakeholders,” says Garg. “They should actively promote open data sharing frameworks that break down silos [among] companies, suppliers, and even competitors to unlock greater insights and drive efficiency at scale.”
Underpinning these connected insights is the concept of industrial intelligence, the combination of integrated data, enriched with industrial AI, and the power of human insight. When applied at scale, this industrial intelligence can empower teams to improve decision-making and execution in ways that spark innovation at scale. When this happens, the door opens to sweeping, simultaneous progress for organizations seeking greater productivity and profit, for employees finding new ways to collaborate and innovate, and ultimately, for the planet as industries work toward a greener, more sustainable future.
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Reshaping industrial operations with AI and cross-industry collaboration
Progress through partnership
AVEVA Industry’s next transformation, defined by software
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How Schneider Electric uses data to drive industrial innovation
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How Schneider Electric uses data to drive industrial innovation
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