The Challenge
There is no question that in the face of global competition, workforce shortages, and constantly shifting demand, industrial companies are under more pressure than ever to deliver more efficient, optimized operations across their enterprises. For years, digital transformation has been the go-to strategy to accomplish those goals. However, in recent years, that digital transformation strategy has evolved in parallel with the rise of artificial intelligence (AI).
Why This Matters Now
Industrial AI adoption has moved beyond pilots. Today’s manufacturers are actively deploying AI to drive competitive advantage – but only the smart ones are doing it without tearing apart trusted systems. They’re embedding AI directly into the automation platforms they already depend on. That’s the difference between expensive, risky transformation and sustainable, nondisruptive modernization.
Leveraging more than 30 years’ experience in essential industries, Claudio Fayad, CTO of Emerson’s AspenTech business, recently outlined three nondisruptive strategies for unleashing industrial AI in InformationWeek. His insights reflect Emerson’s leadership in automation technologies and data management and address the exact modernization barriers facing essential industries today.
Three Nondisruptive Strategies for Industrial AI Success
- Evolve Your Automation Stack – Layer AI and software-defined solutions onto legacy infrastructure without mission-critical disruption; preserve prior investments while unlocking new optimization.
- Break IT/OT Silos – Modern platforms create seamless data highways between operational and information technology teams, enabling true collaboration on AI deployment.
- Build Your Unified Data Fabric Today – Invest now in contextualized, enterprise-wide data infrastructure; it’s the foundation for adaptive, self-learning AI applications that improve as data volume grows.
How Can Companies Modernize Without a Costly Rip-and-Replace?
One of the primary challenges of digital transformation was the deep bench of legacy equipment and patchwork of interconnected systems that siloed data. Ripping and replacing such extensive infrastructure is overwhelming at best, and impossible in the most challenging cases. Since a constant stream of quality, contextualized data is critical to AI, many organizations see these complexities as nonstarters.
However, there’s a better path forward: nondisruptive modernization. “Instead [of rip-and-replace], companies should pursue nondisruptive modernization; advancing their automation architectures by layering AI and software-defined solutions on top of their existing installed base. The goal is to create a flexible and secure platform, one that connects legacy assets with modern technologies to deliver continuous, enterprise-wide visibility and optimization.”
This is exactly the approach Emerson is pursuing as part of its enterprise operations platform vision for next-generation automation. AI tools for optimizing processes and delivering key analytics will be seamlessly integrated into the powerful automation tools users already know and understand, streamlining adoption and allowing organizations to preserve their automation investments while unlocking new optimization capabilities.
Why Is Bridging IT/OT Collaboration Critical to AI Adoption?
Islands of automation are not the only silos hindering the smooth adoption of AI. In many cases, operational technology (OT) and information technology (IT) also operate within their own isolated worlds. Differing goals and technology strategies can often leave the two groups at odds – a significant obstacle to effective industrial AI adoption.
“In industrial AI adoption, effective partnership between IT and OT is essential. While IT brings cloud infrastructure, cybersecurity and enterprise-scale thinking, OT holds the domain expertise crucial for plant reliability, safety and throughput.”
Yet IT and OT can and should work closely together. Modern technologies like the DeltaV™ Edge Environment help build a seamless data highway between IT and OT without introducing security and uptime concerns. They provide the perfect bridge for IT and OT to work together to supply and consume data both across the plant and across the enterprise – creating the foundation for collaborative, enterprise-wide AI deployment.
How Does a Unified Data Fabric Enable Industrial AI?
As CTO of AspenTech, I know firsthand that no concept is more central to industrial AI success than seamless data mobility. This is exactly what Emerson’s Boundless Automation framework delivers.
Moving data quickly and easily from the intelligent field, through the edge, and into local systems and the cloud is a key enabler of AI implementation. That vision is built on Emerson’s cohesive data fabric across all the software in its automation platform.
“A robust industrial data fabric unifies and contextualizes data from all sources – legacy and modern, IT and OT. It enables organizations to not only move and aggregate data, but also to build models and analytics around evolving use cases. The right data fabric allows for the creation of AI applications that are adaptive and insightful, and improving as more data becomes available.”
The industrial data fabric empowers teams to move away from complex custom engineering to connect each OT and IT system. It’s the first step toward an effectively plug-and-play ecosystem for industrial software, helping teams not only preserve the information generated by their automation solutions, but also the essential context surrounding that data.
The Industrial AI Future Is Here
Effective deployment of AI workloads in industrial settings is no longer theoretical. Today’s organizations have moved from the pilot phase and are actively implementing AI technologies to help drive competitive advantage in their operations. The ones doing so effectively are not accomplishing their goals by ripping and replacing their systems or with bolt-on external solutions, but rather using the modern tools built seamlessly into the software they already know and trust.
