Companies are sprinkling their press releases and Websites with Artificial Intelligence (AI) like sugar on your cornflakes. Now we even have Artificial Intelligence of Internet of Things—AioT. One of my favorite series of questions these days runs something like: what do you mean by AI; how is it used; what do operators see; what does it do—really?
One outgrowth of a series of meetings from the recent ARC Advisory Group Industry Forum was an interview with serial entrepreneur Mike Brooks. Most recently he was President and COO of Mtelligence (Mtell) when it was acquired by AspenTech a bit over four years ago.
He clued me into a number of Aspen products based on the Mtell technology. More on those below. First, some insights from someone who has witnessed a lot in the industry.
Talking about machine learning, Brooks told me that it’s not just AI on its own that gives value. Look for a combination of AI plus domain knowledge. This gives you causation, not just correlation. It is also important to build AI from first principles (I’m betting many miss that one). Mostly, AI is a tool for providing event analytics for front line workers.
I’ll combine Brooks and other sources to describe the more practical AspenTech solutions:
From a blog post by Adi Pendyala, Sr. Director, Market Strategy—Aspen AIoT Hub: The Cloud-Ready Infrastructure for Industrial AI
Artificial intelligence (AI) and the Industrial Internet of Things (IIoT) are two of the most prominent technological forces driving digital transformation for capital-intensive industries today. Collectively, they’re like the body and brain of industrial digital transformation: IIoT is the body, creating and transmitting data from a variety sources that is sometimes acted upon, while AI is the brain, turning data into intelligence for smarter decisions and enabling the digital future of industrial organizations.
The confluence of these technological forces gives rise to a new digital solution category – the Artificial Intelligence of Things (AIoT) – that centers on unlocking the hidden business value in industrial data.
Impact of IT-OT Convergence: Sharp market volatility means that capital-intensive industries have to be more agile than ever before to survive and thrive in every cycle – an area that has thwarted the OT-side of industrial organizations in the past. Enterprises are looking to exploit the rapid convergence of IT and OT to significantly reduce the technological implementation risk and the time-to-market risk for introducing AI-rich, real-time applications to complex industrial operations. The rise of the digital executive, i.e. the CTO/CDO/CIO, in driving the digital transformation strategy of industrial organizations is a key influencer of this trend.
Unlock Industrial Data Value: There is a critical (and growing) need for access to industrial analytics and actionable insights in making business decisions – across all levels of the enterprise. Efforts to mine pools (silos) of data across the enterprise are often stalled by the challenges of data collection and integration, with promised business insights and agility never materializing. Organizations are switching their focus from mass data accumulation to strategic industrial data management, specifically homing in on data integration, data mobility and data accessibility across the organization – with the goal of using AI-enabled technologies to unlock the hidden value in these previously unoptimized and undiscovered sets of industrial data.
Lowering the Digitalization Barrier: Industrial organizations are increasing investment in lowering the barriers to AI adoption by deploying fit-for-purpose Industrial AI applications that combine data science and AI with software and domain expertise. This will be the key to overcome a lack of in-house skills and drastically reduce the need for an army of data scientists. To scale this effort, many enterprises are adopting new measures to reduce complexity in interoperability, overcome information silos and harmonize towards a cloud-ready infrastructure that bridges legacy systems with next-generation solutions.
The Aspen AIoT Hub – Cloud-Ready Industrial AI Infrastructure
The AIoT Hub provides the integrated data management, edge and cloud infrastructure and production-grade AI environment to build, deploy and host Industrial AI applications at enterprise speed and scale. It also serves as the foundational infrastructure to realize the transformative vision for the Self-Optimizing Plant. In fact, as part of our recent aspenONE V12 release, the AIoT Hub provides the underlying cloud-ready, enterprise-scale infrastructure that powers V12 Industrial AI applications such as Aspen AI Model Builder and Aspen Event Analytics.
Key Capabilities of the Aspen AIoT Hub
Data Integration & Mobility
On average, between 60% and 73% of all data within an enterprise goes unused. This challenge is further exacerbated by the lack of a scalable data infrastructure to power Industrial AI models from training to productization. Through the AIoT Hub, organizations will be able to access and leverage fully integrated data, from sensors to the edge and cloud, across the enterprise.
Scaling AI requires providing the tools, infrastructure and workflows for powering Industrial AI across the solution lifecycle. It also requires the software, hardware and enterprise architecture needed to productize AI in industrial environments, including broader collaboration between development, data science and infrastructure capabilities such as CloudOps, DevOps, MLOps and others. This dimension is critical to helping organizations mature beyond sporadic AI proof-of-concepts to an enterprise-wide Industrial AI strategy.
Industrial organizations are seeking to aggregate data from different sources across the enterprise, transforming it into analytics and visualizations to guide better decisions at every business level. The goal is to translate real-time data into faster, smarter, profitable business decisions to visualize deviations, sequences and trends automatically and identify risks and opportunities early. The AIoT Hub enables enterprise users’ access to real-time data and analytics to do all of this – improving collaboration, project efficiency and operations by tapping into the power of accelerated insights and enhanced visualizations.
Industrial AI Applications Ecosystem
Enterprises are looking for purpose-built, fully integrated AI environments for their data scientists to accelerate the transformation from raw data to productized AI/ML algorithms. The AIoT Hub provides an embedded workbench for feature engineering, training and rapidly productizing machine learning (ML) models, as well as supports versioning and collaboration. It empowers data scientists, at customers and partners, to collaborate and build their own data-rich AI apps.