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Honeywell Researches Opinions About Industrial Use of AI

Honeywell commissioned Wakefield Research to survey AI leaders around the world. The online research, which was conducted from April 22 through May 2, 2024, involved 1,600 executives in 12 global markets (US, Brazil, Canada, China, France, Germany, India, Japan Mexico, United Kingdom, Kingdom of Saudi Arabia, and United Arab Emirates.) Each respondent works at a company with at least 1,000 employees that is currently using AI to automate processes and tasks. All respondents are influencers or decision makers related to the use of AI within their departments or across their organizations.

This research includes the construction industry.

Here are a few out-takes.

Surveys of people using AI, rather than those just thinking about it, seem to be pushing aside thoughts of AI as evil. People are learning to use the various forms of the technology—as humans always have.

According to AI leaders in the construction industry, the top benefits of implementing industrial AI include:

  • Improved job satisfaction – 51%
  • More time for skills development – 49%
  • Less manual work – 44%
  • More creative thinking – 44%

Forms of artificial intelligence including machine learning have powered software for many years. Here are some updated examples:

AI-enabled buildings can help reduce power consumption, which accounts for approximately 37% of global CO2 emissions, while supported by other solutions to reduce overall energy use.

Sometimes it is downright scary what C-Suite occupants think about technology. The survey included these nuggets.

C-Suite Insight: AI leaders agree with AI’s potential as 94% expect their organization to expand the use of AI beyond its initial implementation. Even though a little more than a third (37%) of respondents expressed concern that their C-Suite does not fully understand AI, they and almost all of their peers (94%) said their corporate leadership is all in.

This finding should not surprise any of us who have worked in an organization for longer than a week.

Uneven Readiness: Uncertainty shows up in discussions of capital costs. Nearly half (48%) of respondents report they are constantly having to justify or request sufficient resources to implement AI plans. At the same time, two-thirds (63%) say a quarter of more of their equipment isn’t properly enabled for AI compatibility, yet most (59%) plan to let non-AI compatible equipment run through its lifespan before replacement.

Of course we are still in the early stages. We will be until it is no longer a buzz word—just another tool we use.

Still Early Stages: Just 17% of those surveyed have launched their initial plans for AI, and many are still in the scaling (43%) or prototyping (12%) stages. Why? Potentially, because it’s expensive. Most AI leaders (74%) believe that their organizations will replace non-AI-compatible equipment, but only 41% of them will do so early to maximize the benefits from AI. The other 33% will wait out the lifespan of legacy equipment.

Everyone I talk with is concerned with workers lacking skills. Of course, even though many say that “people are our greatest asset”, pay rates do not seem to reflect that. I wonder what the people who work there actually think.

Upskilling and Reskilling Workers: With a growing skills gap and the retirement wave of the baby boom generation, employers increasingly rely on AI to bridge the gap. Nearly two-thirds (64%) of respondents to our research cited increasing worker efficiency and productivity as AI’s most promising use in their organizations.

At the same time, a quarter of those surveyed agree that people are their company’s greatest asset (25%). So, it makes sense that when looking at implementing AI, benefiting employees was top of mind. AI leaders say the technology will enhance flexibility (16%) as a key benefit for workers, along with improving efficiency and productivity (52%) and streamlining hiring and training (17%).

Generative AI for Project Estimation and Digital Engineering

My last post concerned using new technologies for specification development. Extending use cases of technology into other realms of administrative work, this release from Galorath announces their new product, SEERai, an advanced chat-based generative artificial intelligence (AI) built specifically to assist digital engineers and engineering professionals plan and estimate projects. (Something else I could have gladly used in my earlier career.)

Use cases for this GenerativeAI technology include information technology (IT), software, hardware, manufacturing, aerospace, military, space, and more.

SEER enables businesses to streamline cost and cost estimation and project planning with real-time insights into cost drivers, risk factors, and the potential impact of hundreds of project variables. With SEER, digital engineers are empowered with industry-leading methodologies in digital engineering and keen, unprecedented insight into actionable data. With the availability of SEERai, cost estimation professionals have easy, generative access to Galorath’s proprietary knowledge base assembled and validated over its forty-year legacy, allowing users to engage in chat-based strategic conversations with SEERai and gain action-oriented insights for their projects and initiatives.

SEERai is built upon the existing industry-leading capabilities of the SEER Cost Estimation Platform. 

SEER’s features include:

  • Predictive Analytics: Accurately forecast project costs, schedules, and potential risks.
  • Machine Learning Algorithms: Improves predictions over time as project data is shared and easily adapts to new information and changing conditions.
  • Data-Driven Decision Making: Provides insights into cost drivers, risk factors, and the impact of different variables on project outcomes to make informed decisions.
  • Risk Analysis and Mitigation: Evaluates the probability/impact of various risk factors on a project, identifying potential issues before they occur and developing mitigation strategies, and planning for program uncertainty.
  • Customization and Scalability: Adapts to projects of different sizes and complexities across various industries with a high degree of AI algorithm customization and scalability.
  • Integration with Existing Systems: Integrates seamlessly with other project management and engineering software tools to facilitate the exchange of data and enhance analysis.

DeepHow Introduces Smart AI Quizzing for Skilled Workforce Training

Some people feel that Generative AI on its own will be detrimental to human civilization—or indeed even existence. I think, as always, it is the human use of it just as every other technology for the past several thousand years that matters. For instance, look at the latest episode of the old “tech bro” culture from Silicon Valley—Sam Altman and OpenAI. One way or another he used Scarlett Johannson’s voice for the voice of the audio ChatBot.

Every week a couple of new uses for generative AI in manufacturing or industrial applications enter my inbox. This one touts leveraging advanced generative AI to provide personalized learning experiences and continuous skills verification, boosting employee retention and operational efficiency.

DeepHow, an award-winning AI-powered, video training platform for the skilled workforce, announced the release of AI Quizzing, which redefines the learning and development landscape by offering real-time assessments generated from training video content. Leveraging advanced generative AI technology, this feature ensures personalized learning experiences while continuously verifying employees’ skills. 

AI Quizzing takes training to the next level by empowering leaders to assess employees’ understanding and retention of training material. This allows them to accurately gauge readiness for skill advancement and increased responsibilities. AI Quizzing fosters a more competent workforce, equipped to tackle complex challenges with up-to-date knowledge and skills, which in turn reduces training costs, decreases onboarding time, and minimizes temporary employee attrition.

Additional benefits of AI Quizzing: 

  • Reinforcement of Learning: Users may utilize the quizzing feature to reinforce their understanding of the content covered in the training video.
  • Self-Assessment and Feedback: Quizzes offer users an opportunity for self-assessment, allowing them to gauge their comprehension and identify areas where they may need further review or clarification.
  • Active Participation and Engagement: Quizzing encourages active participation and engagement with the training material, as users are prompted to recall information, apply concepts, and solve problems.
  • Knowledge Check and Progress Tracking: Users may use the quizzing feature to assess their progress and evaluate their mastery of the training content over time.

Hexagon Partners with Microsoft Using Cloud Technology for Team Collaboration

Partnering with Microsoft continues to be an important part of manufacturing software development. This news is from Hexagon partnering with Microsoft to integrate engineering with Microsoft 365 to foster data collaboration among engineers. There’s a lot of marketing overkill in the release, but the essence is they hope to improve innovation through engineers and designers using improved collaboration tools.

  • Hexagon has contributed significantly to the open-source Fluid Framework data architecture that connects any manufacturing system and will integrate with Microsoft 365 creating agile, simplified workflows and productive collaboration using engineering and productivity software
  • Hexagon will roll-out applications that integrate the Microsoft Azure OpenAI Service to empower experienced employees to be more productive and assist less skilled users
  • These innovations form a significant foundation for new real-time co-engineering applications that combine Hexagon’s digital twin technologies with Microsoft Azure

Hexagon and Microsoft have partnered closely on the development and scaling of the open-source Fluid Framework and Azure Fluid Relay service to support the real-time sharing of data across a wide range of manufacturing industry processes and systems, allowing data created in one system to be immediately available to any other person or machine operating in another. Under the new partnership, the Microsoft 365 ecosystem will plug into this data layer, enabling customers to connect their day-to-day office documents and processes with manufacturing tools. This gives teams the freedom to innovate with the tools they already use; for example, tooling cost data from a Microsoft Excel worksheet could be easily shared with a CAM programmer, so simplifying work practices and decision-making between roles.

Microsoft Teams calls can become interactive working sessions, with CAD, simulations or metrology point clouds seamlessly visualised from the source data to allow on-the-spot collaboration and fast, iterative teamwork across disparate engineering and manufacturing functions. Hexagon has already demonstrated this capability in its 3D Whiteboard Nexus tool, which is also now available as an native app in Teams.

Hexagon is working with Microsoft to integrate generative AI models into its manufacturing software, helping users to make better use of their capabilities and analysing existing datasets to learn and suggest the best practices for achieving desired outputs. These AI experiences include contextual advisors, offering expert users productivity-boosting automation while also helping new users to upskill faster and achieve good results with less supervision – a valuable tool as the industry faces a growing skills shortage in many essential roles.

SymphonyAI announces IRIS Foundry, an AI-powered Industrial Data Ops Platform

Now that ChatGPT has been out for a while, people playing with it have discovered the shortcomings. Today at MIT, Sam Altman acknowledged the shortcomings of GenerativeAI. But that doesn’t stop companies from jumping on the GenAI bandwagon. Yes, they are using it. I’d just suggest doing a test drive or asking a lot of questions to discover just what it can do for you.

This is news from a company using Generative AI for predictive applications. Check it out.

SymphonyAI, a leader in predictive and generative enterprise AI SaaS, announced IRIS (Industrial Reasoning and Insights Service) Foundry, an industrial data operations platform for the rapid creation of robust digital industrial applications that improve process efficiency, reduce unscheduled asset downtime, and enhance connected worker capabilities. IRIS Foundry, powered by SymphonyAI’s award-winning predictive and generative EurekaAI platform, uses AI-enabled data contextualization at enterprise scale and is both open and composable.

IRIS Foundry provides the differentiating building blocks of industrial data management and governance needed to deploy AI-embedded manufacturing solutions at enterprise scale. IRIS Foundry has prebuilt connectors to extract data from IT, OT, and enterprise data sources into polyglot dataops storage to ensure versatile handling and integration of multiple data contexts. Data is organized into a structured asset hierarchy using AI-powered P&ID ingestion or through an existing asset historian framework. This process, enhanced with sophisticated contextualization services, automatically maps data into a unified namespace. The result is a dynamic industrial knowledge graph, simplifying access to and navigation of information. The IRIS Foundry knowledge graph is a foundational layer for enriched analysis and insights, empowering IRIS copilots for user-based interactions and guiding the exploration and understanding of complex data landscapes. Industrial applications built on IRIS Foundry adhere to data governance, audit, and security standards.

IRIS Foundry offers a low-code, drag-and-drop user experience, easy integration with programming tools, and an ability to deploy in various modes ranging from SaaS to customer-hosted models in a private cloud. Built on a lightweight architecture with cloud and edge computing in scope, the install footprint is synergistic with manufacturers’ operational technology (OT), information technology (IT), and external data ecosystems and contains hundreds of prebuilt connectors, reducing the effort to unify industrial data.

Guardrails—Guiding Human Decisions

A personal development speaker I often listen to delivers a set of talks on developing personal guardrails designed to prevent us from going off the deep end emotionally and relationally. Similarly as we explore this new age of artificial intelligence (AI) people are recognizing that we could use a set of guardrails to help guide our collective decisions using this new technology.

Collective guardrails generally include social norms, laws, and rules. Do we have any existing guardrails that will help us navigate AI? Where might they come from? What guardrails might work? Which might fall short?

Guardrails: Guiding Human Decisions in the Age of AI by Urs Gasser and Viktor Mayer-Schönberger came out recently. I promised to read and review it a couple of months ago. It got buried amongst other reading, plus it is not one of those “skim through” business books. This book has real meat. Based on the latest insights from the cognitive sciences, economics, and public policy, Guardrails offers a novel approach to shaping decisions by embracing human agency in its social context.

The authors with meticulous research lead us through technology approaches and social approaches through laws and regulations revealing the benefits but also the shortcomings of each.

From the press release: In this visionary book, Urs Gasser and Viktor Mayer-Schönberger show how the quick embrace of technological solutions can lead to results we don’t always want and explain how society itself can provide guardrails more suited to the digital age, ones that empower individual choice while accounting for the social good, encourage flexibility in the face of changing circumstances, and ultimately help us to make better decisions as we tackle the most daunting problems of our times, such as global injustice and climate change.

They conclude, “We hope that our readers—and everyone in governments, companies, and communities tasked with confronting some of humanity’s biggest challenges—will embrace this timely opportunity to think about and experiment with smarter guardrails to work toward better, fairer, and more sustainable futures.”

Urs Gasser is professor of public policy, governance, and innovative technology and dean of the School of Social Sciences and Technology at the Technical University of Munich. His books include (with John Palfrey) Born Digital: How Children Grow Up in a Digital Age. Viktor Mayer-Schönberger is professor of internet governance and regulation at the University of Oxford. His books include Delete: The Virtue of Forgetting in the Digital Age (Princeton).

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