Embracing AI in the Post-COVID World

As the world looks to towards the future post the coronavirus pandemic, there is an undeniable fact.  The way people live and work has changed.  What does this mean for Industrial companies?

Cisco Investments and McRock Capital teamed up to create a framework to illustrate how AI can optimize assets, improve processes, and enhance human capabilities through a mix of existing and emerging applications. They take a look at how Artificial Intelligence (AI), a technology which was in the nascent stages of adoption in industrial verticals, will lay the foundation for significant investments in digitization and automation in the post-COVID world.

Praemo is honored to be included in this framework through examples of the class of problems we are solving, as well as our unique approach through our industrial expertise.

“Of Industry, For Industry”


Companies with combined expertise in data science and machine learning as well as industrial domain can help customers solve critical business problems and build a good rapport through customer support. Intelligent customers are especially adept at catching lack of information, misdirection, or ineffective communication. An organization’s in-depth knowledge of every aspect it handles will not only separate it from its competition, it will also impress upon the customer that the said organization is best suited to tackle the problem at hand.

The Promise of Next-Level Automation

Digital transformation provides the ability for manufacturers to dive deeper into their operations and expose hidden factories. Even with new buzzwords constantly being talked about, these emerging technologies are driving tangible results within industry when implemented correctly.

In the April issue of Manufacturing Engineering, Bob Puhr highlights the key phrase above “when implemented correctly” as he discusses the challenges and potential pitfalls.  Our own CTO, CRO, and Director of Solution Architecture join Bob and other industry experts to provide insights.

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Deploying AI within Industrial Operations


Deploying AI within Industrial Operations should be fast, lean and valuable. For most, it’s not. The key may be the need to think differently when deploying it. 

In future posts I’ll go deeper into each aspect, but here are a few thoughts to get started… 


There are insights buried everywhere in the data from Industrial Operations.  Unlocking those insights and using them to mentor teams to drive sustainable improvement is at the heart of Industry X.0.   

For decades we’ve been breaking supply networks into small, digestible siloes of disparate data.  It seems completely logical that we’d need to reconstitute those networks and harmonize them in order to apply advanced tools like AI that pour thru the data from end to end and top to bottom. We’ve never been closer to unlocking what my friend Marco Annunziata theorized was ten to fifteen trillion dollars in benefit from exploiting the IIoT.  But that prediction was from over 7 years ago, and in some cases we seem as far from that goal as ever.  

“Innovation has always been the single most powerful ingredient to help us create more with less…”  Link 


Maybe we need just one more universal standard for data connectivity?  Or a common asset hierarchy, or a digital twin of a process or machine all visualized in a new type of dashboard? 

“It’s like déjà vu all over again.” Yogi Berra 

Think of it this way. What if, in order to stem the advance of a new virus, we took all the travelers coming thru the world’s largest airports, and applied the same approach used to deploy AI within industrial operations.  First, we’d establish a connection to each human/device, determining their language and dialect. Then employ a team of translators to interrogate them.  How do you feel overall today? Warm? More or less tired than usual? You’re out of breath, is that typical?  I’ve now got a large body of data, but sometimes feeling “warm” is an emotional state as opposed to physiological, so we’d have to clean up the data before proceeding. 

Of course, this approach sounds absurd. Why not simply check body temperature and heart rate – baseline data – to profile those in distress.  The approach I described above is precisely what we do when we accumulate signals from an endless variety of what look like unique devices, machines and systems in our facilities.  


Discrete, batch or continuous – manufacturing and industrial operations have patterns and rhythms within the data at the lowest level.  Those patterns tell us everything from cycle times and wear patterns to product families, but most importantly what healthy and unhealthy behavior looks like.  When properly applied, analytics and AI will detect the emergence of behavior that if left unchecked will manifest itself as downtime, quality failures or loss of performance.   


  • The ability to start with the data you already have, as-is, where-it-is, reduces risk and cost, and accelerates time to value. 
  • Insights from your existing data should guide the next best action – for everyone from an operator, to an engineer deploying new sensors or tech, to the COO. 
  • Tools like AI should allow you to see your operations in a new light. 

You already have the data. Use it. Today. 


If your industrial AI efforts aren’t scaling, don’t produce valuable insights within thirty to ninety days – with a lean, low risk deployment – you might want to see how Praemo’s technology, Razor™ can help. 

For more on transforming your industrial operations and driving new levels of performance, follow me on LinkedIn. 

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Author - Paul Boris, Executive Vice President and CRO

Paul has driven digital transformation within industrial operations for decades. Starting his career at GM, he’s held P&L responsibility for facilities in all manufacturing modes. In the mid-90’s, Paul led the trend of deployment of technology within manufacturing. As the GVP of Enterprise Operations Management at SAP and CIO of Advanced Manufacturing Solutions at GE, he worked closely with senior executives and operational teams to deliver hundreds of millions of dollars in sustainable performance improvements.

Boris is considered a thought leader on topics of the Industrial Internet of Things (IIoT) and Digital Transformation, with extensive industrial experience.

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