AiThority Interview With Cyril Perducat, EVP IoT & Digital Offers at Schneider Electric

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How do you interact with smart technologies like IoT, AI, and Cloud-based analytics platforms?

We approach smart technologies as the enabler, the tool to solve specific customer problems (vs. leading with the technology / trying to find a problem for the technology to solve). So, we always start with the customer problem/business need. With this customer-centric approach, you need to combine technology with a certain level of domain expertise, productized services, and applications combined in order to solve some specific customer problems. That’s the approach we’ve taken at Schneider Electric as we’ve leveraged IoT, AI, and analytics to create predictive maintenance applications and connected assets that enable real-time monitoring, etc.

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What galvanized you to start at Schneider Electric?

What attracted to me to Schneider was the diversity of the portfolio is the focus on sustainability and a truly global company. Initially, more than 20 years ago, Schneider approached me with the opportunity to move to China to build a new business to grow its presence in that emerging market. It was an exciting endeavor with many unknowns and opportunities watching the city build its infrastructure, business practices, and societal norms while building a business from scratch, adapting to the culture and language. Moreover, Schneider’s approach to empowering people to innovate that is, encouraging a “dare to disrupt” mindset and empowering problem-solvers. I’ve had the privilege to work with many teams and evolve in an environment that embodies more of the mindset of a start-up, versus a huge, multi-national company.

Which technologies and tools you currently leverage in your role? What’s the composition of your team at Schneider Electric?

My team develops IoT solutions and digital service offers using Schneider’s IoT-enabled architecture EcoStruxure, a three-tiered technology stack that delivers customer-centric solutions across 4 end-markets (industry, data center, building infrastructure) and 6 domains of expertise (power, building, data center, plant, machine, grid). The technology stack includes:

1. Connected assets
2. Edge control
3. Apps, Analytics & Services

We leverage Pervasive sensing, Embedded Computing, Edge and Cloud Solutions, AI and analytics and cybersecurity. In 2016, we needed to adapt our digital offer innovation model, bringing cross-domain technology capabilities together and centered around the customer. This is when my team launched the Digital Services Factory, which enables digital business development and expertise/capabilities to our lines of business. We partner with the business R&D teams to speed up the development of digital offers. As a result, we’ve cut time from ideation to commercialization from 2-3 years to less than a year.

• Our goal is to apply these same practices internally in Schneider, bringing the approach of a startup: Lean startup and design thinking, coupled with a certain sense of urgency and accountability to bring a viable product to the market.

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What is the product roadmap for IoT at Schneider Electric? How does it leverage Data Science in its operations?

We continue to evolve our Ecostruxure platform features based on real life offers developments, making our products, equipment, and systems smart and connected. We are bringing to the market a new category of offers that we call “Advisors”, which combine Software and digital services to solve real business problems, transforming raw data from connected systems in actionable insights for customers. Thanks to those offers, right now, we have more than 2 million assets under management (products, equipment, and systems) – and growing. We leverage data within the context of Schneider’s long-time domain and segment expertise to build customer-relevant digital offers: EcoStruxure Asset Advisor: Asset performance management services to enhance security and reduce downtime for greater peace of mind (e.g., healthcare applications) EcoStruxure Machine Advisor: Track, Monitor, Fix (SOMIC GROUP case study)

What is the state of IoT and Big Data technology in 2019 for the market that Schneider Electric caters? How much has it evolved since the time you first started here?

The biggest evolution I’ve witnessed within the last few years is with Artificial Intelligence. AI has moved from being a “bright and shiny new technology” to having concrete value for our business customers. In fact, it is essential for our customers to make use of all the data captured since IoT and industrial IoT has become so widespread but avoiding the data tsunami requires focus and a pragmatic approach to leverage AI. AI applications can help customers understand what it really means to push forward new digital business models in a disruptive yet profitable way allowing non-traditional ways to turn all that data into actual business insights. Integrating an AI strategy can seem like a daunting task, but I recommend that any company embarking on this journey start with a pragmatic, practical approach to individual projects, and of course work with the right partners across the IoT ecosystem

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Tell us more about Schneider Electric Exchange. How would it benefit your customers and technology partners?

We built Schneider Electric Exchange as a diverse, global ecosystem of communities that bring together experts and innovators from across the industry, software, and startups, empowering them to solve very specific business challenges. Having experienced the “before and after” of what it takes to build out an idea from the ground up, I can say that Schneider Electric Exchange eliminates much of the old-school groundwork. It is a collaborative workspace, technology resource, and marketplace all at the same time. It is a digital business platform that brings together a community to tackle specific business challenges in a much more frictionless way than we could have imagined just a decade ago. Schneider Electric Exchange rewrites digital innovation’s playbook by re-centering technology development, software iteration, and R&D on customer problems instead of simply building better technology. It gives tech companies access to technical resources and tools to build and deploy digital solutions easily, while also managing data. For tech companies, the ability to scale their offers (broadening both geographic and market reach) is their “make or break” moment.

How do you see the trend of including AI and Machine Learning in a modern CIO/ CMO’s stack budget?

For Schneider’s CIO organization, AI already is at play: RPA For Schneider’s own digital transformation, we’re creating chatbots for various services (e.g., IT services, work tools) Using chatbots between geographies and across time zones For customer-facing applications, using chatbots to free up Customer Care Center representatives to be more available as customer consultants For Schneider’s CMO organization, AI as integrated with CRM Salesforce / Einstein Lead-gen (Digital Opportunity Factory) and better customer services via connecting product to customer care of that product across its lifecycle

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What are the biggest challenges and opportunities for businesses leveraging IoT technology for Digital Transformation?

Businesses need to look beyond just building a digital offer. There are other essential elements for a successful transformation as a business strategy. I like to say that when you embark on your digital product R&D and innovation strategy, balancing three layers at once is essential:

• Creating connected, intelligent products Creating ways to deliver the product and capture its business value
• Creating an amazing digital customer experience

The digital experience is the glue to parts 1 and 2 and, by extension, your customer-driven business at large. There is no point in delivering the product if the user experience is poor.

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How potent is the Human-Machine Intelligence for businesses and society? Who owns Machine Learning results?

As always, customers own their data, and its protected by regulations, like GDPR, etc. and Schneider’s T&Cs very clearly describe how we use data – we extract data to return actionable insights re business value — and this will always be the case when it comes to data sharing and leveraging data to build AI models. When it comes to Human-Machine intelligence, it’s important to keep in mind that automated processes often are replacing a task or two — not an entire full-time employee. There is value in eliminating repetitive tasks so an employee can focus on more strategic responsibilities (e.g., analyzing what the data say vs. data input). We believe that AI can augment and empower people, so they could create more added value in their roles, but also be safe and efficient. We do focus on AI ethics: it’s very important for a domain expert to work in conjunction with the AI specialists to understand what the data are revealing in context and how to act on those data insights.

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Where do you see AI/Machine Learning and other smart technologies heading beyond 2020?

We are coming back around to what’s happening at the IoT edge. The cloud was and remains huge. The cloud delivers highly scalable computing power, low-cost data storage, and easier access to that data. With Machine Learning and edge analytics capabilities, does it make sense to push everything to the cloud, especially in critical environments? The answer is probably no. So, I see the rise of IoT edge integrations connected to back-office ERP systems to enhance the customer experience (e.g., shipping apart if an asset flags a potential maintenance issue).

What is the Good, Bad and Ugly about AI that you have heard or predict?

In Dec 2017, Forrester said that “The honeymoon for enterprises naively celebrating the cure-all promises of artificial intelligence (AI) technologies is over: AI and all other new technologies like big data and cloud computing still require hard work.” Today, with Schneider Electric Exchange, we’re cutting down that hard work. Having strong domain expertise is critical to making AI projects successful. Businesses can leverage the capabilities of the data engineers in the ecosystem, as well as technology partners that can provide existing applications (SaaS) that can be enriched by specializing for the exact requirements of the end user. The benefits of this approach reach all ecosystem stakeholders: insights that streamline the industrial enterprise’s maintenance budgeting and scheduling (protecting uptime), on the one hand, and better data for the data engineers to improve their own models, on the other hand. Schneider Electric Exchange brings together these stakeholders in a collaborative, open ecosystem.

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The Crystal Gaze

What Cloud Analytics and SaaS start-ups and labs are you keenly following?

We have a new partner in our Schneider Electric Exchange ecosystem, InUse, a tech company that provides a connected maintenance SaaS application that makes machines talk to improve the production performance of factories. By embedding industrial expertise within the machines, data is transformed into concrete recommendations for operators in factories. It enables the machines to request the maintenance operations they need and to guide the operators towards the best action to be taken according to the production context.  As one of the Top 5 winners in the Schneider Open Innovation Challenge, InUse innovated a way to optimize the “rinse overrun” process among Food & Bev customers. Through Machine Learning analytics and applications an average of 20% water savings can be found in the Cleaning In Place process. Customer successes include Hellenic Diaries, ALFI Technologies, Sidel, and Shem (Engie). 

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As a tech leader, what industries you think would be the fastest to adopt Analytics and AI/ML with smooth efficiency? What are the new emerging markets for these technology markets?

We’re seeing industrial enterprises adopt AI to take advantage of data captured from thousands of sensors (standalone or embedded) About 90% of data isn’t used for business insights today. We need to extract this trapped value. AI can capture this value to enabling the industry to balance energy and process efficiency at the same time — in the face of constraints (e.g., regulations, market changes). For cybersecurity, there is a balance to find. Yes, new connected solutions might create a new risk that needs to be mitigated. But I always say, too, that being connected is more cyber secure than not — as connectivity allows you to know what’s happening across the environment and, if necessary, to act quickly to threat or abnormal data flows

What’s your smartest work-related shortcut or productivity hack?

As any innovator would say, “Fail fast and repeat.” Taking the long-time software approach of developing a MVP is the way to go with most digital innovation to keep pace, iterating with the customer. But embedding cybersecurity at the beginning of development is essential, addressing “cybersecurity by design” with every iteration/update.

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Thank you, Cyril ! That was fun and hope to see you back on AiThority soon.