Business transformation towards sustainability: Embracing AI at scale

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At a time of complex, competing business priorities, most progressive and responsible organizations agree on two things. Firstly, to ensure our collective future, we all have a responsibility to act in the fight against climate change. Secondly, we have no choice – we must use digital technologies and data to innovate.

The real difference for businesses, however, lies in bringing these two imperatives together.

By harnessing data-led technologies, companies are empowered to advance measurable decarbonization and efficiency ambitions while driving business value and innovation. As the World Economic Forum highlights: “Businesses that integrate digital and sustainable transformation into their operations and value chains are 2.5 more likely to be successful in the future.”

The rise of an AI-based company

This ambition is coalescing around the promise of Artificial Intelligence (AI). While still relatively new in the enterprise toolbox, AI has moved beyond a theoretical discipline. It is already delivering transformative practical solutions.

It’s worth demystifying AI.  Paradoxically, AI can’t do as much as some people think, like completely replacing humans. At the same time, it can do much more than other people believe. It can, as an example, offer excellent predictions and optimization opportunities.

The power of AI lies in its ability to help organizations make more intelligent decisions that rely on hard facts and enormous amounts of data. Leveraging data provides unparalleled insights in the areas that are part of the solution to energy dilemma – operational efficiency, electrification, and digitization.

At Schneider, we believe that AI has the potential to transform entire industries, to make a sustainable future a reality. That’s why, as mentioned in my previous blog, AI is our next milestone, and we continue our AI at scale journey with three key priorities.

1. Spot high value use cases

We currently have more than 6M assets under management on EcoStruxure, our interoperable IIoT platform. This translates into a tremendous amount of data generated and captured every day. We’ve helped thousands of customers to do more with less. Now we strive to put that data to work for even better insights, speed, agility, and decision making.

Let’s spotlight buildings, responsible for 44% of global emissions. As the International Energy Agency says, “Digitalization can improve energy efficiency through technologies that gather and analyze data to effect real-world changes to energy use.”

Now with AI solutions it is possible to dynamically control on-site energy resources, automatically forecast and optimize how and when to consume, produce, and share energy. Real-time insights make it easy to understand the savings, earnings, and CO2 emissions data and make further informed decisions. We can also use AI at scale to examine complex market trends and dissect data to manage energy spend better and reduce risk in a volatile market, as our CDO, Peter Weckesser, described in his blog.

AI at scale is the path to sustainable business

In practice? IntenCity is an energy neutral, the most efficient Schneider Electric’s flagship building located in Grenoble. The site aims for a consumption of 37 kWh/m2/year. It is 8-9 times less than average consumption of existing buildings in Europe. A smart grid-ready building, IntenCity is already a part of the energy landscape of tomorrow. It is equipped with predictive control algorithms enabling the microgrid’s optimization strategy across dynamic use cases such as tariff management, demand charge reduction, grid ancillary services or optimized self-consumption. Microgrid Advisor allow it to interface with the other buildings in the neighborhood as part of a local network, with the possibility of opting out in the event of a high demand in electricity or a high tariff, to store the renewable energy produced, and to defer consumption in favor of neighboring buildings.

Projects like this demonstrate that AI is not the future; it is the strategic lever of today, delivering tangible business returns while also accelerating the journey to net zero and contributing to collective societal goals. As such, investing in digitization and embracing transformative AI is not a nice to have. Indeed, it is a “must-have” strategy with strong business value.

Business transformation towards sustainability: Embracing AI at scale

2. Tap into the potential of employees to capture business value

While many organizations are convinced of its power, they still struggle to harness AI within their operations. The challenge is to go beyond the pilot projects and infuse AI into the whole organization – to run AI at scale.

Successful AI requires more than data science. It must be business oriented. Usually, companies with their own manufacturing legacy capture the industry challenges and business trends well. But if they don’t democratize the AI understanding, AI projects risk to remain siloed unscalable successes with no growth potential for the future.

When employees are familiarized with digital technologies, they are better equipped to spot relevant business use cases where AI brings tangible value for customers. Additionally, domain expertise is essential for selecting features, models, training and perfecting AI models, and ensuring the ethical unbiased algorithms’ creation.

Within organizations themselves, this means supporting talent to explore how AI can enhance their long-term strategies while also using it to unburden them from day-to-day repetitive and low-value activities, for the benefit of having more time for analysis, continuous improvement and real-time decision making. The good news is that everyone wants to learn AI now!

3. Find the right partners

Co-innovation and partnerships are key to accelerate the AI-journey for everyone. The reality is that AI expertise often extends beyond a company’s core competencies. Tech start-ups often lack data and domain knowledge. On the contrary, long legacy firms sit on amounts of so-called dark data (the one you know you have but you don’t use it) and expertise in their fields. In both cases, bringing in AI experts unlocks the business-relevant value of using open-source data for start-ups or limiting the dark data for experienced players, ultimately making the most of data analytics that can yield business outcomes, and enable putting in place agile strategies.

Partnerships of the Future

Nobody innovates alone. In order to gain the momentum needed to seize the AI opportunity, an ecosystem approach is required. For example, in the Schneider Electric Exchange, an open ecosystem for IoT energy management and automation solutions, efficiency-focused analytics and software are becoming a key area of partnerships.

Such partnerships enable organizations to come together. Their aim? To reset and revitalize connected infrastructure, sustainability commitments, renewable energy usage, and digital innovation for a better, cleaner, and greener tomorrow.

Commercially valid sustainable goals

Many companies are going through an AI transformation, or will soon join this path. What makes us excited about artificial intelligence at Schneider, is that we are challenging AI with the biggest problem of our generation – climate change and energy dilemma.

“AI for the planet” and “AI for good” use cases can be executed alongside ambitious business objectives. World Economic Forum analysis shows that “84% of IoT deployments are currently addressing, or have the potential to address, the Sustainable Development Goals (SDGs) as defined by the United Nations,” without compromising the commercial drivers of AI and IoT integration.

I’m personally keen on Microgrid Advisor use cases, that show perfect alliance between business targets and planet protection. Using AI at scale, we help our customers to avoid consuming energy during pricing peaks. At the same time, high cost electricity usually means generating energy from carbon-based sources (gas or coal). When aiming at off-peak hours with your electricity usage, you also increase your usage of green electricity!

AI is not a panacea, but it opens up far-reaching possibilities for companies willing to embrace collaboration. The clock is ticking in the race to save our planet, and we are all in this together. Let’s use everything at our disposal to achieve it: technology, AI, human skills, and trusted partnerships.

Join us in our AI at scale journey – we are hiring!

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Discover our key priorities on the journey to AI at scale with a clear commitment to sustainability and efficiency.

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