Proactive AI solutions
How is AI enhancing customer experiences and what’s next in this field? In this episode of the AI at Scale Podcast, Audrey Hazak, the SVP of Digital Customer Relationship at Schneider Electric, unveils how her teams are applying AI to drive digital transformation and what she thinks is the next frontier of digital customer experience.
Throughout her career, Audrey was leveraging advanced analytics, API integration, then AI and Gen AI to meet evolving customer needs. In the conversation with the podcast host, Gosia Gorska, Audrey explains how Schneider Electric uses AI to continuously improve customer support and strive to deliver hyper-personalized journeys, managing over 50 AI use cases, including AI-driven chatbots.
Audrey highlights opportunities to improve the digital customer experience through self-service options, omnichannel support, and proactive customer support using connected products and predictive analytics. Furthermore, she points out the importance of trust in AI solutions, achieved through strict quality assurance processes and continuous improvement based on employee feedback.
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Transcript
Gosia Gorska:
Hi everyone, this is AI at Scale Podcast. My name is Gosia Gorska and today I’m excited to meet Audrey Hazak, SVP Digital Customer Relationship at Schneider Electric. With an impressive 25-year journey at Schneider across various leadership roles, Audrey has been advancing digital transformation and customer experience. Her background also includes a significant role at APC as VP of Business and IT Transformation for the EMEA region. Alongside her professional accomplishments, Audrey holds an MBA from Grenoble Graduate School of Business and brings unparalleled expertise in AI, API technology, digital services, and cybersecurity. I’m very happy to host you today, Audrey.
Audrey Hazak:
Thank you for having me today.
Customer Centric Approach
Gosia:
Thanks, Audrey. In your current mission, you lead a global team of 45,000 frontline employees with a mission to enhance business performance and customer satisfaction. With your expertise in API, AI, sales, marketing, and digital services, you’ve gained a comprehensive understanding of the digital ecosystem. Can you elaborate on what a customer-centric approach means to you and also how it changes with the new digital habits of customers?
Audrey:
To me, a customer-centric approach means putting the customer at the very heart of everything and every decision we make. It’s about listening closely to understand their needs, challenges, and goals and then delivering solutions that make a meaningful impact on their lives and businesses. With today’s digital habits, customers now expect personalized experiences and instant access to information wherever they are. They want self-service options and digital channels that give them control and convenience. This means we are not only optimizing our traditional services but also leveraging new technologies like AI, Gen AI, advanced analytics, and API integration to anticipate needs and provide real-time solutions. At Schneider Electric, our goal is to make each interaction easy and impactful, ensuring that we empower our customers at every step—all in the pursuit of sustainability and efficiency.
Challenges in Managing Diverse Customer Needs
Gosia:
That’s a very important mission. And I think along all the areas that you mentioned, the personalization is especially the Holy Grail today of all the companies. And I want to go deeper on this topic later in the conversation. But firstly, you mentioned, and you described basically the complexity of managing diverse customer needs. What are some specific challenges that you’ve encountered in this area? And what are these areas that we can use AI tools to solve them to get some support?
Audrey:
So, at Schneider, we serve around 14 different types of customers from electricians to electrical distributors, system integrators, panel builders and end users, each with very specific needs and expectations and looking about the industry demands we have ready to fit with that. So, this is adding a significant complexity to our customer management and relationship approaches. Key challenges include where do we start, what are the top use cases, how do we drive the change management and what are really the considerations for trust, safety and responsible AI.
So, moving from a pure industrial company to a more services and software company, the key priority for us are the following areas: the personalization at scale to deliver the right solution efficiently, understanding personal context to deliver intelligence that is helpful and relevant. It’s a kind of moving from a pure B to B type of approach to a more B to C kind of approach. We have also to adapt to diverse regulations, each customer type and region has unique regulatory and operational requirements which we must comply with. It’s also to maintain this high-quality customer interaction across the multiple touch points, making sure that in the full value chain end to end, it’s easy to work with us and we make really the life easier for our customer.
So, AI has been absolutely essential in addressing this complexity, enabling the real-time data analysis in order really to provide meaningful insights into customer behaviors, preferences, pain points, allowing us to respond quickly and accurately. It’s a kind of what should be the best next action, and we can prevent common issues and address needs before they arise. And we have developed some offers to support this new type of services. And definitely the personalized marketing, which is really important to tailor all the campaigns to each customer based on the past interaction, ensuring more relevant and impactful communication.
So overall, AI helps us transition from reactive to a proactive approach, making it possible to deliver customized, efficient services that meet the different needs of our global customer base.
Harnessing AI and Gen AI
Gosia:
While we were speaking, I was already imagining how many different AI use cases can be developed—multiplying the areas you mentioned. How is Schneider harnessing the power of AI and Gen AI for customer support and delivering these hyper-personalized customer journeys?
Audrey:
Schneider Electric drives AI at scale both internally and for our customers and partners, enabling greater overall efficiency and sustainability through data-driven insights. In the digital customer relationship area alone, we manage over 50 AI use cases across different functions. The first use case focuses on optimization and productivity, enhancing operational activities in customer care with various chatbots. For example, our AI-driven chatbot assists with handling technical questions and material availability for our customer care agents. The chatbot is designed to be well-informed with Schneider’s specific knowledge so that agents find the responses useful, accurate, and content rich. This dual approach of leveraging AI both internally and externally ensures that we share augmented intelligence that drives real value. Additionally, we have integrated AI into our product portfolio for energy management and analytics in offerings like Micro Grid, Advisor, Ecocare, and Compass Data Center, among others.
Enhancing Digital Experience with AI Tools
Gosia:
And if we look at the different areas managed by your team under your leadership, where do you see opportunities to enhance the digital experience of customers?
Audrey:
In different areas, we see opportunities primarily in self-service. Our objective is to empower customers with quick, on-demand access to information and support, while also enhancing communication to deliver targeted insights and updates tailored to their needs. We are focused on omnichannel support—ensuring seamless and consistent support across mobile chat, voice, and more. Our teams benefit from enhanced capabilities in writing and communicating more effectively, crafting emails that are perfectly tuned to the audience and task. We also see significant opportunities in proactive customer support through connected products and predictive analytics, which help us anticipate issues, minimize downtime, and boost satisfaction by providing real-time, actionable performance insights and notifications. Enhanced security is another critical aspect, as it instills trust and confidence in our digital solutions.
Ensuring Trust and Quality in AI Solutions
Gosia:
That’s an impressive portfolio of projects. Now, if we deep dive into your own operations, how are you using AI and Gen AI? Could you share a specific example?
Audrey:
Let me select the knowledge bot as an example. This tool assists our customer care agents by preparing responses rather than directly answering customer inquiries. Its goal is to augment agent capacity by providing quick, accurate, and up-to-date information in real time, significantly reducing the time spent searching for answers. In large companies like ours, where information is scattered across many databases, the bot helps agents resolve issues faster and more efficiently. The development process involved evaluating various chatbot platforms, piloting the solution, and training our global team—including communication experts—to establish proper engagement rules and ensure the bot accurately reflects the voice of the customer.
Gosia:
In one of your previous interviews, you mentioned that while the chatbot augments employee capabilities, an employee still reviews and validates the answers, even having access to the source information. This not only builds trust but also creates an opportunity for learning as employees can deepen their understanding of the subject matter. With trust being a cornerstone of Schneider’s operations, how do you ensure that the AI technologies you implement are both trustworthy and maintain that trust with customers and employees alike?
Audrey:
It’s a very important point. We ensure trust by implementing strict quality assurance processes, checking that AI-generated answers are accurate and reliable. For any AI initiative, the human element is crucial because employees are the final gatekeepers of quality. We gather employee feedback—especially from customer care agents interacting with the knowledge bot—to continuously improve the system. We operate in an open loop of learning, constantly refining our processes to ensure that the level of quality meets expectations. Additionally, we regularly assess whether the AI products are delivering the expected value for both our employees and customers, keeping a close eye on time-to-market and overall benefits. This continuous improvement is key to building and maintaining trust.
Best Practices in AI Adoption
Gosia:
Could you share some best practices with our audience—what have you learned when integrating AI into customer support systems?
Audrey:
The key is to be extremely clear about what you want to achieve. We often overestimate what technology can solve and underestimate the importance of articulating our exact goals. Learning from initial implementations is essential. Anticipating the change curve and planning for longer timelines than originally expected are critical factors. Furthermore, adoption varies across our global teams—operating in over 150 countries means that some teams are more ready for digital shifts than others. Recognizing and addressing these differences is crucial to ensuring a smooth transition and maximizing the benefits of AI-driven systems.
The Future of Hyper Personalization
Gosia:
I see the emphasis on human aspects of the transformation—change management is the key success factor for all these projects. Before we close, what do you hope to see in AI technology that could further enhance digital customer experiences and drive hyper personalization?
Audrey:
The future really lies in hyper personalization, which I mentioned earlier. The biggest challenge for us is achieving truly deep personalization by having AI understand not just the initial engagement, but the entire customer journey. We need insights into what the customer wants, who they are, and the value they seek. Although we know our customers, we have yet to reach that deep contextual level—from lead generation to pricing, competition, and finance. Our aim is to provide the right answer at the right time, delivering self-service everywhere while improving customer retention through tailored experiences that keep them engaged and satisfied. This evolution in AI will drive more dynamic and meaningful interactions across every touchpoint, boosting productivity, efficiency, and sustainability for both our teams and our customers.
Gosia:
I recall that in one of your interviews you were asked for one word to describe yourself, and you said “innovation.” After this conversation, no one can doubt that you are indeed driving innovation at Schneider and for your customers. I really appreciate the time you spent with us today, Audrey.
Audrey:
Thank you so much.
Gosia:
Thanks for joining us today on the AI at Scale Podcast. Be sure to visit our se.com AI website to learn more about our AI at Scale solutions. Head over to our Schneider blog platform to read more. Don’t forget to subscribe to the show on your preferred platform and share it with your network. Thank you for listening and stay tuned for the next episode.
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