While the AI conversation remains dominated by speculation about what might be possible, some organizations have moved beyond experimentation to deploy AI systems that handle millions of transactions, automate complex workflows, and deliver measurable business value. Their experiences reveal both the extraordinary potential and sobering realities of implementing artificial intelligence at enterprise scale.
We spoke with seven industry leaders who have successfully integrated this technology in real-world production environments, with projects ranging from processing over 300,000 municipal documents a month to automating customer support for more than 500 representatives. Their insights go beyond the hype, offering a clear-eyed view of what it truly takes to unlock AI’s transformative potential at scale.
Our distinguished interviewees
Frederik Severin leads AI initiatives at SumUp, a major German fintech company, where he has overseen the simultaneous deployment of over 200 AI projects. Thanks to these efforts, the organization has achieved 50% automation in customer support while upholding enterprise-grade quality standards.
Clémentine Lalande is the co-founder and CEO of kelvin, a French company that has developed specialized AI engines for analyzing the energy efficiency of residential buildings. Her team has created entirely new frameworks for AI applications in the energy retrofit sector.
Kimmo Parviainen-Jalanko serves as Engineering Lead at Vainu, a Finnish company that processes vast amounts of business data for the Nordic region. They have been implementing AI and machine learning solutions for over eight years, well before the current GenAI boom.
Ariel Rosenfeld is CEO of 3d Signals, an IoT company that connects physical manufacturing assets to cloud systems. With a background spanning from founding M-Systems (the inventor of the USB flash drive) to ultra-marathon running, he brings a unique perspective on digital transformation in industrial environments.
Gil Matzliah is the CEO and co-founder of NoviSign, a global SaaS company specializing in digital signage. He has been integrating AI content generation capabilities into their platform, carefully balancing innovation with operational stability.
Rosaria Silipo is the Head of Data Science Evangelism at KNIME. She brings nearly three decades of experience in data science and neural networks to help organizations integrate AI capabilities into their data analytics workflows.
John Smith (a pseudonym), who has requested anonymity, is a seasoned professional in e-commerce advertising optimization. His company uses predictive AI to optimize PPC bidding across major platforms like Amazon, eBay, and Walmart, with a focus on aligning customer success with business outcomes.
Industry use cases: Where AI is delivering value
AI is no longer confined to the realm of experimental technology—it’s becoming a core part of how businesses operate. According to McKinsey, 78% of organizations report using AI in at least one business function. For leaders navigating this landscape, understanding the broader implications of current AI adoption statistics is critical for benchmarking strategies and identifying emerging opportunities.
From manufacturing and finance to marketing and customer service, companies across industries are finding concrete, value-generating use cases.
The following examples from industry leaders illustrate how different sectors are adopting AI not just to enhance efficiency, but to fundamentally rethink how work gets done.
Customer support: When AI handles 500 jobs
Customer support is one of the most resource-intensive functions in any large organization, often requiring hundreds of staff to manage inquiries quickly and accurately. It has traditionally been viewed as a necessary overhead with limited room for innovation. That’s what makes Frederik Severin’s story so remarkable—if the results weren’t measurable, they might seem almost unbelievable.
As the leader of AI initiatives at a major German fintech company, Severin has overseen the simultaneous deployment of over 200 AI projects. The transformation of customer support is the biggest initiative SumUp has done so far. And it clearly demonstrates the true scale of what’s possible with AI.
“At our company size, that would have required 1,000 customer support people. We used to have 1,000 or almost 1,000 customer support people in the past, and now 50% of the cases coming in are handled solely by AI.”
The mathematics is staggering: AI now manages the equivalent of 500 full-time customer service representatives. But this wasn’t achieved through a simple ChatGPT integration. “There are a couple of engineers working full-time on it for almost a year, because they have been connecting all relevant data sources,” Severin notes.
This level of implementation marks a fundamental shift from seeing AI as just a productivity tool to treating it as essential business infrastructure. Severin’s team has organized this approach into what he calls a “three-stream strategy”:
- Internal development led by dedicated engineering teams to build use cases.
- Collaborations with startups that apply AI on top of foundation models for rapid deployment.
- Providing democratized access to over 40 AI tools across all employee roles.
“We categorize our strategy into three different streams,” he explains. “One workstream is building out use cases internally. The second workstream, AI projects, utilizes an AI layer based on the foundation model, with the application case being conducted by a startup. The third one is that we try to get contracts with big enabling tools where everybody inside the company can apply for certain seats.”
The sophistication becomes clear when he explains the security requirements: the requester must be signed into their account, and only a specific set of information is accessible. “To achieve a 50% success rate, it is usually necessary to access their financial history.”
Perhaps most importantly, Severin has recognized that enterprise AI requires different quality standards than those of startup implementations. “If you have a big operating business, in contrast to running a startup, you need to work towards 99 point something in accuracy, in uptime, in non-hallucinating. It requires way more testing, training, reviewing, and doing QA than in a startup.”
Building AI for problems that don’t exist yet
While Severin’s organization focuses on scaling proven AI applications, Clémentine Lalande, co-founder of kelvin, has built something entirely unprecedented: an AI system that can analyze any residential building in France and recommend optimal energy efficiency improvements.
“We have developed an artificial intelligence to massify energy retrofits in residential buildings,” Lalande explains. Her company has created three interconnected AI engines that work together in ways that would be impossible for humans to replicate at scale.
The first engine performs geo-statistical analysis, cross-checking dozens of databases to create comprehensive building profiles. “We are connected to databases that range from land registers and Google Maps, to more confidential databases, and they give us estimations about the building’s construction date, its area, wall material, and the orientation—whether it faces south or east.”
At this stage, data availability isn’t the main challenge—consistency is. “We have a lot of data, but the different sources don’t really talk to each other,” Lalande explains. This forces the engineering team to continuously find “common ground across all the data sets. The quality is very heterogeneous, so the first AI layer focuses on predicting a high-performing building profile and its 1,000+ features.”
The second AI engine takes this data and creates 3D models of buildings, making intelligent assumptions about shape, height, and roof configuration. “We go from something that’s just a data set to completing the missing parts and finding what the house actually looks like,” Lalande describes.
The third engine models energy efficiency and calculates optimal retrofit scenarios. Together, these systems can analyze buildings and generate recommendations that would take human experts days or weeks to produce manually.
What makes kelvin’s approach remarkable is that her team had to invent entirely new frameworks.
“The field of application is so new for AI that we have to come up with our own frameworks and models. We have to go back to the existing literature on how to calculate energy efficiency, but all of that is just literature, not data models.”
This cutting-edge development carries real risks. “One of the complexity factors of what we are trying to achieve is that there is close to little ground truth, or at least not at scale. Plus, this is a massive unsupervised ML problem: there is no right or wrong in the energy retrofit space. So we had to aggregate the data for our models to learn and iterate,” she recalls.
The complexity extends beyond technical challenges to regulatory differences across European countries. “The way we calculate energy performance in France is different from Germany, and it’s different from Poland,” Lalande notes, highlighting how AI applications often face obstacles unrelated to the technology itself.
From clicks to revenue: The next generation of AI ad optimization
In the high-stakes world of e-commerce advertising—where profit margins are razor-thin and competition is relentless—artificial intelligence is becoming a powerful tool for gaining an edge. John Smith, an industry expert, shared insights into how advanced AI is being used to optimize bidding systems, revealing just how sophisticated these applications have become in today’s most dynamic markets.
“Our job is to set the optimal bids,” he explains. “If your bid is too high, then you lose money because you pay too much for a click. If your bid is too low, then there’s no volume, you won’t win an auction.”
The challenge requires predictive AI that can make forecasts while incorporating seasonality, product attributes, audience behavior, location data, and device information. “We really need to do two things: forecast the conversion rate and forecast the market volume, depending on the bids or number of impressions and clicks.”
This AI platform stands out for its ability to address a fundamental misalignment in digital advertising. “Google’s interest is not to make the customer profitable. They want to earn money, “the expert explains. In response, his company shifted to a model that charges clients based on the revenue generated from ads rather than the ad spend itself, showing how AI can enable business models that better align the interests of providers and customers.
Behind the scenes, the technical implementation relies on sophisticated risk management practices. “We usually ask customers if they want to join a beta, and we have a soft rollout system. We usually have three versions running at the same time, just to switch back and forth if the new version causes some problem.”
Looking ahead, Smith sees critical challenges that extend beyond technical capabilities. “I think these are the challenges for the future: to somehow enable users to fine-tune the system, to talk about data privacy, to control it, to know what’s going on, to tweak it to make sure it’s compliant.”
Smith’s concerns about authenticity are especially timely:
“One big challenge in the future is not knowing anymore what’s real and what’s not. Is it a fake product or a real product? Is it a fake company or a real company?”
A related development gaining momentum is what Smith refers to as paid agentic commerce. Modern AI agents can already assist with product discovery, and they have also started to integrate a direct purchasing option, allowing customers to buy desired items without leaving the conversation or interface. The next step, Smith believes, will be enabling sellers to actively advertise through these agents. This evolution will blur the line between organic recommendations and paid placements, creating fresh challenges around bias, transparency, and fairness in AI-driven commerce.
Data intelligence at unprecedented scale
Kimmo Parviainen-Jalanko from Vainu offers insights into processing business data across Nordic countries. “We collect company data