Innovation and business transformation: How to introduce them efficiently?

AI and other emerging technologies can open up a lot of new opportunities for businesses. But there are certain critical conditions that should be met. Without them, even the most promising innovation risks becoming just another project stuck in the proof-of-concept (PoC) phase.

How to avoid this? And how to make sure that your next project will bring the highest possible value? The answers can be found in the latest episode of the Innovantage podcast hosted by Sigli’s CBDO, Max Golikov. This time, the podcast guest was Adriana Grüschow, Industry Lead Business Development Manager at Zühlke Group.

Originally from Guatemala, Adriana moved to Germany to pursue technical studies in industrial and power systems engineering. Her early career explored various domains, including project engineering, quality control, and risk management. But sales were never on the radar.

Like many engineers, she once viewed sales as a sphere that lacks technical depth and strategic value. But over time, that perception changed. There were three key things that had the strongest impact:

First, across all her roles, the moments Adriana enjoyed most were those spent directly engaging with clients, understanding their challenges, and translating them into tailored solutions. It became clear that she didn’t want to work behind the scenes. She wanted to be where the real impact happens.
Second, while at ABB, she learned that sales in an industrial or tech environment isn’t about pushing products. It’s about listening, advising, and building trust.
Third, she found herself increasingly drawn to digitalization and innovation. Namely, in these areas, transformation happens fastest.

That passion led to a series of international experiences, including an assignment in Tokyo, and eventually to a role as Product Manager for SaaS and IoT solutions at ABB.

Today, at Zühlke Group, Adriana co-creates complex innovation projects with clients in the industrial sector. Many of them sit at the intersection of AI and hardware.

Bridging the gap between tech and business

Early in her career, Adriana noticed that professionals in engineering, business, and software often seemed to speak entirely different languages. That disconnect quickly became one of her biggest challenges and greatest learning opportunities.

Throughout her journey, she discovered the vital role of being a translator:

Between business and technology,
Between hardware and software,
And between differing priorities (for example, technical robustness is the key priority for engineers, while ROI expectations remain vital for business leaders).

Bridging those gaps became an essential and highly valued skill.

It wasn’t easy, especially in the traditional manufacturing sector. There is often deep-rooted resistance to change. But despite those hurdles, Adriana has witnessed significant progress over the past six years. More and more companies are becoming open to digital transformation and emerging technologies.

According to Adriana, innovation isn’t just about adopting new tools or platforms. Mainly, it’s about aligning people, culture, and business models around meaningful value.

In her view, technology, and especially AI, plays a powerful role, but only when it’s grounded in real human needs and when it is strategically integrated into the processes.

Today, Adriana helps hardware-driven companies unlock the potential of AI in a way that respects both technical realities and human dynamics.

This process requires patience and persistence. In her experience, gaining trust starts with small, tangible wins. As value becomes visible, credibility and confidence in the solution grow.

Adriana recalled a project during her time in the turbochargers division at ABB, where she was tasked with integrating a new quality inspection process for cast parts. Until then, inspections were done manually. A quality manager had to assess parts once a day visually, often relying on simple tools like rulers and intuition.

Her goal was to introduce automated inspection using computer vision. Initially, there was resistance from both quality managers and production leads. And it was quite understandable as these were established workflows, and change could feel disruptive.

But instead of pushing the solution aggressively, Adriana focused on building trust through demonstration. Once the teams saw how much time could be saved and how the accuracy of inspections improved, they became more receptive. Gradually, the team accepted the new system.

Is the AI hype a bubble?

Today, we can observe a massive hype around AI, the same as around blockchain and the metaverse. Without any doubt, these technologies hold real potential. But there is also a serious risk associated with them. Businesses might rush to adopt them simply because everyone else around them is doing it.

Too often, organizations feel pressured by what they see at conferences or on LinkedIn and may assume that they are already behind. Nevertheless, LinkedIn isn’t reality, and conference case studies often demonstrate only the most advanced, niche projects. Innovation shouldn’t be about chasing trends. It should focus on solving existing problems.

As Adriana highlighted, AI, no matter how advanced, is only a tool. And the main task is to define how to apply it to meet real user and business needs.

The value of discovery

To avoid falling into the hype trap, Adriana and her team apply an “innovation filter” before launching any project. It focuses on three critical questions:

Desirability. Do users actually want this?
Viability. Does it make business sense?
Feasibility. Can it be built and scaled with available resources?

In most cases, the bottleneck isn’t technical feasibility, but a lack of clear user demand or business alignment. That’s why every project should begin with a structured discovery phase. Talking to users, validating assumptions, and ensuring the project supports the broader business strategy are crucial steps.

Without that, companies risk investing in solutions no one needs.

In nearly every project, Adriana sees the same pattern. Clients arrive with well-formed ideas, confident in their understanding of the market. And they are simply looking for implementation support.

But Adriana’s team never jumps straight into development. They challenge assumptions and strongly advocate for a discovery phase.

Initial reactions are often hesitant. Very few clients are eager to invest more time and budget into research.

Nevertheless, Adriana always recommends taking time to focus on what truly delivers value.

It’s vital to stay open to questioning assumptions, even long-standing ones. While scientists need to test each hypothesis, companies need to actively challenge their own thinking instead of just taking it as fact. This mindset shift is essential for meaningful innovation.

Another practical advice is not to copy others. Just because a larger competitor has adopted a particular solution doesn’t mean it will work in your specific business context. Every company operates in its own niche with unique needs and clients. Every assumption should be validated in your particular context.

Challenges of business transformation

One of the most underestimated shifts a company can undergo is the transition from a product-based model to a service-oriented business. At first glance, it might seem like a simple revenue model change. But in reality, it is a deep transformation that impacts every part of the organization, including its strategy, operations, product development, culture, and many other aspects.

This transformation marks a collision of two fundamentally different business worlds. In the traditional model, success revolves around ownership and one-time transactions. The new model focuses on usership, data, and building long-term customer relationships.

However, many companies mistakenly perceive this as a technical upgrade, without realizing that it demands a complete rethinking of the business logic.

In a service-based model, success is not defined by making a sale. It’s about whether the customer continues to see value month after month.

Change management and its role in business transformation

Change management is not just one component of a transformation process. It is the work that should be done.

Effective change management goes far beyond sending memos or presenting strategy decks. It requires leadership, patience, and a culture that supports learning and experimentation. Without this foundation, resistance will arise across all departments, even at the executive level. And that resistance is entirely human. People naturally push back when asked to let go of familiar routines and redefine how they create value.

A major part of successful change management involves giving employees the tools, clarity, and confidence to succeed in a new model. This is especially true when shifting from product sales to service-based business models.

Starting small, with MVPs and pilot projects, is often the right strategy for innovation.

But one of the most common missteps companies make is expecting existing product teams to take on service transformation efforts. In reality, success often comes when organizations establish a separate service-focused unit. This new team should be empowered with its own KPIs, processes, and go-to-market strategies.

But should companies rely on existing staff or bring in entirely new talent?

The answer lies in striking the right balance.

In most cases, it’s possible and beneficial to identify people within your current sales team who are open to change and capable of shifting their mindset from product features to service value.

However, relying solely on internal talent is rarely enough. That’s why it’s also important to bring in fresh perspectives, either by hiring externally or collaborating with innovation partners. These newcomers can introduce new thinking, best practices, and experience.

IoT after the hype: From connectivity to intelligent value

The hype around the Internet of Things may have peaked around 2018, but that doesn’t mean the technology has faded into irrelevance. While it has been overshadowed by newer trends like AI, IoT is very much alive, but just under a different name and narrative.

Today, a lot of companies don’t speak about IoT in isolation. Instead, they talk about smart, connected solutions. This evolution marks a significant shift: the focus is no longer on simply connecting devices and collecting data. Now, it’s about why devices are connected and what actionable insights that connectivity can produce.

In the past, a connected product might have meant being able to control a vacuum cleaner from your phone. Now, the same devices can map your home, learn from your habits, and adapt to your routines.

This same principle applies in industrial settings. Connected machines used to be about avoiding breakdowns. Now, they’re about predictive performance, energy optimization, and extending lifecycle value.

Many IoT initiatives in the past failed to move beyond the PoC stage. Today, organizations are more ROI-driven. They ask themselves:

What’s the return on this initiative?
How soon will we see value?
Is it scalable?

This helps them avoid investing in solutions that don’t drive transformation.

Escaping “PoCititis” in AI and IoT projects

“PoCititis” might sound like a joke, but it’s a very real phenomenon, which describes PoC projects that never scaled.

In the early days of IoT, companies were captivated by the technology’s potential. Teams built PoCs to test new sensor-based applications, but often left those experiments behind. The technical feasibility was not the problem.

The real bottlenecks were related to business viability and users’ desire to pay for such solutions.

Adriana explained that it is helpful to move from “proof of concept” to “proof of value.” The goal is to ground innovation in real-world impact from the very beginning. Teams need to focus not just on what can be built, but on what should be built.

This more “boring” approach results in scalable, sustainable solutions. And with the rise of AI, there’s hope that the industry will take a similar route.

Deploying AI models at scale demands energy, infrastructure, and talent. Pilots without a clear path to value can just lead to wasted resources.

Three P’s for meaningful innovation

To build lasting, impactful solutions, innovators need a clear understanding of the potential outcomes, not just capabilities. Adriana shared a simple framework that can help organizations refocus their efforts.

It can be called “Three P’s”:

People. Does the solution genuinely improve lives? In sectors like healthcare, this could mean better patient outcomes.
Profit. Does the solution reduce operational downtime? Improve process efficiency? Open up new revenue streams or strengthen competitiveness?
Planet. Does the innovation help reduce emissions, cut waste, or improve energy efficiency? Does it support the company’s environmental goals or contribute to a greener supply chain?

It’s rarely realistic to achieve all three P’s in a single project. But the key is to focus on at least one of them.

What new opportunities does the EU Data Act bring?

The EU Data Act marks a significant turning point in the conversation around data ownership and access.

Historically, the data generated by connected products has been tightly controlled by the manufacturer. Users have had little access to the data it produces. That data often remained locked within proprietary ecosystems, which limited interoperability and innovation.

The EU Data Act changes that. It grants users the legal right to access and share the data their devices generate.

This shift will drive the creation of new ecosystems where third-party service providers can aggregate and analyze data across devices and brands.

While new laws like the EU Data Act and the Cyber Resilience Act (CRA) are being introduced with urgency, the full positive impact will take time to materialize.

Curiosity is a superpower

The only constant in tech is change. It doesn’t matter whether it’s AI, IoT, cloud, or the metaverse; cutting-edge technology today can quickly become legacy tomorrow.

To stay ahead, leaders must embrace continuous learning and adapt quickly. That means testing new tools, reading regularly, and being open to challenging their own assumptions.

“Fail fast” isn’t about defeat. It is about learning. Launching minimum viable ideas, validating them with real data and users, and refining the approach is key.

Those who stay curious and make adaptability their superpower are best prepared for the future.

Real-life applications of AI

At the end of their discussion, Adriana also shared a couple of real-life use cases of AI based on her practical experience.

Burckhardt Compression. To speed up the quoting process, Adriana’s team helped the company create a GenAI-powered knowledge assistant. Instead of manually searching through hundreds of technical specification documents, users can now chat with the assistant to instantly retrieve validated requirements. This is a classic example of Retrieval-Augmented Generation (RAG), which is quite common already in sales and service across industries like insurance and banking.
Bosch. Identifying spare parts from a catalog of over 13,000 items is a very time-consuming task. A computer vision solution powered by neural networks now allows users to simply snap a photo of a part. The system then validates the image, matches it to the catalog, predicts the correct part, and can even trigger an automatic order.

These examples show how AI is already driving real impact in industrial and customer-facing processes.

Want to know more about how technology is transforming the world of business and how organizations can benefit from innovation? Don’t miss the next episodes of the Innovantage podcast.