AI’s tangible business impact: 5 key learnings from Zühlke & ETH Zürich

This blog post shares the five key insights from the study. It helps decision-makers look past the AI hype and concentrate on effective solutions. We also explore how to avoid common AI pitfalls and how businesses can build scalable, responsible AI strategies.

1. Custom AI models and proprietary data drive competitive advantage

Off-the-shelf AI solutions rarely deliver maximum value. The most impactful AI applications are built around custom models and proprietary data, ensuring they align with business needs and provide a competitive edge. The study shows that 82% of successful AI solutions depend on internal, proprietary data. This shows that organisations that put money into their data infrastructure and adapt their models tend to be more successful.

Decision-makers should focus on creating AI models that address their organisation's specific challenges. Leveraging continuous learning by embedding AI into daily workflows enhances accuracy and adaptability over time. Companies that use AI for internal process optimisation gain a sustainable advantage over competitors. Additionally, companies that fail to harness their proprietary data risk being overtaken by more AI-savvy competitors.

Learn more on how we help companies build AI-powered solutions.

2. AI’s sector-specific impact: Where it delivers the most value

AI adoption and its business impact vary significantly across industries. According to the study, different sectors prioritise AI applications based on their specific challenges and strategic goals. The impact of AI adoption also differs by region, with some industries experiencing higher maturity levels in specific countries.

Information technology and financial services are leading adopters in various regions. On the other hand, healthcare and industrial sectors display different levels of AI maturity. This variation may come from regulatory and operational complexities. Understanding the differences between sectors and regions helps decision-makers shape AI strategies. This way, they can tailor their approach to meet the unique needs of their industry and leverage regional strengths. 

Understanding AI adoption trends across industries

AI adoption varies by industry. Each sector uses the technology to fit its own priorities and needs. While some industries focus on efficiency gains, others invest in AI for innovation, compliance, or customer engagement. The study shows key trends in how AI is changing different sectors. It helps decision-makers see where investments have the biggest impact.

Key insights from industry leaders:

• Manufacturing: AI helps with predictive maintenance and production optimisation. This cuts downtime and boosts efficiency.
• Healthcare: This sector uses AI for better diagnostics and improved patient outcomes. It helps ease medical decisions-making.
• Financial services: AI is changing risk management and personalisation. It helps companies spot fraud and customise customer experiences.
• Telecommunications: AI is key for optimising networks and automating customer service, which cuts costs.
• Logistics: AI-driven demand forecasts and supply chain analytics boost resilience and cut waste.

These sector-specific applications show that successful AI adoption relies on matching technology to industry needs. It's not a one-size-fits-all approach.

3. AI adoption varies by region

The study shows big differences in AI adoption between regions. Each country has its own strengths. The US is the leader in AI adoption, especially in consumer discretionary and information technology. This success comes from large investments in automation and improving customer engagement. The UK is right behind, showing strong AI maturity in several sectors. Notable progress is seen in financial services and healthcare, especially in risk management and diagnostics. Germany, Austria, and Switzerland (DACH region) show strong AI use in manufacturing industry, and finance. However, they still fall behind the US and UK in AI development.

These insights point out the need for a tailored AI strategy. It must take into account regional strengths and the unique needs of each industry. By recognising each country's strengths, businesses can improve their AI strategies and boost their impact.
Decision-makers should use regional expertise and encourage collaboration across regions. This way, they can create a balanced approach to AI implementation. Global companies can blend USA’s customer-focused AI strategies with Europe’s precise AI skills in niche sectors. This dual approach helps businesses seize local market opportunities and maintain global AI scalability.

4. Generative AI holds promise but requires structure

Generative AI is gaining traction, with 68% of organisations considering it strategically important. Yet, its success hinges on structured implementation. The most impactful use cases integrate generative AI into existing business processes rather than treating it as a standalone tool.
Functions such as marketing (27%), R&D (26%), and HR (25%) are already benefiting from generative AI’s capabilities. Companies must ensure quality control and use GenAI responsibly. This means aligning GenAI applications with business goals and rules to reduce risks.

Best practices for implementing Generative AI

• Use it to augment existing AI models, not as a replacement.
• Ensure human oversight, to mitigate bias and inaccuracies.
• Apply it in high-impact areas, such as automating document processing or enhancing customer interactions.
• Develop clear governance frameworks to prevent security risks and data misuse.

Learn more on how to migitate AI risks with an AI governance framework.

5. Ethical AI frameworks are a key success factor

The study shows that companies with clear AI ethics and governance are much more likely to achieve meaningful AI results. 

These organisations use AI with clear policies. They focus on bias reduction, following regulations, and using data responsibly. This approach builds greater trust among stakeholders.

Key parts of a solid AI governance framework are clear decision-making, regular risk checks, and established accountability structures. Companies that incorporate these factors into AI development not only meet legal requirements but also improve efficiency. This approach helps reduce errors and biases. Therefore, embedding AI ethics into strategic decision-making is essential for long-term adoption and success.

Learn more in our blog article "Building an ethical AI framework".

From AI potential to tangible business impact

AI success is not just about technology—it’s about strategy, governance, and execution. The insights from Zühlke and ETH Zürich show that businesses must focus on customisation, operational efficiency, and ethical AI to achieve meaningful results.

For decision-makers looking to explore AI’s full potential, we provide expert guidance in AI strategy, development, and deployment.

By approaching AI strategically and responsibly, companies can unlock new efficiencies, drive innovation, and create long-term competitive advantages. To learn more about how AI can transform your organisation, explore our insights and expertise in AI-powered solutions.