AI: Rapid market growth - but how to invest?

by Michael Buttkus - Head of Consulting, Germany
| minute read

Businesses are tasked with harnessing the potential of artificial intelligence (AI) strategically, ensuring that their investments yield tangible returns. To achieve this, they require market analyses tailored to the decision-making needs of executives, not data scientists, says Michael Buttkus, Head of Sopra Steria Next (Germany). 

Sopra Steria Next’s study, Navigating the AI Era, categorises the AI market into four application areas: AI for Machines, AI for Processes, AI for Humans, and AI for Software. This segmentation provides companies with a solid framework to guide their AI investment strategies effectively. 

Two key AI growth areas emerge 

The study identifies AI for Humans and AI for Processes as the categories with the highest expected growth. Demand for generative AI, which supports employees in knowledge management and decision-making, is anticipated to rise exponentially. For instance, Klarna deployed an AI-based assistant that handled 2.3 million conversations within a single month, reducing average response times by a factor of 5.5 and cutting repeat inquiries by 25%. 

Process automation, powered by technologies such as Intelligent Process Automation (IPA), is also expected to gain traction. One global retail giant implemented an IPA platform to automate processes and integrate AI-based tools, such as chatbots. Over two years, the automation of more than 200 business processes resulted in significant cost savings and noticeably improved customer satisfaction. The implications of AI, however, go beyond efficiency gains – the technology is transforming entire business models. 

Sector-specific AI investment priorities 

The challenge for business leaders lies in aligning AI potential with their respective business models as follows: 

Financial services: Prioritise investments in AI for Processes and AI for Software. The combination of automation and software solutions can drive efficiency, ensure compliance with regulatory requirements, and facilitate the development of generative AI-based services. 

Healthcare: Focus on AI for Humans to create personalised treatment options and enhance patient experiences. 

Manufacturing: Invest primarily in AI for Machines. Digital twins and improved simulations can enable companies to reorganise supply chains. Sopra Steria’s IoT-Finance platform, for example, integrates machine data to support innovative financing models, such as pay-per-use schemes. By distributing investment costs, businesses can attract a wider pool of investors, creating new investment opportunities. In an environment of fluctuating production levels, usage-based financing offers companies greater flexibility to explore new business models. 

Value-driven investment strategies and AI scaling 

Businesses should allocate 70–80% of their AI budget to mature use cases that can be scaled within 18 months. The remaining 20–30% should be reserved for exploratory projects that drive long-term innovation. This dual approach allows for experimentation while ensuring immediate gains. 

Many organisations struggle to transition from AI pilots to widespread AI deployment. A robust AI transformation strategy should encompass the following five elements: 

  1. Focus on proven use cases: Prioritise AI projects that have demonstrated success in your industry to avoid costly mistakes and accelerate implementation. 
  2. Modernise your data infrastructure: Ensure your data infrastructure can efficiently process both structured and unstructured data. 
  3. Adapt your IT landscape: Eliminate siloed solutions that fragment processes, and focus on interoperability. 
  4. Secure top talent: Invest in recruiting and training experts capable of developing and applying AI technologies. 
  5. Establish governance structures: Avoid unchecked AI proliferation. Clear guidelines and centralised oversight are essential to integrate individual AI components seamlessly. 

AI as a lever for long-term business success 

Companies that strategically manage their AI initiatives and prioritise scalable use cases will secure sustainable competitive advantages. The era of small-scale proofs of concept is over – the focus must now be on scaling AI solutions to unlock their full potential. Businesses that invest in a clear roadmap will not only achieve short-term wins but also strengthen their market position in the long run. 

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