Thomas Kiessling, CTO Siemens Smart Infrastructure  

For businesses to become sustainable, they need to navigate the dual challenge of achieving digitisation while striving for meaningful decarbonisation. At the heart of this shift lies a critical intersection of Artificial Intelligence (AI) and data. 
 

The two are inextricably linked, with AI reliant on vast, high-quality datasets to function effectively, and data requiring AI’s advanced capabilities to extract actionable insights. Together, they form the foundation upon which sustainable industrial and infrastructure transformation is built, particularly as organisations grapple with the urgent need to modernise while addressing environmental obligations. 

Decarbonising our future  

If we think about decarbonisation, we often view it in the context of renewable energy and electrification. But to achieve it we need to have data. For example, smart grids rely on real-time data to optimise energy distribution, while industries like manufacturing use advanced analytics to pinpoint inefficiencies. However, data on its own cannot drive outcomes. It is through AI that patterns emerge, inefficiencies are flagged, and optimisation can occur. 
 

From energy and utilities to manufacturing and healthcare, data serves as the starting point for identifying inefficiencies, optimising resources, and tracking progress toward net-zero goals. Yet, as a recent Siemens study revealed, many organisations lack the data readiness required to make substantial progress. According to the study, more than half (56%) of respondents say that digital technologies have significant or massive potential to advance the decarbonisation of their operations. 
 

AI as catalyst – data as enabler 

AI acts as a catalyst for change, turning raw data into actionable insights quickly and at scale. In industries where vast amounts of data are generated daily, such as manufacturing, energy, and logistics, AI-powered platforms are revolutionising operations. Predictive maintenance systems powered by AI, for instance, can identify potential equipment failures before they occur, reducing costly downtime and improving efficiency. However, the success of such applications depends on the availability and quality of data. Without comprehensive, well-structured, and interoperable datasets, even the most advanced AI systems are rendered ineffective. 
 

The digitisation of industries is incomplete without addressing the challenges posed by fragmented data ecosystems. Many organisations operate within silos, where data is stored across disconnected systems. Breaking down these silos is essential if we want AI-driven solutions to thrive. Cloud computing and edge technologies act as enablers for this as they facilitate seamless data integration across platforms and geographies. Furthermore, decentralised data ecosystems empower organisations to harness real-time insights, ensuring that decisions are informed by the most current information available. 
 

According to the report, the lack of well-integrated and accessible data infrastructure has directly impacted progress, with only a minority (30%) saying they have all the energy consumption data they need, leaving huge room for improvement.
 

Knowing the challenge ahead  

For many organisations, the first challenge to digitisation is establishing a baseline of digital literacy across the workforce. Employees need to understand and engage with the technologies driving transformation. This requires continuous upskilling and often a culture shift, where innovation is embraced as a core value, and change is seen as an opportunity rather than a disruption. 
 

Infrastructure plays a crucial role in this transition. Legacy systems, designed for an era of simpler, slower operations, are often ill-equipped to handle the demands of modern AI and data-driven environments. Transitioning to scalable, flexible infrastructure is, therefore, a priority. Organisations must evaluate their current capabilities and invest in solutions that support long-term growth, interoperability, and resilience. 
 

Collaboration is another vital element for overcoming barriers to digitisation and decarbonisation. No organisation can navigate this journey alone. Partnerships between technology providers, industry leaders, and academic institutions can foster the development of tailored solutions that address specific challenges. Co-innovation enables industries to leverage expertise from across the ecosystem, ensuring that AI and data strategies are aligned with both operational needs and sustainability goals. 
 

AI and data in action 

In the energy sector, AI-powered platforms are starting to play a role in revolutionising grid management. These systems analyse consumption patterns and predict future energy demands, allowing for the integration of renewable sources while minimising waste. In manufacturing, predictive maintenance systems powered by AI have proven transformative, enabling real-time monitoring of equipment and dramatically reducing downtime. Meanwhile, the healthcare industry is leveraging AI and data to personalize patient care, from diagnostics to treatment plans, improving outcomes and operational efficiencies alike. 
 

While these examples demonstrate the potential of AI and data to create value across industries, data fragmentation, inconsistent quality, and security concerns are still a very real threat. To unlock the full potential of AI, organisations must prioritise data governance and standardisation. Establishing clear frameworks for data collection, storage, and analysis will ensure that AI applications can access reliable and relevant information. 
 

Overcoming regulatory hurdles 

The regulatory landscape adds another layer of complexity. As data flows across borders, compliance with local and international standards becomes increasingly critical. Organisations must navigate a patchwork of regulations governing data privacy, security, and sovereignty. AI solutions are beginning to incorporate compliance mechanisms directly into their workflows, enabling organisations to adhere to regulatory requirements without compromising efficiency.   
 

The study notes that only 27% of surveyed organisations state that compliance remains one of the biggest risk/barriers to deploying digital business platforms, highlighting the importance of integrating regulatory frameworks early on in an AI deployment strategy.
 

The rewards of integrating AI and data into industrial workflows are substantial. Industries that successfully navigate this transformation will enhance their operational capabilities and position themselves as leaders in sustainability and innovation. By adopting AI and data-driven approaches, organisations can achieve greater efficiency, reduce environmental impact, and deliver better outcomes for customers and stakeholders. 
 

Leadership must lead 

The role of leadership cannot be overstated. CTOs and other technology leaders have a unique responsibility to champion these efforts, bridging the gap between technical capabilities and business goals. Their vision and commitment are essential in driving cultural and operational change, ensuring that digital transformation efforts are not just implemented but fully embedded into the organisation’s DNA. 
 

With growing access to modern digital tools, the opportunity to leverage AI and data for digitisation and decarbonisation grows ever more compelling. But success will be defined by investing in the right tools, frameworks, and partnerships. 
 

The intersection of AI, data, and industry represents a transformative frontier. It is here that the challenges of our time—climate change, resource scarcity, and economic volatility—can be met with solutions that are not only effective but sustainable. The journey is complex, but the destination is clear: a world where industries are not only digitised but decarbonised, achieving progress that benefits both business and society.