Why the path to sustainability transformation is built on data and AI

Fujitsu / March 7, 2024

Digital Shifts AI Data and Security Data Driven Digital Transformation

Digital transformation has driven a seismic period of change for enterprise. But as the world faces an unprecedented combination of challenges – from climate change and war to economic fragility and social issues – a more radical approach is required. One that puts robust, enduring transformation first.

At Fujitsu, we use the transformative potential of digital technologies to foster sustainability across enterprises and communities. And we call this Digital Shifts.

To achieve this whist improving financial and operational performance, organizations need to harness data effectively. This involves proficiently overseeing the complete data life cycle and transforming raw data into actionable insights to make SOF (Sustainability, Operational, and Financial) decisions . Crucially, this requires ensuring trust, transparency, and auditability in both the data and the decisions derived from it.

Creating positive environmental and social impact will remake industries and create new waves of growth. But at the heart of this shift is data and artificial intelligence. Business leaders can no longer rely on manual processes, spreadsheets, and informal, siloed disclosure to signpost the way forward.

We’ve already discussed how accurate ESG reporting is the key to agile, sustainable enterprises . (*1) Now let’s consider the role of data and AI.

(*1) accurate ESG reporting is the key to agile, sustainable enterprises

The trouble with data
Enter AI
Prioritizing change management
Decoding sustainability

The trouble with data

Data makes the world go round and businesses are collecting more than ever before. But what to do with it poses real challenges for modern-day enterprise. Before embarking on sustainability transformation organizations need to consider what metrics they’ll need or want to report on, the data they already hold, and whether there are any changes to the processes and legacy systems already in place.

“There is quite a lot of valuable data held within a company already, but the big question is how it can be better leveraged for sustainability targets. Making clear what sustainability targets to focus on allows businesses to understand what data is required to take actions.” This is the view of my colleague Koen Vingerhoets, Blockchain Evangelist at Fujitsu. “There’s a big challenge with knowing where and what to improve, and which parts of your business you are going to tackle first from the sustainability perspective. Having a data-driven approach in place will help you prioritize the right actions once you know which business challenges you’re going to tackle first.”

Beyond organizational requirements, there can also be data quality concerns, particularly when the information is collected from a variety of sources. If data is outdated or incomplete, for example, any insights are likely to be incorrect, result in wasted resources or worse, wrong decisions. There may be changes to make around access – in an ideal world, access to relevant data would be democratized in a safe way, so that the wider business is able to drive innovation.

But the sheer volume of data can be overwhelming and make analysis difficult. Meaningful insights are only possible with a robust framework that can scale up and down as required. With such insights, business leaders can add another resource to their toolkit, helping them make the right decisions.

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Enter AI

Next-generation technology, such as Generative AI, Self-Serve Analytics Platforms, and Neural Networks Algorithms are capable of empowering leaders to take their organizations to the next level. Automating analysis and data insights helps businesses to be more efficient and agile, taking vast amounts of data into account, identifying complex patterns, and predicting future events in a much more accurate way than ever possible before.

But the right guardrails need to be in place. All algorithms rely on complete, organized, accurate data. Without it, enterprises will have the infamous “garbage in, garbage out” problem. Don't ever assume you can just jump right to AI deployments without setting up the foundations first. Make sure that the data is accessible, clean, validated, and the process makes sense.

It’s also important to keep security and privacy front of mind. Businesses have legal obligations to keep the data they collect safe under a variety of legislation, including the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), with hefty fines for non-compliance. Data must be protected at every stage of the processing lifecycle, from collection to disposal, and the policies and procedures that document this must be up to date.

Focusing on these elements also enables organizations to target sustainability goals. Challenges centered around sustainability transformation also have the capacity to drive improved business performance. Think of being able to reduce energy consumption without impacting operational capabilities. Or repurposing waste materials to open new revenue streams.

This ‘sustainability-first’ mentality is beginning to take hold. Recent Fujitsu C-suite research in partnership with FT Longitude, a Financial Times Company, found that the ‘Change Makers’ – respondents identified as successfully achieving sustainability goals and delivering significant results through innovative technology – have produced positive results from this approach. They have seen average organizational revenue grow 5% over the past 12 months - in comparison to the average of just 1% revenue growth generated by the remainder of executives surveyed . (*2)

Put simply, those embracing next-generation technology and implementing a sustainability-led focus are not just hitting sustainability targets; they’re hitting revenue targets too.

(*2) Based on a Fujitsu survey of 600 C-suite executives in 15 countries on their attitudes toward sustainability management. The responses were captured in November and December 2023, full results were published in April 2024.

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Prioritizing change management

Change management is often overlooked when it comes to technology projects, but it plays an essential role, particularly when it comes to AI. There is still a lot of anxiety among some workers about how such technology will impact their lives – one recent Gallup poll, for example, found more than one in five US employees are worried AI will make their jobs obsolete. According to the World Economic Forum, AI will disrupt 85 million jobs globally by 2025 and create 97 million new job roles. It’s estimated 40% of the workforce will need to reskill. With Generative AI in particular, most businesses simply aren’t prepared. Fujitsu can help.

When explaining the benefits of this technology to staff, leaders need to tread carefully. They need to ensure they have the skills needed to make the most of the higher-level insights generated, as most data problems in an organization are not technical, but cultural. When business leaders assume the improvement of a business, or a particular business outcome will just come with technology, they are lying to themselves. This is relevant to any type of technology, but particularly AI.

According to our research (*3), organizations that invest in change management are more likely to report that AI initiatives exceed expectations and achieve outcomes than those that don’t. By implementing digital solutions in a human-centric way, employees can be empowered to focus more on higher level work and less repetitive tasks that can be automated. They’ll have the right data and insights to be more productive and deliver a better customer experience. And there are benefits for trust and engagement.

While AI tools are becoming significantly more powerful every day, they simply cannot function properly without human interaction. The knowledge, creativity, and flexibility of people will continue to play an integral role in the future. While the insights themselves hold value, the true impact of AI and data is realized when insights are effectively interpreted and acted upon by knowledgeable individuals.

Without effective synergy with staff, AI technology simply cannot reach its full potential. So, it’s vitally important that the workforce is on board with an AI strategy and happy to play their role.

(*3) Based on a Fujitsu survey of 600 C-suite executives in 15 countries on their attitudes toward sustainability management. The responses were captured in November and December 2023, full results were published in April 2024.

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Decoding sustainability

Data has never been available in such abundance before, but that’s just the start of the story. Thankfully business leaders also have access to technology, such as artificial intelligence, to transform their sustainability plans.

Start with clear parameters about what you’re looking to achieve, because AI is just an enabler. Without a real connection to a business problem, there is no added value. A lot of people jump straight to technology and don’t think enough about their current situation and where they want to be. With proper integrations, quality of data, and validated pipelines, AI models are more likely to be successful.

By establishing good data management systems, coupled with an appreciation for change management, and the support of people, organizations can create a sustainable transformation strategy that works. Setting up a good foundation is key to success, so assess your data readiness. Then, when you’re ready, start small – test the solution, identify potential problems, and understand if the workforce is ready to use it.

If you’re looking for further guidance, visit our previous blog on bridging the gap in your sustainability transformation.

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Diogo Santos

Director, Data & AI Portfolio, Uvance Digital Shifts, Fujitsu

Diogo Santos is a seasoned data leader with extensive experience in machine learning, analytics engineering, and data product management. He has held various leadership positions across multiple consulting and IT strategy firms, and has implemented a diverse range of data engineering, AI projects and data strategy plans for clients across multiple industries.