Imagine this for a future work experience: a user in procurement starts the day by asking their virtual assistant to create a purchase order.

This is an action that requires only the basic facts, ranging from vendor and quantity to item and date, with no manual data entry needed to complete the process.

Likewise, a manufacturing user asks their system to tell them what orders they are likely to miss today, and receives not just a detailed report of real-time progress against plan, but also a series of options for addressing potential problems.

These and countless other use case scenarios form the true vision for business AI in the modern work environment. This vision is already building significant momentum, even while introducing various organizational and technical challenges – and it’s only a couple of years away from transforming the everyday interaction with digital applications.

Early gains

The pace of AI and Gen AI adoption is obviously going to differ by organization, depending upon individual business use cases and perceived benefits. But to identify the tangible value underpinning these considerations is first to identify a future state, and imagine a way of working that combines human and machine components into a complete and harmonized whole.

Such thinking typically puts the focus on “quick wins” made possible by AI, including:

• Automating manual, repetitive tasks, which can extend from data entry to scheduling and report creation, thereby freeing people up to focus on more creative and complex work.
• Boosting user productivity: where individuals no longer need to access a range of systems to complete tasks and find answers, and instead rely on AI agents to do the heavy lifting – while proactively delivering insight before they even seek it.
• Streamlining business processes: where agents offer recommendations and autonomously taking actions across a range of commonly completed tasks.
• Increasing business resilience by proactively designing response plans to critical scenarios.
• Supporting complex SAP implementations: for example, supporting the project teams activities on RISE with SAP and GROW with SAP integration, working with an Augmented Software Development Life-cycle, and ensuring high data quality.

According to the Capgemini Research Institute’s report, Data-powered enterprises 2024, AI has the capacity to streamline business processes and enhance business resilience. This aligns perfectly with the potential of Gen AI to transform user experiences and create new revenue opportunities.

All told, Gen AI promises to transform the user experience in terms of the way we interact with information and back-end systems, discover insights, and find inspiration. Just as importantly, the technology is rapidly introducing new revenue opportunities and removing “skills barriers” – such as by enabling people to create complex spreadsheet analysis based on a simple query.

Bumps in the road

When the potential of AI is combined with industry and business use cases, the reasons to act become even harder to ignore. Hence the growing focus today on removing any obstacles in the way – with the headlines being:

• A lack of trust: a concern that spans ethical considerations as well as a resistance inside many organizations to actively experiment with new – and therefore unproven – technologies.
• A seeming lack of maturity: where decision-makers are waiting on the technology to become “perfect” before committing, held back by talk of AI hallucinations and output bias.
• Regulatory concerns: where frameworks such as the European Union’s AI Act 2024 aim to ensure AI systems are safe, transparent, and respectful of fundamental rights – but can impact future innovation.
• Human nature: which sees people preferring the “comfort zone” that comes with traditional ways of working.

This last point is understandable given the fact that AI brings with it a demand to change standard operating procedures. A transformation takes place in the way tasks are completed, to optimize the mix of human and artificial intelligence required at distinct touchpoints along the way.

A dynamic move forward

Overcoming these impediments is an important next step that requires the continued evangelization of AI and Gen AI from technology leaders. This is a task that Capgemini is heavily involved in, helping our customers to better understand the most suitable options for Gen AI – while also providing training and education to master the different aspects of change management.

Such support is a vital way station for any AI roadmap, as organizations seek guidance on the right approach and common pitfalls, as well as ways to introduce the necessary safeguards and appropriate ways to keep a “human in the loop.”

The good news, certainly from a technical perspective, is that Gen AI does not require major changes to existing IT environments, especially when the AI capabilities offered by SAP and global hyperscalers are taken into account. This situation might change with the advent of multi-agent AI systems, and the number of AI agents interacting autonomously – but that is a bridge most will worry about crossing when they finally reach it.

Final thoughts

Gen AI is often described as a train that is gaining speed. In this context, the key question facing organizations is when to get onboard: should they join now while advances are steady, or risk trying to gain access when the locomotive is hurtling through the station at full throttle?

What’s emerging as best practice is the idea of starting small and validating the potential of Gen AI for different use cases. This approach focuses on non-business-critical processes that can be addressed by out-of-the-box functionality available from providers like Capgemini and SAP. Once these initiatives prove their value, organizations gain the confidence to proceed with more advanced design strategy to tackle the bigger task of integrating Gen AI into the very fabric of day-to-day operations.

Ultimately, it comes down to one overriding thought: how to ensure your business doesn’t get left behind.

Watch this space for our next blog post.

Both artificial intelligence (AI) and generative AI (Gen AI) play a critical role in RISE with SAP transformations. The latter though, Gen AI, offers specific capabilities for both enriching the deployment of RISE with SAP, and maximizing its potential.

Let’s take a brief look at what these capabilities are.

From a big-picture perspective, Gen AI and RISE with SAP share many common goals. Both, after all, are dedicated to driving business transformation. They also increasingly share the same “space” with Gen AI assistants such as Joule, which is now integrated into SAP’s RISE bundling.

But as with any technology that for many sits somewhere between potential and practical value, Gen AI – particularly in the context of RISE with SAP – still raises many “how” questions:

• How can it reduce the risk and help optimize the actual deployment of RISE with SAP?
• How can it enable clear business value, in areas such as procurement and accounts payable?

The first step to answering these questions is understanding what’s realistically possible. Yet it’s also important to understand what’s suitable, given the fact that a mix of standard AI and automation will most likely be the answer for 80 percent of current Gen AI use cases.

Gen AI for delivering RISE with SAP transformations

The true value of Gen AI, in the context of a large, multi-year change program, is its ability to accelerate delivery, reduce the risk profile, and improve efficiency. For example:

• It can be used as an assistant during implementation workshops, helping answer questions, and creating in-depth reports.
• Based on these discussions, Gen AI can then create functional specifications, technical designs of custom objects, and configuration documents to significantly ease the manual burden.
• There’s also an important data migration benefit with Gen AI automatically mapping and transforming data from legacy systems to SAP S/4HANA.
• Last but not least, Gen AI can be used to generate code, thereby accelerating the software development lifecycle and ensuring quality assurance.

According to the latest Capgemini Research Institute report, Generative AI in organizations 2024, organizations have seen a fourfold increase in the deployment of generative AI, with 20 percent boosting investments and realizing tangible benefits like enhanced customer engagement and operational efficiency. These benefits are also key drivers behind using Gen AI for RISE with SAP implementations.

Overall, the correct application of Gen AI can have a huge impact and cut the costs of a RISE with SAP implementation by up to 15–20 percent.

Gen AI for delivering business value as part of the implementation

Outside of the delivery conversation, which certainly helps accelerate the benefits of the transformation, what really makes Gen AI a long-term enabler of the value of a transformation based on RISE with SAP are the business use cases. These create the transformational outcomes that provide the all-important business case justification.

That is why the activation of embedded AI/Gen AI use cases, as well as the identification of additional ones, should always be part of the solution design.

SAP is heavily investing in integrating Gen AI features into core business processes to automate, optimize, and bring contextual navigation to any task. These capabilities sit at the heart of the Joule offering, which is available via the RISE and GROW with SAP offerings.  These are usually industry-specific and cover a wide range of areas such as:

• Supply chain resilience
• Recruitment matching
• Predictive analytics.

The value underpinning these activities will manifest in the form of more automated and independent processes, enhanced productivity, and more informed decision-making. Yet equally, it’s about streamlining the way users interact with systems – and making the process easier and more intuitive for creating highly specific outcomes.

Obstacles to change

Gen AI may represent a major change in enterprise technology, but its introduction alone does not guarantee success. This is where change management enters the picture, because in reality Gen AI demands changes to standard operating procedures:

• For people, that means overcoming long-established habits (“I’ve always done it this way…”) and skills resistance (“I’m an expert developer, and don’t need Gen AI…”).
• For processes, Gen AI inevitably requires a degree of fine-tuning to maximize the outcomes it delivers.

There can also be an understandable wariness of the technology itself, with concerns extending from ethical considerations to practical day-to-day issues relating to data security and the introduction of bias into any system.

Embracing what’s possible

Despite these potential obstacles, it’s an undisputed fact that Gen AI drives better business outcomes. Depending upon the use case, there is also significant value in using the technology now, as an ever-growing number of organizations can confirm. The difference made by RISE with SAP is that it makes adoption far easier and more compelling.

RISE with SAP might not represent the totality of an organization’s AI strategy, but it does enable the key capabilities such as SAP Business Technology Platform (SAP BTP) that are critical to ongoing innovation.  Embedding Gen AI into RISE with SAP is also central to SAP’s long-term AI roadmap, making it important for customers to embrace the opportunity. This is where Capgemini can help, working with our SAP clients to help them:

• Identify the right use cases for Gen AI, while also deploying standard AI, automation, and hyper-automation
• Access the tools and accelerators needed to speed up the delivery of projects
• Utilize our deep relationship with SAP to surround their Gen AI journey with added reassurance
• Identify new use cases that enrich the ones out of the box from SAP
• Provide tools and frameworks for a safe, trusted, and cost-effective use of Gen AI.

Final thoughts

Improving forecast accuracy, lowering inventory costs, and detecting fraud – these and more use cases represent the ultimate goal of Gen AI projects. With Gen AI, a user can chat with a system, ask it to create a report, a purchase order, or a line of code, and receive increasingly personalized responses. This is the new reality as enabled by RISE with SAP and supported by all the experience and insight available from Capgemini.

It all points to an exciting future.

Read our next blog part of the series.

Promising benefits with potential challenges

Diversity and inclusion are more than just ethical imperatives—they are critical drivers of innovation and serve as competitive advantages in financial services. In an industry defined by its complexity and the demand for rapid, data-driven decision-making, diverse teams have been shown to outperform homogeneous teams when tackling complex challenges. Sheryl Sandberg’s assertion that “diverse teams make better decisions” captures the essence of why inclusion is a necessity for any organization striving for success, including financial firms. For example, research conducted by Development Dimensions International highlights that top-performing organizations feature 29% women leaders compared to only 23% in underperforming ones. Diversity isn’t a “nice-to-have” feature, it’s a strategic necessity that enhances creativity, improves problem-solving, and drives tangible business outcomes.

However, the benefits of diversity are not guaranteed, as they can be hindered by challenges that arise during team formation and collaboration. A study on organizational dynamics found that diverse teams often fall into one of three categories: destroyers, equalizers, or creators. Destroyer teams fail due to mistrust, negative stereotyping, and poor collaboration. Members in these teams waste valuable energy on conflicts rather than focusing on innovation. Equalizers, while maintaining surface-level harmony, suppress differences to avoid potential conflict. While these teams may achieve mediocrity, they fail to leverage the creative potential of their diversity. On the other hand, creator teams excel by explicitly recognizing, embracing, and nurturing differences. These teams deliver exceptional results by transforming diverse viewpoints into innovative and effective solutions. The challenge lies in turning destroyer and equalizer teams into creators, and Generative AI (Gen AI) offers a powerful means to achieve this transformation.

Source: https://www.sciencedirect.com/science/article/abs/pii/S0090261600000127

Transforming team dynamics and enhancing decision-making

Gen AI has the potential to fundamentally reshape how diverse teams interact and perform by providing data-driven insights and addressing systemic biases. One of its most significant contributions lies in challenging stereotypes. For instance, women leaders in financial services are often mislabeled as risk-averse or overly emotional, while male leaders may be perceived as hasty or less inclusive. Gen AI can offer objective insights that empower leaders of all genders to make more informed decisions, enabling them to leverage their strengths and address areas of improvement. By doing so, Gen AI fosters balanced leadership and enhances team dynamics.

When equipped with Gen AI, leaders gain access to comprehensive data analysis and summarized insights that would traditionally take weeks or even months to process manually. By receiving these distilled results, leaders can apply their expertise and strengths to make strategic decisions and determine the best course of action. Gen AI ensures that data is distributed fairly across the team, minimizing biases that may inadvertently influence conclusions when handled solely by individuals. Additionally, Gen AI enables leaders to make more informed decisions by processing significantly larger volumes of data than traditional tools or human efforts alone. When integrated with interactive prompts, Gen AI allows team members to “ask questions,” positioning it as an active participant at the decision-making table and fostering a collaborative dynamic.

Gen AI can also expose hidden gaps in diversity and reveal areas where inclusion efforts may be falling short. By analyzing patterns in team interactions and decision-making processes, Gen AI provides organizations with actionable insights to improve team composition and inclusivity. For example, it can identify biases in credit approval processes, wealth management strategies, or risk assessments that arise from homogeneous perspectives. This ability to spotlight areas for improvement enables financial firms to build teams that truly reflect diverse viewpoints. Additionally, Gen AI supports balanced leadership by offering data-driven recommendations, reducing reliance on subjective judgments, and encouraging equitable collaboration.

The benefits of integrating Gen AI into decision-making processes extend beyond addressing biases. Gen AI reduces the influence of subjective factors such as emotions or personal relationships, leading to more efficient and accurate decisions—qualities essential in a highly regulated industry. Its ability to process large amounts of data and identify patterns introduces innovative perspectives that may not be apparent to human team members. By acting as an impartial collaborator, Gen AI fosters constructive engagement among team members, helping them move beyond the superficial harmony of equalizer teams to the dynamic innovation of creator teams.

Balancing innovation with human diversity

Despite its potential, integrating Gen AI into financial services team dynamics requires careful consideration. Gen AI should be viewed as a complement to human diversity, not a replacement for it. The unique perspectives brought by individuals remain essential, and AI should not overshadow them. Furthermore, for Gen AI to deliver inclusive insights, it must be trained on diverse and representative datasets. In financial services, this means incorporating global market trends, varied client demographics, and cultural nuances into its training data. Without this, the AI risks perpetuating biases instead of mitigating them. Organizations must also navigate the risks associated with integrating AI into decision-making processes. While the introduction of Gen AI may initially seem daunting, the potential for innovation and strategic advantage far outweighs the challenges. The key to high-performing diverse teams lies in how they interact and leverage their differences, and Gen AI serves as a catalyst for these interactions.

Driving excellence through Gen AI in financial services teams

For financial services firms, the integration of Gen AI into diverse teams represents a turning point for decision-making processes in today’s complex and competitive business environment. By addressing biases, fostering inclusivity, and enhancing collaboration, Gen AI enables organizations to move beyond the status quo and build high performing teams. It introduces new perspectives, facilitates engagement, and transforms team dynamics. In an industry where cultural and geographic diversity is both inevitable and invaluable, financial institutions that embrace Gen AI as an active participant in decision-making are poised to lead the charge in innovation and success.

Capgemini Business for Planet Modeling uses the intelligence of Google Cloud capabilities to assess the impact of climate change on corporate financials and accelerate sustainable growth.

A 2023 study calculated that climate change costs the world $16 million per hour, with the global annual cost estimated between $1.7 trillion and $3.1 trillion by 2050. These costs include infrastructure, property, agriculture, and human health and they are expected to increase over time as climate change becomes more severe.

Big costs mean big impacts on the financial services industry. Banking, asset management, and insurance companies are facing increasing financial risks due to climate change. Understanding climate shifts has become essential to assessing their financial impacts, and the physical risk on banking and insurance portfolios. But there are a huge number of data points to consider at macro, sector, company and asset levels.

Failing to assess the impacts of climate risks could strongly undermine portfolio performance and competitiveness, especially when adding in the pressures from regulatory bodies to perform stress tests to model and mitigate the impact of climate change on financial services companies. These and other variables mean there is a need for reliable data and predictive models to make more informed business decisions.

The explosion of new technologies is transforming how we monitor Earth, presenting an incredible opportunity to better understand our planet. Thousands of satellites capture millions of images daily, and advanced sensors continuously gather data on temperature, precipitation, wind, and more-sometimes as often as every second. This unprecedented flow of information provides a comprehensive view of Earth’s systems like never before in history. By leveraging this vast and ever-growing amount of data, we have the potential to unlock critical insights that can empower decision-makers to address climate change more effectively and shape a sustainable future.

A different modeling approach

Most financial services institutions struggle with the complex data integration needed for modeling to assess how global variables like economy or energy evolution may be interconnected with climate change. To increase performance and competitiveness, the financial services industry must transform its approach to climate risk modeling. It needs to embrace new scenario generation capabilities and connect macro variables with granular asset-level risk assessment to produce financial statements that consider climate impact.

To help financial institutions overcome these challenges, Capgemini has developed Business for Planet Modeling (BfPM), a set of climate risk technology and advisory services built on the strength of Google Cloud and its partners. The solution embraces the power of Google Cloud’s geospatial analytics and artificial intelligence to simulate the financial impact of transition, the physical risks of climate change and global variables to enhance forecasting and support better decision-making to reduce risks and uncover new opportunities.

Unlike conventional methods, BfPM combines a holistic and granular analysis of climate risks, including those related to energy transition, leveraging extensive geospatial data and digital twin technology to stress-test scenarios. Additionally, BfPM’s customizable and scalable solutions seamlessly integrate into existing systems, enhancing forecasting capabilities, reducing risks, and accelerating the sustainability journey, ultimately leading to better financial and environmental outcomes.

We collaborate with Google Cloud and its partners to leverage Earth observation technologies in Google Earth Engine, Big Query, and Vertex AI, to understand their impact on physical assets. This partnership leverages 300 models and more than 265,000 variables to enable continuous climate risk monitoring and impact assessment. We aggregate and harmonize data from multiple sources, applying climate data science and machine learning on Google Cloud to deliver insights in Google Looker.  

“At Google Cloud, we are dedicated to leveraging our advanced technologies to drive sustainability and address climate change. By integrating our geospatial analytics, Vertex AI, and Earth observation technologies, we empower organizations like Capgemini to bridge the gap between corporate financials and climate impact. Together, we can create innovative solutions that not only mitigate financial risks but also promote sustainable growth and a healthier planet.”

Denise Pearl, Global Partner Lead, Sustainability and New Energy, Google Cloud

Designed to be secure and scalable, BfPM integrates into existing systems, providing easy access to rights management and a user-friendly environment. It harnesses the power of structured and unstructured data and insights to help accelerate the sustainability journey, reduce risk and unlock new opportunities to enhance returns.

How BfPM is different

Capgemini’s Business for Planet Modeling (BfPM) for Financial Services stands out by offering platform-based climate risk modeling services to address all use cases for financial services institutions’ including: climate stress testing, scenario analysis, financial planning, sustainability reporting, equity and loan portfolio management. By leveraging the power of Google Cloud’s analytics and AI, BfPM enhances the risk management and forecasting capabilities of financial institutions, enabling them to better understand and mitigate climate risks.

Key features and benefits:

• Integration services: BfPM services leverage an extensive ecosystem of specialized partners to integrate the best climate risk modeling solutions that will augment existing risk management tools for better business decisions.
• Integrated assessment models: BfPM uses a reliable and open-source integrated assessment model (IAM) to generate climate-influenced financial statements and financed emissions projections that will help drive portfolio transition and higher returns.
• Hybrid approach: by combining global variables such as economy, climate, energy, and carbon taxes with asset-level physical risk analysis, BfPM provides a holistic view of potential impacts on financial statements for equity and loan portfolios.
• Strategic digital twins: utilizing digital twin technology, BfPM can augment climate stress-testing capabilities and benchmark future business states against climate scenarios. This includes reliable forecast and models on climate, economy, energy, and planetary boundaries, ensuring secure and accurate simulations.
• Granular data analysis: by leveraging Google Cloud’s extensive data and partners, BfPM pinpoints the geolocation of all assets and analyzes the evolving impact of physical risks on them. This granularity allows for detailed market segment and asset-level analysis, making climate risk actionable.
• Customizable, scalable & modular solutions: designed to be secure and scalable, BfPM integrates seamlessly into existing systems. It simplifies the integration process, provides auditable outcomes and enhances returns.
• Advanced scenario generation: BfPM generates scenarios that integrate global variables and assess physical risks based onCoupled Model Intercomparison Project Phase 6 (CMIP6) data. Using NGFS and tailored scenarios co-developed with banks, it simulates climate change impacts on equity and loan portfolios, providing essential key performance indicators (KPIs) for risk executives.

By combining these advanced features, BfPM empowers financial organizations to dynamically analyze business scenarios and plans. This not only supports sustainable transformation but also enhances profitability while reducing the carbon footprint.

From quantum threats to AI-driven attacks, discover the key trends shaping the future of security and how organizations can stay ahead.

As we usher in 2025, the cybersecurity battleground has never been more complex. New technologies bring transformative opportunities, yet they also open the door to increasingly sophisticated threats. For business leaders, the mission is clear: anticipate risks, adapt to challenges, and take decisive action to build continuous resilience.

Here’s a closer look at the key cybersecurity trends shaping 2025, and how organizations can stay ahead in the face of tomorrow’s threats.

Quantum computing: A new frontier for security challenges

Quantum computing is no longer a distant dream but an emerging reality with profound implications for cybersecurity. Its potential to break traditional encryption methods threatens the foundations of secure communication and the digital economy. With the quantum computing market projected to reach $5.3 billion by 2029, the urgency for organizations to act has never been greater. Organizations must proactively adopt a flexible and resilient cryptographic approach, transition to post-quantum cryptography, and revamp their cryptographic protocols to remain secure in this new era. Embracing quantum-safe encryption today is the only way to safeguard tomorrow.

AI and machine learning: A double-edged sword

Artificial intelligence (AI) and machine learning (ML) continue to revolutionize cybersecurity, enabling faster, more accurate threat detection and response. However, these same tools are being weaponized by adversaries, creating a new breed of AI-driven attacks such as advanced phishing campaigns and deepfake scams. According to our latest Capgemini Research Institute (CRI) report, 55 percent of organizations are already prioritizing Gen AI to advance their cybersecurity.

To stay ahead, organizations must adopt AI-enabled solutions, not as a luxury but as a necessity. Balancing the benefits of AI while preparing for adversarial uses is critical to staying resilient in this rapidly shifting battlefield.

The zero trust imperative: Continuous resilience in a borderless world

“Never trust, always verify” is no longer a buzzword; it’s a cornerstone of modern cybersecurity strategy. As remote and hybrid work become entrenched in our business models, the zero trust framework is critical to securing every interaction – inside and outside the organization. By 2025, Gartner predicts 60 percent of enterprises will have embraced zero trust architectures.

For leaders, this means adopting a zero trust framework isn’t just about enhancing security; it’s also about enabling agility and maintaining business continuity in a perimeterless world.

Privacy and compliance: Navigating a regulatory landscape

As public scrutiny and governmental regulations around data privacy intensify, compliance has shifted from being an operational challenge to a reputational imperative. By 2032, the global data protection market is set to reach $505.98 billion.

Stay ahead of regulatory changes and use compliance as a differentiator. Transparency and robust data handling can build trust and set you apart in a crowded marketplace.

Ransomware’s growing shadow

Ransomware is evolving beyond encryption threats. Today’s attackers weaponize data, leveraging extortion tactics that combine encryption with public data leaks. With ransomware costs projected to hit $71.5 billion by 2026, it’s clear this is no longer just an IT problem – it’s a business risk.

Beyond technical defenses, build a culture of readiness. Incident response plans, cross-functional drills, and employee awareness are your frontline defenses.

Evolving security operations: The hyperautomated intelligent Security Operations Center (SOC)

As threat actors become more sophisticated and early adopters of automation and AI increase the speed, reach, and depth of their attacks, organizations must raise the bar in their security operations.

With more solutions running in the cloud, leveraging bidirectional API connections to automate hundreds or thousands of playbooks has become imperative. Additionally, security orchestration, automation, and response (SOAR) systems are evolving to automate complex tasks such as phishing takedowns, vulnerability patching, and malware analysis.

Moreover, AI can be used to substantially remove false positives and improve context during an incident investigation, increasing the productivity of SOC analysts and reducing alert fatigue.

2025: An era of disruption and opportunity

Cybersecurity in 2025 is about much more than mitigating threats – it’s about embedding resilience, innovation, and trust into the core of your organization. It’s a business enabler, a trust builder, and a critical competitive differentiator.

The path forward requires bold decisions, strategic investments, and a commitment to staying ahead of an ever-evolving threat landscape. Continuous resilience is no longer a choice – it’s a strategic advantage.

Let’s shape the future of continuous cyber resilience, together.

As we enter 2025, the global energy sector faces a volatile and fast-moving landscape. Pressures from rising electricity demands, geopolitical shifts, and digital advancements converge to redefine the way we produce, manage, and consume energy.

Five critical themes are poised to impact us this year, offering both challenges and opportunities for governments, businesses, and consumers alike.

Conclusion: navigating a complex transition and peaking emissions?

The interconnected nature of these trends highlights the complexity of the global energy transition.

Rising electricity demands, the nuclear revival, geopolitical pressures, grid modernization, and digital innovation all intersect to shape the sector’s future.

To succeed, stakeholders should embrace collaboration within an innovation ecosystem, and adaptive policies. Governments, businesses, and consumers alike have a role to play in navigating these challenges and seizing the opportunities that 2025 presents. By aligning technological advancements with sustainability goals, the energy sector can accelerate its journey toward a more resilient and equitable future.

2025 promises to be transformative, setting the stage for long-term progress in building a sustainable energy ecosystem. The path forward requires bold action and a shared commitment to fostering the innovations needed to power a cleaner, smarter, and more sustainable world.  The most powerful example of this could be that global emissions of greenhouse gases might peak.  The link between economic development and emissions is steadily weakening.  China has been making significant progress and if the demand for fossil fuels in China continues on its current trajectory it’s possible that global emissions could peak in 2025 and that would be a very significant development.