乌鸦传媒’s Job Fair solution improves CV processing and communication by making the recruitment process more efficient and transparent for candidates.

At one time or another we have all felt the frustration of sending out dozens of job applications, each tailored to a specific job or company, only to receive no response from any HR team whatsoever.

This lack of communication can leave candidates wondering if their application was even considered or how they could improve for future opportunities.

Simplifying CV processing

乌鸦传媒 saw overcoming this problem as a challenge which is why we set out to improve our CV processing capabilities, helping us deliver an intelligent and connected “consumer-grade” people experience to any potential candidate who engages with us.

Our Job Fair solution ensures timely communication with candidates, including those who will not move forward in the recruitment process.

Our process allows HR teams to quickly inform candidates when their recruitment journey has concluded without an offer. These messages are phrased positively to motivate candidates for future efforts while maintaining transparency and efficiency.

This approach helps 乌鸦传媒 build a reputation as a company that cares about career growth, even for those who haven’t worked with us.

Tackling CV processing challenges with precision

乌鸦传媒’s Job Fair solution uses Optical Character Recognition (OCR) and Microsoft’s Power Automate technology to extract key information such as email addresses, and phone numbers from various documents quickly.

Furthermore, our Job Fair solution鈥檚 simplicity and flexibility, combined with its straightforward interface, requires minimal training to operate effectively. This ensures improved CV processing comes with minimal disruption, and enables HR teams to meet a wide range of business needs without developing a new, expensive solution.

Delivering award-winning HR processes

We understand that companies need to focus on bringing their people, processes, and technology together to deal with whatever their business might face, moving them closer to becoming a truly Connected Enterprise.

This mindset is why 乌鸦传媒 recently won a Gold Medal in Brandon Hall鈥檚 Excellence in Technology Awards, 2024. This highlights 乌鸦传媒鈥檚 commitment to implementing effective, easy-to-use applications that address its clients鈥� needs at speed, leaving a positive impact on their businesses.

But that鈥檚 not all. 乌鸦传媒 also won a Silver Award in Brandon Hall鈥檚 HCM program, 2023, which clearly demonstrates that 乌鸦传媒 is among an elite group of exceptional HR service providers.

To discover more about how 乌鸦传媒鈥檚 Intelligent People Operations put your employees at the heart of HR operations, across your talent acquisition, HR administration, payroll, and HR analytics functions, to deliver strong and sustainable business value, contact: alicja.watorek@capgemini.com

There鈥檚 a crucial need to demystify bio-engineering solutions to demonstrate their value, gain public trust and understanding, and drive adoption and economic growth.

The COVID-19 pandemic was a moment in history, when global governments, private institutions, academia, and society as a whole, coalesced around the need and to benefit global wellbeing. When a vaccine was produced and rapidly approved through extraordinary measures, the collective relief was palpable, but there was an unexpected problem.

Public trust in science, representation, and a lack of bio-literacy around mRNA vaccine technology challenged the progress of the global vaccination program. Governments worldwide invested hundreds of millions in fighting disinformation about bio-engineering solutions.

For a new innovative solution to be adopted, the people it serves must trust that it will work in their best interests, and trust that those developing solutions have their best interests at heart. In the case of such new and emerging deep-tech as bio-engineering solutions, with their implications to natural evolution, building trust and understanding is more important than ever.

There are clear steps we can take to build this trust, to drive adoption of bio-engineering solutions and related socio-economic growth.

Low levels of literacy and trust: policy makers, citizens, and markets must better understand bio-engineering solutions to unlock their potential

Recent data from the 乌鸦传媒 Research Institute highlighted the growing divide between industry innovation and public understanding: 96% of companies reported active engagement with, or plans to develop, bio-solutions. And yet, research by the showed that 76% of UK respondents feel they lack sufficient information to make informed decisions about bio-based products. The same report found that 61% have never heard of engineering biology. Although these findings are UK-specific, similar trends are evident globally. For example, the CRI report also revealed that 65% of startups and corporations view low public bio-literacy as a significant barrier to adoption.

The consequences of this gap are profound. One showed that decades-old public resistance to the distinct domain of genetically modified organisms (GMOs) shaped overly cautious regulatory frameworks that impede advancements in plant genome editing to this day. As a result, solutions to critical challenges, such as food security, continued to be delayed. More recently, hesitancy around mRNA vaccines has shown how a lack of communication can erode trust and create a vacuum for misinformation to grow. Without proactive public engagement, societally-valuable bio-based innovations such as bio-based plastics, precision farming, and sustainable fuels risk similar skepticism, potentially stalling their adoption. Governments and industries must act collaboratively, ensuring consumers feel like active participants in this journey, not passive spectators.

Business and government: shared responsibility to unlock economic growth through engineering biology vision

The challenge of bridging the gap between innovation in bio-engineering solutions and public understanding is a shared responsibility. It demands coordinated action from both businesses in industry and policymakers.

Businesses must go beyond simply creating innovative bio-products 鈥� they should actively engage citizens and a broad stakeholder group throughout the development process by employing user-centered and co-design techniques. This not only builds trust and understanding with consumers but of course ensures stronger market fit and a greater chance of commercial success.

To unlock the economic growth potential of bio-engineering solutions, policy makers also need to engage in similar user-centered and co-design techniques early in the policy making process through techniques like 鈥楶olicy Labs.鈥� Policy Labs help rapidly map 鈥榩olicy whitespace鈥� where a new emergent theme or technology means there are few existing policy ideas or policies to build on. If policy teams become blocked due to a lack of understanding of complex new technologies, this prevents private markets from progressing due to a lack of support or legislation and regulation that unnecessarily blocks development. 

The importance of Policy Labs

Policy Labs are a proven technique to test early policy hypothesis, to ensure policy solutions will work for all stakeholders. For instance, if there鈥檚 to be a proposed investment by the Department of Environment Farming and Rural Affairs (DEFRA) into alternative proteins or GM crops, the farming industry should be engaged to test early policy hypothesis to see whether proposed solutions, services, regulations or legislations are likely to empower farmers with new economic means. Or disempower them by creating new competition in the markets or an even more complex post-Brexit funding landscape.

A Policy Labs approach also ensures better cross-departmental working and inputs from the relevant government bodies that should be involved, across such a broad problem space. For example, in the UK, the Department of Business and Trade (DBT) or Department of Science Innovation and Technology (DSIT) might work with DEFRA to provide a better holistic approach and collaborative understanding to launching new solutions by data sharing agreements. Subject Matter Experts (SMEs) and private sector specialists in Engineering Biology could also be engaged through the policy labs process to ensure policy ideas are technologically viable, operationally feasible, and economically scalable in markets.

Open and honest dialogue

Governments should also be creating an ongoing dialogue with citizens to ask what assurances they need to feel confident about bio-engineering solutions (products and services) and how perceived risks can be addressed. Transparent, accessible communication mechanisms are key, alongside independent oversight to ensure safety and accountability. As the puts it: 鈥楶ublic engagement, improvement of public perception, and building trust are critical factors for the growth of the bio-economy and market.鈥�

A call to action for bio-economy solutions

Engineering biology has the potential to solve some of the world鈥檚 most pressing population scale challenges, from climate change to healthcare. But without better collaboration between governments, industry, and the markets they serve, progress on bio-engineering solutions will stall. By employing co-design both in the product development and policy making lifecycles, we will build better understanding and trust among stakeholders and citizens, while enabling policymakers and businesses to ensure bio-solutions not only innovate but also meet public needs.

At 乌鸦传媒, we provide end-to-end support in Engineering Biology and AI for Science strategy, helping public and private sector clients accelerate bio-solutions while addressing the challenges outlined here. From fostering public bio-literacy and engaging citizens to building transparency into innovation and driving growth through regulatory collaboration, we leverage our insight drawn from practical laboratory bio-engineering, to advise our clients on delivering impactful, trusted, and scalable outcomes. Together, we can draw up a bio-economy strategy that maximizes public trust in the solutions that will shape the world for years to come.

The time is now to act. Together, we can do more than just create bio-solutions; we can create a bio-ready society. Get in touch to explore how we can help accelerate your engineering biology journey.

The jump from cloud FinOps to On-Demand consumption FinOps is underway, it鈥檚 time to embrace the evolution and unlock greater business value.

Cloud FinOps was built around utilizing the public cloud to manage financial investments and has become a standardized part of today鈥檚 technology landscape. However, FinOps cloud cost management is no longer enough 鈥� it鈥檚 time to evolve. To do so, it鈥檚 worth considering what cloud FinOps is currently defined as to better highlight our solution.

The FinOps Foundation, a project to advance the community who practices FinOps, defines cloud FinOps in their Framework as: 鈥渁n operational framework and cultural practice which maximizes the business value of cloud, enables timely data-driven decision making, and creates financial accountability through collaboration between engineering, finance, and business teams.鈥�¹

However, with the growth of on-demand technologies like SaaS and Gen AI, FinOps needs to go further. And with this evolution in business and technology comes a new focus on operating expenses (OpEx), particularly in how these technologies affect cost. Companies need to evaluate the value of OpEx spend, while FinOps need to underline the benefits.

To put it simply it鈥檚 time to ask: What鈥檚 the cost of a click?

It is undeniable that cloud FinOps has proved its worth both quantitatively and qualitatively in terms of cutting the fat out of technology investments. It鈥檚 driven elevated business value and has become a staple discipline across organizations.

Still, there are a number of limitations that contemporary FinOps practices have run into. These primarily circle around common themes such as isolation and lack of strategic input. And with companies seeking to manage their OpEx spend, ensuring that FinOps and on-demand technology integration continues can become complicated by a lack of visibility among FinOps teams.

Let鈥檚 examine these challenges in more detail. 

Isolated FinOps Teams鈥�

There鈥檚 no denying FinOps teams do a great job in their areas of influence, however, those areas are often too limited. This is especially true when executive sponsorship wanes and other priorities take precedence. The focus on cloud FinOps seems to return only when a negative cost incident occurs, and this only further isolates these teams.鈥�

Low demand management influence鈥�

Even the most successful cloud FinOps capabilities are limited in their influence in demand management. Specifically, in how FinOps teams influence acquiring new technologies and resources to support the growth of the discipline. Sure, they can highlight cost implications and even help shape cost-effective solutions, however, they can rarely influence demand management directly 鈥� especially as part of a wider, end-to-end system.鈥��

Limited strategic reach鈥�

FinOps teams usually report to a technology leader who isn鈥檛 likely sitting in the C-Suite. Naturally, this limits the influence on strategic decision-making that FinOps has. This is a compounding problem, as other areas of the business group beyond the IT organization are driven by C-Suite priorities and bridging that gap is hard 鈥� if not impossible 鈥� in a large enterprise.鈥��

Isolated from other initiatives鈥�

To add to this gap, initiatives from senior leadership most often do not happen in concert with FinOps teams. The consequence can impact how architectural principles are re-designed, or result in frugal investments made in architecture that should support FinOps. It translates into a lack of FinOps integration with the wider business. The result? Unplanned or unexpected complexity later.鈥��

Singularly focused on cloud and public cloud services鈥�

In the current market, there is an ever-increasing variety of consumption-based technologies in use. FinOps teams who are only using public cloud are limiting the potential value they can generate. This in turn results in the web challenges we鈥檝e described. It鈥檚 precisely the place where traditional cloud FinOps stumbles, and where exactly is that leading us?

While the FinOps Foundation is widening their scope to include SaaS and AI, within the overall FinOps community, these concepts are still in their infancy. This is true both in terms of implementation but also integrating them into current FinOps practices.

However, we believe this is a vital moment to start the shift towards acquiring new technologies and re-defining goals. This is reinforced by the fact that there is an increasing amount of tooling available to not only identify, but also to introduced automated optimization of various FinOps processes. And with the continued adoption of machine learning and AI (which is already rolling out as AI for FinOps) 鈥� this will only accelerate.鈥� 

Welcoming the new era of FinOps:

Cloud Consumption On-Demand鈥�

From the very beginning, the rise of Cloud Consumption On-Demand in FinOps must consider all consumption-based technologies and business strategies. This includes everything from SaaS, Gen AI, AI infrastructure, and other cloud FinOps services. In turn, these technologies must be introduced to encompass the entire business value chain.

To do this, FinOps teams need to be challenging traditional capital expenditure (CapEx) governance systems. In a world where a growing number of technologies are purchased under OpEx scenarios, this can be complex. In other words, FinOps teams must not only prove long-term financial benefits but provide a solid short-term business case that tangibly shows how Cloud Consumption On-Demand technologies create cost savings and better efficiency.

It also means re-defining how FinOps is perceived. From their current periphery, FinOps teams must harness on-demand technologies to demonstrate their value with faster and automated solutions, reduced expenses, and elevated data-led decision making. This will help showcase the strategic value FinOps teams are creating.

Additionally, FinOps teams need to underline that the challenges they face are business problems. It is not a question of managing IT systems or technologies. This is a matter of delivering better business value thanks to the benefits of on-demand technologies, such as accurate budgeting and elevated forecasting results.

FinOps teams can also deepen collaboration across business units with on-demand technologies, For example, providing more flexible scalability that best fit the immediate business need. This can help keep various disciplines, like finance and engineering, connected and transparent in terms of financial accountability via integrated tools and platforms with accurate, real-time data sharing. 

Transforming company culture

While harnessing on-demand technologies and more modern FinOps practices are vital, it is education that will really make an impact. FinOps teams have the opportunity to transform a company鈥檚 culture by pushing for stronger education around on-demand technologies and their potential. Such a step is not only desirable but business critical.鈥�

These cultural practices that on-demand FinOps can create will help spark greater levels of integration across business lines. This translates into the reduction of silos across the organization. Ideally, the new FinOps will fully integrate with demand architecture, purchasing, on-demand technology, reporting, analysis, finance forecasting, and more.

It’s time to evolve the FinOps framework 

This new world of FinOps will play a far more active role. Powered by on-demand technologies, the discipline can escape the limitations that reliance on public cloud has introduced and in turn, generate greater value. Most importantly, it gives FinOps teams deserved recognition as fundamental players in modern business practices. 

Do you know the cost of a click?

With Cloud Consumption On-Demand, we can help diagnose the potential of FinOps has in store for companies, while supporting them with action plans to help them make it a reality.

It鈥檚 time to welcome in the new era of cloud FinOps鈥�

鈥� It鈥檚 time to know the cost of a click.

Reference: 1.

The challenge of text summarization in financial services

The financial services industry generates an immense volume of documentation daily. From customer interactions and regulatory filings to legal proceedings and risk assessments, organizations must process, interpret, and act upon large amounts of unstructured data. Traditionally, this has been a time-consuming and labor-intensive process, often susceptible to human error and inconsistencies. As regulatory frameworks evolve and customer expectations rise, the demand for accurate, efficient, and standardized document summarization has never been more critical.

In banking, institutions must navigate a constantly shifting regulatory landscape. Compliance teams are responsible for reviewing extensive regulatory filings, risk reports, and audit documents鈥攁ny misinterpretation can result in significant financial and legal consequences. Beyond compliance, customer service operations require rapid access to key insights from call center interactions to enhance service efficiency. Additionally, loan and credit risk assessment teams manually analyze financial statements, credit histories, and other documents to determine creditworthiness, a process that is both time-intensive and costly.

The insurance sector faces similar challenges, particularly in underwriting, policy management, and claims processing. Insurance providers must constantly interpret complex regulatory changes while ensuring accurate policy underwriting and risk assessment. Claims processing teams review medical reports, legal documents, and third-party assessments to determine coverage and fraud risk. Manual document reviews in these areas not only slow down operations but also introduce inconsistencies that can impact decision-making.

The increasing complexity of financial services documentation makes manual summarization an unsustainable approach. Generative AI (GenAI) offers a powerful solution by enabling automated summarization of key insights from various documents. However, assessing the quality of AI-generated summaries remains a challenge. Traditional evaluation methods, such as and , rely on human-generated references, which are not always available or practical for large-scale financial services applications.

Introducing QAG-based automated summarization evaluation

Question-Answer Generation (QAG) for provides a breakthrough, offering a reference-free approach to ensuring both completeness and accuracy in AI-generated summaries. Instead of comparing summaries to predefined references, QAG-based evaluation gauges summarization quality by generating factual questions from the original document and checking whether the AI-generated summary provides correct answers.

Experimental results

Optimization techniques for QAG were implemented that included limiting truth extraction and using custom question templates to improve evaluation performance.

This enhanced QAG-based evaluation approach was then tested on four real-world transcripts. In each test, both the default QAG model and our optimized approach were implemented. The following table summarizes the results:

Overall, the experimental results reveal a significant leap in alignment scores, rising from a baseline of 56% to over 70%, while coverage scores experienced an even greater boost, increasing from 70% to 90%. These enhancements demonstrate the effectiveness of the refined approach in producing more accurate and comprehensive AI-generated summaries.

Wide-ranging use cases in banking and insurance

By implementing QAG-based evaluation, financial institutions can improve the reliability and accuracy of GenAI-powered summarization across multiple business functions. In banking, it ensures that compliance reports, customer interactions, and financial risk assessments maintain factual integrity. In insurance, it enhances underwriting decisions, policy management, and claim evaluations. The following is a sample of several key use cases in financial services.

Banking use cases

• Call center interaction summarization: Customer service teams manage a high volume of customer interactions, often recorded in call center transcripts, chat logs, and emails. GenAI can summarize these conversations, extracting key themes, customer concerns, and sentiment trends, enabling more efficient issue resolution. With QAG-based evaluation, AI-generated summaries ensure that no critical customer concerns are overlooked, allowing for more personalized and proactive customer support.

• Audit report summarization: Internal audits are a critical part of risk management in banking, yet the process is often time-consuming and labor-intensive. AI-powered summarization helps highlight key discrepancies, compliance violations, and recommended actions from audit reports, improving the efficiency of risk and compliance teams. With QAG-based evaluation, banks can ensure that summarized audit findings remain aligned with the original reports, reducing the chances of oversight in risk assessments.

• Credit risk assessment: Evaluating a borrower鈥檚 financial health requires the review of credit reports, financial statements, and loan histories, often spread across multiple documents. GenAI can consolidate key financial indicators into a structured summary, allowing risk analysts to make faster and more informed lending decisions. By applying QAG-based evaluation, banks can verify that these summaries accurately reflect the borrower鈥檚 financial status, reducing errors in credit risk assessments.

Insurance use cases

• Underwriting and risk assessment: Insurance underwriting requires the evaluation of extensive data, including health records, financial documents, and previous policy claims. GenAI-generated summaries allow underwriters to quickly assess risk factors, policy eligibility, and pricing considerations. With QAG-based evaluation, insurers can confirm that these summaries capture the full scope of risk assessment criteria, reducing underwriting errors and improving decision-making efficiency.

• Policy management: Managing policies involves handling a large amount of unstructured documentation throughout the policy lifecycle. Any modifications initiated by insurers or customers require careful reassessment. GenAI streamlines this process by efficiently condensing information from various sources. By applying QAG-based evaluation, insurers can confirm that AI-generated summaries align with policy terms and regulatory requirements, enabling them to allocate more time to strategic tasks such as customer service and relationship management.

• Claims processing: Whether for auto, healthcare, or commercial policies, claims processing is a complex, documentation-heavy task that demands significant time and effort when done manually. GenAI automates the extraction of critical details from diverse records. QAG-based evaluation ensures that all necessary claim details are preserved, reducing operational costs, expediting claim settlements, and improving overall customer satisfaction.

These use cases highlight just a few of the many ways QAG-based evaluation can be applied in financial services. Potential applications extend far beyond these examples. Depending on an organization鈥檚 specific needs, QAG-based evaluation can be adapted to review AI-generated summaries across a wide range of business functions, including regulatory reporting, contract analysis, investment research, internal policy compliance, and more.

Driving accuracy, efficiency, and trust in AI-generated summarization

As financial institutions increasingly rely on GenAI to streamline document processing, ensuring the accuracy and reliability of AI-generated summaries is paramount. QAG-based automated summarization evaluation provides a reference-free, scalable, and precise method to assess summarization quality, addressing one of the key challenges in AI adoption. By evaluating summaries based on factual correctness and content coverage, QAG-based evaluation offers a structured approach to verifying AI outputs without the need for human-generated reference summaries.

The benefits of integrating this approach in banking and insurance are far-reaching. Banks can enhance decision-making by quickly extracting key insights from financial reports, compliance documents, and customer interactions. This leads to faster responses to regulatory changes, improved operational efficiency, and a more seamless customer experience. In the insurance sector, QAG-based evaluation improves underwriting accuracy and claims processing efficiency, ensuring that AI-generated summaries are both comprehensive and aligned with business objectives.  

Now is the time for financial institutions to embrace AI-powered summarization with QAG-based evaluation. To explore how this approach can elevate your organization鈥檚 AI-driven summarization efforts, contact 乌鸦传媒鈥檚 Financial Services 乌鸦传媒 & Data team today.  

Call centers are seeing gains but reliability and consistency need to be a focus. Adopting a copilot approach is the best way to ensure real efficiencies and positive customer experience. 

It has been said that AI agents could be a . Intelligent software agents capable of learning to manage actions and tasks have the potential to transform almost everything. Work and life will be impacted by the drive for productivity and efficiency. But AI agents will also democratize access and help overcome barriers to empower more people and drive innovation.

The road to agentic AI is still being built and there will be many routes to explore but, today, one of the biggest pushes is coming from the telecommunications industry. Call centers have been early adopters for this kind of generative AI because it is a natural evolution from bots. Existing chat features were interesting but they do not always work well, or customers were so annoyed by the menu systems on phones that they were unhappy before they even spoke to someone.

Moving beyond bots

Generative AI brings a much better experience to the call center structure, and it enhances existing technology. For example, (CES) was built on its Contact Center AI and enhanced with generative AI technology. It has better engagement with customers in both the chat channel and live. With the emerging capabilities of large language models (LLMs) and the growth of companies like OpenAI, AI agents can take on expanded tasks.

Creating a multi-modal experience allows an AI agent like Google Gemini to intake text, visuals, and audio, and add a communications layer through actual voice and text-to-voice features that are extremely realistic.

Combining Gen AI, language features, the ability to understand a vast amount of context instantly, and better and more human communication with text-to-voice capabilities creates systems with huge potential.

Enhancing the AI agent

We have recently launched the concept of thinking models that are capable of handling much more complex tasks. This is achieved through reinforcement learning based on human feedback, which means these models can actually think, process, and approach problems from multiple angles and explore different paths to find the best solution. It is very reminiscent of how a human would work to solve a problem.

Agentic AI has the capability to not only understand what a customer needs but to communicate in our own language with the right nuances and even slang. Communication is there. The thought process is there. The ability of AI agents to think through problems at length is there. And they bring the ability to use tools during interactions.

For example, a customer calls in with multiple inquiries. The AI agent can quickly understand the intent of the call so there is no longer a need to sift through menus or listen to a bunch of options. Because the intent is read in the early stages of the call, the problem resolution process operates better, as the AI agent has the information to solve the problem and the tools to execute it.

The right AI team

After detecting the true intent of a customer call, a master AI agent can act as the interactive layer with a customer, while simultaneously accessing a team of subagents to delegate tasks. The subagents can specialize in different areas, like billing issues or new installations. There is no more waiting on hold to be transferred to a different department or a manager. The master agent can access a whole host of tools and know what it needs to take action.

For example, a customer may want to process a payment. The master agent can identify the request and decide how to proceed. It can give a credit, research a billing discrepancy, or initiate other searches to complete the request. It can call different APIs to get information, update the account, and process the bill. With access to tools, there is really no end to what an AI agent can do.

These reasoning capabilities and tools mean agents can do very similar things to humans. However, it is still early days in the process and there are concerns to be addressed. Reliability and consistency are two factors. The monitoring and evaluating are improving to help ensure the responses and decisions by AI agents are correct.

Improving the call center experience

We worked with one telco client to deliver better knowledge searching, to leverage LLMs to use new methods of data acquisition summarization. The goal was to make technical documentation more accessible so when someone calls to troubleshoot a modem, for example, the answer is readily available.

Call centers are also a common sales channel. Agents can provide additional information or offer specific deals. That requires the agent to understand the needs of the customer, align them with a product, make an offer, address objections, and close the deal. Now an AI sales agent can interview the customer to understand the needs and wants and match them with potential solutions. They can even address objections and concerns to help get to the sale.

Copilots: Finding the agentic balance

According to a recent 乌鸦传媒 Research Institute report, being an agent is not an overly satisfying career choice, with only 16 percent of human agents surveyed report overall satisfaction with their roles. They face a number of pressures, from rising customer expectations to inefficient systems and a high attrition rate. There are efficiency gains to be made by employing AI agents that can help humans do a better job. In addition, AI agents can help resolve issues more quickly so the customer and employee have positive experiences.

This is why the copilot effect is a popular AI agent option. Google has Agent Assist to support live agents to resolve queries and issues more quickly. It is like having an expert in the room at all times with a call center agent. For example, the human agent can use it to help digest what is being said, with information automatically appearing on dashboards to assist with the call resolution. The copilot can also provide real-time assessments of the sentiment of the caller. Now the human agent has prompts with potential resolutions, rather than having to bounce between different systems for information or consult with a manager.

The human in the middle

So the concept of the human in the middle is very important. AI agents are a powerful tool meant to enhance experience, but sometimes a model can hallucinate or produce an error 鈥� and a company is responsible for an AI agent鈥檚 output. That means companies have to own the net result. So employing copilots with the human in the middle is happening even in new call centers. Once a system is proven, the role of AI agents can expand but, since call centers have a major impact on customer experience, there needs to be a high level of comfort with the system.

Call centers that use Google Customer Experience Suite (CES) engage customers with generative AI for many tasks, like determining what a client needs and other lower-level processes, to make calls more efficient and get to resolution quicker. AI agents can, for example, engage with back-office operations so humans can focus on more high-value tasks.

It takes time for companies to be comfortable with exploring generative AI solutions.  Companies need to focus on the business case and ensure innovation results in efficiency and savings.

Working with Google Cloud, 乌鸦传媒 can help companies move into the agentic future. We can help companies build a competitive edge with agents to drive real customer service transformation. Google Cloud鈥檚 advanced AI capabilities enable businesses to build and deploy intelligent virtual agents easily. It is time to create, frictionless environment to scale agents where everything supports the needs of the organization and its customers.

Join us at Google Cloud Next to discover how we’re helping companies embrace the agentic era and benefit from the intersection of innovation and intelligence.

Forget chatbots. The age of the agent is here. Imagine a digital workforce that understands, empathizes, and anticipates customer needs as a trusted advisor 鈥� a network of AI agents collaborating to deliver truly human-centric experiences.

This isn’t science fiction; it’s the dawn of the Agentic AI era, and it’s poised to revolutionize customer interactions.  is projecting the global Agentic AI market will be valued at $196.6 billion by 2034, a dramatic leap from $5.2 billion last year. This exponential growth is not just exciting; it signals a fundamental shift. While the possibilities are vast, companies must move beyond simply creating “cool agents” to building robust, collaborative systems.

Agentic AI is rapidly evolving, and the conversation needs to shift towards building networks of interconnected AI agents. This next stage, focusing on multiagent systems, is where real value will be unlocked. 

Next-level hyper-personalization: The game changer 

The true power of multiagent systems lies in their ability to deliver hyper-personalized experiences. Imagine AI agents seamlessly orchestrating across different business areas, instantly accessing client information to tailor interactions in real-time. This level of hyper-personalization, incorporating individual preferences, creates a genuine sense of personal connection. 

Multiagent systems represent the next evolution in personalized interactions. We’ve moved beyond deterministic chatbots and automated processes to a realm where embedded generative AI enables faster, more personalized interactions that build loyalty and connection. The impact is already evident: according to the 乌鸦传媒 Research Institute, 31 percent of organizations using generative AI see faster response times, and 58 percent anticipate further improvements. 

Efficiency and beyond: Connecting agents across departments 

Beyond enhancing customer experience, connecting agents across departments drives efficiency and productivity through automated, complex workflows. The ability for agents to communicate and operate seamlessly at faster speeds across departments unlocks significant potential. 

This also expands service capabilities. For example, overcoming language barriers in global call centers becomes possible with multilingual digital agents. Research indicates that 60 percent of consumers would pay more for premium customer service, highlighting the value of these enhanced capabilities. provides the AI technology and natural language processing (NLP) that can provide enhanced customer experiences.  

Connecting agents and data: Unlocking deeper insights 

Multiagent systems generate valuable data on information and conversations, which, when shared, provides a deeper understanding of customer behavior and trends. 

This data spans various departments 鈥� sales, order management, supply chain, ERP, and marketing 鈥� highlighting that inquiries rarely fit neatly into departmental silos. Agents need to be able to access data across these silos is crucial for providing cohesive responses to complex customer questions. 

This is why cross-department collaboration is crucial. Agents need seamless handoffs and access to different departments so that when a person engages with them, the conversation continues without waiting for the next agent to be updated. 

However, simply opening up data is not enough. Robust security protocols are necessary to ensure that not all information is accessible to every agent. Agents must pull information in a way that maintains visibility, requiring a deep understanding of systems for effective deployment. Data security and privacy are paramount. Accessing various data sources requires clear guidelines and governance to ensure compliance with existing data rules. 

Agentic change management: Blending the human workforce with the 鈥渄igital workforce鈥� 

Ideally, digital and human workforces will seamlessly blend, working in unison on daily tasks and customer interactions. Generative AI will continuously learn from feedback and algorithms, while large language models adapt. However, potential biases must be addressed to ensure fairness. 

Companies must also address the impact of multiagent systems on the human workforce. Clear communication early in the process can prevent resentment toward AI agents. Reassuring employees is a crucial part of change management. If employees fear job losses, they will be less inclined to engage with companies using AI agents. Multiagent systems offer exciting possibilities, but everyone must be part of the solution to maximize the benefits. 

Building a resilient agentic infrastructure 

Agentic AI does not mean creating a single, all-encompassing agent. Companies must prioritize resilience. Humans have bad days, and so can AI agents. If a single agent fails, the entire operation can grind to a halt. A multiagent system allows agents to focus on specific areas, ensuring that if one fails, others remain unaffected. 

The challenge for companies lies in the complexity of the infrastructure required for seamless agent communication. While technology is increasingly sophisticated, the talent to make it work is scarce. Companies need the right skills to build and effectively operate these agentic systems. 

is an orchestration platform that allows companies to deploy agents easily. The Google ecosystem integrates seamlessly with any system, ensuring smooth information flow, regardless of whether a company is using Google applications and infrastructure. 

Working with Google Cloud, 乌鸦传媒 can support customer service transformation that creates seamless, quality interactions that deliver an exceptional level of service, support, and delight to all stakeholders. Advanced AI capabilities and scalable infrastructure means Google Cloud can build and deploy intelligent virtual agents, enhance agent productivity, and personalize customer experiences easily. We can leverage the power of Google鈥檚 Customer Experience Suite to innovate for growth and reinvent business models to unleash what is possible. 

Join us at Google Cloud Next to discover how we’re helping companies embrace the agentic era and benefit from the intersection of innovation and intelligence.

As the Australian public sector navigates an increasingly complex landscape, new strategies are emerging to address multifaceted challenges with greater agility and impact.

In 2025, a key focus is on leveraging collaboration, innovation, and technology to create more resilient, citizen-focused solutions across areas such as cross-jurisdictional governance, digital trust, leveraging agentic systems, workforce capability-building, and digital inclusion.

Cross-Jurisdictional and Departmental Collaboration for Problem-Solving

A prominent trend in the Australian public sector for 2025 is the increased collaboration across jurisdictions and government departments. Faced with complex, interconnected challenges like climate change, healthcare, and cybersecurity, governments are moving toward more unified strategies. This shift reflects a growing recognition that many problems cannot be solved in isolation. Rather, cross-jurisdictional and cross-departmental collaboration is essential for achieving better policy outcomes and delivering more effective public services.

The Australian cross jurisdictional Data and Digital Ministers’ Meeting (DDMM) exemplifies this trend, facilitating coordination among states and territories to leverage shared resources and expertise. The focus has shifted from simply allocating resources to individual departments toward funding initiatives that foster collective problem-solving. This collaborative approach is proving particularly effective in addressing complex, multi-faceted challenges that require coordinated responses across multiple jurisdictions and agencies. For example, the . This significant funding aims to create a consistent approach across states whilst enhancing public safety through effective inter-agency cooperation.

Building Digital Trust and Trust in Government Services

In 2025, establishing digital trust and confidence in government services remains a critical focus for the Australian public sector. As societal expectations evolve and public scrutiny intensifies, government institutions face a more complex trust landscape. Citizens increasingly prioritise and expect data security, transparency, and privacy, necessitating public sector leaders to implement robust frameworks for managing sensitive information.

To cultivate citizen confidence, through responsible data practices and stringent security measures. This includes:

• Collecting and Using Data Responsibly: Government agencies are committed to transparent data practices that allow citizens to understand how their information is collected, used, and protected.
• Enabling Safe and Secure Information Sharing: Modern digital infrastructure will facilitate secure information sharing across government entities. Notably, the Federal Budget has allocated $1.8 billion over the next three years to bolster the nation’s cybersecurity environment, underscoring the commitment to safeguarding digital interactions.
• Reducing Legacy Technology: Transitioning away from outdated systems is essential for enhancing security and efficiency.

These initiatives aim to create a trustworthy environment where citizens feel assured that their data is protected and utilised ethically.

The Rise of AI and Agentic Platforms in Government

The adoption of AI agents signifies a strategic evolution from merely digitising public services to leveraging artificial intelligence for improved service delivery. This shift extends beyond large language models (LLMs) to encompass AI agents capable of executing complex automated tasks鈥攕uch as resource allocation, policy analysis, and citizen assistance. These innovations empower governments to enhance operational efficiency and responsiveness, meeting public needs with increasingly sophisticated tools.

Inter-agent communication is a particularly exciting development, enabling AI systems within government agencies to collaborate, optimise workflows, and improve service delivery across functions. demonstrated tangible benefits, with 64% of managers reporting improvements in efficiency and quality within their teams through AI tool adoption. This aligns with the , which projects generative AI could contribute $115 billion to the Australian economy, with approximately 70% of this value derived from productivity gains.

Additionally, AI’s transformative potential in public service delivery is evident in its capacity to free up human resources for higher-value activities. During the Copilot trial, 40% of participants noted reallocating their time to strategic tasks such as staff engagement and planning. Together, these advancements position AI as a cornerstone for smarter, more responsive government operations that deliver increasingly personalised and efficient services to citizens.

Expanding Digital Inclusion in Public Service Delivery

Ensuring equitable access to digital government services remains a critical priority in all jurisdictions. Digital inclusion efforts are particularly important for First Nations communities, culturally and linguistically diverse (CALD) groups, individuals in remote areas, seniors, people with disabilities, and socially disadvantaged populations. Governments are implementing targeted strategies to bridge the digital divide, ensuring all citizens can access essential services.

Key initiatives include:

• First Nations Digital Inclusion: A nationwide priority, aligned with Closing the Gap Target 17, which aims for digital equivalency by 2026. The Federal Government allocated $68 million in the 2024-25 Budget to enhance First Nations digital access.
• Compliance Mandates for Web Accessibility: Suppliers are now expected to comply with stringent accessibility standards to ensure inclusivity.
• Connectivity Enhancement: The NBN will replace Telstra for the Universal Service Obligation in regional, rural, and remote communities, supported by a $3 billion equity injection from the Federal Government as of January 13, 2025.
• State-Level Initiatives: An example of one of the many state initiatives to lift Digital Inclusion is the which measures the quality of digital connectivity across NSW via considering factors such as access, affordability and demographics.

By prioritising digital inclusion, governments can deliver services that are not only more efficient but also more equitable.

Resilience & Modernisation in Public Service Delivery

. Flexible cloud architectures play a key role in enabling scalability, interoperability, and resilience. Ensuring alignment with national standards and best practices, including compliance with the ASD Essential Eight, supports the secure and efficient transition to modernised systems.

The Growth of One-Stop Digital Whole-of-Life Government Services

The push for seamless digital government experiences continues to gain momentum in 2025, with an increasing emphasis on “One-Stop Digital Whole-of-Life” services. States like NSW, VIC, WA, and SA have successfully implemented integrated digital service platforms such as , while other regions including Queensland, Tasmania, and New Zealand are now prioritising similar initiatives to centralise public service access.

By consolidating government services into single-entry digital portals, citizens can efficiently access key lifestage events such as birth registration, healthcare, business licenses, tax services, and retirement planning from a unified platform. This streamlining effort enhances service efficiency, reduces administrative burdens, and improves user experience, making government interactions more intuitive and responsive.

Our Global Data Science Challenge is shaping the future of learning. In an era when AI is reshaping industries, 乌鸦传媒’s 7^th Global Data Science Challenge (GDSC) tackled education.

By harnessing cutting-edge AI and advanced data analysis techniques, participants, from seasoned professionals to aspiring data scientists, are building tools to empower educators and policy makers worldwide to improve teaching and learning.

The rapidly evolving landscape of artificial intelligence presents a crucial question: how can we leverage its power to solve real life challenges? 乌鸦传媒’s Global Data Science Challenge (GDSC) has been answering this question for years and, in 2024, it took on its most significant mission yet 鈥� revolutionizing education through smarter decision making.

The need for innovation in education is undeniable. Understanding which learners are making progress, which are not, and why is critically important for education leaders and policy makers to prioritize the interventions and education policies effectively. According to UNESCO, a staggering 251 million children worldwide remain out of school. Among those who do attend, the average annual improvement in reading proficiency at the end of primary education is alarmingly slow鈥攋ust 0.4 percentage points per year. This presents a sheer challenge in global foundational learning hampering efforts made to achieve the learning goal as set forth in the Sustainable Development Agenda.

The Grade-AI Generation: A collaborative effort

The GDSC 2024, aptly named “The Grade-AI Generation,” brought together a powerful consortium. 乌鸦传媒 offered its data science expertise, UNESCO contributed its deep understanding of global educational challenges, and Amazon Web Services (AWS) provided access to cutting-edge AI technologies. This collaboration unlocks the hidden potential within vast learning assessment datasets, transforming raw data into actionable insights for decision making that could change the future of millions of children worldwide.

At the heart of this year’s challenge lies the PIRLS 2021 dataset 鈥� a comprehensive global survey encompassing over 30 million data points on 4^th grade children鈥檚 reading achievement. This dataset is particularly valuable because it provides a rich and standardized data that allows participants to identify patterns and trends across different regions and education systems. By analyzing factors like student performance, demographics, instructional approaches, curriculum, home environment, etc. the AI-powered education policy expert can offer insights that would take much longer time and resources to gain from traditional methods. Participants were tasked with creating an AI-powered education policy expert capable of analyzing this rich data and providing data-driven advice to policymakers, education leaders, teachers, but also parents, and students themselves.

Building the future: Agentic AI systems

The challenge leveraged state-of-the-art AI technologies, particularly focusing on agentic systems built with advanced Large Language Models (LLMs) such as Claude, Llama, and Mistral. These systems represent a significant leap forward in AI capabilities, enabling more nuanced understanding and analysis of complex educational data.

鈥淕enerative AI is the most revolutionary technology of our time,鈥� says Mike Miller, Senior Principal Product Lead at AWS, 鈥渆nabling us to leverage these massive amounts of complicated data to capture for analysis, and present knowledge in more advanced ways. It鈥檚 a game-changer and it will help make education more effective around the world and enable our global community to commit to more sustainable development.鈥�

The transformative potential of AI in education

The potential impact of this challenge extends far beyond the competition itself. As Gwang-Chol Chang, Chief, Section of Education Policy at UNESCO, explains, “Such innovative technology is exactly what this hackathon has accomplished. Not just only do we see the hope for lifting the reading level of young children around the world, we also see a great potential for a breakthrough in education policy and practice.鈥�

The GDSC has a proven track record of producing innovations with real-world impact. In the 2023 edition, “The Biodiversity Buzz,” participants developed a new state-of-the-art model for insect classification. Even more impressively, the winning model from the 2020 challenge, “Saving Sperm Whale Lives,” is now being used in the world’s largest public whale-watching site, happywhale.com, demonstrating the tangible outcomes these challenges can produce. 

Aligning with a global goal

This year’s challenge aligns perfectly with 乌鸦传媒’s belief that data and AI can be a force for good. It embodies the company’s mission to help clients “get the future you want” by applying cutting-edge technology to solve pressing global issues.

Beyond the competition: A catalyst for change

The GDSC 2024 is more than just a competition; it’s a global collaboration that brings together diverse talents to tackle one of the world’s most critical challenges. By bridging the gap between complex, costly collected learning assessment data and actionable insights, participants have the opportunity to make a lasting impact on global education.

A glimpse into the Future

The winning team 鈥榠nsAIghtED鈥� consists of Michal Milkowski, Serhii Zelenyi, Jakub Malenczuk, and Jan Siemieniec, based in Warsaw Poland. They developed an innovative solution aimed at enhancing actionable insights using advanced AI agents. Their model leverages the PIRLS 2021 dataset, which provides structured, sample-based data on reading abilities among 4^th graders globally. However, recognizing the limitations of relying solely on this dataset, the team expanded their model to incorporate additional data sources such as GDP, life expectancy, population statistics, and even YouTube content. This multi-agent AI system is designed to provide nuanced insights for educators and policymakers, offering short answers, data visualizations, yet elaborated explanations, and even a fun section to engage users.

The architecture of their solution involves a lead data analyst, data engineer, chart preparer, and data scientist, each contributing to different aspects of the model’s functionality. The system is capable of querying databases, aggregating data, performing internet searches, and preparing elaborated answers. By integrating various data sources and employing state-of-the-art AI technologies like Langchain and crewAI, the insAIghtED model delivers impactful, real-world, actionable insights that go beyond the numbers, helping to address complex educational challenges and trends.

Example:

Figure 1: Show an example of the winning model. The image has the model answering the following prompt 鈥淰isualize the number of students who participated in the PIRLS 2021 study per country鈥�

As we stand on the brink of an AI-powered educational revolution, the Grade-AI Generation challenge serves as a beacon of innovation and hope. It showcases how the combination of data science, AI, and human creativity and passion can pave the way for a future where quality education is accessible to all, regardless of geographical or socioeconomic barriers.

Start innovating now 鈥�

Dive into AI for good
Explore how AI can be applied to solve societal challenges in your local community or industry.

Embrace agentic AI systems
Start experimenting with multi-agent AI systems to tackle complex, multi-faceted problems in your field.

Collaborate globally
Seek out international partnerships and datasets to bring diverse perspectives to your AI projects.

Interesting read?乌鸦传媒鈥檚 Innovation publication,Data-powered Innovation Review – Wave 9 features 15 captivating innovation articles with contributions from leading experts from 乌鸦传媒, with a special mention of our external contributors from, and. Explore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.

Implementing business transformations incorporating artificial intelligence (AI) and fueled more recently by the excitement around generative AI (Gen AI), is an increasingly popular course of action for companies.

According to 乌鸦传媒鈥檚 recent survey 鈥�AI-led Generative Business Services: The future of Global Business Services (GBS)鈥� conducted in partnership with , over 80% of respondents agree it is time to rethink Global Business Services as Generative Business Services – better defined as AI-led data-driven services focused on driving growth and the enterprise innovation agenda.

It is worth remembering, however, that business transformations with tangible business outcomes are enabled through a comprehensive approach, i.e., applying suitable technology platforms, together with operating model and process transformation, not necessarily just AI alone.

The key to their effectiveness is a thorough diagnosis of the company’s needs, a strategic approach and individually tailored and industry specific solutions that collectively transform business operating models, processes, technology, and people.

In business transformations, we are seeing significant reliance and focus on the latest technology solutions, but the basic principles remain the same. The customer, i.e., the end recipient of products or services, must always be at the centre of the design. It is unlikely the transformation will be successful if we forget to identify their needs and solve them.

An increasingly popular and effective method of building a transformation strategy is the Outcomes Based Model, which focuses more on the business impact of the transformation program, rather than typical process performance measures and fixed or variable fee pricing models. Transformation initiatives or services provided are aligned with business outcomes, e.g., working capital improvements through reduction of aged debt, and increasing revenue through revenue leakage detection, prevention, and recovery. We are seeing cases where such models are applied are resulting in significant cashflow improvements, and outcomes realized in millions of euros for our clients.

This approach significantly improves the effectiveness of business transformation and goes beyond traditional priorities focused on productivity or labour arbitrage. It is then much easier to get the attention of C-level Executives, who are typically the decision makers and buyers of business transformation services.

We choose to work in this model because we are confident that well-planned transformations will deliver the expected results. We need to have a deep understanding of the clients we work with to enable the development of optimal strategies for them. We can then create tailored and industry specific solutions and prepare and support them through the change journey. We also rely on detailed data analysis and insights to drive informed decision making. This is coupled with an outcome-based commercial model which incentivizes the clients and 乌鸦传媒. This is what makes this model an interesting and beneficial formula for both parties.

In the context of strategic business transformation, very often the key role is played by organisations known as Global Business Services (GBS) or Business Process Outsourcing (BPO) Providers. This sector is strongly represented in Poland, and other countries in the region such as Romania, due to the availability of highly qualified specialists, expertise, and still relatively low wage costs in comparison to other countries. While the traditional roles and benefits of GBS/BPO remain vital and relevant, there is an urgent need to redefine the GBS/BPO narrative to appeal more to Business Leaders who are demanding more than just cost reduction.

乌鸦传媒 is no longer just a transactional services vendor, it is an ecosystem orchestrator that brings new skills, technologies, and capabilities to its clients, and thus is not just providing support but drives the strategic objectives of modern enterprises. 乌鸦传媒 and its services and business transformation programs more often are expanding their scope of responsibility and expansion into more business lines and functional areas of their clients. which is allowing for greater bottom- and top-line financial impact for clients.

The power of simplicity

Today, technology is evolving at a dizzying pace, leaving companies constantly bombarded with innovative solutions that have the potential to improve their operations. This rapid pace of change often prevents full adaptation, resulting in technologies not used to their best advantage, which in turn inhibits the maximization of business impact. Many organizations implement only partial solutions, and do not always exhaust the possibilities of the standard technology deployed, limiting their effectiveness and return on investment.

Technology should be used to its fullest extent and that allows a greater part of the organisation to leverage the solution and its benefits. Such extensive use optimizes costs. Simply put, it means our clients can make the most of what they pay for. Companies should also look for technology platforms that allow them to fulfil many diverse needs, moving away from a multi-tool approach, and focusing on full and proper adoption.

The company’s growth is based on the development of the teams’ competencies

The key to successful business transformations, apart from good strategic planning, is change management & communication. Change is only effective if the people working in companies understand it, are convinced of it, and ideally when they have the chance to co-create it.

Business transformation also means developing competencies for the people the company employs. It is important to let people know from the very beginning of the process what role they will play during and after the change. In parallel with changes to business processes, it is important to plan and deliver robust training and equip staff with the right tools and resources to ensure there is no or limited disruption to business as usual and people can excel in their roles.

At 乌鸦传媒, we are focused on developing our teams in Gen AI and Industry specific certifications. People working for us have the opportunity, and sometimes even the obligation, to obtain key certifications in this area. This is to ensure we stay up to date with the current trends and changes and apply tailored solutions that are optimal for our clients. This is the only way we can be a dependable partner for our clients.

One of the best-performing ways of implementing change is through a 鈥減ilot鈥� approach. This allows for testing a solution in a selected and sometimes isolated area before a wider roll out. This method works most effectively in large companies with a regional and global reach. The choice can be made on a geographical level, e.g., starting in a particular country or city according to business lines or business functions, and where people are most suited and willing to participate in the change process. The success of operating on a smaller scale allows you to de-risk, and with proven and positive results, to convince people who are less supportive of change before proceeding on an organisation-wide scale.

Twilight of the old technologies

Even the best implemented changes take time. In the case of large platforms such as S/4HANA, for example, the process can take years. Business Managers and their teams need to be prepared for a period of operating in different realities simultaneously. This is necessary to ensure business continuity, and it is worth taking the time to act in a comprehensive way because well-planned transformations, based on clearly defined business goals, produce long-term, measurable results and outcomes.

The race to develop and exploit the extraordinary capabilities of AI and other breakthrough technologies is accelerating at a dizzying pace. But while governments, businesses and citizens are scrambling to take advantage of the seemingly limitless ability of AI to transform almost every aspect of our lives, there鈥檚 another challenge looming on the horizon.

As economies in general, and tech companies in particular, are striving to transition to renewable energy sources and to reduce carbon footprint, the boom in AI-related data processing is producing a huge surge in demand for power. But, as the need for clean and secure electricity supplies soars, could nuclear be set to play a vital role in bridging the potential energy gap?

Powering AI will require 9% of US grid capacity by 2030

Powering the world鈥檚 rapidly expanding network of data centres has already had significant impacts on society and public policy. In Europe, major data centre clusters, around Dublin and Amsterdam for example, require so much electricity that further data centre expansion in those cities is on hold until new, additional sources of energy come on stream.

As recently as 2020, UK data centres used just over 1% of the nation鈥檚 electricity. By 2030 this figure is forecast to reach 7%. Demand is set to be even greater in the US, the global centre of AI innovation, with predictions that, by 2030, 9% of all grid capacity will be used to power AI technologies alone. It鈥檚 a monumental challenge that traditional energy utility organisations cannot meet alone.

SMRs will change the game for businesses transitioning to low carbon energy

New research published by 乌鸦传媒 to coincide with the 2025 World Economic Forum in Davos reveals that 72% of business leaders say they will increase investment in climate technologies, including hydrogen, renewables, nuclear, batteries, and carbon capture, with nuclear energy in their top three climate technology investment priorities for 2025.

This direction of travel chimes with statements made during Davos by the International Energy Agency (IEA). The IEA heralds 鈥渁 new era for nuclear energy, with new projects, policies and investments increasing, including in advances such as small modular reactors (SMRs)鈥�.

According to IEA Director General Rafael Mariano Grossi: 鈥渙ne after another, technology companies looking for reliable low-carbon electricity to power AI and data centres are turning to nuclear energy, both in the form of traditional large reactors and SMRs.鈥�

Around 60 new reactors are currently under construction in 15 countries around the world, with 20 more countries, including Ghana, Poland, and the Philippines, developing policies to enable construction of their first nuclear power plants. The US Energy Information Administration (EIA) estimates that, by 2025, global nuclear capacity could have increased by up to 250% compared to the end of 2023.

Clean, reliable, available 鈥� and safe

It鈥檚 easy to understand why nuclear is set to play an increasingly significant dual role in both powering the AI revolution and decarbonising industry. Its 99.999% guarantee of stable energy availability compares with just 30-40% from weather-dependent wind or solar generation.

Decades of continuous improvements in reactor design and operation make nuclear the second safest source of energy in the world after solar, according to the International Atomic Energy Authority (IAEA), although the Agency also points out that large scale solar power systems need 46 times as much land as nuclear to produce one unit of energy.

But it鈥檚 the potential to rapidly deploy SMRs that could have the most significant impact in preventing the looming energy gap, as AI-driven data processing requirements grow exponentially. It鈥檚 important to remember that most light-water SMRs are simply smaller versions of the large-scale GEN III+ technology with proven safety and operational records, with small generally defined as having a maximum output of 300 MWe. The underlying scientific and operational principles are not technologically new in themselves.

As the name suggests, SMR鈥檚 modular design enables major components to be constructed at speed in a factory environment, for bespoke assembly on site, located flexibly close to consumers. With a footprint the size of a sports stadium, they can easily be placed near the demand, such as data centres or industrial estates.

Reduced construction times, lower investment and running costs and the ability to add or reduce capacity as demand increases or decreases, are just some of SMRs鈥� obvious advantages. They鈥檙e ready-made replacements for fossil-fuel based generation, and as nuclear is less vulnerable to price fluctuations, owners and consumers of SMR generated power have more budget certainty and can plan more accurately for the long-term accordingly.

SMRs, specifically the advanced reactor designs, can also be adapted to supply heat for industrial applications, district heating systems and the production of hydrogen, and are increasingly regarded as catalysts for economic development and job creation.

Tech giants at the front of the queue

Many of the global tech giants are actively working on plans to develop their own SMR-based generating capabilities, to provide their own independent sources of safe, stable, low-carbon power, protected from the increasingly volatile open market.

It鈥檚 a race that鈥檚 not only vital that they win to ensure that we fuel the AI revolution, but by doing so we will accelerate the transition to a low carbon world economy.