As we get ready for ServiceNow Knowledge 2025, it’s an opportune time to reflect on the insights and discussions we recently had on the Data-powered Innovation Jam RightHere, RightNow podcast.

Automation is a cornerstone of innovation. On the Data Powered Innovation Jam RightHere, RightNow podcast, Toby Isaacson, Senior Advisory Solution Architect at ServiceNow, shared invaluable insights on how automation is reshaping industries and unlocking new possibilities. Hosted by Capgemini’s Ron Tolido, Weiwei Feng, and Robert Engels, this episode delved into the transformative power of automation and ServiceNow’s pivotal role in this journey. 

The evolution of ServiceNow  

ServiceNow has come a long way from its origins as a ticketing tool. Toby Isaacson elaborated on how the platform has evolved to encompass a comprehensive suite of automation capabilities. He emphasized that while ServiceNow still excels in IT Service Management (ITSM), its true potential lies in automating diverse business processes across HR, CRM, supply chain, and more. This evolution reflects ServiceNow’s commitment to driving efficiency and innovation in every aspect of enterprise management. 

Automation and AI 

A significant portion of Toby’s discussion focused on the integration of AI and automation within ServiceNow. He highlighted the importance of AI in enhancing productivity by automating repetitive tasks and allowing employees to focus on more valuable work. Toby shared examples of how AI agents and process mining tools are used to identify bottlenecks and optimize workflows, ultimately leading to more efficient and effective operations. 

Unified data and process management

One of the standout points from Toby’s talk was the emphasis on unifying data and process management. He explained how ServiceNow’s single data model and workflow data fabric enable seamless integration and orchestration of data from various enterprise systems. This unified approach ensures that businesses can leverage accurate and consistent data to drive their workflows and AI initiatives, fostering a more cohesive and efficient operational environment. 

Industry-specific solutions   

ServiceNow’s commitment to providing industry-specific solutions was another key highlight. Toby discussed how predefined workflows and best practices tailored to industries like healthcare, manufacturing, and retail can help businesses quickly adopt automation without reinventing the wheel. These solutions not only streamline processes but also allow companies to focus on innovation and differentiation in their respective fields. 

The future of work  

Looking ahead, Toby painted a picture of a dynamic and fluid future of work, driven by automation and AI. He emphasized that the goal is not just efficiency but also creating a work environment that is autonomous and perfectly timed. ServiceNow’s platform is designed to support this vision, ensuring that businesses can navigate the complexities of modern work with ease and agility. 

Toby Isaacson’s insights on the Data Powered Innovation Jam podcast underscore the transformative power of automation and AI in today’s business landscape. As ServiceNow continues to innovate and expand its capabilities, it remains a crucial partner for enterprises seeking to enhance their operations and drive meaningful change. 

Join us at ServiceNow Knowledge 2025, to embrace our theme “Intelligence, meet experience. Welcome to your agentic-powered business,” highlighting how our intelligent solutions are designed to enhance customer experiences and empower businesses with agentic AI capabilities.

Listen to the RightHere, RightNow podcast episode now!

Today’s customers demand more than convenience. They expect speed, consistency, and empathy – across every channel and interaction.

Whether they’re paying a bill, reporting an outage, or navigating a complex public service, customers want seamless, human-centric support. Yet most organizations are still struggling with disjointed systems, manual processes, and a reactive service mindset.

The result? Frustrated customers, overworked service teams, and a widening gap between expectations and delivery.

According to the Capgemini Research Institute:

• 52 percent of customers have switched providers due to poor experiences
• 43 percent  of executives cite limited cross-department alignment and collaboration as a top challenge
• 38 percent point to siloed systems and fragmented customer data as key barriers to delivering seamless experiences
• There is $98 billion left on the table each year by failing to provide simple customer experiences.

The research also highlights three critical takeaways:

• Customer service matters: 58 percent of those surveyed say it is extremely important to overall brand perception.
• Customer service faces serious operational challenges: 65 percent of executives report low efficiency within the function, directly leading to reduced satisfaction.
• Gen AI offers real promise: already, 33 percent of organizations using generative AI are seeing improved first-contact resolution rates.

It’s time to change the game. And that’s where Customer Experience (CX) with ServiceNow comes in.

The Capgemini plus ServiceNow vision for CX

Capgemini believes that delivering great customer experience doesn’t start at the front-end. It starts deep in the back office – where the real magic (or mess) happens. ServiceNow gives us the platform to streamline and orchestrate customer journeys end-to-end, breaking down silos and automating complexity.

CX with ServiceNow is a strategic offering that combines:

• ServiceNow’s powerful AI-driven workflows
• Capgemini’s industry and functional expertise
• A library of real-world demo scenarios across energy, utilities, public sector, and more.

We’re not just fixing broken experiences. We’re reimagining them.

From fragmented to frictionless

In most organizations, customer service is a relay race. CRM passes the baton to IT. IT passes it to field services. Field services hands it to billing. It’s disjointed, inefficient, and slow.

CX with ServiceNow changes the game.

We create unified workflows that treat the customer journey as a single, intelligent flow – no matter how many departments are involved. With ServiceNow, we automate case routing, streamline approvals, integrate legacy systems, and enable real-time visibility for agents and customers alike.

And most importantly, we embed agentic AI to power decision-making at every step.

Agentic AI: The smart core of CX

Agentic AI is not basic chatbots. They’re intelligent digital agents that:

• Monitor data from across the ecosystem
• Detect issues early (sometimes before the customer even notices)
• Trigger the right workflows based on business logic and intent
• Escalate and communicate in human-friendly ways
• Learn and improve over time.

In our demos, you’ll see how agentic AI enables:

• Autonomous power outage triage and dispatch
• Auto diagnosing and fixing machinery in manufacturing
• Tax office support driven by natural language understanding
• AI-based customer notifications and smart triage for service disruptions
• Seamless, integrated journeys across CRM, GIS, ERP, and field ops.

Tailored for the real world

We’re not building these solutions in a vacuum. Every CX demo we deliver is based on real-world pain points we hear from clients across sectors:

• In energy and utilities, we show how to proactively detect outages, trigger repair workflows, and keep customers informed every step of the way.
• In the public sector, we demonstrate how intelligent workflows reduce the strain on overstretched services and make it easier for citizens to get the help they need.
• In smart factory and industrial, we show how CX isn’t just about customer service – it’s about operational continuity, safety, and compliance.

Each scenario is brought to life with working demos that showcase how the NOW platform and Capgemini’s expertise come together to deliver value.

Delivering the bottom line

Customer experience isn’t just a feel-good metric. It impacts trust, loyalty, efficiency, and brand reputation. It’s the difference between a complaint and a lifelong advocate.

CX with ServiceNow gives organizations the tools to automate what should be automated, humanize what needs empathy, predict problems before they happen, and respond at speed and scale.

CX with ServiceNow gives organizations the tools to automate what should be automated, humanize what needs empathy, predict problems before they happen, and respond at speed and scale.

Want to see it in action? Get in touch for a live demo or to explore how CX with ServiceNow could work for your organization.

Join us at ServiceNow Knowledge 2025, to embrace our theme “Intelligence, meet experience. Welcome to your agentic-powered business,” highlighting how our intelligent solutions are designed to enhance customer experiences and empower businesses with agentic AI capabilities.

We cannot have a sustainable energy system without storage, and lots of it. For signatory countries to achieve the commitments set at COP28, for example, global energy storage systems must increase sixfold by 2030.

Batteries are expected to contribute 90% of this capacity. They also help optimize energy pricing, match supply with demand and prevent power outages, among many other critical energy system tasks. Put simply, batteries are the beating heart of the energy transition – so what are the opportunities and challenges of this pivotal market? To find out, the Capgemini Research Institute surveyed 750 senior executives globally, including in the energy and utilities sectors. Its research report, The battery revolution: shaping tomorrow’s mobility and energy, generally reflects what I hear from clients, but I would add a couple of other factors.

As battery prices fall, their prevalence goes up

Let’s start with the good news: the falling price of batteries as production capacity increased over the past decade or so. It will be interesting to see how these ongoing price drops impact two of the challenges highlighted in the report – the extended payback period for investors and the profitability of manufacturers.

Likewise, as batteries become more available and affordable, I believe that every solar photovoltaic site will have some form of battery storage. I also expect that we will see more residential battery use. That could be people buying their own battery energy storage system (BESS) to capture energy from their solar panels and discharge it at peak times. Or it could be EV owners with Vehicle-to-Load (V2L) functionality renting or leasing a battery through the growing trend for Batteries-as-a-Service (BaaS).

Innovation could lead to surplus batteries and energy demand outstripping supply

This rise in availability, and corresponding drop in cost, also has its downsides. First, with so many batteries coming on to the market, how can we track where they are all are, or how well they are cared for during their life cycle? And what happens when an innovation arrives like “Flash Charging”, from the Chinese company BYD, which allows an EV battery to charge in 15 minutes? How do we know that the stock it supersedes will be properly disposed of, not just thrown into landfill?

Second, if increasing numbers of homes have a BESS and an EV charging point, it will create a level of demand that the grid was not designed to meet. For example, it makes sense to charge your EV overnight. But if everyone does the same, it puts a huge stress on the system. This could conceivably lead the Distribution System Operator (DSO) to tell consumers they can’t have an EV charger or a BESS – the political implications of which could be huge.

One final issue I would call out, which is also in the report, is the growing need for sovereignty in battery and energy production. We have all seen the impact on energy prices of being overly reliant on other countries for our supply. But having just one country – China – produce 83% of the world’s batteries, and mine around of one fifth of its lithium, creates similar vulnerabilities. Only time will tell how the trade tariffs the US administration recently imposed on China will affect the price of batteries and their raw materials stateside, for example.

Data and AI will be a big part of any solution

Currently, there is no single, defined solution to the issues I have described. But here are some ideas to consider.

1. In today’s distributed energy system, data and AI are king – so why not use them to help ease the stresses I have described? For example, advanced battery management systems can extend the life of batteries by constantly monitoring and maintaining their health and optimizing the way they charge and discharge. Data and AI can also speed up research and increase visibility of the supply chain.
2. Collaboration between governments and industry could help to overcome the issue of an overstrained power grid. For example, Great Britain’s energy regulator, OFGEM, has tasked the UK’s National Energy System Operator (NESO) with coordinating the delivery of a data sharing infrastructure (DSI) for the sector (until 2028). Having a DSI in place will ultimately make it easier to connect all the battery storage devices on the grid and optimize when and how they are used.
3. Europe and the US have invested heavily in rebalancing the scales for battery production. (Though again, it remains to be seen how the tariffs imposed by the US will affect its own efforts.) Meanwhile, governments that are serious about sovereignty will need to keep incentivizing local production. That’s as well as supporting the research and development of technologies that rely on different components or materials, like solid-state and sodium-ion batteries.
4. As I said before, new innovations risk making old ones redundant. Yet according to our report, just one in three organizations has taken meaningful steps to establish a circular economy. To move the dial, we need more initiatives like the EU Sustainable Batteries Regulation, more research into recycling and repurposing methods and a ‘sustainable-by-design’ approach to battery manufacture.

Batteries are not the only storage

These challenges will not be solved overnight. But meanwhile, other innovations are emerging. For example, the giant solar array at New York City’s John F Kennedy International Airport will also feature 3,84 MW of hydrogen fuel cells. I’m sure London’s Heathrow Airport would have appreciated this capacity when a fire in one of the electrical substations supplying its power forced it to close for over a day in March 2025.

Far from being the be all and end all, then, batteries are part of a bigger picture of energy storage – one that is constantly evolving. In future, this could mean we have a sustainable energy system that deploys different types of storage to help it manage, monitor and optimize energy use. With AI technologies developing at pace, we have more opportunity than ever to achieve it.

Product-centric innovation fuels business growth in a unified ecosystem

Service management was a bit of a solo act not too long ago. It stood alone, as an IT cost center, but now it’s taking center stage to drive an integrated and AI-driven world that’s revolutionizing service management. Once impossible, enterprises across sectors can adopt these technological leaps to enhance their operations with cloud-first models, intelligent automation, and digital-first experiences, among other solutions.

As a ServiceNow consulting partner, Capgemini’s experts are helping organizations unlock the full potential of ServiceNow’s integration platform, going beyond ITSM. Below are five top trends we believe position businesses as digital transformation leaders.

1. Unleashing AI-powered autonomy – The prospect of unleashing artificial intelligence might seem daunting, but the technology has matured. Companies can confidently loosen the reins and make AI and intelligent automation the nerve center of modern service management. According to Gartner, AI and machine learning are rapidly getting smarter and now have the capacity to resolve up to 40 percent of IT incidents automatically. This real-world progress uses proactive and predictive services to transform everyday operations and positively impact bottom lines. For instance, a large retail company implemented AI-driven solutions, such as Agentic AI and next-gen virtual assistants, to manage its IT incidents. Since this technology continuously learns from historical data and real-time inputs, it could predict – and resolve – issues before they escalated, as well as automate routine tasks and make context-aware decisions. The company achieved a 40 percent reduction in IT incidents, decreased downtime, and improved overall productivity. AI’s evolution to handling even more complex decision-making paves the way for a resilient and agile operational landscape.
2. Shifting from silos to seamless ecosystems – Customer needs are always evolving – and every service offering must also align with their expectations at every touchpoint and deliver a seamless customer experience. To achieve this, modern service management requires a consolidated view that breaks down silos between SIAM, ITSM, and ESM, thereby creating a unified approach, which shifts from cost centers to product-centric solutions. In one example, Capgemini partnered with a US public agency to enhance its ServiceNow integration and management services (SIAM) through the platform, delivering secure, reliable, and recoverable IT services for state agencies. The benefits were a 20 percent reduction in IT costs, doubled customer satisfaction, increased agility, and faster innovation, ultimately improving collaboration and transparency across business functions. 
3. Prioritizing responsive cloud-first and hybrid service models – As remote work and digital transformation accelerate, flexible and scalable service models become essential. The transition to cloud-first and hybrid service models isn’t just an upgrade – it’s a strategic imperative for modern, distributed enterprises. Cloud-first models provide the agility to scale operations up or down with ease, while hybrid environments, which blend on-premises and cloud-based solutions, ensure business continuity and support distributed workforces. Both these models empower organizations to innovate quickly while maintaining cost efficiencies and robust performance. They support a responsive infrastructure that adapts to market disruptions and evolving customer expectations.
4. . Enhancing invisible and intuitive interactions – Customer and employee experiences are now defining and driving competitive advantages. Yet even modern expectations can be surpassed with enhanced digital interactions. At the forefront are seamless omnichannel journeys that can be hyper-personalized at scale. Today’s robust digital experience blends functionality with ease, and it offers intuitive solutions at the point of need to create frictionless experiences. For employees, ServiceNow’s platform enables user-friendly AI-powered interfaces to be built into tools and systems, while also integrating other collaboration tools and virtual assistants such as Microsoft Copilot or Dynamics 365. Staff can perform the task at hand, self-provision workloads, and resolve issues quickly and easily – without waiting for traditional support channels. Workflows are streamlined. Cumbersome, human-dependent processes are replaced with intelligent, “invisible” systems that deliver a consumer-like experience. This approach enhances usability and elevates overall service quality.
5. Committing to transparency in AI ethics and governance – As AI and automation become central to service management, guaranteeing ethical governance and seamless infrastructure help safeguard an enterprise’s operations from privacy risks and cyber threats, while boosting client trust. Capgemini has recently developed ServiceNow solution that helps organizations comply with the EU’S new Digital Operations Resilience Act (DORA). It uses automated methodologies to assess compliance maturity, resilience mapping and reporting, third-party risk, and delivers improved security and information sharing.

It’s critical to embed security into every layer of service management. Transparent decision-making processes, clear audit trails, and robust compliance protocols ensure that automated systems align with ethical standards and regulatory requirements, which are continuously changing. 

The fascinating future of service management is already here, and these five trends can help organizations dream smarter and innovate faster. With Capgemini’s support, companies can leverage the ServiceNow platform as its foundation to integrate AI intelligently and fulfill its vison for the future.

Join us at ServiceNow Knowledge 2025, to embrace our theme “Intelligence, meet experience. Welcome to your agentic-powered business,” highlighting how our intelligent solutions are designed to enhance customer experiences and empower businesses with agentic AI capabilities.  

Technology is bringing innovation to the life sciences sector and transforming the patient experience.

Clinical trials would be impossible without willing participants. Vaccines, drug treatments, medical devices, and other clinical services designed to prevent disease, treat illnesses, and improve quality of life could be delayed or even derailed without effective trials.

For life-sciences organizations and pharmaceutical companies, the work of coordinating the complex and interdependent stages of clinical trials – especially the critical recruitment and retention of patients – has long been a slow-going, labor-intensive, and manual process. But that’s swiftly changing, thanks to the power of technology to automate, accelerate, and scale the clinical trials process. As a ServiceNow consulting partner, Capgemini is helping organizations undertake this type of business transformation, delivering operational services to support all participants in clinical trials, especially patients.

A holistic approach to transforming trials

During the COVID-19 pandemic, the ways in which people interacted and went about their daily lives changed dramatically. With people directed to stay at home, Signant Health, the company awarded clinic-trial management for Pfizer’s coronavirus vaccine, faced a monumental challenge. It had to urgently find a way to organize the tens of thousands of patients required to bring a vaccine to the world and concurrently manage large-scale data, patient engagement, and communication – all under stringent deadlines. Millions of lives were at stake. If people couldn’t participate in clinical trials in person, there had to be a way to reach them in their new, largely virtual, lives.

With Capgemini’s support, experience, and industry expertise, Signant Health moved to ServiceNow’s NOW platform to support a more robust patient experience. Within four weeks, the people, processes, and tools were in place and integrated to execute a highly complex study with more than 46,000 patients. Signant Health now had the technological capacity to deliver an innovative method of clinical service orchestration – the seamless integration and delivery of operational services to patients, healthcare providers, and pharmaceutical companies.

Automating the patient experience from mobile phones to chatbots

That process generated a 95 percent patient satisfaction rate, notably because it was innovative, simple, and intuitive, but there was another reason: it was human-centric, putting participants first. That is critical, as a major hurdle in clinical trials is that, on average, 30 percent of patients drop out over the course of the process. That results in 85 percent of clinical trials failing to retain enough participants.

So, why are people exiting trials that could improve their own lives and potentially those of millions more? Reasons include onerous record-keeping requirements in paper journals, the need to travel to in-person appointments, and barriers in accessing timely information and support, like troubleshooting issues with wearables.

The NOW platform’s patient portal, which is accessible around the clock, helps remove these obstacles. And much like any customer journey, like shopping, banking, or streaming a movie, the online user experience is critical, for patients and for investigators and other participants. People expect an omnichannel experience and the platform delivers, ensuring seamless operations, enabling patients to access the portal via mobile and other connected devices.

Driving the patient experience also delivers business outcomes

Clinical trials take time and the best way to improve outcomes is to provide high-touch multi-channel support, where patient engagement and communication are key. Imagine the continuum and volume of “contacts” required throughout the trial’s life cycle and the impact that has on patients. The portal is designed to anticipate and guide the non-linear patient journey, thereby expanding accessibility and boosting the overall experience.

Between in-person visits, patient data is collected through mobile phones, IoMT devices, and other connected technologies, such as wearables that track health metrics and patient diaries. This eliminates the need for manual record-keeping and the potential for patients to miss reporting or forget key details that could create gaps in, or reduce the reliability of, the data.

Patient privacy, however, remains paramount. Medical data is not stored on the ServiceNow platform. Its core focus is ensuring seamless operations and powering and optimizing workflows to enable a quick resolution. If a patient has a concern or feedback, they can provide that through an automated channel, like a chatbot, or live support. The result is two-way communication where patients can drive improvements and also find quick solutions to frequent queries. This supports patient retention, which promotes speedier and more successful clinical trials, accelerating time to care for life sciences organizations.

The platform we developed with ServiceNow and Signant Health was an unqualified success and helped develop a vaccine for the deadliest health crises to occur in many generations. For life sciences companies, this translates into better IoMT device management, reduced operational friction, improved trial efficiency, and increased patient satisfaction. It is an example of technology helping humans when we needed it most.

 Join us at ServiceNow Knowledge 2025, to embrace our theme “Intelligence, meet experience. Welcome to your agentic-powered business,” highlighting how our intelligent solutions are designed to enhance customer experiences and empower businesses with agentic AI capabilities.  

The convergence of biology and technology has unlocked unprecedented scientific breakthroughs. Fueled by data science and artificial intelligence, bio-innovation is reaching new heights. And Generative AI is poised to be a catalyst of this bio-revolution – a transformative force that promises to accelerate discovery, enhance precision, and optimize operations across the pharmaceutical value chain.

For decades, the challenges of drug development have seemed to be set in stone: it takes well over a decade and costs upwards of $1 billion to bring a new drug to market. Even then, 90% of potential treatments fail somewhere along the way. But what if we could rewrite this equation?

Reimagining Drug Discovery: AI as the Co-scientist

At the heart of every breakthrough medicine is a molecule—a tiny structure with the power to change lives. Finding the right molecule, however, has traditionally been a laborious process of trial and error, relying on time-consuming screening, costly experiments, and unpredictable outcomes.

GenAI is redefining drug discovery with deep learning models trained on vast chemical and biological datasets that predict promising candidates as well as identifying drug targets with unprecedented accuracy. These AI-driven systems don’t just analyze known compounds; they can design entirely new molecules, simulate their interactions, and flag potential failures before they reach the lab.

For pharmaceutical innovators, this means not only shortening R&D timelines but also expanding the pipeline of high-quality drug candidates, reducing the risks associated with late-stage failures. In an industry where speed and accuracy are everything, AI is shifting the balance from guesswork to data-driven certainty.

Revolutionizing Clinical Trials: Smarter, Faster, More Predictive

Clinical trials remain a bottleneck in drug development. Recruiting the right patients, ensuring trial adherence, and managing vast amounts of regulatory data all contribute to delays and rising costs. Here, too, AI is proving to be a game-changer.

AI-powered models can now identify ideal patient subpopulations by analyzing real-world data, ensuring trials enroll individuals who are most likely to respond positively. This not only improves success rates but also lays the groundwork for precision medicine, where treatments are tailored to specific genetic or biomarker profiles.

Meanwhile, AI-generated synthetic data is reducing dependence on traditional control groups, allowing trials to run faster and with greater statistical power. GenAI-assisted automation is also transforming the regulatory process—drafting protocols, ensuring compliance, and streamlining interactions with health authorities.

For pharma executives, this means fewer trial failures, faster regulatory approvals, and a clearer path to market success.

”The promise of AI in the life-sciences is to transform it from an industry focused on hunting for ever-smaller needles in ever-larger haystacks, to one where new therapies are purposely designed and engineered with precision” – Dr Mark Roberts, CTO Applied Sciences, Capgemini Engineering”

Beyond the Lab: AI-Optimized Manufacturing and Digital Therapeutics

While much of AI’s promise lies in discovery and trials, its impact extends into pharmaceutical manufacturing and patient engagement.

AI-driven predictive analytics are optimizing production processes, reducing waste, and improving scalability, making drug manufacturing leaner and more sustainable. Given the growing emphasis on ESG (Environmental, Social, and Governance) initiatives, AI-driven efficiency gains are not just about cost savings—they’re also about meeting global sustainability targets.

At the same time, the rise of digital therapeutics (DTx) is redefining how we think about patient care. AI-powered applications are enabling personalized health interventions, from managing chronic diseases to real-time medication adjustments. As pharma companies explore hybrid models that combine traditional therapeutics with AI-driven digital health solutions, new revenue streams and business models are beginning to emerge.

The AI-Powered Pharma Enterprise: What Comes Next?

Despite the promise of GenAI, pharma organizations must take strategic steps to unlock its full potential. Investing in AI-first R&D strategies, curating high-quality data ecosystems, and fostering AI-literate teams will be critical to long-term success. Regulatory frameworks must evolve alongside AI capabilities, ensuring ethical AI adoption and transparent validation of AI-driven discoveries.

The question is no longer if AI will transform pharma R&D—it already is. The real challenge is how quickly organizations can adapt. In the life-sciences, and other complex industries, autonomous and agentic systems will soon start to challenge existing norms and shorten value chains. Those who act now will define the future of medicine, setting new standards for speed, precision, and impact.

AI isn’t just changing the way we develop drugs—it’s reshaping the very fabric of healthcare. Are we ready to embrace this transformation?

Click here to read the research paper.

About AI Futures Lab 

We are the AI Futures Lab, expert partners that help you confidently visualize and pursue a better, sustainable, and trusted AI-enabled future. We do this by understanding, pre-empting, and harnessing emerging trends and technologies. Ultimately, making possible trustworthy and reliable AI that triggers your imagination, enhances your productivity, and increases your efficiency. We will support you with the business challenges you know about and the emerging ones you will need to know to succeed in the future.   We create blogs, like this one, Points of View (POVs), and demos around these focus areas to start a conversation about how AI will impact us in the future. For more information on the AI Lab and more of the work we have done, visit this page: AI Lab. 

I’m thrilled to share that Capgemini has achieved a triple win at the Google Cloud Partner of the Year awards.

These awards recognize our innovative solutions and the significant impact we have made across various industries.

• Global Industry Solutions
• Sustainability Industry Solutions
• Country: Denmark

With nearly 15 years of collaboration with Google Cloud, we’ve unlocked incredible potential and value through our joint efforts. This partnership has consistently demonstrated the power of a combined approach in driving business transformation and exploring new possibilities.

Reflecting on our growth from last year, this year highlights our strategic focus towards sustainability and industry-specific solutions. We are more committed than ever to addressing global challenges and creating value for our clients through sustainable and innovative solutions.

Sustainability industry solutions

One of the awards we received is “Sustainability Industry Solutions”. This award recognizes partners that helped customers in the sustainability industry achieve outstanding success through Google Cloud. Sustainability is a core component of Capgemini’s DNA, and it is embedded in every service and solution we develop. Our collaboration with Google Cloud has enabled us to help clients become more sustainable. For instance, our Fractals solution enables end-to-end product-level data collaboration on pre-competitive supply chain issues, including ESG challenges such as food waste, health, decarbonization, human rights, and living wages.

Additionally, our Business for Planet Modeling (BfPM) solution with Google Cloud is a set of climate risk advisory services designed to drive better climate risk analysis for the financial services industry. BfPM leverages Google Cloud’s analytics and artificial intelligence to simulate the financial impact of climate change and global variables, enhancing forecasting and supporting better decision-making.  We’ll be exploring these solutions in person, at Google Cloud Next.

Global industry solutions

In addition, we received an award for Global Industry Solutions. This award recognizes partners that leveraged Google Cloud solutions to create comprehensive and compelling solutions that made a significant impact across multiple industries and regions. Our deep industry expertise and use of Google Cloud resources, including generative AI, have enabled us to provide clients worldwide with tailored solutions. For example, our Industry Cloud for Grocers on Google Cloud has helped grocers enhance customer experiences while improving inventory visibility and profitability.

Partner of the Year Award, Country: Denmark

At Google Cloud Next, we’re hosting a breakout session with Danfoss to discuss their AI-driven demand forecasting approach using Google Cloud. This session will highlight our work in Denmark with Danfoss, a leader in energy-efficient solutions, and how they partnered with Google Cloud and Capgemini to tackle demand forecasting challenges, stay competitive, and support global sustainability goals.

Impact on our clients

Our partnership with Google Cloud has brought significant benefits to our clients, and we’re proud of the successful projects which have driven value for them. In our recent lookbook, we talk about this in more depth. For instance, we modernized IT infrastructure with data cloud solutions at Wind Tre, processing 1,000 events per second and making 100 million decisions per day. We also created the first generative AI chatbot in Catalan using Google Cloud’s Vertex AI, preserving language and improving response times. Additionally, we helped L’Oreal connect the physical and digital worlds using a digital twin solution on Google Cloud.

These accomplishments showcase our ability to leverage Google Cloud’s capabilities to deliver innovative solutions that address specific industry challenges and enhance customer experiences.

A big thank you

These achievements would not have been possible without the hard work and dedication of our teams and the incredible partnership with Google Cloud.

Looking ahead, we have ambitious goals for our partnership with Google Cloud, and we really look forward to bringing these accolades to life through our participation at Google Cloud Next, as a Luminary sponsor. It would be great to meet you there at booth #2240, Apr 8-11 or connect with me to discover how we’re helping companies achieve the potential of Innovation, meet intelligence.

The energy and utilities sector is indeed undergoing a significant digital transformation, driven by the rapid adoption of emerging technologies.

Generative AI (Gen AI) is set to play a pivotal role in this shift, enhancing innovation, operational efficiency, and uncovering new opportunities, thereby advancing the transition towards a more digital and sustainable global economy.

The growth of drone technology in the energy and utilities sector is expected to remain strong, particularly for asset inspection and management. Integrating drone-based inspections with SAP Asset Management, edge AI models, and AI agents can profoundly impact field operations, sparking business transformation across industries. This combination of technologies will enable the automation of workflows, predictive maintenance, and the generation of actionable insights, transforming asset management, risk mitigation, and operational efficiency.

Drones equipped with high-resolution cameras and 3D laser technologies are becoming essential tools for industries, offering the ability to capture real-time, detailed asset information. This capability helps reduce time, costs, and human errors. However, the true value of drone inspections lies in analyzing the vast amounts of data generated. Integrating drone technology with AI agents in the SAP Business Technology Platform (BTP) stack enables in-depth data analysis and decision-making capabilities, providing actionable insights. This combination creates a seamless and intelligent ecosystem for managing inspections, optimizing workflows, and driving improved business outcomes.

To further illustrate the transformative impact of Gen AI and AI agents, the  CRI report Unleashing the value of customer service highlights how customer service, augmented by Gen AI, can transcend its traditional role and evolve into a driver of commercial opportunities.

Unleashing the power of Gen AI in drone-based inspection

• Automated report generation: Agents can process drone data to generate detailed inspection reports, reducing manual workload and speeding up decision-making.
• Real-time data interpretation: Agents can analyze live drone feeds to identify anomalies and generate actionable insights, enhancing decision-making.
• Predictive maintenance: By analyzing historical data from previous field inspections and maintenance records, AI can predict potential future issues or failures, enabling proactive maintenance and reducing downtime.
• Natural language query: Field engineers can interact with inspection data using natural language, simplifying access to information.
• Knowledge extraction: Agents analyze unstructured text data from inspection logs to identify patterns and inform decision-makers about recurring issues.
• Automated fault categorization: Agents can categorize and prioritize inspection findings by severity and urgency, improving workflow efficiency.
• Training and knowledge sharing: Agents will be able to assist in training new employees by providing detailed explanations and answering questions about inspection issues.
• Natural language summary: AI summarizes drone-collected data into easy-to-understand insights for non-experts, aiding informed decision-making.

Will integrating Gen AI into SAP Business Technology Platform (BTP) bring value to the field operations and management?

Integrating Gen AI into the enterprise systems can significantly expedite inspections and enhance workforce efficiency by automating and streamlining complex processes. By leveraging the power of AI, data from drones and maintenance logs can be analyzed in real time, enabling faster identification of anomalies, defects, or potential risks in energy and utilities assets. With Gen AI Hub integrated into SAP BTP stack, the system can process vast amounts of unstructured data and provide actionable insights, helping field workers make informed decisions quickly. AI-driven models can automatically generate inspection reports, flag critical issues, and recommend maintenance actions, reducing the time spent on manual documentation and improving the speed at which problems are addressed. Furthermore, AI capabilities can predict asset failures and maintenance needs by analyzing historical data and real-time conditions, allowing organizations to perform proactive maintenance and avoid costly repairs or downtime. This predictive ability ensures that the workforce is always prepared with the right information, enabling more efficient task assignments and better resource allocation.

Additionally, agents can assist field workers by offering real-time support, troubleshooting suggestions, and guidance during inspections, reducing their dependency on experts and ensuring tasks are completed more effectively. The integration of Gen AI within SAP BTP stack allows for seamless scalability across multiple sites, assets, and workflows, ensuring that the technology grows with the organization’s needs. By automating routine tasks such as work order creation, inspection reporting, and issue prioritization, AI agents empower the workforce to focus on higher-value activities, improving operational performance and overall productivity. Ultimately, integrating agents into SAP BTP stack can lead to faster, more efficient inspections, optimized workforce performance, and more reliable asset management, driving operational excellence and sustainable growth.

Capgemini and industry AI in the energy and utilities sector

Gen AI is transforming field operations and engagement for energy and utilities assets by boosting efficiency, accuracy, and sustainability. By automating tasks like inspections and predictive maintenance, Gen AI helps energy and utility companies enhance the lifespan of their assets, minimize waste, and reduce their environmental footprint. These advancements foster long-term sustainable growth within the energy and utilities sectors, making operations more environmentally friendly, while also lowering costs and optimizing overall performance. AI-driven solutions not only streamline workflows but also ensure that maintenance is more proactive, preventing costly repairs and maximizing asset utilization. Ultimately, these innovations contribute to a more efficient and sustainable future for energy and utilities operations.

Final thoughts

With Gen AI for asset management, we help utilities unlock AI’s transformative power by building tuned models and navigating complexities. Our digital labs foster collaboration and innovation, guiding clients through challenges like cost, scale, and trust. This approach ensures seamless transition from pilot to deployment, delivering innovative, transformational journeys faster and at scale.

For CFOs, and the finance and procurement functions in general, Gen AI continues to offer significant potential.

Indeed, only two years ago a Gartner survey suggested that 80 percent of CFOs expected to increase spend on the technology by the end of 2024.

This is unsurprising of course given the CFO’s ongoing preoccupation with cost and risk, and the many repetitive, time-consuming, manual tasks that routinely need to be undertaken to manage these metrics. Hence the attraction of Gen AI as a tool able to generate insights at speed to transform the way organizations answer questions such as:

• What suppliers should we avoid doing business with?
• How can we make the entire bank reconciliation process touchless?
• How can we maintain oversight on the key trends relating to contracts?

With its ability to process vast amounts of data quickly and accurately, alongside the use of natural language processing to generate human-like text, the opportunities presented by Gen AI across both finance and procurement are extensive. AI agents can further enhance these processes by acting as intelligent assistants that can interact with various systems and users. They can automate routine tasks, provide real-time insights, and facilitate decision-making by integrating data from multiple sources. All of which makes the first challenge that of identifying specific use cases for delivering more dynamic operations. So, let’s take a brief look at the main candidates to help shape expectations.

Enabling a dynamic transformation of P2P

The delivery of Gen AI often involves the building of a bot to generate content for end users. This makes perfect sense for CFOs when the content supports their wider efforts to streamline processes, mitigate risk, and reduce levels of manual intervention. Hence a common starting point being the procure-to-pay (P2P) process to enable outcomes such as:

• Sourcing and procurement: analyzing supplier data (delivery times, quality standards, etc.) to identify the most reliable and cost-effective suppliers.
• Vendor invoice matching: complementing the invoice reconciliation process and augmenting vendor invoice matching to replace Optical character recognition (OCR)technologies.
• Payment anomaly detection: enabling the early identification of accounting irregularities or deviations from established policies, procedures, or thresholds.
• Financial insights: generating recurring financial reports, automating the import of data into templates, and delivering board-level insight into performance.
• Planning and analysis: reducing forecasting cycles down from weeks to minutes and conducting more sophisticated data querying from different sources.

Getting more intelligent about suppliers

Another area ripe for Gen AI utilization is vendor management. Again, the objective being to help simplify and streamline standard processes, from onboarding a new supplier to validating invoices. These processes involve vast third-party data sets, ranging from audit information, sustainability data, quotations, and even social media reviews. The result of all this data, analyzed and turned into actionable insight by Gen AI, is an elevated capability for assessing and quantifying suppliers based on the key metrics of cost and risk – alongside more time efficient processes. For CFOs, this means having a virtual assistant that can handle everything from generating financial reports to analyzing market trends, thereby freeing up time for more strategic activities.

For example, Gen AI can help select a supplier, based on detailed cost comparisons (including all related costs), then convert a request for quotation into a purchase order. It can also help detail potential supply gaps and recommend alternative suppliers – both existing and new – based on a stated risk profile. These capabilities and more are introducing new cost efficiencies to both finance and procurement teams, while easing the move toward more dynamic sourcing.

Streamlining the AP process

Alongside benefits for the P2P process comes Gen AI’s involvement in a touchless accounts payable (AP) process. The value here is easy to imagine, given the huge amount of data involved in picking the correct invoices and translating them to the required format – before the manual scrutiny even begins!

Where Gen AI offers huge potential is its ability to automatically scan all these paper invoices and extract the correct data and processing invoices – while also providing recommendations on the fields being captured. In doing so, the Gen AI bot is bringing a greater “touchless” element to AP, as it can:

• Be trained on historic data – to understand key fields and what marks an invoice as “correct” versus “problematic”
• Match invoices to purchase orders and receipts – while handling complex scenarios like partial deliveries and multiple invoices for a single order
• Clarify whether invoices are PO or non-PO based
• Route invoices for approval based on predefined rules and exceptions
• Analyze patterns and anomalies to detect potential fraud or duplicate invoices.

AI agents can also ensure compliance by continuously monitoring transactions and flagging any anomalies, thus reducing the risk of fraud and errors. By leveraging AI agents, CFOs can achieve greater efficiency, accuracy, and agility in their operations, ultimately driving better business outcomes.

The Intelligent procurement study 2024 report by Capgemini Research Institute highlights how generative AI is reshaping procurement by driving efficiency, mitigating risks, and fostering innovation in a rapidly evolving landscape. Ultimately, these capabilities are helping redefine AP workflows and are significantly cutting back on what tasks need to be completed by humans – and the time required to do them. This is a development that quickly leads to a sizeable cut in invoicing processing costs, while also helping inspire greater velocity of cash flow.

Final thoughts

It’s important to note that these Gen AI capabilities and more are already in the here and now. Capgemini has clear offerings in each area, built within the RISE with SAP framework, delivered in the SAP BTP layer, or through hyperscaler tools like Microsoft Power Platform, and available as plug and play solutions. With them, CFOs and procurement leaders can finally remove the burden associated with traditional tasks. From contract reviews and validations to fraud detection and financial forecasting, Gen AI can be called upon to do much of the heavy lifting – or at least the more time-consuming, repetitive tasks.

Not that it finishes there. Gen AI is also now able to assess the strategic insights needed by the board, reviewing key dashboards, and providing personalized summaries for those involved. This “narrative design” further reduces the time needed to pull such insights together, while automating the alignment of summary insight with key performance indicators. It’s just another example of how Gen AI is helping fast-track journeys toward the intelligent business.

Gen AI is certainly one of today’s hottest boardroom topics. Organizations of every shape and size are looking at the technology and asking questions of how it can help optimize the core tasks, processes, and workflows that underpin everything they do.

Equally, and as covered in previous blogs, the application of Gen AI is almost boundless – from inspiring large RISE with SAP transformations to reimagining the software development life cycle.

Yet while big-picture thinking on the full potential of Gen AI is a critical, ongoing endeavor, it’s also vital to follow up such analysis of what’s possible with practical use cases.

Knowing what Gen AI can do in creating new content, simplifying the analysis of complex data streams, and streamlining information access is important – but so is understanding how this capability can be applied to day-to-day realities. So, with that in mind, let’s look at opportunities for applying Gen AI within a specific industry sector – in this instance manufacturing.

Getting productive

Speak to any manufacturer about their operational challenges, and common issues soon emerge relating to the seemingly endless task of improving productivity. A task made excessively complex by the myriad variables involved, ranging from material availability to unexpected machine breakdowns.

What’s more, surrounding any manufacturing process is a wealth of structured and unstructured data – typically residing in SAP and non-SAP systems – that if available in a timely manner can provide both advance warning of upcoming problems and options for immediate resolution.

In effect, efforts to overcome the “productivity barrier” are ultimately focused on turning this raw data into actionable insight. To this end, many technologies, from manufacturing execution systems (MES) to advanced analytics, are already employed. Indeed, much of the required insight can be made available and embedded into established workflows.

But with its ability to be trained to follow precise rules, analyze vast data sets, detect discrepancies, and provide tailored responses, Gen AI can truly “democratize” the flow of insight across manufacturing operations. This is a capability that in turn lowers the barrier for people to discover (or receive) timely intelligence on which to base their decisions.

Advancing the journey to Industry 4.0 (and beyond)

The Capgemini Research Institute’s report, Harnessing the value of generative AI: 2nd edition – Top use cases across sectors, highlights how organizations are leveraging generative AI to enhance operational efficiency, foster innovation, and unlock new revenue streams across industries, including manufacturing.

Here are a few examples of manufacturing capabilities currently being developed by Capgemini:

Asset availability – with Gen AI models able to create real-time forecasts of capacity, predict potential machine stoppages, and maintain a more dynamic form of production scheduling that can react instantly to any stoppages or lack of available resources/labor.

Product quality – where quality checklists incorporate voice controls and real-time updates based on product-specific quality intelligence, to fast-track the process both at the assembly line and within the warehouse operation.

Workforce self-service – with Gen AI used to diagnose the root cause of high priority issues, suggest possible resolutions to users, and propose long-term solutions to prevent recurrence – as well as the process and time needed to implement them.

Supply chain optimization – where Gen AI combines historical data with real-time forecasts to create highly detailed bills of materials (BOMs) and matching this to existing stock levels and known supplier availability to cover predicted shortfalls.

Time to insight

With all these use cases, the value of Gen AI comes in its ability to provide a simplified interface between users and a bewildering array of complex data. Answers can be found without using the technology, but often in a way that requires too much time and effort to make the process viable – as well as a basic skill level for performing such analysis. Which is why, in the past, key insights that could transform both user productivity and customer relationships were often left hidden in the detail.

An example of Gen AI turning vast data sources into meaningful insight can be found in a current project with a semiconductor manufacturer using SAP solutions. As with any Gen AI initiative, the work has originated from a clear operational problem:

• The client has a portfolio spanning thousands of products and components – each accompanied by a mass of documentation.
• Responding to customer queries means people having to navigate through these design and specification documents to find answers – an exhaustively inefficient process.
• As a result, customers may not always be presented with the ideal product recommendations, and receive a slow response to any urgent sales inquiry.

Where Gen AI can help is in bringing together the structured and unstructured documentation surrounding each product. This data can then be queried via a conversational chat window, with responses increasingly tailored to the expectations and personalities of individual users. As a result, sales and technical employees can now ask questions – such as “what’s the best components mix to meet a customer’s precise specification?” and “what’s the fastest, most economical and sustainable way to supply these products from our global operation?” – and receive answers in seconds.

Final thoughts

Gen AI opens a window into an organization’s collective knowledge and intellectual property. It is a trained intelligence able to interpret a user’s intent and produce the most relevant data possible. It’s about radically shortening time to query and providing a layer of contextual understanding that helps advance the collective ambition for Industry 4.0 and beyond.

Challenges exist in introducing an industrialized Gen AI solution that can be trusted to consistently deliver authentic answers, from architecting the right solution to implementing the correct policies and controls. But as Capgemini is routinely demonstrating, these issues can be quickly solved with a robust implementation process and in-depth industry expertise. The hardest part remains the conceptualization of different use cases and imagining how and where Gen AI can complement existing processes – while inspiring new ways of tackling old problems.

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