The energy and utilities (E&U) industry is undergoing a dynamic transformation. Driven by emerging technologies from smart grids to the integration of renewable energy sources, the landscape is evolving rapidly.

Generative AI (Gen AI) is poised to play a pivotal role in accelerating this shift, fostering innovation, efficiency, and new opportunities across industries worldwide. You can read about this transformation and more in our “Future of” series, here.

What effect will Gen AI have on E&U end customers?

Customer complaints are a longstanding challenge for E&U suppliers, particularly during peak seasons or service outages. A recent Ofgem report revealed troubling statistics about the service quality of 17 major UK energy suppliers. Customers often face chaotic experiences marked by inconsistent messaging, long wait times, and unresolved issues leading to frustration and dissatisfaction.

The surge in call volumes (some suppliers reported a 300% increase since 2018), combined with a shortage of experienced agents, complex regulatory issues, and missing root cause analysis reports, has overwhelmed support teams. This results in high complaint rates, declining customer satisfaction scores, and reputational damage.

How Gen AI can vastly improve customer interactions with E&U businesses

Gen AI is already transforming customer support by streamlining complaint resolution, enhancing personalization, and reducing staff turnover. Key use cases include:

• Automated bill summaries: Gen AI can generate clear, concise summaries of complex bills, enabling faster query resolution and empowering customer self-service.
• Contextual routing: By analyzing historical queries, Gen AI can match current issues with agents who have relevant expertise, improving resolution speed and satisfaction.
• Real-time knowledge assistance: Gen AI can interpret technical manuals and guides, presenting information in simple language to both customers and staff.
• Sentiment analysis and personalized responses: Gen AI can assess customer tone and emotional state across channels, tailoring responses to foster empathy and clarity.
• Predictive maintenance support: In IoT-enabled environments, Gen AI can predict service disruptions and proactively suggest preventative actions.
• Email summarization: Gen AI can extract key information from lengthy emails, allowing agents to quickly understand and address issues.
• Routine task automation: AI-powered chatbots can handle scheduling, payments, and FAQs using natural language.
• Error reduction and consistency: Gen AI ensures accurate, consistent information across agents, improving service quality.
• Operational insights: Gen AI enables better call center audits, agent coaching, and customer-agent matching, reducing average hold times and boosting productivity.

These improvements not only enhance customer satisfaction but also drive profitability and reduce agent attrition. Root cause insights from Gen AI can inform future system and process design, creating a cycle of continuous improvement.

As highlighted in the Capgemini Research Institute’s report Harnessing the value of generative AI: Top use cases across sectors, organizations are increasingly prioritizing Gen AI to elevate customer experience, with tools like ChatGPT becoming the preferred interface for product and service recommendations.

What agentic AI and embodied AI mean for the E&U industry

The next frontier is agentic AI, autonomous software agents that interact with their environment, gather data, and perform tasks to achieve defined goals. These agents leverage large language models (LLMs) to reason, act, and adapt dynamically.

In customer service, agentic AI can autonomously manage enquiries, request additional information, and resolve issues, sometimes even overriding standard procedures when necessary. This autonomy enhances customer satisfaction and allows human agents to focus on complex, high-value tasks.

Increasingly, we are also seeing the rise of embodied AI: AI systems integrated into physical or digital environments that can perceive, interact, and respond in real time. In the E&U context, embodied AI agents can be deployed in smart meters, grid management systems, and field service robotics to autonomously monitor, diagnose, and act on operational data. These agents combine Gen AI’s reasoning capabilities with sensor inputs and real-world feedback loops, enabling more adaptive and intelligent infrastructure.

Capgemini and Gen AI in the E&U industry

Generative AI presents a transformative opportunity for the E&U sector to transform customer experiences, optimize operations, and drive sustainable growth. However, successful adoption requires careful governance to mitigate risks and maintain control over AI processes.

Capgemini’s Generative AI for Customer Experience offering helps E&U companies unlock Gen AI’s potential by building tuned foundation models and navigating implementation complexities. By leveraging our global network of certified Gen AI for CX experts, we accelerate deployment of industry-specific use cases that deliver tangible business value.

With over 500 enterprise-ready use cases and demonstrators, and a track record of successful client engagements, we empower CxO leaders to drive high-impact transformation initiatives.

Gen AI is here to transform the customer satisfaction. Get in touch with us to learn how we can partner with you on your transformation journey.

Compiling and communicating upstream and downstream data on industrial batteries is key to increased circularity and transparency throughout the electric vehicle (EV) battery’s lifecycle. 

The metals used in an EV’s battery travel an average of 90,000 kilometers via multiple actors across the value chain before they are even incorporated into the battery (its components, cells, and stacks). This is just one example of how global and complex the EV battery supply chain is.  

Collecting, storing, and sharing data from every step of the product’s journey will be a complex, collaborative process. With the EU’s Digital Battery Passport (DBP) on the horizon for 2027, addressing this challenge has never been more pressing.  

As a result of this urgency, many suppliers are asking, “Which kind of data do I need to collect today to prepare, and how can I ensure compliance tomorrow?” 

Data enables upkeep and circularity  

The DBP is a set of regulations governing the collection and sharing of data for batteries relevant for the industrial and transportation sector, including those used in EVs. The data stored in the DBP will help provide transparency on raw materials impact, usage and wear of EV batteries, which today are relatively hard to track. Without this data, EVs are difficult to resell and maintain. 

A fully operational DBP will include upstream data to ensure due diligence, and downstream data for lifecycle management.  

Upstream data refers to information about the raw materials and production of the battery. Collected from various parties, including miners and traders, it accounts for a huge share of the battery’s overall carbon footprint. This poses a logistical challenge, as it is not data that battery manufacturers traditionally collect and share.  

Downstream data relies on a battery management system (BMS) to track the health and performance of the battery once it is installed in the EV. This data is either held locally in the car or communicated back to the manufacturer and is available for free to different stakeholders including the car owner, maintenance centers, recyclers, and legislators. This information can be stored safely in cloud- or blockchain-based systems, accessible via QR code. 

A transformational journey 

There is no doubt that collecting and communicating both upstream and downstream data poses a challenge, particularly at this pivotal moment in the automotive industry’s sustainability journey.  

But if they focus on the challenges, suppliers risk missing the forest for the trees.  

By providing an easily accessible cache of data on the battery’s origins, age, and performance over time, the DBP facilitates reselling, revamping, and upgrading. It tracks the level of wear on the battery in terms of residual autonomy, improving accuracy in circularity. 

In other words, the DBP will be a vital tool for facilitating the long-term growth and profitability of the EV market. It will push the automotive industry into its next phase, a more sustainable and transparent one.  

But first, DBP compliance requires an unshakeable foundation of comprehensive, reliable data so companies understand where they stand today. They also need data from across the value chain. For this, they must be able to rely on data from the entire ecosystem and in turn, share that information with consumers.  

Traceability fosters transparency 

Capgemini can help create and foster data strategy, architecture, and communication. Working with upstream and downstream inputs, and leveraging our partnerships across the industry, we can ensure the BMS is compatible with data collection in the cloud. Our Product Traceability for Automotive offer creates efficiency and reduces costs. 

We are united in the effort to shape a more sustainable future. Now, we must work together to embrace regulations like the DBP, a key step along the way to achieving a more circular value chain.  

To learn more about how to collect and incorporate data into strategic decision-making, contact

Mobility, meet action. 

You can also meet me at the upcoming IAA Mobility 2025 event to discuss about how we can go beyond compliance with data-driven product lifecycle intelligence to increase circularity and transparency throughout the electric vehicle (EV) battery’s lifecycle. 

^{September 9-12, 2025 | Find us at Hall B1, Booth 22}

Climate change isn’t a distant threat—it’s a reality to deal with now.

Businesses need to rethink how they operate, especially when it comes to supply chains, which are crucial for global trade. Just like in the movie Interstellar, where survival depended on data, AI, and adaptability, today’s supply chains need to be flexible and smart to handle disruptions and climate challenges. AI-powered insights and actions are like the movie’s robot TARS: helping predict risks, optimize logistics, and reduce waste. Data ensures that every decision is as precise as a gravity equation. AI enhances precision in supply chains by analyzing vast data in real time, predicting risks, and optimizing logistics. It’s the key to transforming supply chains into smarter, greener, and more resilient systems that balance profitability with ecological responsibility.

Supply chains aren’t just stretched — they’re under siege. Disruption is no longer the exception; it’s the norm. That’s why resilience — the ability to anticipate, adapt, and recover fast — has shifted from nice-to-have to non-negotiable. A recent report from The Business Continuity Institute delivers the reality check: 80% of organizations faced supply chain disruptions last year, most more than once. That’s an uptick despite better planning — proof that we’re still reacting more than we’re preparing. Meanwhile, sustainability pressures are mounting. With supply chains responsible for over 60% of global carbon emissions, according to the World Economic Forum, they’re no longer just operational engines — they’re climate liabilities too.

Let’s face it—what we’re doing right now isn’t cutting it. The cracks in our supply chains are showing, and incremental fixes won’t be enough. It’s time for bold moves. If we want supply chains that can truly withstand shocks and stay ahead of the curve, we need to lean into smarter, faster, more adaptive solutions. That’s where AI steps in—not just as a tool, but as a game-changer. With its ability to forecast disruptions, optimize operations, and accelerate response times, AI is shaping the supply chains of the future. To stay ahead, companies must embrace green supply chain management (GSCM), where sustainability is built into every step. AI supercharges this shift, turning GSCM into a smart, data-driven engine. From cutting carbon to driving circular economies, AI enables supply chains that are not just efficient, but truly green.

Resilience, not yet autonomous: Supply chains still heavily rely on people

Supply chains are navigating a perfect storm: geopolitical instability, extreme weather, shifting consumer expectations — and growing uncertainty in global trade. Disruptions are no longer outliers; they’re part of the operating environment. While many organizations are embedding risk management into supply chain strategy, execution is still stuck in manual mode. Too much effort goes into collecting, cleaning, and stitching together data — leaving little room for insight, foresight, or speed. AI and machine learning are still underused, and critical response actions often rely on human intervention alone. The result? Slow reactions, mounting workloads, and talent focused on firefighting instead of forward-thinking.

What’s missing? Technology that doesn’t just capture and store data, but actively turns it into prescriptive insights and clear, actionable recommendations. Unfortunately, most tools in the market today still fall short of that promise. Instead, businesses are left stitching together manual processes and siloed teams to make sense of a rapidly changing environment. To build truly resilient supply chains, we need to shift from reactive, human-heavy models to intelligent, tech-augmented systems. The future isn’t about replacing people—it’s about empowering them with tools that amplify their decision-making, speed up response times, and free them to focus on what matters most.

Greening the chain: How AI and data are changing the game

Data and AI are at the core of this transformation, delivering unmatched insights, predictive accuracy, and optimization potential. By leveraging real-time data and predictive analytics, AI can identify potential risks—such as supplier delays, extreme weather, or geopolitical issues—before they impact operations. This early warning capability allows businesses to proactively mitigate threats through alternative sourcing, dynamic rerouting, or inventory adjustments. AI also enables scenario modeling, helping organizations test various disruption scenarios and build contingency plans with data-backed confidence. As a result, companies can maintain continuity, reduce downtime, and ensure customer satisfaction, even in the face of unexpected challenges. In today’s volatile global environment, AI is no longer a luxury but a critical enabler of resilient and future-ready supply chains.

AI-enhanced supply chain resilience framework

The AI-enhanced supply chain resilience framework strengthens supply chain agility and robustness by harnessing advanced AI technologies. It integrates real-time data from IoT devices into a centralized system for comprehensive analysis. Through predictive analytics and machine learning, the framework forecasts demand and detects potential risks—like supplier disruptions or market shifts—enabling proactive risk mitigation and smarter decisions in areas like inventory and logistics.

AI-driven communication tools improve collaboration with suppliers and stakeholders, ensuring seamless, transparent information flow. Continuous monitoring and adaptive feedback loops allow the supply chain to respond swiftly to changing conditions, driving ongoing improvement and innovation. By adopting this framework, businesses gain end-to-end visibility, reduce vulnerabilities, and ensure operational continuity—ultimately building a more resilient and high-performing supply chain.

Leveraging AI enables businesses to streamline operations, improve efficiency, cut costs, and elevate customer experiences. One powerful application is demand forecasting, where AI analyzes historical data to accurately predict customer needs. This leads to smarter inventory management—minimizing overstock and stockouts while optimizing capital use. Another key use case is route optimization. AI-driven tools evaluate factors like weather, traffic, and transport costs to determine the most efficient delivery paths. This reduces time and expenses while ensuring faster, more reliable service that meets growing customer expectations.

How organizations can harness it effectively:

According to the International Data Corporation (IDC), 55% of Forbes Global 2000 OEMs are projected to have revamped their service supply chains with AI and by 2026, 60% of Asia based 2000 companies will use generative artificial intelligence (GenAI) tools to support core supply chain processes as well as dynamic supply chain design and will leverage AI to reduce operating costs by 5%. This signifies a widespread adoption of AI to improve efficiency and gain a competitive advantage in supply chain management. Further, Generative AI can be harnessed to monitor global events and proactively identify emerging risks. It can automatically generate risk assessments, simulate scenarios, and suggest strategic mitigation plans—empowering supply chain teams to manage risks more effectively. Its conversational interface enhances user experience and accelerates response times. Over time, this evolves into a system-guided, data-driven approach, drawing from a rich library of scenarios and mitigation strategies to deliver contextual, timely responses to risk events.

Considering all of the facts

The fusion of data and AI isn’t just a tech upgrade — it’s a strategic shift for building supply chains that can bend without breaking. Organizations that embed intelligence into their operations now won’t just survive the next disruption — they’ll lead the transition to greener, faster, more adaptive ecosystems. By 2025, global supply chains will be reengineered out of necessity and powered by innovation. AI won’t just help companies — it will help nations stay resilient, competitive, and climate-conscious. It will redefine how we make, move, and manage everything. And like TARS in Interstellar, the most effective systems won’t just follow instructions — they’ll anticipate, adapt, and act as true copilots. What supply chains need now isn’t just visibility. It’s vision.

Start innovating now –

Give your supply chain an AI-enabled sixth sense

• Plug your supply chain into real-time feeds—from IoT sensors to storm trackers—and let AI act like your all-seeing oracle. Spot trouble (like delayed shipments or political curveballs) before it hits the fan

Make generative AI your strategic co-pilot

• Leverage Generative AI to generate real-time risk assessments, simulate disruption scenarios, and recommend mitigation strategies, all in a conversational interface

Build a digital twin—your virtual supply chain lab

• Think of it as a flight simulator for your supply chain. A digital twin lets you mirror operations in a virtual space to test “what-if” scenarios—from port delays to carbon constraints—without breaking a sweat in real life.

Interesting read? Capgemini’s Innovation publication, Data-powered Innovation Review – Wave 10 features more such captivating innovation articles with contributions from leading experts from Capgemini. Explore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.  Find all previous Waves here.

“AI is rapidly becoming embedded in the systems that power modern business, but its success still depends on the people behind it. Human oversight, intuition, and contextual understanding are what give automation its edge. As organizations evolve, those that build with ethics, adaptability, and purpose in mind will set the pace for their respective industries.” – Priya Ganesh 

Automation has become a central talking point for today’s businesses. But as the discussions grow more technical, something has been notably absent from the conversation: humans. In the race to automate, one thing is increasingly clear; the real value of AI lies in not replacing humans, but in leveraging machine efficiency to empower us to reach new heights. 

As AI and automation capabilities continue to scale across industries, organizations have both a unique opportunity and a growing responsibility to bring the human element back into focus. AI has the power to amplify human potential by offloading the repetitive tasks that consume so much of our time each day. But to remain effective, human oversight is an essential aspect of every AI system. From critical decision-making to ethical oversight, humans provide the adaptability, judgment, and real-world experience that are vital to the success of automation. Building robust collaborations between humans and machines ensures a future where human expertise and AI advancement drive ethical, creative, and innovative outcomes together. 

What happens when humans stay at the core 

By leveraging AI across more mechanical tasks, companies are streamlining operations and unlocking opportunities for people to focus on more strategic, creative, and value-driven work. Whether it’s accelerating product development cycles, improving customer experiences, or enhancing decision-making, automation is enabling improved outcomes for businesses. 

When humans remain at the heart of intelligent systems, the benefits begin to multiply. Predictive analytics powered by AI can deliver real-time insights that help organizations anticipate market shifts, tailor offerings, and improve business agility. When data-driven intelligence is combined with human knowledge and judgment, the result is more thoughtful, nuanced, and effective action.  

Human-centered AI in action 

Human intuition, ethical reasoning, and contextual understanding provide the framework within which automation can truly thrive. Across industries, the integration of AI into business operations is accelerating, but so is the realization that technology must be tempered by human judgment. Companies at the forefront are embedding oversight mechanisms into their AI systems not only to protect against risk, but also to reinforce trust in the technology.  

An American cloud-based software company recently launched Einstein AI – a generative AI platform that integrates large language models (LLMs) directly into user workflows to power predictions, recommendations, and chatbots for the organization’s full product line. Understanding the increased decision-making power this platform grants to AI, the organization has concurrently developed The Office of Ethical and Humane Use (OEHU), which ensures that human oversight and a set of core regulatory principles responsibly govern the platform – underlining its commitment to trusted AI. 

In retail, the conversation around responsible AI is equally urgent. One American multinational retailer is expanding its use of AI to enhance customer service, streamline operations, and boost productivity. However, the threat of AI hallucinations – misleading or inaccurate outputs – presents real risks, including customer misinformation, financial exposure, and legal liability. To mitigate these risks, the retailer is strategically incorporating human oversight at key checkpoints within its AI systems, focusing intervention on high-risk areas where accuracy and accountability matter most. 

In the telecommunications sector, a major American provider is taking a proactive approach to govern its AI initiatives. The organization has developed an internal AI Leadership Council, which consists of leaders from its legal, IT, security, and network teams, to set policies that prioritize ethical AI use, data privacy, and risk mitigation. The primary purpose of the council is to ensure human judgment shapes the trajectory of the company’s AI evolution along every step of the journey.  

As these examples show, responsible AI isn’t just about what technology can do. It’s about who remains in control. Whether through cross-disciplinary councils, ethical use advisory teams, or targeted human checkpoints, organizations are designing systems where human oversight is a guiding principle. 

Building a more human operating model 

As automation matures and AI becomes more deeply embedded into business operations, organizations are entering a new chapter defined by intentional design, ethical guardrails, and true human-machine partnership. The focus is shifting from implementation to impact, with a growing understanding that how we build matters just as much as what we build.  

This trend marks the beginning of a more collaborative approach to innovation. It’s a vision of the future where human insight shapes intelligent systems and technology is developed to support adaptability, creativity, and trust. This is not a temporary adjustment, but a long-term shift that will transform how businesses grow, compete, and evolve. Organizations that embrace this mindset will lay the foundation for more resilient, responsive, and responsible systems. When automation is designed around people and not just processes, it opens the door to a future that is as human as it is high performing.  

Learn more 

• TechnoVision 2025 – your guide to emerging technology trends 
• CTRL-ALT-Human – a new trend in Process on the Fly 
• Voices of TechnoVision – a blog series inspired by Capgemini’s TechnoVision 2025 that highlights the latest technology trends, industry use cases, and their business impact. This series further guides today’s decision makers on their journey to access the potential of technology. 

As identity and password-based cyberattacks increase, the urgency grows for clients to migrate from legacy identity solutions quickly as they transition to zero trust and start adopting AI, while protecting themselves from sophisticated cyberattacks. At Capgemini, we know identity governance, security, and productivity can align through innovation, but the process of selecting, procuring, and implementing effective solutions can take years, while the challenges are here and now.

Identity tool sprawl has created overlapping policies, features, and functionalities. Managing legacy on-prem identity technology alongside cloud identity continues to be a difficult area to manage and requires fresh thinking to navigate through today’s identity security challenges. 

Organizations need to consolidate and standardize identity governance and automation for productivity at speed, so that their security teams to have the best chance of protecting the identity attack surface, fast.

Risk through complexity

The identity lifecycle’s complexity, and risk to security, often only becomes apparent during breaches when a rogue identity can gain permission, explore the network, and find crucial unsecured systems and datastores to expose, export, or encrypt. Security risks come from all angles as we try to accommodate internal and external collaboration needs, machine and human identities, JML journeys, and privileged access.

Good control over the identity and access management lifecycles is possible, but needs strong, automated policies and procedures and a transparent control plane.

Multiple requirements across different personas

Mature organizations will have well-structured identity data stores, constructed so that personas and other logical groups of entities are well-defined and can be administered and controlled according to local criteria defining what they can or cannot do. 

Privileged identity management and conditional access policies need to be in place to ensure that access to privileged resources is controlled and that a zero-trust ethos pervades across the organization.

Recertification campaigns back this up, ensuring access is restricted to personas who still need it and automatically removed from those who don’t. Segregation of duties policies prevent dangerous combinations of permissions and help demonstrate good governance to auditors.

Managing third-party identities can be especially challenging, but there are cross-cloud tools available that can bring it back under control, ensuring that “3P” identities can be monitored, recertified, and automatically removed from your AD when they leave the provider, along with any permissions they held.

But getting all of this in place in a reasonable timeframe can seem overwhelming.

Capgemini’s new Microsoft Entra FastTrack

Increasingly Capgemini is contacted by organizations, often already on the Microsoft platform, seeking advice on rapidly implementable solutions to these IAM security challenges, especially as the cybersecurity world changes quickly around us. Responding to this demand, Capgemini has developed a Microsoft Entra-specific version of our tried and tested FastTrack assessment methodology.

Today, many organizations use Microsoft Entra tools, even if only as the main corporate Active Directory. While many CISOs rely on this as the organization’s main identity store and authentication and authorization mechanism, they do not necessarily understand the extent to which Entra can be used as a tool to enforce good governance of identity and access management processes and that there may be an opportunity to exploit or extend existing capability to fulfill their requirements.

With the frequency of cyber-attacks on the rise it is becoming increasingly important that organisations are able to respond to the threat landscape as quickly as possible, preferably using existing tools and minimising disruption to collaboration and mission-critical business functions. In many cases, Entra, with its widespread adoption and multi-use-case capability, may be the best option to fulfil these criteria.

Launched recently, our Entra FastTrack analyzes an organization’s current state of IAM maturity, assesses its requirements, and creates a detailed report and roadmap based on the exploitation or extension of existing Microsoft Entra assets, leading to the right path to security and compliance fast, without the need to spend months or years reaching for new solutions.

This FastTrack leverages Capgemini’s long history delivering Microsoft implementations across multiple sectors, allowing us to bring our own real-world experience and recommendations to the delivery, and accelerating the migration away from existing legacy infrastructure.

Conclusion

Capgemini’s new Microsoft Entra FastTrack could be your organization’s best and fastest way to improving your IAM and IGA processes and securing your assets against cyberattacks, without the need for expensive research and deployment of new solutions.

As security data volumes continue to grow, organizations face the dual challenge of retaining data for long periods while managing storage costs. Microsoft Sentinel Data Lake addresses this with the introduction of a new cold storage tier – an innovation that brings flexibility, scalability, and cost-efficiency to security data management.

Understanding the cold storage tier

The cold storage tier is designed for long-term retention of infrequently accessed data. It complements the existing hot and warm tiers, enabling organizations to implement a tiered storage strategy that aligns with their operational and compliance needs. With seamless transitions between tiers, security teams can access historical data when needed without incurring high costs.

This is particularly valuable for industries with stringent regulatory requirements or those conducting forensic investigations. Cold storage ensures that data remains accessible and secure, even years after it was collected.

Benefits for security operations

The new storage tier offers several advantages:

• Significant cost savings for long-term data retention
• Simplified compliance with data governance policies
• On-demand access to archived data for threat hunting and analysis.

By optimizing storage costs, organizations can allocate more resources to proactive security measures and advanced analytics.

Capgemini’s MXDR services: Maximizing storage efficiency

Capgemini’s MXDR services are uniquely positioned to take advantage of Sentinel’s new storage capabilities. Through its Cyber Defense Centers, Capgemini helps clients implement intelligent data retention strategies that balance performance and cost.
With the cold storage tier, Capgemini can:

• Store historical telemetry for extended periods without budget strain
• Enable retrospective threat analysis and compliance audits over longer periods of time
• Integrate storage policies with real-time monitoring and response workflows.

This holistic approach ensures that clients not only meet regulatory requirements but also enhance their overall security posture.

Strategic value for the future

The addition of cold storage to Microsoft Sentinel Data Lake is more than a technical upgrade – it’s a strategic enabler. It empowers organizations to retain valuable data, derive insights from it, and respond to threats with greater agility. When combined with Capgemini’s MXDR expertise, the result is a powerful, cost-effective solution for modern security operations.

Increased competition means companies must understand their customers like never before. Using Agentic AI to harvest insights and drive marketing KPIs is game-changing, but marketers need the right plan to take full advantage of it.

Enterprises must continually improve their understanding of audiences and how to engage them to effectively respond to increasing competitive pressures. It’s critical for a firm’s marketing experts to take advantage of every tool to inform smarter decisions.

New, Multi-AI Agents can deliver the insights that drive winning campaigns, but marketing departments must be prepared to take full advantage of these powerful tools. It starts with the right roadmap and strategic technology partner.

Challenges for every marketing pro

In my conversations with Chief Marketing Officers, I’ve identified several common goals for improvement. These include:

• Converting contact center data into valuable insights that help to design effective, customer-centric strategies
• Minimizing customer churn
• Using market intelligence to increase customer conversions

A company’s own data is an important source of the information required to help CMOs, Chief Experience Officers, and other marketing professionals achieve these goals. Unfortunately, legacy business intelligence systems often fail to deliver, for several reasons:

• Analytics systems rarely support strategic foresight and transformative innovation, instead providing business users with yet another dashboard.
• The results are often, at best, a topic for discussion at the next team meeting. This is not sufficient for a decision-maker to act upon immediately and with confidence.
• Systems typically fail to personalize their output to provide insights contextualized for the person viewing them, instead offering a generic, unsatisfying result.
• Systems often aggregate data within silos, which means their output still requires additional interpretation to be valuable.

In short, many legacy systems miss the big picture, miss actionable meaning, miss the persona and miss the point.

Based on my experience, I recommend an organization address this through multi-AI agent systems.

The Gen AI Strategic Intelligence System by Capgemini bridges the gap between the old way, and a value-driven future. This system converts the vast amounts of data generated by each client across their enterprise, into actionable insights. It is agentic, meaning that it operates continuously and is capable of independent decision-making, planning, and execution without human supervision. The solution examines its own work to identify ways to improve it rather than simply responding to prompts. It’s also able to collaborate with multiple AI agents with specialized roles, to engage in more complex problem solving and deliver better results.

How would organizations potentially go about doing this?

A well-crafted plan for Agentic AI-powered insights

First, organizations must establish a clear roadmap to take full advantage of Agentic AI-enabled decision-making. This should align technology with business objectives.

It starts by identifying the end goals, the core business objectives and associated KPIs relevant to the marketing team. These are the basis upon which the team contributes to the organization’s business value. Strengthening them is always a smart exercise. The good news is that even small improvements to any of these KPIs can deliver enormous benefits.

The roadmap should take advantage of pre-existing AI models to generate predictive insights. It should also ensure scalability, reliability, and manageability of all AI agents, not just within the realm of marketing and customer experience, but throughout the organization. And it should be designed to leverage domain-centric data products from disparate enterprise resource planning and IT systems.

Finally, the roadmap must identify initiatives to ensure the quality and reliability of the organization’s data by pursuing best-in-class data strategies. These include:

• deploying the right platform to build secure, reliable, and scalable solutions
• implementing an enterprise-wide governance framework
• establishing the guardrails that protect data privacy, define how generative AI can be used, and shield brand reputation

The right partner delivers more than technology

Second, the organization must engage the right strategic partner – one that can provide business transformation expertise, industry-specific knowledge, and innovative Agentic AI solutions.

Capgemini uses its technology expertise, partnerships with all major platform providers, and experience across multiple industrial sectors to design, deliver, and support generative AI strategies and solutions that are secure, reliable, and tailored to its clients unique needs.

The solution draws upon the client’s data ecosystem to perform root-cause analysis of KPI changes and then generates prescriptive recommendations and next-best actions that are tailored to each persona within the marketing team. The result is goal-oriented insights aligned with business objectives, ready to empower the organization through actionable roadmaps for sustainable growth and competitive advantage.

*Applying agentic AI to customer experience

Here’s a use case that demonstrates the potential of an agentic Gen AI solution for customer experience.

A marketing department wants to leverage its contact center data to improve customer experience, boost operational efficiency, and manage costs. This requires a comprehensive view of contact center operations, including insights into customer interactions, interaction channels, and outcomes.

An analytics solution powered by agentic generative AI can deliver hierarchical views of customer service level and KPI metrics, conduct near real-time (NRT), around the clock trend analysis for service level agreements, highlight correlations between dependent KPIs for continuous improvement initiatives, provide early warning systems for emerging customer experience challenges, and enhance churn prediction.

The impact can include a 10 percent boost to upsell closure, and a 20 percent improvement to customer satisfaction. Capgemini enables this use case through an AI CX insights 360 solution offered for the Gen AI Strategic Intelligent System by Capgemini.

Just imagine this agent working 24/7 on your behalf. They don’t sleep, they don’t get tired, they don’t take vacation, and they’re completely autonomous. 

Meaningful, actionable results  
With the right implementation and support, the potential benefits include better access to market intelligence, as well as significant opportunities for growth through cross-selling, up-selling, and capitalizing on both marketing white spaces and competitive insights. 

Capgemini’s modeling suggests such a solution would accelerate the speed and rate of customer acquisition by 75 percent, while lowering the cost. It would also boost customer satisfaction scores by 20 percent and increase customer conversions by more than 50 percent.

Given the direct relationship between customer-experience excellence and revenue generation, those are meaningful advantages that cannot be ignored.

^*Results based on industry benchmarks and observed outcomes from similar initiatives with clients. Individual results will vary.

The Gen AI Strategic Intelligence System by Capgemini works across all industrial sectors, and integrates seamlessly with various corporate domains. Download our PoV here to learn more or contact our below expert if you would like to discuss this further.