Intelligent decision-making has never been so important, and agentic AI is a technology that can deliver the actionable insights the chief supply chain officer needs to build resilience and agility.

Intelligent decision-making has never been so important, and agentic AI is a technology that can deliver the actionable insights the chief supply chain officer needs to build resilience and agility.

To call the current business climate volatile is an understatement – and at enterprises across multiple industrial sectors, the people most keenly impacted by the resulting uncertainty are likely those responsible for managing their organization’s supply chains. These vital, logistical links are subject to powerful external forces – from economic and political factors to environmental impacts and changes in consumer behavior. It’s critical that the executives in charge of supply chains, and their teams, take advantage of every tool to make smarter decisions.

New, multi-AI agent systems can deliver the insights that not only make supply chains more resilient, but also help executives identify opportunities to reduce logistics costs. But organizations must be ready to take advantage of these powerful tools. Preparing for success includes creating the right roadmap and engaging the right strategic technology partner.

Common pain points in the chain

In my conversations with chief supply chain officers, I’ve identified several common pain points they’re keen to address. Most are being challenged to improve supply planning, reduce inventory cycle times and costs, better manage logistics investments, and do a better job of assessing risks associated with suppliers and other partners across their ecosystem.

A company’s own data is an important source of the information required to help CSCOs achieve these goals and to enable agentic AI. Unfortunately, legacy business intelligence systems are not up to the task. There are several ways in which they fail to deliver:

• 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 – 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 result that satisfies nobody.
• 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.

With the introduction of Gen AI Strategic Intelligence System by Capgemini, this could be the very system that 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: it operates continuously and is capable of independent decision-making, planning, and execution without human supervision. This agentic AI 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?

Establish an AI-driven KPI improvement strategy

First, organizations must establish a well-defined roadmap to take full advantage of AI-enabled decision-making – one that aligns technology with business objectives.

For CSCOs, this starts by identifying the end goals – the core business objectives and associated KPIs relevant to supply chain management. These are the basis upon which the supply chain contributes to the organization’s value, and 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 supply chain management, but throughout the organization. That also means it should be designed to leverage domain-centric data products from disparate enterprise resource planning and IT systems without having to move them to one central location.

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.

An experienced technology partner

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

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

Capgemini’s 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 – tailored to each persona within the supply chain 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 the supply chain^*

Here’s a use case that demonstrates the potential of an agentic AI solution for supply chain management.

An executive responsible for supply chain management is looking for an executive-level summary and 360-degree visualization dashboard. They want automated insights and recommended next-best actions to identify savings opportunities.

An analytics solution powered by agentic AI can incorporate multiple KPIs into its analysis – including logistics spend, cost per mile, cycle time, on-time delivery rates, cargo damage, and claims. It can also track performance of third-party logistics service providers – including on-time performance, adherence to contractual volumes, freight rates, damages, and tender acceptance.

The solution can then apply AI and machine learning to optimize asset use through better design of loadings and routes. Partner performance can be analyzed – including insights into freight rates, delays, financial compliance, and lead times – and used to negotiate better rates.

The impact of this can include a reduction in logistics spend of approximately 10 percent, an opportunity to save approximately five percent through consolidation of routes and services, and a 15 percent improvement in transit lead time.

Capgemini enables this use case through an AI logistics insights 360 solution offered for the Gen AI Strategic Intelligence 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.

Real results that relieve supply chain pressures

Capgemini’s modeling suggests that with the right implementation and support, the potential benefits include reducing overall supply chain spending by approximately five percent – including a 10-percent reduction in logistics spend. Other benefits include a three percent improvement in compliance, plus 360-degree order visibility and tracking.

Given that today’s supply chains are being subjected to so many pressures from so many sources, 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.

A revolution in trade and transaction reporting 

With rising regulatory pressures and data challenges, financial institutions increasingly demand efficient trade and transaction reporting. In response, there is a noticeable shift toward managed services solutions, with India emerging as a key destination. 

India’s rise in this domain can be attributed to several factors, including a robust talent pool, cost advantages, technological innovation, and operational efficiency, organizations like Capgemini are playing a central role in transforming how trade and transaction reporting is managed. In today’s financial landscape, institutions are under mounting pressure to meet increasingly complex and evolving regulatory requirements. From trade and transaction reporting (TTR) to data reconciliation, regulatory submissions, and analytics, the operational burden is growing – and so are the associated costs. Compliance is no longer just a legal necessity; it’s a strategic imperative that demands specialized expertise, scalable infrastructure, and round-the-clock operational support. 

These challenges are compounded by the need for agility, accuracy, and cost-efficiency. Financial institutions must navigate a web of jurisdictional rules, manage vast volumes of data, and ensure timely reporting – all while keeping operational costs in check. This is where India’s value proposition becomes particularly compelling. 

India has rapidly emerged as a global hub for managed services, offering a unique blend of deep domain expertise, advanced technological capabilities, and cost-effective delivery models. Its workforce is not only technically proficient but also increasingly specialized in financial services operations, regulatory compliance, and digital transformation. 

At Capgemini, we are leveraging this strategic advantage through our Post-Trade Transaction Reporting Practice. By expanding the scope of managed services beyond reporting, Capgemini is helping financial institutions transform compliance from a cost centre into a source of strategic value. This blog explores how India is not just supporting this shift – but leading it 

Leveraging a vast talent pool 

India has long been recognized for its diverse and highly skilled workforce, and the financial services sector is no exception. With a large pool of professionals possessing a unique blend of expertise in finance, technology, and regulatory compliance, India is increasingly recognized for its ability to manage complex reporting requirements. These professionals bring a strong understanding of global financial markets, regulatory standards, and the technologies required to handle large-scale data processing, making India an ideal base for supporting trade and transaction reporting needs. 

For financial institutions, this talent pool offers deep expertise in navigating regulatory landscapes such as MiFID II, EMIR, Dodd-Frank, and SFTR. These frameworks demand stringent data reporting and reconciliation processes.  This is an area where India’s workforce excels. 

Cost-effectiveness and operational efficiency 

In addition to technical expertise, India offers a significant cost advantage, making it an attractive destination for financial institutions aiming to optimize operational costs. Institutions are under constant pressure to streamline processes and reduce overhead while maintaining compliance and reporting accuracy. Leveraging managed services in India can significantly lower operational costs, as labor expenses are considerably lower than in many Western markets. 

Moreover, the cost-effectiveness extends beyond just labor. Infrastructure and technology investments in India can be more easily scaled, allowing financial institutions to adopt cutting-edge solutions at a fraction of the cost. This provides access to best-in-class capabilities without the need for substantial capital expenditures. 

Technological innovation and automation 

India is increasingly becoming a global leader in IT infrastructure and innovation, with a focus on technologies transforming the trade and transaction reporting landscape. At Capgemini, there is a strong emphasis on integrating advanced technologies such as automation, data analytics, and AI into managed services offerings. 

AI and machine learning streamline data aggregation, reconciliation, and validation, significantly reducing manual errors and improving speed and accuracy. These technologies enable financial institutions to achieve shorter turnaround times, ensuring that they meet regulatory deadlines and respond quickly to market changes. 

As adoption of these technologies accelerates, India is becoming a key player. By partnering with Indian service providers, financial institutions can stay ahead of regulatory and technological curves as well as emerging market trends. 

24/7 operational capabilities

Financial markets operate continuously, requiring reliable, round-the-clock support for reporting functions. India, with its well-established infrastructure, offers a 24/7 operational model, ensuring financial institutions meet their reporting obligations across time zones. 

Indian teams offer continuous monitoring and rapid response. This uninterrupted support is critical for global financial institutions with operations in multiple regions, ensuring seamless compliance and reporting activities across different time zones. 

Post-trade transaction reporting and specialized expertise

One of the key areas in which India excels is in post-trade transaction reporting. This includes critical processes like data reconciliation, regulatory submissions, and compliance checks that ensure transparency and reduce market risks. By focusing on building specialized talent pools, including subject matter experts, India enables firms to navigate the complexities of global regulations, such as EMIR and the U.S. Dodd-Frank Act. 

Capgemini, for example, has established a dedicated Post-Trade Transaction Reporting Practice that helps financial institutions optimize operations by streamlining these processes. Using advanced analytics, automation, and regulatory expertise it helps clients reduce risk and ensure compliance. Centralizing these delivers cost-effective, high-quality services vital to managing regulatory obligations. 

Regulatory change management   

As financial regulations evolve globally, institutions must be agile and adapt their systems and processes in real time. Regulatory change management is a key area where Indian managed services providers add value. Changes in regulatory frameworks can be complex and costly to implement, particularly when new rules require re-architecting internal systems or updating reporting platforms. 

Capgemini offers specialized solutions to help financial institutions navigate these changes. Whether it’s adapting existing systems to meet new regulations or developing entirely new platforms for reporting, Capgemini supports its clients through every phase of the change management process. This proactive approach ensures that financial institutions remain compliant with evolving regulations while avoiding costly penalties or operational disruptions. 

Conclusion

India’s emergence as a hub for trade and transaction reporting managed services reflects a broader shift toward outsourcing and automation in the financial services industry. With a wealth of talent, cost advantages, and a strong focus on technological innovation, India is transforming the way financial institutions manage regulatory compliance and reporting.  

Sarah wants to access her new online banking account to pay an urgent bill. Frustrated and anxious, she spends close to an hour trying to complete a simple task that should only take a few minutes. Unfortunately, her bank’s digital interface is not compatible with her screen reader, which she relies on as a person with a visual impairment.

If you think this is an extremely uncommon scenario, roughly 80 million people, or one-fifth of the EU’s population, live with a disability.[1] A disability can affect anyone at any time and can include temporary conditions like someone recovering from surgery or suffering from a short-term injury that prevents them from accessing a once routine service.

As a brand in the digital space, you should strive to design and develop more accessible services, especially since digital accessibility can suddenly become very important for any of your customers.

Here are five more reasons why this topic deserves your attention.

1. Tap into a large and valuable audience

People with disabilities will naturally favor brands that ease their accessibility challenges. A study in the Netherlands revealed that 45% of iOS and 61% of Android users “have one or more accessibility settings activated on their phone.”[2] By making features like screen reader compatibility, video captioning, and voice recognition standard practice in software development, your business will see a significant uptick in transactions thanks to new satisfied customers.

2. Your brand reputation is on the line

Whether physical or mental, temporary or permanent, disabilities can limit people’s access to essential services in areas like banking, transportation, healthcare, and education. As digital technologies become increasingly integral to our daily lives, addressing accessibility is not only a matter of social justice but also an economic necessity. If your business can design products and services that are easier to access by everyone, your new and existing customers will have a favorable view of your brand, which can engender strong customer loyalty.

3. Non-compliance will be costly

Starting June 28, 2025, the European Accessibility Act (EAA) will introduce measures requiring EU businesses to adhere to the updated digital accessibility guidelines, as it aims to reduce barriers to entry and ensure everyone can participate in the digital realm.[3] This new standard will catch many affected businesses by surprise. A January 2025 survey revealed that only 11% of organizations feel confident they will meet the June deadline, while another 35% aren’t even sure if their changes are enough to be in scope of the EAA.[4] By prioritizing accessibility today, you’ll avoid potential legal pitfalls tomorrow.

4. A better user experience (UX) benefits everyone

Accessibility is about more than just meeting legal requirements; it’s about creating a highly accessible service that’s more enjoyable to use. By adding new features, you enhance the overall experience for all users. Plus, did you know search engines favor accessible websites?

5. Continuous improvement leads to long-term growth

Accessibility is not a one-time fix; it’s a continuous journey that will only become more relevant in the future. Disabilities such as visual impairments, hearing loss, cognitive decline, and reduced mobility are a fact of life as populations age, so if you want to be a business that values a diverse customer base, you’ll make accessibility a foundational core of your business strategy. And by doing so you’ll set a positive example in your industry, winning you respect and loyalty from customers, employees, and stakeholders alike.

For example, a national postal company recognized the importance of enhancing accessibility and collaborated with Capgemini to evaluate over 100 of their omnichannel journeys. This led to the identification of more than 250 recommendations for improving the UX of their app and website.

Ready to create a more inclusive digital environment? Stay tuned for our follow-up article, Five steps to widespread digital accessibility.

Contact us to learn more about how we can help you with your digital accessibility journey.

• [1] https://www.who.int/health-topics/disability#tab=tab_1
• [2] https://appt.org/en/stats
• [3] https://creative-boost.com/european-accessibility-act/#:~:text=European%20Accessibility%20Act%20Exemptions,less%20than%20€2%20million
• [4] https://abilitynet.org.uk/news-blogs/eaa-only-11-organisations-confident-they-will-meet-june-deadline

Marketing guru Shep Hyken once said “make every interaction count, even small ones.” This quote has always stuck with me because it’s so human, and because it explains why we feel a strong emotional connection to certain brands. We are more likely to become repeat customers if we experience good customer service, even in a small interaction.

It is well known that contact center agents are the face of any bank. They are on the front lines dealing with customer interactions and shaping your bank’s perception. Alas, the unfortunate reality is that today’s customer service isn’t standing up to customers’ needs. Consumers in 2025 expect more, and it’s on banks to step up.

Today’s consumer won’t stand for generic banking – they expect a personalized, seamless experience. More than that, they want it to feel human. Often, this demand lands with the staff at a contact center. But can we expect this staff to keep up with ever-growing customer expectations unaided? Or, even worse, can we expect the contact center to deliver a great experience when the perception is that banks are actively trying to automate away their jobs?

Capgemini’s World Retail Banking Report 2025 finds that only 16% of agents appear satisfied with their jobs. Attrition continues to rise, increasing the cost of recruitment and time spent training agents. In between, customers are looking for empathy in basic interactions – and instead find things impersonal and procedural.

I’m convinced we’ll do right by customers if we deploy technology to help overworked agents. Technology, after all, is a tool. The use of AI can help eliminate friction and let these agents deliver the kind of frictionless experiences that customers are hungry for. By implementing predictive AI capabilities, banks can prevent issues before they even occur based on historical patterns and trends, reducing the number of complaints and anomalies in real-time.

In the World Retail Banking Report, we sought to understand how 8,000 millennial and Gen Z customers view perhaps the single most important feature of their banking relationship: the card. The consensus was clear: there is room for improvement at every point of the customer journey. And there is a clear need for personal connection.

The worrying part of our research findings was the extent to which bank teams seemed aware of dissatisfaction among customers. Consider this: 68% of banking institutions acknowledged poor customer satisfaction as a major issue. What’s more concerning is that over 60% of bank marketing staff say they are overwhelmed by the number of applications they receive, and many banks acknowledge the KYC process can take days.

All of this is taking place against a backdrop of profound technological change. These changes have benefited nimble, digital-first players such as Monzo and Revolut. While they may seem small compared to the scale of US megabanks, they have succeeded in capturing valuable market niches. They did so by creating smooth digital experiences, broadening the aperture of services available and sidestepping much of the friction that can hinder established banks. They created real customer connection.

AI can let US banks build this connection too, removing bottlenecks in manual processes such as card applications. At a strategic level, it can inform banking strategy, create products with in-built personalization and close the customer service gap with the emerging neobank players.

By proactively predicting and addressing trends, the technology can assist banks in staying ahead of customer complaints and operational bottlenecks, making the process smoother for both agents and customers. 

However, AI can’t do it alone – many customers will still want the option to connect with a human being. After all, personal finance is personal, whether it’s a customer loan application or resolving a disputed charge. But AI can empower those humans, giving them a better insight into the customer’s situation and request.

For example, if a customer is angry about an unauthorized credit card transaction, a human agent augmented by AI can use sentiment analysis to detect the customer’s anger. The AI can then direct the query to an agent who has a high success rate in managing similar complaints and calming frustrated customers. AI can even proactively anticipate scenarios to help agents better serve customers.

Furthermore, by automating routine inquiries, AI allows agents to focus on complex, high-value tasks that require empathy, creativity, and judgment – attributes that customers are increasingly expecting. In this way, AI enables agents to provide more personalized service at scale, bridging the gap between human empathy and efficiency.

To put it simply, AI can make customer service agents much happier and more productive in their work. This takes more than a technology strategy: bank leaders will have to implement a thorough change management plan. That means educating employees about the potential of AI and their role in augmenting human capabilities, as well as clearly delineating what work will be done entirely by AI, and where AI will play a supporting role.

It’s also crucial that banks adopt a customer-centric AI strategy, focusing not only on operational efficiencies, but also how these technologies can directly enhance customer experience and employee experience. AI’s role is not just to solve problems faster, it’s to solve them better and with more empathy, while providing seamless self-service options and empowering agents to be more competent with contextual insights and continuous learning.

The bottom line: bank executives must push the boundaries of innovation to explore the potential of AI – in a safe and controlled fashion – that strives to deliver enhanced client engagement. It’s time to make every interaction count.

Geothermal energy is a clean and reliable power source, but making it more efficient can be difficult. Systems like organic Rankine cycles (ORCs) are commonly used because they work well with moderate temperatures and are environmentally friendly.

However, improving their performance requires careful control of factors like temperature, pressure, and flow.

Traditional design and simulation tools can be slow and hard to use. That’s where Gen AI, Bayesian optimization, and large language models (LLMs) come in. These advanced technologies can make the process faster, smarter, and more user friendly.

• Gen AI can create useful data, suggest design improvements, and support decision-making.
• Bayesian optimization helps find the best settings to boost system efficiency.
• LLMs can explain complex data and offer clear, actionable insights.

By combining these tools with traditional engineering methods, we can build smarter, more efficient geothermal systems. This approach supports greener energy solutions that are easier to design, manage, and scale.

How can Gen4Geo help to optimize the geothermal energy process?

We partnered with one of India’s top institutes (IIT) to explore how geothermal power plants perform under different conditions. Our goal was to better understand and improve their efficiency.

• Simulation and modeling
  We built detailed models of geothermal systems using Python and REFPROP to get accurate data. We focused on key parts of the organic Rankine cycle (ORC) and calculated important values like energy output and efficiency. To ensure accuracy, we also recreated the model in Aspen HYSYS, a trusted industry tool.
• Smart predictions
  We used Gen AI to create a model that can predict how the system should operate to reach certain efficiency goals. This model was trained on real data and tested to make sure its predictions were reliable.
• System optimization
  To find the best setup for the system, we used Bayesian optimization with a fast-learning model (XGBoost). This helped us quickly identify the most efficient configurations without heavy computing.
• User friendly interface
  We developed a chatbot called Gen4Geo, powered by a large language model (LLM). It allows users – even those without technical backgrounds – to ask questions and get clear, helpful answers about the system.
• A smarter, closed loop system
  By combining simulation, AI generated data, optimization, and a natural language interface, we created a smart, self-improving system. It helps design and manage geothermal plants more easily and efficiently.

Bringing value to the geothermal extraction domain with AI and physical modeling

Traditional methods for designing geothermal power plants can be slow, expensive, and hard to use without deep technical knowledge. Our new approach solves these problems by combining the power of artificial intelligence (AI) with proven physical models.

• Faster, smarter design
  We use Gen AI to quickly create realistic data, which helps us test different design ideas much faster than before. This speeds up the entire process and leads to better, more efficient systems.
• Cost effective optimization
  With Bayesian optimization, we can find the best system settings using fewer tests. This saves time and money while still delivering high performance.
• Easy to use for everyone
  A breakthrough is our use of large language models (LLMs). These allow anyone from engineers to decision makers to ask questions and get clear, helpful answers. No need for deep technical skills.
• Always improving
  Our system learns and adapts over time. As new data comes in, it gets smarter, helping us stay ahead in geothermal technology and improve performance under changing conditions.
• A greener future
  By making plant design faster, cheaper, and more accurate, our method helps speed up the use of geothermal energy. It supports cleaner, more sustainable energy solutions that are also more profitable.

Key insights and learnings

We’re combining the power of thermodynamics and artificial intelligence (AI) to solve real world energy challenges. By using smart data models alongside traditional simulation and optimization tools, we can make geothermal power plants more efficient, faster to design, and more affordable. A key part of our approach is using Gen AI to create useful data for testing and improving system performance. Bayesian optimization helps us make smart choices quickly, saving time and money. We’ve also added a large language model (LLM) interface that lets users interact with the system using everyday language. This makes advanced tools easier to use, even for people without a technical background. This approach isn’t just for geothermal energy; it can also be used in other industries like oil and gas or hydrogen production. It opens the door to smarter, more sustainable, and more accessible energy solutions across the board.

We’ve entered a new phase of AI – one where systems no longer wait for instructions but actively reason, plan, and act. This shift from generative to agentic AI raises a defining question:

Who will lead the next wave of transformation?

 Will it be the tech companies building the foundation models and platforms, or the industries embedding AI into real-world business workflows? The answer is clear: neither side can win alone. Agentic AI isn’t a plug-and-play solution—it’s a systemic leap that demands AI-native infrastructure, new talent roles, a culture of experimentation, and trust in autonomous systems. The future belongs to those who can bridge the gap between breakthrough technology and scalable, responsible value creation. In this article, we explore the evolving power dynamic between builders and adopters—and why service providers may be the unlikely accelerators of this new era.

Agentic AI: Beyond Implementation to Transformation

Unlike prior tech cycles, Agentic AI isn’t simply implementing a new tool or channel. It demands a complete rethink of how work is done, how decisions are made, and how value is created. To truly harness its power, industries need more than APIs and dashboards.

They need:

• Infrastructure readiness: scalable compute, data pipelines, and model orchestration.
• Talent transformation: from prompt engineers to AI product managers, the skills needed are nascent and niche.
• Mindset shift: a culture of experimentation, agility, and comfort with co-creating alongside AI.

In this context, the true differentiator isn’t just having access to AgenticAI; it’s being prepared to reimagine how you operate with AI at the core.

ROI, Talent, and the Black Box Problem

While tech companies dazzle with breakthrough models and autonomous agents, industries face grounded realities:

• ROI is uncertain unless use cases are tightly coupled with business outcomes.
• Niche talent is hard to find, and even harder to retain.
• The black-box nature of LLMs challenges observability, governance, and trust.
• Security, privacy, and compliance must be rethought in the age of generative automation.

This isn’t a plug-and-play revolution. It’s a systemic shift. Industries must invest not only in tools but also in readiness and resilience.

The Evolving Power Dynamic

Tech companies lead the way in building foundation models, toolchains, and agentic platforms. They control the tech stack, drive innovation velocity, and shape the ecosystem. Yet, they face challenges around monetization, trust, and the long tail of enterprise needs.

On the other hand, industries hold the real-world context, proprietary data, and deep knowledge of customer behaviour. They define high-value use cases, drive adoption at scale, and ultimately determine where AI delivers impact. But they must also tackle integration complexity, change management, and readiness gaps.

The new power players will be those who can navigate both worlds — translating the potential of Agentic AI into practical, governed, and scalable transformation across domains.

Strategic Implications for Service Providers

For service companies working with both tech builders and enterprise consumers, this creates a unique strategic opportunity:

• Act as translation layers between Agentic AI innovation and industry needs.
• Provide platformization strategies (moving from isolated tools and pilots to creating scalable, reusable AI foundations inside an enterprise) to help industries build internal capability, not just consume tech.
• Build AI governance frameworks that bridge the black-box risks and enterprise trust requirements.
• Offer talent incubation and skilling programs tailored to AI-first roles.

Service companies must evolve from implementation partners to AI transformation enablers.

The Real Winners: Co-Creators of Value

Ultimately, the winners in the Agentic AI era will not be defined solely by who builds the most powerful models or the most dazzling demos. They will be the ones who can:

• Align AI with business strategy.
• Drive adoption with speed and responsibility.
• Build ecosystems that are trustworthy, explainable, and human-centric.

This is not just a race to innovate — it’s a race to transform. And those who can blend technology, context, and trust will define the next era of value creation.

In this new landscape, co-creation is the new competitive advantage.