As warehouse operations evolve, smart warehouses and new technologies are helping organizations balance growing operational challenges and deliver goods efficiently

Running a warehouse today involves overcoming a changing set of obstacles. Alongside the rapid growth of e-commerce, warehouse managers must navigate an ever-changing landscape of customer demands, sustainability challenges, and resource shortages. Additionally, they must consider regulatory requirements and skills management. In this environment, cost optimization becomes an increasingly difficult task. To improve their chances of success and stay competitive, warehouse managers need to adopt the latest technologies such as a smart warehouse management system. The concept of the “smart warehouse” represents an evolution, understanding the maturity of your warehouse operations today is crucial to seizing future opportunities.

Warehousing operations are faced with multiple challenges

Navigating the complexities of today’s warehouse operations involves addressing a variety of challenges, including:

• Customer needs: Managing continually changing customer needs requires the agility to quickly adjust to fluctuating volumes and meet increasingly high service level expectations.
• Climate challenges: Environmental responsibility is a significant and pressing issue, compelling warehouses to embrace their role in ensuring adherence to sustainability targets.
• Resource scarcity: Labor shortages and inventory shocks create a scarcity that clashes with the growing demands of consumers. Efficient operations and focusing labor on complex client value driven tasks is becoming paramount in finding innovative ways to navigate these constraints and deliver results.
• Strict regulatory frameworks: Regulations, particularly concerning traceability, add layers of complexity to supply chains. Warehouses must be compliant with these regulations while effectively managing logistics across geographical borders.
• Competency management: The constant turnover of employees and subsequent loss of knowledge become significant hurdles to long-term productivity, demanding constant effort to maintain a skilled and knowledgeable workforce.

The first step in warehouse optimization and transformation 

To meet customer needs, warehouse managers must optimize warehouse space and identify areas for improvement. For that to happen, warehouse managers need to be able to assess and visualize capacity and demand at the facility level and below. As new companies enter the market, the need for warehouses to be more competitive – and provide complex service offers – has increased. 

The evolving role of warehouse management systems

Warehouse management systems (WMS) fulfill a critical role in warehouse transformation. They provide a centralized software interface for processing, managing and monitoring operational processes. WMS have significantly evolved over the last few years. And this evolution includes cloud warehouse management systems which are currently in high demand. The popularity of the move to cloud is due, in no small part, to the fact that WMS that use cloud computing offer unparalleled flexibility, scalability, and cost efficiency. 

Applying a core model to your deployment approach shapes your WMS in a way that addresses your needs across all geographies to enable faster transformation projects at scale. 

But, nowadays, modern WMS are moving beyond streamlining core processes. They are increasingly incorporating new use cases like workforce management, enabling dynamic task allocation and improved labor efficiency. This is ultimately driving operational excellence in warehousing. 

As well as preparing a foundation for warehouse operations, WMS also set the stage for further optimization efforts, such as integrating with automation technologies to provide established business rules, communicate orders and enhancing the data visibility needed to unlock data-driven improvement use cases. WMS systems act as a pivotal enabler for the transformation of traditional warehousing into smart, connected operations.  

Warehouse automation and robotics

Warehouse automation that integrates into WMS has been accelerated by multiple technological leaps and fueled by massive investments from marketplace players. The need to address both traditional objectives (cost efficiency, service and quality), as well as new challenges (labor scarcity, assortment complexity) has made the decision to invest in automation easier to justify. 

While automated guided vehicles (AGV) and autonomous mobile robots (AMR) are the most visible tip of the iceberg within the solutions landscape, there is no “one-size-fits-all” approach making it important to consider the full scope of solutions within the warehouse and all the variables at hand. 

Warehouse automation and robotics solutions can boost labor productivity by 85%¹ allowing businesses to not only reduce labor costs but also face head on the shortages some experience. For instance, Exotec’s Skypod system with its put-to-light cells and orchestration engine enables end-to-end automated order fulfillment and achieved a throughput and efficiency increase of more than 600% for one of its customers. 

Data-driven optimization and IT/OT integration

A proper IT/OT strategy, including operational tools and IT integration, and a data factory approach, is just as important as the technology itself. It is key to empowering the supply chain as a strategic asset and to continuously identifying new business cases. Data analytics enables warehouses to harness vast amounts of data, created by their systems, to drive efficiencies, costs and CO2 footprint reduction through real-time monitoring and data-driven decision making. This data can be harnessed to drive extended visibility solutions such as digital twin, transportation control towers and process mining solutions to take operational issue diagnosis and resolution to the next level.   

Towards smarter and more sustainable warehouses 

Generative and agentic AI tools unlock a new enhanced layer of data intelligence. Revealing a realm of unprecedented adaptability and resilience will certainly revolutionize supply chains. Warehouse space optimization is a great example of how Gen AI allows you to go one step further than with traditional AI.  Discriminative AI can be used for warehouse layout constrained optimization, considering factors like distance travelled and tasks duration minimization, reducing congestion, and products category. Generative AI then enables layout generation for visualization, simulation of a wide range of scenarios (e.g. seasonal peaks) and suggestion of optimal layout. 

Finally, it is equally as important for industry stakeholders to prioritize sustainability as an integral part of warehouse optimization. By leveraging the power of technology and adopting sustainable practices, warehouses can not only enhance operational efficiency and customer satisfaction but also contribute significantly to mitigating the environmental impact associated with their operations. We believe this can be achieved through a combination of sustainable execution and sustainable assets. 

Sustainable execution involves implementing initiatives that promote the more sustainable use of existing assets and resources within warehouses, while “sustainable assets” focus on designing and utilizing assets that are inherently sustainable, such as eco-friendly warehousing facilities and sustainable primary materials. From reducing energy consumption and optimizing packaging to implementing effective waste management practices, new technologies also offer the potential to revolutionize the way warehouses operate in an environmentally conscious manner.  

Partnering for end-to-end warehouse transformation

As warehousing solutions continue to advance, it is crucial for industry stakeholders to collaborate with the right partners in their warehouse transformation journey. At Capgemini we provide end-to-end support for organizations, from the analysis of your current situation and exact context, the selection of the most suitable technologies that align with your goals and needs, the definition of the right implementation strategy and delivery execution, to the set-up of a continuous improvement approach to ensure your warehouse remains at the forefront of innovation. 

“As manufacturers face increasing pressure to deliver faster, smarter, and more sustainable operations, the way we design and build factories is undergoing a radical transformation. At Capgemini, we’ve been working with global leaders to rethink traditional approaches – leveraging digital twin technology to bring agility and intelligence to the factory floor.” 

–  Alexandre Embry  

A global consumer products company wanted to make building new factories simpler, smarter, and more efficient. Instead of starting from scratch each time, we helped them create a digital tool that lets teams design and compare factory setups virtually, choosing everything from product types to packaging lines. With built-in visuals, data dashboards, and AI-powered insights, the tool is now helping them plan better, move faster, and make more informed decisions. 

Reimagining factory design for the digital era

Designing a new factory is a complex, capital-intensive endeavor. Our client wanted to eliminate disruption points and boost both capital efficiency (CapEx) and operational efficiency (OpEx). The question: how could they standardize factory design globally while tailoring it to specific consumer goods?  

So, we innovated the process from the ground up. Instead of treating each new factory as a bespoke project, we built a plant configurator that lets engineers design production lines using a modular and digital-first approach. From selecting product types and packaging sizes to choosing suppliers and automation levels, users can now configure entire factories digitally, complete with 3D models, scanned documents, and real-time KPI dashboards. 

Building the Digital Twin: How we made it real 

We assembled an innovation team of business experts, data modelers, business analysts, 3D and digital twin specialists, and programmers, to develop the Digital Twin Configurator. Our solution helps create new digital twin content dynamically, on demand. To achieve this, we leveraged our Digital Twin Cockpit solution based on Microsoft assets and developed as part of Capgemini’s AI Robotics and Experiences Lab. It merges the assets built in our Lab with Microsoft data, AI and cloud standards, such as Copilot, Power BI, and several Azure components, enabling faster and consistent review of source standards and produced plant models. 

The tool guides users through each step of setting up a new production line—letting them choose product types, factory layouts, and equipment options, much like customizing a kitchen. Teams can compare different designs based on cost, energy use, and water consumption. The AI speeds up data entry, and built-in dashboards help track key metrics like emissions and operating costs. 

One of the biggest challenges was making sure the tool could handle many different factory types and still keep everything connected from the first design to final construction. 

Results delivered and the road ahead 

Our client now has a centralized, standardized, and replicable architecture for factory design. The digital twin configurator enables: 

• Setting up factories faster and more efficiently 

• Making smarter decisions about where to invest and how to maintain equipment 

• Comparing different factory setups using key data like energy use, water consumption, and operating costs 

The system is already helping top management make data-driven decisions. As the configurator evolves, it’s poised to become a blueprint for global factory design—scalable, smart, and sustainable. 

Learn more about our AI Robotics & Experiences Lab

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.

The convergence of scientific computing and engineering is accelerating innovation in unprecedented ways.

As different sectors seek to tackle complex physical systems, optimize design and simulation, and unlock the next wave of scientific breakthroughs, Capgemini’s association with Wolfram stands as a powerful milestone. Together, we’re combining decades of expertise in symbolic computation, generative AI, and systems engineering to create what we call the Capgemini co-scientist framework—an intelligent assistant built for engineering rigor.

At the heart of this collaboration lies a shared belief: generative AI is transformative, but it must be grounded in scientific accuracy, auditability, and domain reasoning to truly serve the engineering and scientific community. To enable the robustness that scientists expect from their tools, Wolfram Language brings all the infrastructure needed for a scientific reasoning engine: unmatched breadth of symbolic computation, algorithmic modelling, and knowledge representation, resulting in a co-scientist that doesn’t just generate answers in a typical LLM way—it actually understands the problem and queries verifiable facts to produce trustworthy results.

From Natural Language to Verified Computation

What makes the co-scientist novel is its ability to go beyond text-based generation. By combining large language models with Wolfram’s symbolic reasoning capabilities and curated computational knowledgebase, users can input a natural language query and receive responses that are not only contextual but computationally synthesized.

Imagine asking co-scientist to simulate the thermal behaviour of a new material under varying conditions—or to optimize the control logic of a complex mechatronic system. Rather than simply returning a list of suggestions, the co-scientist can use real Wolfram Language code to compute precise equations, model dynamic systems, and integrate directly with engineering workflows in different environments.

Hybrid AI in Action

This collaboration brings the vision of Hybrid AI to life—an approach that blends language model fluency with symbolic reasoning, scientific simulation, and rigorous rules. It’s this hybridization that unlocks reliability and traceability in safety-critical domains such as aerospace, automotive, and industrial automation.

Hybrid AI enables iterative co-design, traceable decision-making, and seamless collaboration between AI systems and human experts. Our joint solution with Wolfram represents a concrete step toward AI systems that are not only assistive but trustworthy.

Engineering a Better Future, Together

Capgemini’s Augmented Engineering strategy is about more than just productivity—it’s about elevating human expertise through AI, enabling organizations to solve harder problems faster. Our work with Wolfram builds a bridge between natural language interfaces and the rigorous world of scientific computing, ultimately empowering engineers, researchers, and product teams to think more freely, design more confidently, and innovate more responsibly.

As this collaboration evolves, we are excited to bring the power of the co-scientist to real-world use cases—from sustainability analytics and advanced manufacturing to systems engineering and intelligent product development. This is the future of engineering: collaborative, explainable, and scientifically grounded.

Read more about the collaboration with Wolfram here

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 foundational 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 foundational 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.