ÎÚŃ»´«Ă˝ and NVIDIA’s strategic collaboration provides an innovative AI solution designed to transform the way enterprises build and scale AI factories.

This work is aimed to assist organizations, particularly those in regulated industries or with substantial on-premises infrastructure investments, deploy agentic AI into their operations. By leveraging NVIDIA AI Enterprise software, accelerated infrastructure, and the ÎÚŃ»´«Ă˝ RAISE platform, companies can expect a seamless, high-performance AI solution ready for the future.

Managing AI at scale

ÎÚŃ»´«Ă˝ RAISE is our AI resource management platform, able to manage AI applications and AI agents across multiple environments within a single managed solution. This enables organizations to separate their solution from systemic risk and, leveraging NVIDIA NIM microservices, can centralize AI evaluation, AI FinOps, and model management. The business can then focus on delivering AI-augmented work, while the AI Risk Management team focuses on managing risk, costs, and technical challenges. 

This is a paradigm shift, placing the AI Factory at the center – and not only for private implementation, but as the global point for AI management.

“This new collaboration with NVIDIA marks a pivotal step forward in our commitment to bringing cutting-edge AI-powered technology solutions to our clients for accelerated value creation. By leveraging the power of the NVIDIA AI Stack, ÎÚŃ»´«Ă˝ will help clients expedite their agentic AI journey from strategy to full deployment, enabling them to solve complex business challenges and innovate at scale.” – Anne-Laure Thibaud, EVP, Head of AI & Analytics Global Practice, ÎÚŃ»´«Ă˝

Benefits for modern enterprises

Imagine the ability to deploy agentic AI capabilities with a single click. Our partnership extends the reach of the ÎÚŃ»´«Ă˝ RAISE platform, bringing these capabilities to NVIDIA’s high-performance infrastructure. This enables companies to realize value more swiftly, and reduce total cost of ownership and deployment risk. Additionally, with the NVIDIA Enterprise AI Factory validated design, we guide organizations in building on-premises AI factories leveraging NVIDIA Blackwell and a broad ecosystem of AI partners.

Some of the other key features to support the navigation of complex, agentic AI solutions include:

• Rapid prototyping and deployment: Speeding up the deployment of AI agents through ready-to-use workflows and streamlined infrastructure, minimizing time-to-market.
• Seamless integration: Embedding AI agent functionalities into current business systems to enhance automation, operational efficiency, and data-informed decision-making.
• Scalability and governance: Deploying AI agents within strong governance models to ensure regulatory compliance, scalability, and consistent performance. ÎÚŃ»´«Ă˝ RAISE provides specialized agentic features – such as governance, live monitoring, and orchestration – to provide centralized management and measurable outcomes.

Scaling AI in private, on-premises environments

Our solution is designed to help organizations rapidly scale AI in private, on-premises environments. It supports key requirements such as data sovereignty and compliance to meet regulatory and data residency mandates. It also ensures resiliency and high availability for business continuity, security, and privacy controls for air-gapped environments. This solution delivers ultra-low latency for a diverse set of real-time use cases like manufacturing or healthcare imaging, and edge or offline use cases for remote, disconnected environments.

ÎÚŃ»´«Ă˝ RAISE and Agentic Gallery: Demonstrating the future

Alongside NVIDIA, we are bringing the power ofÎÚŃ»´«Ă˝ RAISE to on-premises infrastructure. This open, interoperable, scalable, and secure solution paves the way for widespread AI adoption. To illustrate our capabilities, we are launching the Agentic Gallery, a showcase of innovative AI agents designed to address diverse business needs and drive digital transformation.

ÎÚŃ»´«Ă˝ and NVIDIA have collaborated on over 200 agents, leveraging the NVIDIA AI Factory to create a robust ecosystem of AI solutions. This collaboration has led to the development of the Agentic Gallery, which is set to revolutionize the way businesses approach AI.

Is your organization ready to place the power of an AI Factory at the center of its business? Get in touch with our experts below.

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.

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 ÎÚŃ»´«Ă˝, 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.

ÎÚŃ»´«Ă˝ 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.

ÎÚŃ»´«Ă˝â€™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.

ÎÚŃ»´«Ă˝ enables this use case through an AI logistics insights 360 solution offered for the Gen AI Strategic Intelligence System by ÎÚŃ»´«Ă˝. 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

ÎÚŃ»´«Ă˝â€™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 ÎÚŃ»´«Ă˝ 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.

AI is reshaping the way enterprises operate, but one fundamental challenge that still exists is that most applications were not built with AI in mind.

Traditional enterprise systems are designed for transactions, not intelligent decision-making, making it difficult to integrate AI at scale. To bridge this gap, MongoDB and ÎÚŃ»´«Ă˝ are enabling businesses to modernize their infrastructure, unify data platforms, and power AI-driven applications. This blog explores the trends driving the AI revolution and the role that ÎÚŃ»´«Ă˝ and MongoDB play in powering AI solutions.

The challenge: Outdated infrastructure is slowing AI innovation

In talking to many customers across industries, we have heard the following key challenges in adopting AI:

• Data fragmentation: Organizations have long struggled with siloed data, where operational and analytical systems exist separately, making it difficult to unify data for AI-driven insights.
  
  In fact, according to the , 59 percent of C-suite executives said their organizations’ data is somewhat or completely siloed, which results in inefficiencies and lost opportunities. Moreover, AI workloads such as , , and recommendation engines require vector databases, yet most traditional data architectures fail to support these new AI-driven capabilities.
  
• Lack of AI-ready data infrastructure:The lack of AI-ready data infrastructure forces developers to work with multiple disconnected systems, adding complexity to the development process.
  
  Instead of seamlessly integrating AI models, developers often have to manually sync data, join query results across multiple platforms, and ensure consistency between structured and unstructured data sources. This not only slows down AI adoption but also significantly increases the operational burden.

The solution: AI-ready data infrastructure with MongoDB and ÎÚŃ»´«Ă˝

Together, MongoDB and ÎÚŃ»´«Ă˝ provide enterprises with the end-to-end capabilities needed to modernize their data infrastructure and harness the full potential of AI.

MongoDB provides a flexible document model that allows businesses to store and query structured, semi-structured, seamlessly, a critical need for AI-powered applications. Its vector search capabilities enable semantic search, recommendation engines, RAG, and anomaly detection, eliminating the need for complex data pipelines while reducing latency and operational overhead. Furthermore, MongoDB’s distributed and serverless architecture ensures scalability, allowing businesses to deploy real-time AI workloads like chatbots, intelligent search, and predictive analytics with the agility and efficiency needed to stay competitive.

ÎÚŃ»´«Ă˝ plays a crucial role in this transformation by leveraging AI-powered automation and migration frameworks to help enterprises restructure applications, optimize data workflows, and transition to AI-ready architectures like MongoDB. Using generative AI, ÎÚŃ»´«Ă˝ enables organizations to analyze existing systems, define data migration scripts, and seamlessly integrate AI-driven capabilities into their operations.

Real-world use cases

Let’s explore impactful real-world use cases where MongoDB and ÎÚŃ»´«Ă˝ have collaborated to drive cutting-edge AI projects.

• AI-powered field operations for a global energy company: Workers in hazardous environments, such as oil rigs, previously had to complete complex 75-field forms, which slowed down operations and increased safety risks. To streamline this process, the company implemented a conversational AI interface, allowing workers to interact with the system using natural language instead of manual form-filling. This AI-driven solution has been adopted by over 120,000 field workers, significantly reducing administrative workload, improving efficiency, and enhancing safety in high-risk conditions.
• AI-assisted anomaly detection in the automotive industry: Manual vehicle inspections often led to delays in diagnostics and high maintenance costs, making it difficult to detect mechanical issues early. To address this, an automotive company implemented AI-powered engine sound analysis, which used vector embeddings to identify anomalies and predict potential failures before they occurred. This proactive approach has reduced breakdowns, optimized maintenance scheduling, and improved overall vehicle reliability, ensuring cost savings and enhanced operational efficiency.
• Making insurance more efficient: GenYoda, an AI-driven solution developed by ÎÚŃ»´«Ă˝, is revolutionizing the insurance industry by enhancing the efficiency of professionals through advanced data analysis. By harnessing the power of MongoDB Atlas Vector Search, GenYoda processes vast amounts of customer information including policy statements, premiums, claims histories, and health records to provide actionable insights.
  
  This comprehensive analysis enables insurance professionals to swiftly evaluate underwriters’ reports, construct detailed health summaries, and optimize customer interactions, thereby improving contact center performance. Remarkably, GenYoda can ingest 100,000 documents within a few hours and deliver responses to user queries in just two to three seconds, matching the performance of leading AI models. The tangible benefits of this solution are evident; for instance, one insurer reported a 15% boost in productivity, a 25% acceleration in report generation – leading to faster decision-making – and a 10% reduction in manual efforts associated with PDF searches, culminating in enhanced operational efficiency.

Conclusion

As AI becomes operational, real-time, and mission-critical for enterprises, businesses must modernize their data infrastructure and integrate AI-driven capabilities into their core applications. With MongoDB and ÎÚŃ»´«Ă˝, enterprises can move beyond legacy limitations, unify their data, and power the next generation of AI applications. For more, watch this by Steve Jones (EVP, Data-Driven Business & Gen AI at ÎÚŃ»´«Ă˝) and Will Shulman (former VP of Product at MongoDB) to learn about more real-world use cases. And how ÎÚŃ»´«Ă˝ and MongoDB are driving innovation with AI and data solutions.

Read more about our collaboration with MongoDB here.

Generic proposals are losing deals

Insurance RFP responses are starting to feel like they’ve been photocopied over and over. Brokers and clients today are no longer just flipping through proposals hoping to find a winner—they’re expecting them to speak directly to their unique needs. The days when you could get away with templated, one-size-fits-all responses are behind us. In insurance, trust is built on understanding, and understanding is signaled through specificity.

In fact, many proposals don’t even get past the first skim because they sound like they were written for any client, not this client. The root issue is that generic responses signal a lack of investment in the relationship. Insurers risk losing out on high-value deals, wasting time and resources crafting responses that don’t convert. As our work with numerous global insurers has revealed, many of these generic documents—especially cover letters and executive summaries—were not even being read by brokers due to their lack of relevance.

Generative AI for hyper-personalization in insurance

Now, let’s imagine a private, enterprise-trained generative AI assistant that doesn’t just regurgitate past language, but crafts messages so tailored they make your clients feel like VIPs. That’s the magic of a custom, private GenAI assistant.

This assistant is no off-the-shelf chatbot. It’s trained on your historical RFP data, your previous client interactions, your industry nuances, and even your internal product literature. It understands how you communicate and what your clients care about. More importantly, it learns and evolves. With the help of Agentic AI, a modular framework powered by specialized AI agents, this assistant goes far past simple auto-fill. It reads the RFP, summarizes the client ask, constructs the top winning themes, and proactively drafts personalized responses, summaries, and even intelligent suggestions for improvement.

This is where hyper-personalization becomes real. By utilizing structured and unstructured data alike, the Gen AI assistant pulls out the most relevant insights and shapes them into messaging that resonates. It compiles data from its entire knowledgebase to craft a tailored solution to the client’s problem. It’s not guessing, it’s contextualizing. That means proposals land stronger, faster, and with far better chances of hitting the mark.

MongoDB: The motor powering AI-driven personalization

Behind the scenes, plays a crucial role in making all this magic possible.

Their flexible document model allows for rapid ingestion of diverse data types including past RFPs, client correspondence, marketing decks, and everything else imaginable. This structure is perfect for insurers juggling massive volumes of semi-structured and unstructured data.

is particularly crucial here.  It enables the Gen AI assistant to rapidly identify, rank, and re-rank the most relevant information based on contextual relevance, delivering responses that are both timely and precise.

Its globally distributed architecture—available across AWS, Azure, and GCP in over 115+ regions—makes it an ideal foundation for building large-scale, enterprise-grade Gen AI applications. By embedding Vector Search directly into the core database, MongoDB eliminates the need to sync data between separate operational and vector databases. This simplification reduces complexity, minimizes the risk of errors, and significantly shortens response times.

Keeping both operational and vector data in a single system also improves performance through reduced latency and advanced indexing capabilities. For organizations building out agentic Gen AI capabilities, MongoDB further supports Graph RAG (Retrieval Augmented Generation) architectures, enhancing contextual accuracy and scalability across use cases.

However, insurance is a heavily regulated industry and data security is critical. MongoDB also offers enterprise-grade encryption, access controls, and supports compliance with key data privacy regulations.

Case study: Less robotic, more calibrated and compelling RFPs at a global insurer

A recent standout example of our custom, private GenAI assistant in action comes from a global insurer who started with a modest request: Can we hyper-personalize our RFP cover letters better? The ask was simple and they were merely looking for a few bullet points to make things feel less robotic.

What we were able to create for them was a revolution in how they respond to RFPs. In just five weeks, our team implemented our custom, private GenAI assistant that not only delivered personalized bullet points but also crafted full executive summaries and tailored cover letters. These were not piecemeal templates—they were coherent, compelling, and calibrated to the specific opportunity at hand.

The feedback we received was immediate and enthusiastic. The Chief Innovation Officer and the Sales leadership team pushed for scaling the solution to other areas. It wasn’t just a productivity gain, it was a reputation builder. Brokers began to take notice. The insurer wasn’t just responding faster; they were responding smarter.

Business impact, check! Strategic outcomes, check!

By implementing a custom, private GenAI assistant, insurers gain access to a scalable, cloud-native platform that integrates easily with existing systems—whether it’s a CRM, document management platform, or internal knowledge base. Beyond the technical flexibility, the real impact lies in how this approach transforms stagnant, siloed data into living insights that power tailored client engagement.

The platform supports more consistent and efficient proposal development by reducing manual effort, accelerating turnaround times, and improving the quality and relevance of responses. Teams can focus less on reformatting and more on building client relationships. Meanwhile, the built-in security and governance measures ensure that every interaction meets enterprise compliance standards, protecting both client data and institutional knowledge.

Insurers using this model report stronger broker engagement, better win rates, and faster RFP response times. Operational costs drop due to reduced manual formatting and response drafting. From a technical perspective, compared to full LLM inference on raw content, thanks to targeted document retrieval and short-form reasoning tasks.

As organizations use this solution over time, feedback loops from won/lost deals can be fed back into the model for retraining, improving response quality and alignment. As the assistant matures, it can serve as a strategic enabler across adjacent workflows—claims review, renewal briefs, or even sales coaching.

The future of insurance RFPs

Custom private GenAI assistants represent a rare intersection of technical maturity and business impact. When combined with MongoDB’s robust data orchestration capabilities and ÎÚŃ»´«Ă˝â€™s proven technology blueprint, this solution becomes more than a digital enhancement—it becomes a strategic advantage.

Organizations that embrace this model transition from reactive, templated proposal development to proactive, context-rich client engagement. With the ability to generate intelligent, personalized content at scale, they not only improve operational efficiency but also strengthen their competitive position in a high-stakes market.

This isn’t just about responding faster—it’s about responding better. As expectations around relevance, precision, and value continue to rise, the future of insurance RFPs will belong to those who invest in intelligent automation and meaningful personalization.

The path forward isn’t generic. It’s personal, scalable, and ready to deliver lasting impact.

Read at leisure. Download a copy of this expert perspective.

To drive business value, it is important to leverage all the data from within your organization as well as from partners outside of it. Such a collaborative data ecosystem is an alignment of business goals, data, and technology, among one or more participants, to collectively create value that is greater than each can create individually. It is both combining and collaborating on that data.

With a little help from your friends

John Lennon and Paul McCartney met by chance in 1957 when Lennon’s band The Quarrymen was performing in Liverpool. McCartney then joined The Quarrymen and, after the band had already changed its name to The Beatles, they were by chance discovered by Brian Epstein, at that time a local record store manager who became the band’s manager in 1962.

The way we see data ecosystems is similar: it is sometimes about a chance encounter and then bringing various elements together. We could refer to the well-known Beatles song from 1969 Come Together as the unifying theme of this article but instead let’s choose another one, namely With a Little Help from My Friends, which was released in 1967. In the context of this story, a little help comes in the form of a little data. Bringing together data from your friends (customers, suppliers, partners, vendors, whoever) is what we would call “organized serendipity.”

Imagine you’re a retailer operating in a competitive market needing to stay on top of trends, having to make sure your shelves (whether physical or virtual) are filled and are appealing to your customers. As an example, out-of-stocks remain the single largest problem in retail. The challenge with keeping products stocked involves a complex value chain that must anticipate and respond to dynamic market forces. Extreme weather, local events, and even activity from social influencers can quickly alter the demand for a product. In an optimal world, suppliers, distributors, retailers, and other partners would have visibility to changing dynamics and consumption in real-time, enabling them to optimize their operational decisions on-the-fly. And yet supply chains across retail and consumer goods still operate much as they have for decades, making decisions on data that is days or weeks old. It is this delay between changes in demand and our ability to respond that lead to out-of-stocks.

The main sources for retail data are operations by the retailer, data from their ecosystem, competitive data from syndicated sources, and external environmental data from governments and commercial sources.

• Retail operational data comes as a result of business operations, and includes everything from customer-facing retail sales data, advertising, e-commerce, customer support, reviews, and loyalty to back-of-house data from inventory, distribution, planning, and other management systems.

• Retailers operate in a complex value chain, with data coming upstream from suppliers, wholesalers, and distributors, and integrating downstream with advertising and delivery partners.

• Competitive data sources help retailers understand how their key competitors are operating in similar areas. Competitive distribution, assortment, pricing, promotions and advertising, sales, and other sources help retailers index their performance.

• Environmental data helps retailers understand the context in which consumers are making decisions. This includes environmental data such as weather, local economic forces, census information, local events and foot traffic data, legal and regulatory changes, social data, keyword searches, and more.

Finding a cost-effective technology

No two organizations leverage the same data in the same way. The differences in their strategies, operations, competitors, geography, and the systems that support them are designed to help the company succeed. But this means that no two businesses have the same data ecosystem. Companies may exchange data in key areas but increasingly the differences in data between companies is perceived as a competitive advantage. Legacy data-sharing technologies were designed to support the lowest common denominator of collaboration but have struggled to meet the needs of real-time data sharing, quality, and governance and decisioning. Companies want the flexibility to communicate in real-time with a variety of information and across platforms.

The key to achieving this is to select a cost-effective technology that enables the broadest range of sharing options without proprietary technology or vendor lock-in, facilitates real-time data sharing and collaboration, ensures the control of quality and governance of data, and enables companies to focus on immediately leveraging all types of data to drive better decisions.

A retail lakehouse simplifies collaboration

A data lakehouse is a modern data-management architecture that combines the features of both data lakes and data warehouses. It is a unified platform for storing, processing, analyzing, and sharing large volumes of data, both structured and unstructured, in its native format, with support for batch and real-time data processing.

Databricks’ Lakehouse is built on open-standards and open-source, which avoids proprietary lock-in. This importantly extends to data sharing and collaboration. Databricks introduced Delta Sharing, which is an open-source project started by Databricks that allows companies to share large-scale, real-time data between organizations in a secure and efficient manner.

A Lakehouse is the optimal method for data collaboration as it addresses the critical needs in retail.

• Real-time collaboration. Not only can companies share data that is being continuously updated, but Delta Sharing also enables sharing without movement of data.

• Collaborate on all of your data. Unlike legacy systems, Delta Sharing enables companies to share images, video, data-science models, structured data, and all other types of data.

• Centralized data storage. The Lakehouse architecture makes it easier for different users or groups to access and share data from a single source of truth, eliminating data silos and enabling seamless data sharing across various stakeholders.

• It supports quality and compliance. A Lakehouse architecture helps ensure data integrity, traceability, and compliance with regulatory requirements, which are important considerations when sharing data with external users or organizations.

• It simplifies data management and discovery. The Lakehouse architecture includes a robust data catalog and metadata management system that helps in documenting and organizing data assets.

“Collaborative data ecosystems hold immense potential for retail companies looking to thrive in an increasingly competitive and data-driven industry.”

With Delta Sharing, companies can securely share data with other organizations without having to copy or move data across different systems. Delta Sharing uses a federated model, which means that data remains in the original location and is accessed remotely by the recipient organization. This approach allows organizations to maintain control over their data while still sharing it with others.

Collaborative data ecosystems hold immense potential for retail companies looking to thrive in an increasingly competitive and data-driven industry. By leveraging these ecosystems, retailers can optimize their supply chain, gain valuable customer insights, make informed decisions, foster collaboration, and ensure data security and compliance. As more organizations recognize the value of such ecosystems, we can expect the retail industry to become even more connected, efficient, and customer-centric.

INNOVATION TAKEAWAYS

EMPOWERING COLLABORATION

By leveraging data from within and outside their organization, businesses can create collective value that surpasses individual capabilities, fostering collaboration and innovation.

BRIDGING THE GAP

Outdated supply chains hinder retailers from effectively responding to dynamic market forces, making real-time data sharing imperative for optimizing operational decisions and reducing out-of-stock issues.

LAKEHOUSE ARCHITECTURE

A modern data-management approach, the Lakehouse architecture combines data lakes and data warehouses, enabling real-time collaboration, centralized storage, and simplified data management for improved decision-making.

DELTA SHARING

Delta Sharing, an open-source project, empowers companies to securely share large-scale, real-time data without data movement, unlocking the potential for seamless collaboration, compliance, and valuable insights in the retail industry.

Interesting read?

ÎÚŃ»´«Ă˝â€™s Innovation publication, Data-powered Innovation Review | Wave 6 features 19 such fascinating articles, crafted by leading experts from ÎÚŃ»´«Ă˝, and key technology partners like ,  ,  ,  and . Learn about generative AI, collaborative data ecosystems, and an exploration of how data an AI can enable the biodiversity of urban forests. Find all previous waves here.

“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 ÎÚŃ»´«Ă˝, 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 ÎÚŃ»´«Ă˝â€™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