As someone once said, modesty is “the gentle art of enhancing your charm by pretending not to be aware of it.” Of course, modesty is one of our seven core values at ��ѻ��ý, too, and I think there’s something in that saying.

For me, modesty provides the opportunity for others to enhance your reputation by observing and highlighting your achievements for you. So, you can imagine how pleased and proud I am that ��ѻ��ý is recognized as a leader in the Forrester Wave: SAP Services, Q1 2025 report.

Of course, such an achievement doesn’t just happen; it is borne of hard work, strong skill sets, assured competence, and a powerful business strategy – all of which have been acknowledged and applauded by Forrester. I was especially pleased to see our unique value proposition being praised: Forrester highlighted our strength in value management and continuous improvement, noting our success rate in moving our clients to composable, clean -core, multi-pillar S/4HANA architecture (they spotted our impressive ability to achieve over 90 percent SAP clean core).

Building a world-leading strategy

It goes without saying that we don’t set out to be positioned favorably in the Forrester Wave – or any other analyst’s rankings. We set out to be a world-leading provider of SAP services with a complete vision and an unchallenged ability to execute. Analyst recognition is a welcome and happy result of our hard work and strategy. Together with an excellent team of leaders around the world, we have built – and continue to refine – a determined strategy to achieve success for our clients. The foundations of this strategy are: 

An end-to-end business transformation approach. This includes:

• A solid industry focus up to and including specific reference models
• A redoubling of our emphasis on value management and continuous innovations
• The incorporation of applied SAP – based Gen AI to improve processes and delivery.
• Tools and methodologies such as our Large Transformation Program (LTP) method supported by the Digital Acceleration Navigator (DAN) platform.

Excellence at cloud transformation, with:

• A focus on the move to composable architecture, clean core, multi-pillar SAP- based architecture
• A strong partnership with and commitment to the big hyperscalers, resulting in a three-year strategic initiative and joint offerings on RISE with Microsoft, AWS, and Google Cloud.
• A concerted drive towards SAP Cloud-enhanced services.

An increased focus on sustainability, requiring us to:

• Create (and continue to evolve) a best-in-class set of sustainable processes around supply chain, manufacturing, and customer services.
• Accelerate our sales effort on new business around battery and electric car models, recycled plastics, wind farms, and other “green” businesses.
• Establish an end-to-end sustainable architecture framework through joint partnerships with SAP, the hyperscalers, and niche solution providers. 

Access to the proven global capabilities and power of ��ѻ��ý, including:

• Capitalizing on the full set of ��ѻ��ý’s capabilities, ranging from superior SAP expertise to systems and services integration and architecture, cloud apps, data and cloud management, and business operations
• Structuring our teams to take full advantage of their capabilities across the globe
• Availing of our great industry transformation consulting practices and digital engineering capabilities from across the Group

I am very aware that I have just described a very inward-centric perspective on our work (a lot of “we did this” and “we did that”) but rest assured, we never lose sight of the imperative to deliver the very best service that we can possibly do for our clients. As the Forrester Wave demonstrates, our clients have a choice, and we will always strive to be the best. That’s why I was particularly pleased to see such positive client feedback. Forrester noted that our clients rated us as “excellent” when it comes to our knowledge of SAP products, and praised the quality of our SAP resources. In fact, we got a “halo” in the rankings, thanks to the strength of our clients’ feedback. 

A strong industry focus

As a leader whose portfolio includes Digital Core and Packaged Solutions, it has always been important to me to provide industry-specific solutions. Every industry comes with its own nuanced service requirements, its own set of regulations and governance, and its own unique market challenges. So, to fit (dare I say shoehorn?) generic processes, methodologies, or even systems into an environment where something considerably more specialized is required just won’t work. Even worse, doing so can make things a lot more challenging, leading to inefficiencies and cost escalation.

Not only that, but it is entirely reasonable for our clients to expect that we know and understand their industry, and have the skills, systems (and sometimes even security clearances) required to work effectively with them. So, I was delighted to see Forrester observe that “��ѻ��ý shines at industry-specific transformation,” specifically noting our strength in “automotive, retail and grocery, consumer products, life sciences, utilities, telecommunications, and semiconductors, with industry process models, reference cloud architectures, Gen AI, and sustainability embedded in all solutions.” Indeed, our industry-centric approach was acknowledged by Forrester several times, including a description of ��ѻ��ý being, “a great fit for industry-specific large SAP transformation programs requiring global-scale deployment.” … precisely what we aim to be.

Take a bow!

As I mentioned, such a great achievement doesn’t just happen. It is preceded by a lot of hard work, solid strategies, and the skills of a great team of more than 30,000 SAP consultants, engineers, and leaders. And, more importantly, it happens thanks to clients who are hungry for innovation and open to change. To you all, I say, “thank you.”

Find out more

Discover the full report and learn more about our SAP services leadership.

��ѻ��ý, a data- and analytics-first organization and global launch partner for SAP Business Data Cloud (BDC), understands the value of integrating data and AI foundations from the earliest stages of a business transformation.

With the recent acquisition of Syniti, we further empower organizations in utilizing data and AI as the cornerstone of their business transformation.

Enterprises across the globe are leveraging data and AI to drive insights, enable intelligent processes, and foster innovation to improve customer intimacy, drive new routes to market, expand business models, and reduce total cost of ownership (TCO).

As businesses strive to stay competitive, the integration of advanced data management and AI capabilities becomes paramount. SAP, a leader in enterprise software, has taken a significant step forward with the launch of SAP Business Data Cloud and the SAP Databricks solution, propelling them ahead of their competitors with a 360-degree view of enterprise data and AI.

The importance of data in business transformation

SAP has long been at the forefront of helping businesses manage their data. With the introduction of SAP Business Data Cloud, SAP is redefining how enterprises harness their data. The SAP Business Data Cloud solution unifies and governs all SAP data while seamlessly connecting with third-party data. By integrating SAP Datasphere, SAP Analytics Cloud (SAC), and SAP Business Warehouse (BW) alongside Databricks, SAP Business Data Cloud delivers a unified experience that empowers businesses to make informed decisions.

SAP Business Data Cloud: A new era in data management

SAP Business Data Cloud represents a paradigm shift in enterprise data management. Together with ��ѻ��ý’s data-first methodology, it provides a trusted and harmonized data foundation, ensuring high-quality data that businesses can rely on. This foundation is crucial for driving impactful decisions and fostering innovation.

One of the standout features of SAP Business Data Cloud is its ability to deliver fully managed SAP data products across all business processes. These curated data products align with a highly optimized and unified “one domain” model, maintaining their original business context and semantics. This means businesses get immediate access to high-quality data without the hidden costs of rebuilding and maintaining data(base) extracts.

Additionally, SAP Business Data Cloud offers a suite of pre-built analytical applications, known as ��ѻ��ý Apps. As a global launch partner for SAP Business Data Cloud, ��ѻ��ý has been working closely with SAP and partners like Syniti, Databricks, and Collibra to integrate our knowledge into these apps. Insight Apps incorporate pre-defined metrics, AI models, and planning capabilities, simplifying how businesses connect and integrate every part of their operations. This accelerates use cases aligned with critical business functions, including ERP, spend, supply chain, HR, customer experience, and finance.

SAP Databricks: Enhancing AI and data engineering

As a Databricks Partner of the Year award winner, we are excited by the integration of Databricks into SAP Business Data Cloud, as it marks a significant milestone in enterprise data management. This partnership brings the power of Databricks directly into the SAP ecosystem, enabling businesses to leverage advanced data engineering and AI capabilities to support the integration of SAP data into the enterprise ecosystem, both internally and externally.

Databricks empowers data professionals to accelerate AI models and generative AI applications on their business data. Native capabilities like Delta Sharing harmonize SAP data products with existing lakehouses bidirectionally. This zero-copy approach allows businesses to apply advanced AI and machine learning models to various use cases, such as predicting payment dates on open receivables, without the need for complicated ETL pipelines.

Moreover, SAP Business Data Cloud with the included Databricks capabilities facilitates the modernization of SAP Business Warehouse, providing additional migration options for existing BW customers under one license. On-premises SAP BW customers can easily transition to an SAP BW Private Cloud Edition, accessing their data as a data product with the object store via Delta Share. This simplifies the modernization journey and maximizes the value of existing SAP BW investments.

Driving innovation with AI and machine learning

AI and machine learning are at the heart of SAP’s new offerings. The integration of Joule AI Copilot into SAP Business Data Cloud exemplifies this commitment. Joule AI leverages a knowledge graph to connect data, metadata, and business processes, enabling AI agents and large language models (LLMs) to understand data within its business context.

This mapping creates clear data links, making insights more reliable for users and applications. Training AI agents and Joule on business knowledge and context drives increased productivity. For instance, users can use AI to complete cross-functional tasks, uncover insights, and summarize critical information across the business – without heavy reliance on IT support. This empowers the usage of AI to automate complex analytics and planning tasks, such as risk assessment, forecasting, and other advanced scenario simulations.

Partner ecosystem and open data integration

SAP Business Data Cloud is built to prioritize openness and customer choice. It supports an open data ecosystem, integrating natively with leading data and AI partners like Collibra, Confluent, and DataRobot. This openness simplifies the data landscape and unleashes transformative insights from all data sources.

SAP has also announced partnerships with the likes of ��ѻ��ý. These partners bring deep business process and industry domain expertise, building insight apps on SAP Business Data Cloud. From data enrichment to data activation, partner insight apps build on top of the data products and core services provided by SAP Business Data Cloud.

Conclusion

Data is the lifeblood of modern enterprises. It fuels decision-making, drives operational efficiency, and enables businesses to respond swiftly to market changes. For many organizations, this means integrating data from various sources, ensuring that it is high quality, and applying advanced analytics and AI to uncover hidden patterns and trends.

The launch of SAP Business Data Cloud and SAP Databricks marks a new era in enterprise data management. By unifying and governing data, integrating advanced AI capabilities, and fostering an open data ecosystem, SAP is empowering businesses to unlock the full potential of their data. As enterprises continue to navigate the complexities of digital transformation, these new offerings provide a robust foundation for driving innovation, enhancing decision-making, and enabling intelligent, AI-driven processes.

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

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

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

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

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

Unleashing the power of Gen AI in drone-based inspection

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

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

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

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

��ѻ��ý and industry AI in the energy and utilities sector

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

Final thoughts

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

Me complace compartir que ��ѻ��ý ha logrado una triple victoria en los premios Google Cloud Partner of the Year.

Estos premios reconocen nuestras soluciones innovadoras y el impacto significativo que hemos logrado en diversas industrias.

• Soluciones para la Industria Global
• Soluciones para la Industria de la Sostenibilidad
• �ʲ�í��: Dinamarca

Con casi 15 años de colaboración con Google Cloud, hemos descubierto un potencial y un valor increíbles gracias a nuestros esfuerzos conjuntos. Esta alianza ha demostrado constantemente el poder de un enfoque conjunto para impulsar la transformación empresarial y explorar nuevas posibilidades.

Tras reflexionar sobre nuestro crecimiento del año pasado, este año destacamos nuestro enfoque estratégico en la sostenibilidad y las soluciones específicas para cada sector. Estamos más comprometidos que nunca con abordar los desafíos globales y generar valor para nuestros clientes mediante soluciones sostenibles e innovadoras.

Soluciones para la industria de la sostenibilidad

Uno de los premios que recibimos es “Soluciones para la Industria de la Sostenibilidad”. Este premio reconoce a los socios que ayudaron a los clientes de la industria de la sostenibilidad a alcanzar un éxito excepcional a través de Google Cloud. La sostenibilidad es un componente fundamental del ADN de ��ѻ��ý y está presente en todos los servicios y soluciones que desarrollamos. Nuestra colaboración con Google Cloud nos ha permitido ayudar a nuestros clientes a ser más sostenibles. Por ejemplo, nuestra solución Fractals facilita la colaboración integral de datos a nivel de producto sobre cuestiones precompetitivas de la cadena de suministro, incluyendo retos ESG como el desperdicio de alimentos, la salud, la descarbonización, los derechos humanos y los salarios dignos.

Además, nuestra solución Business for Planet Modeling (BfPM) con Google Cloud ofrece un conjunto de servicios de asesoría sobre riesgos climáticos diseñados para impulsar un mejor análisis de riesgos climáticos en el sector de servicios financieros. BfPM aprovecha las analíticas y la inteligencia artificial de Google Cloud para simular el impacto financiero del cambio climático y las variables globales, optimizando la previsión y facilitando la toma de decisiones. Analizaremos estas soluciones en persona en Google Cloud Next.

Soluciones industriales globales

Además, recibimos un premio por Soluciones para la Industria Global. Este premio reconoce a los socios que aprovecharon las soluciones de Google Cloud para crear soluciones integrales y atractivas que tuvieron un impacto significativo en múltiples industrias y regiones. Nuestra amplia experiencia en el sector y el uso de los recursos de Google Cloud, incluida la IA generativa, nos han permitido ofrecer soluciones a medida a clientes de todo el mundo. Por ejemplo, nuestra solución Industry Cloud para supermercados en Google Cloud ha ayudado a los supermercados a mejorar la experiencia del cliente, a la vez que mejora la visibilidad del inventario y la rentabilidad.

Premio al Socio del Año, �ʲ�í��: Dinamarca

En Google Cloud Next, para hablar sobre su enfoque de previsión de la demanda basado en IA con Google Cloud. Esta sesión destacará nuestro trabajo en Dinamarca con Danfoss, líder en soluciones de eficiencia energética, y cómo se asociaron con Google Cloud y ��ѻ��ý para abordar los desafíos de la previsión de la demanda, mantenerse competitivos y apoyar los objetivos globales de sostenibilidad.

Impacto en nuestras clientas

Nuestra colaboración con Google Cloud ha aportado importantes beneficios a nuestros clientes, y nos enorgullece el éxito de los proyectos que les han aportado valor. En nuestro reciente catálogo, profundizamos en este tema. Por ejemplo, modernizamos la infraestructura de TI con soluciones de nube de datos en Wind Tre, procesando 1000 eventos por segundo y tomando 100 millones de decisiones al día. También creamos el primer chatbot de IA generativa en catalán con Vertex AI de Google Cloud, preservando el idioma y mejorando los tiempos de respuesta. Además, ayudamos a L’Oréal a conectar el mundo físico con el digital mediante una solución de gemelo digital en Google Cloud.

Estos logros demuestran nuestra capacidad de aprovechar las capacidades de Google Cloud para ofrecer soluciones innovadoras que aborden desafíos específicos de la industria y mejoren las experiencias de los clientes.

Un gran agradecimiento

Estos logros no habrían sido posibles sin el arduo trabajo y la dedicación de nuestros equipos y la increíble asociación con Google Cloud.

De cara al futuro, tenemos objetivos ambiciosos para nuestra colaboración con Google Cloud, y estamos deseando hacer realidad estos reconocimientos a través de nuestra participación en Google Cloud Next como patrocinador de Luminary. Nos encantaría encontrarnos allí, en el stand n.° 2240, del 8 al 11 de abril, o cómo ayudamos a las empresas a alcanzar el potencial de la innovación y la inteligencia.

Data fabric helps organizations overcome the challenge of managing overwhelming data by unifying and streamlining disparate sources. It connects and integrates data across environments, transforming complexity into actionable insights that drive innovation.

Utilizing data is not a passing trend; it’s a core element of business strategy. However, despite its potential, many organizations struggle with turning overwhelming amounts of data into valuable insights. This is where data fabric comes in, offering a solution to unify and streamline disparate data sources. By connecting and integrating data from various environments, data fabric provides a clear path from complexity to actionable insights that drive innovation and improve competitive advantage.

The data challenge: Why data fabric matters

Businesses face the dual challenge of becoming data-driven while navigating an increasingly complex landscape. From data silos to a lack of coordination between business and IT, and growing concerns about privacy and governance, the hurdles are many. But amidst all these challenges, one thing remains clear: data is a key asset in today’s digital world. The real question isn’t whether data is valuable; it is instead how organizations can leverage it effectively. In a fast-paced, competitive environment, businesses that can unlock the value of their data will make more informed decisions, optimize operations, and stay ahead of competitors. This is where data fabric proves its worth, making it easier to integrate, analyze, and secure data, driving both innovation and business success.

The need for data fabric: Bringing order to chaos

A data fabric acts as a unified platform that connects and manages data from various sources – whether on-premises, in the cloud, or in a hybrid setup. It integrates structured, semi-structured, and unstructured data, creating a cohesive environment where all data types work together seamlessly. In other words, data fabric eliminates the silos that stifle innovation and ensures that data is always ready to provide real-time insights.

Today’s data environments are anything but simple. The idea of funneling all data into one central repository doesn’t hold up in a world of distributed systems and multi-cloud ecosystems. Data fabric allows organizations to keep their data where it is while still making it accessible and manageable, so it’s easier to extract the insights that power decision-making and business growth.

Foundation layer: The role of metadata 

The foundation of data fabric is metadata: the data about your data. In a world where data exists in multiple locations, formats, and systems, metadata helps connect the dots. Rather than moving data around unnecessarily, metadata enables data virtualization, allowing real-time access without physically transferring data. This approach streamlines data discovery, integration, and governance, ensuring that the right data is accessible at the right time. This metadata layer is also what makes data fabric “smart.” It doesn’t just connect data; it helps organize it in a way that makes sense. By automating processes like data discovery and governance, metadata enables organizations to manage complex data environments more efficiently while maintaining control over quality and security.

Composable data products: Innovation at speed

One of the most powerful features of data fabric is the ability to create composable data products. These are reusable, modular datasets or services that can be combined in various ways to create new capabilities or services. Instead of reinventing the wheel every time a new business need arises, organizations can use existing data products to accelerate innovation.

This modular approach allows businesses to rapidly respond to market demands, creating new offerings or features without starting from scratch. It’s about leveraging what’s already available, making it easier and faster to innovate while maintaining flexibility.

Data democratization and self-service: Empowering teams to innovate

Traditionally, data has been controlled by IT departments, slowing down decision-making and limiting innovation. With data fabric, that changes. Data democratization means giving everyone in the organization the ability to access and work with data, not just the IT team. This shift empowers teams to experiment, collaborate, and iterate faster, without waiting for IT to process every request.

By enabling self-service analytics and empowering teams to create their own data products, organizations can speed up innovation and improve collaboration. Developers, data scientists, business analysts – everyone gets the tools to explore data and generate insights in ways that drive the business forward. In short, data fabric fosters a more agile and responsive organization.

AI integration: Powering smarter insights 

AI relies on high-quality, structured data to provide valuable insights. Fortunately, data fabric is designed to work seamlessly with various data structures – whether it’s tables, graphs, or lists – ensuring that AI and machine learning models have access to the rich, reliable datasets they need to make accurate predictions.

With strong governance and data lineage, data fabric provides the foundation for AI and machine learning models to thrive. This enables innovations in areas like predictive analytics, personalized recommendations, and automation, while ensuring the integrity and security of the data being used.

Data fabric also enhances the AI process by automating many of the routine tasks that traditionally take up valuable time. Tasks like data integration, quality management, and anomaly detection can be automated using AI, freeing up organizations to focus on more high-value innovations that drive business growth.

“By enabling self-service analytics and empowering teams to create their own data products, organizations can speed up innovation and improve collaboration.”

The transformative power of data fabric 

Data fabric isn’t just a technological solution – it’s a game-changer for how organizations manage their data. By leveraging metadata and AI-driven solutions, data fabric helps organizations create a flexible, responsive, and innovative data environment. This environment fosters faster insights, quicker development cycles, and the ability to respond to market changes with agility.

Perhaps most importantly, data democratization enables a culture of innovation where employees across departments can contribute to business success. As data volumes grow and complexities increase, data fabric will be the key to not only managing these challenges but turning them into new opportunities for growth.

Data fabric is an essential solution for organizations looking to stay competitive and innovative in a world where data is becoming increasingly complex. By integrating AI, automating routine tasks, and empowering teams to access and use data freely, you’ll position your organization for success in the digital economy.

Start innovating now –

Build a unified data fabric

Begin by implementing a data fabric that integrates and manages data across all environments: cloud, on-premises, and hybrid. Real-time access and seamless connectivity eliminate silos, unlocking new possibilities for faster insights and product development. 

Create reusable data products

Transform your data into modular, reusable products. This approach accelerates innovation and enables faster iteration, so you can create new services and capabilities without starting from scratch every time. 

Empower teams with data

Democratize your data by making it accessible to everyone. Self-service capabilities allow teams to experiment and innovate quickly, fostering a culture of continuous, business-driven innovation.

Interesting read?

��ѻ��ý’s Innovation publication, Data-powered Innovation Review – Wave 9 features 15 captivating innovation articles with contributions from leading experts from ��ѻ��ý, with a special mention of our external contributors from, and . Explore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.

While Mulder trusts his gut; Scully trusts the facts – in fraud detection, we need both. Hybrid AI blends the intuition of LLM with the structured knowledge of a knowledge graph, letting agents uncover hidden patterns in real time. The truth is out there—now we have the tools to find it.

Fraud detection can be revolutionized with hybrid AI. Combining the “intuitive hunches” from LLMs with a fraud-focused knowledge graph, a multi-agent system can identify weak signals and evolving fraud patterns, moving from detection to prevention in real-time. The challenge? Rule sets need to be cast in iron, whereas the system itself must be like water: resilient and adaptive. Historically, this conflict has been unsolvable. But that is about to change.

A multi-agent setup

Large language models (LLMs) are often criticized for hallucinating: coming up with results that seem feasible but are plain wrong. In this case though, we embrace the LLM’s gut-feeling-based approach and exploit its capabilities to identify potential signs of fraud. These “hunches” are mapped onto a general ontology and thus made available to symbolic AI components that build on logic and rules. So, rather than constricting the LLM, we are relying on its language capabilities to spot subtle clues in text. Should we act directly on these hunches, we would run into a whole world of problems derived from the inherent unreliability of LLMs. However, this is the task of a highly specialized team of agents, and there are other agents standing by, ready to make sense of the data and establish reliable patterns.

When we talk about agents, we refer to any entity that acts on behalf of another to accomplish high-level objectives using specialized capabilities. They may differ in degree of autonomy and authority to take actions that can impact their environment. Agents do not necessarily use AI: many non-AI systems are agents, too. (A traditional thermostat is a simple non-AI agent.) Similarly, not all AI systems are agents. In this context, the agents we focus on primarily handle data, following predefined instructions and using specific tools to achieve their tasks.

We define a multi-agent system as being made up of multiple independent agents. Every agent runs on its own, processing its own data and making decisions, yet staying in sync with the others through constant communication. In a homogeneous system, all agents are the same and their complex behavior solves the problem (as in a swarm). Heterogeneous systems, though, deploy different agents with different capabilities. Systems that use agents (either single or multiple) are sometimes called “agentic” architectures or frameworks.

For example, specialized agents can dive into a knowledge graph, dig up specific information, spot patterns, and update nodes or relationships based on new findings. The result? A more dynamic, contextually rich knowledge graph that evolves as the agents learn and adapt.

The power is in the teaming. Think of the agents Mulder and Scully from The X-Files television show: Mulder represents intuitive, open-minded thinking, while Scully embodies rational analysis. In software, there always have been many Scullys but, with LLMs, we now have Mulders too. The challenge, as in The X-Files, is in making them work together effectively.

The role of a universal ontology

We employ a universal ontology to act as a shared language or, perhaps a better analogy, a translation exchange, ensuring that both intuitive and analytical agents communicate in terms that can be universally understood. This ontology primarily consists of “flags” –generic indicators associated with potential fraud risks. These flags are intentionally defined broadly, capturing a wide range of behaviors or activities that could hint at fraudulent actions without constraining the agents to specific cases.

The key to this system lies not in isolating a single flag but in identifying meaningful combinations. A single instance of a flag may not signify fraud; however, when several flags emerge together, they provide a more compelling picture of potential risk.

“This innovation shifts the approach from simple fraud detection to proactive prevention, allowing authorities to stay ahead of fraudsters with scalable systems that learn and evolve.”

Hybrid AI adaptability

The adaptability of the system lies in the bridging between neural and symbolic AI as the LLM distills nuances in texts into hunches. They need to be structured and amplified for our analytical AI to be able to access them. As Igor Stravinsky wrote in his 1970 book Poetics of Music in the Form of Six Lessons, “Thus what concerns us here is not imagination itself, but rather creative imagination: the faculty that helps us pass from the level of conception to the level of realization.” For us, that faculty is the combination of a general ontology and vector-based similarity search. They allow us to connect hunches to flags based on semantic matching and thus address the data using general rules. Because we work in a graph context, we can also explore direct, indirect, and even implicit relations between the data.

Now let’s explore how our team of agents picks up and amplifies weak signals, and how these signals, once interwoven in the graph, can lead the system to identify patterns spanning time and space, patterns it was not designed to identify.

A scenario: Welfare agencies have observed a rise in fraudulent behavior, often uncovered only after individuals are exposed for other reasons like media reports. Identifying these fraud attempts earlier, ideally at the application stage, would be extremely important.

Outcome: By combining intuitive and analytical insights, authorities uncover a well-coordinated fraud ring that would be hard to detect through traditional methods. The agents map amplified weak signals as well as explicit and implicit connections. Note also that the system was not trained on detecting this pattern; it emerged thanks to the weak signal amplification.

One of the powers of hybrid AI lies in its ability to amplify weak signals and adapt in real time, uncovering hidden fraud patterns that traditional methods often miss. By blending the intuitive insights of LLMs with the analytical strength of knowledge graphs and multi-agent systems, we’re entering a new era of fraud detection and prevention – one that’s smarter, faster, and more effective. As Mulder might say, the truth is out there, and with the right team, we’re finally close to finding it.

Start innovating now –

Implement a universal ontology

Create a shared ontology to bridge neural (intuitive) and symbolic (analytical) AI agents, transforming weak signals for deeper analysis by expert systems and graph-based connections.

Form specialized multi-agent teams

Build teams of neural (real-time detection) and symbolic (rule-based analysis) AI agents, each specialized with tools for their role.

Leverage graph technology for cross-referencing

Use graph databases to link signals over time and across data sources, uncovering patterns like fraud faster, earlier, and at a lower cost than current methods.

Interesting read?

��ѻ��ý’s Innovation publication, Data-powered Innovation Review – Wave 9 features 15 captivating innovation articles with contributions from leading experts from ��ѻ��ý, with a special mention of our external contributors from, and . Explore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.

Our Global Data Science Challenge is shaping the future of learning. In an era when AI is reshaping industries, ��ѻ��ý’s 7^th Global Data Science Challenge (GDSC) tackled education.

By harnessing cutting-edge AI and advanced data analysis techniques, participants, from seasoned professionals to aspiring data scientists, are building tools to empower educators and policy makers worldwide to improve teaching and learning.

The rapidly evolving landscape of artificial intelligence presents a crucial question: how can we leverage its power to solve real life challenges? ��ѻ��ý’s Global Data Science Challenge (GDSC) has been answering this question for years and, in 2024, it took on its most significant mission yet – revolutionizing education through smarter decision making.

The need for innovation in education is undeniable. Understanding which learners are making progress, which are not, and why is critically important for education leaders and policy makers to prioritize the interventions and education policies effectively. According to UNESCO, a staggering 251 million children worldwide remain out of school. Among those who do attend, the average annual improvement in reading proficiency at the end of primary education is alarmingly slow—just 0.4 percentage points per year. This presents a sheer challenge in global foundational learning hampering efforts made to achieve the learning goal as set forth in the Sustainable Development Agenda.

The grade-AI generation: A collaborative effort

The GDSC 2024, aptly named “The Grade-AI Generation,” brought together a powerful consortium. ��ѻ��ý offered its data science expertise, UNESCO contributed its deep understanding of global educational challenges, and Amazon Web Services (AWS) provided access to cutting-edge AI technologies. This collaboration unlocks the hidden potential within vast learning assessment datasets, transforming raw data into actionable insights for decision making that could change the future of millions of children worldwide.

At the heart of this year’s challenge lies the PIRLS 2021 dataset – a comprehensive global survey encompassing over 30 million data points on 4^th grade children’s reading achievement. This dataset is particularly valuable because it provides a rich and standardized data that allows participants to identify patterns and trends across different regions and education systems. By analyzing factors like student performance, demographics, instructional approaches, curriculum, home environment, etc. the AI-powered education policy expert can offer insights that would take much longer time and resources to gain from traditional methods. Participants were tasked with creating an AI-powered education policy expert capable of analyzing this rich data and providing data-driven advice to policymakers, education leaders, teachers, but also parents, and students themselves.

Building the future: Agentic AI systems

The challenge leveraged state-of-the-art AI technologies, particularly focusing on agentic systems built with advanced Large Language Models (LLMs) such as Claude, Llama, and Mistral. These systems represent a significant leap forward in AI capabilities, enabling more nuanced understanding and analysis of complex educational data.

“Generative AI is the most revolutionary technology of our time,” says Mike Miller, Senior Principal Product Lead at AWS, “enabling us to leverage these massive amounts of complicated data to capture for analysis, and present knowledge in more advanced ways. It’s a game-changer and it will help make education more effective around the world and enable our global community to commit to more sustainable development.“

The transformative potential of AI in education

The potential impact of this challenge extends far beyond the competition itself. As Gwang-Chol Chang, Chief, Section of Education Policy at UNESCO, explains, “Such innovative technology is exactly what this hackathon has accomplished. Not just only do we see the hope for lifting the reading level of young children around the world, we also see a great potential for a breakthrough in education policy and practice.”

The GDSC has a proven track record of producing innovations with real-world impact. In the 2023 edition, “The Biodiversity Buzz,” participants developed a new state-of-the-art model for insect classification. Even more impressively, the winning model from the 2020 challenge, “Saving Sperm Whale Lives,” is now being used in the world’s largest public whale-watching site, happywhale.com, demonstrating the tangible outcomes these challenges can produce. 

Aligning with a global goal

This year’s challenge aligns perfectly with ��ѻ��ý’s belief that data and AI can be a force for good. It embodies the company’s mission to help clients “get the future you want” by applying cutting-edge technology to solve pressing global issues.

Beyond the competition: A catalyst for change

The GDSC 2024 is more than just a competition; it’s a global collaboration that brings together diverse talents to tackle one of the world’s most critical challenges. By bridging the gap between complex, costly collected learning assessment data and actionable insights, participants have the opportunity to make a lasting impact on global education.

A glimpse into the future

The winning team ‘insAIghtED’ consists of Michal Milkowski, Serhii Zelenyi, Jakub Malenczuk, and Jan Siemieniec, based in Warsaw Poland. They developed an innovative solution aimed at enhancing actionable insights using advanced AI agents. Their model leverages the PIRLS 2021 dataset, which provides structured, sample-based data on reading abilities among 4^th graders globally. However, recognizing the limitations of relying solely on this dataset, the team expanded their model to incorporate additional data sources such as GDP, life expectancy, population statistics, and even YouTube content. This multi-agent AI system is designed to provide nuanced insights for educators and policymakers, offering short answers, data visualizations, yet elaborated explanations, and even a fun section to engage users.

The architecture of their solution involves a lead data analyst, data engineer, chart preparer, and data scientist, each contributing to different aspects of the model’s functionality. The system is capable of querying databases, aggregating data, performing internet searches, and preparing elaborated answers. By integrating various data sources and employing state-of-the-art AI technologies like Langchain and crewAI, the insAIghtED model delivers impactful, real-world, actionable insights that go beyond the numbers, helping to address complex educational challenges and trends.

Example:

Figure 1: Show an example of the winning model. The image has the model answering the following prompt “Visualize the number of students who participated in the PIRLS 2021 study per country”

As we stand on the brink of an AI-powered educational revolution, the Grade-AI Generation challenge serves as a beacon of innovation and hope. It showcases how the combination of data science, AI, and human creativity and passion can pave the way for a future where quality education is accessible to all, regardless of geographical or socioeconomic barriers.

Start innovating now –

Dive into AI for good
Explore how AI can be applied to solve societal challenges in your local community or industry.

Embrace agentic AI systems
Start experimenting with multi-agent AI systems to tackle complex, multi-faceted problems in your field.

Collaborate globally
Seek out international partnerships and datasets to bring diverse perspectives to your AI projects.

Interesting read?��ѻ��ý’s Innovation publication,Data-powered Innovation Review – Wave 9 features 15 captivating innovation articles with contributions from leading experts from ��ѻ��ý, with a special mention of our external contributors from, and. Explore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.

Peering into the future is a tricky business – especially in the ever-changing realm of data, analytics, and AI. But if there’s one thing we’ve learned, it’s that uncertainty never stopped us from trying. After all, we’re in a part of the technology profession where predicting the unpredictable is often part of the job description.

We called upon seven of our data-powered innovation movers and shakers to dust off their (frozen) crystal balls and share their visions of what 2025 has in store. Their insights reveal a world where AI balances on the edge of legality, cloud platforms morph into something entirely new, and synthetic data booms with promise – and no, it’s not artificial hype. From “vertical AI” that digs deep into industry needs to conversational AI that knows what you want before you do, these trends give a glimpse of the fascinating, yet challenging, future of data and AI.

Will their predictions come true? Only time will tell, but one thing’s for sure: 2025 is shaping up to be a year we’ll be talking about for a long time to come. And data and AI are right in the middle of it.

Let’s dive in.

AI is not a crime

AI is not a crime, though sometimes it feels like one, given its swift advance beyond legal bounds. While identity theft, deepfakes, and media manipulation emerge from Pandora’s box, many AI ethicists focus on fairness and transparency, skirting around AI’s darker uses. As AI matures and criminal applications surge, discussions will inevitably shift from theoretical ideals to practical realities, especially when organizations face public lawsuits under new AI regulations. This shift will force experts to tackle how AI can harm society directly. So, while AI is not a crime, it certainly invites a compelling conversation about AI and crime. Let’s face it: When it comes to AI, the real crime would be ignoring the conversation altogether. – Marijn Markus, AI Lead, Managing Data Scientist, ��ѻ��ý and Data, ��ѻ��ý

Augment my governance

Prepare to be captivated. AI agents are about to revolutionize data management in the upcoming year. They will shoulder burdensome data tasks, enabling companies to reach new pinnacles of productivity and efficiency. With AI seamlessly managing data collection, analysis, and access for us, we can finally focus on something much more crucial: getting value out of data for the business. It’s high time to achieve that, isn’t it? The future of data management is upon us, and missing the opportunity of augmenting is not an option. Because in this data game, those who augment govern – and those who don’t get governed. – Liz Henderson, Executive Advisor, ��ѻ��ý and Data, ��ѻ��ý

Cloud encounters of the third kind

As we look towards 2025, cloud data platforms apply for a new round of transmitted change. We all recognize the need for high-quality enterprise data as a foundation for relevant, trustworthy AI. Add to that the need to adhere to regulations, data sovereignty, privacy sustainability and cost. It soon becomes apparent that a smart mix of different and diverse cloud approaches for data will play a crucial role in the upcoming year. I expect to see a pendulum swing towards larger investments in cloud data platforms, yet it will be clouds of a different kind. Or, to put it differently, the forecast calls for cloud cover – but with a whole new kind of silver lining. – Prithvi Krishnappa, Global Head of Data and AI, Sogeti

Let’s talk better

Conversational AI will continue to be a hot topic in 2025. Contact center transformation, leveraging “classic” AI and generative AI, will help save labor costs by billions and improve customer service significantly. These technologies can handle routine inquiries and provide instant responses, freeing human agents to deal with more complex issues. Imagine a world where you no longer have to press 1 for assistance – AI will anticipate your every need before you even know you have one. While human agents may become less central, customer satisfaction might reach an all-time high as AI enhances efficiency and personalization in ways we never thought possible. It’ll be the talk of the town in 2025. – Monish Suri, Global Google Partnership Lead, ��ѻ��ý and Data, ��ѻ��ý

When AI goes vertical

We will see a major rise in domain-specific vertical AI solutions that are finely tuned through rigorous test-driven prompt engineering. These purpose-built AI models will deliver more reliable and precise insights tailored to the unique needs of their industries. For instance, in healthcare, imagine AI predicting patient outcomes like a crystal ball, analyzing vast datasets of medical histories and treatment plans to conjure better patient care and optimized resources. In financial services, AI will become the ultimate fraud-buster, identifying unusual patterns in real time and safeguarding assets with previously unseen precision and confidence. Vertical AI solutions will not only streamline operations but also spark innovation by providing industry-specific intelligence and efficiency. The only way is vertical! – Dan O’Riordan, VP, AI and Data Engineering, ��ѻ��ý

The semantics of confidence

We’ve seen many companies adopting the principles of data mesh and semantics as part of their modern data analytics platform strategy. If nothing else, it’s needed in 2025 and beyond to comply. For example, the EU AI Act requires close tracking of the purpose of AI models and the underlying data used to train it. This can only be done by enhancing data platforms with semantics, connecting original data sources, forged data products in AI models, and all business dashboards and AI-infused applications. It creates high levels of confidence in both data and AI, next to many new, innovative opportunities to leverage data. The endgame? Nothing less than a full digital twin of the enterprise, a hallmark of data mastery. – Arne Rossmann, Innovation Lead, ��ѻ��ý and Data, ��ѻ��ý

Synthetic data boom

I predict a boom in synthetic data. But first of all, what is synthetic data? It’s artificially generated but realistic data that mirrors real patterns without using sensitive information. Why is synthetic data crucial? It tackles privacy, security, data scarcity, and control issues. Traditional data sources are hitting their limits. Privacy laws are tightening, and real-world data often lacks the diversity we need. Synthetic data lets companies create datasets that mimic real shopping behavior in retail or complex production processes in manufacturing without exposing sensitive info or being held back by data gaps. I foresee that 2025 will be the year synthetic data moves center stage. Companies ready to leverage it will build powerful, adaptable models faster than ever. The synthetic data boom will be anything but artificial. – Dinand Tinholt, VP, ��ѻ��ý and Data, North America, ��ѻ��ý

Interesting read?

��ѻ��ý’s Innovation publication, Data-powered Innovation Review – Wave 9 features 15 captivating innovation articles with contributions from leading experts from ��ѻ��ý, with a special mention of our external contributors from, and. Explore the transformative potential of generative AI, data platforms, and sustainability-driven tech. Find all previous Waves here.

Earlier this week, our friends at Liquid.AI introduced their first products powered by Liquid Neural Networks (LNNs). This is a new generation of AI models that promise to achieve state-of-the-art performance at every scale, while maintaining a smaller memory footprint, more computing efficiency and much better transparency.

But what are Liquid Neural Networks exactly? And why should you care?

To understand, let’s consider the classical Large Language Models we’ve been building over the past few years.

LLMs, like ChatGPT, are great statistical learners. Meaning they have to ‘’memorize’’ trillions of variations and patterns from an immense training dataset in order to coherently mimic those patterns in their outputs. This is why models are becoming better but also exponentially larger with each iteration. This reliance on scale is why LLMs have grown into models with trillions of parameters. To produce increasingly complex and nuanced outputs, LLMs need always more parameters, which means more data, more computational power, and a larger model size.

This approach is becoming extreme, as constantly increasing the number of parameters to improve performance is both resource-intensive and costly. In our race to develop Generative AI, we are also increasingly scaling black boxes with little explainability.

In contrast, Liquid Neural Networks (LNNs) offer the promise of fundamentally more adaptive and efficient model architecture.

Instead of relying on larger and larger networks of simple neurons, LNNs use smaller networks of more capable neurons that adjust in real-time to new inputs. In simplified terms, these neurons are mathematical formulas that are adaptive – they can change their behavior based on new inputs. They adjust their connections and processing methods dynamically, like a formula that updates itself as new information comes in.

Since these neurons are not static, they continuously evolve based on the information they process, allowing LNNs to learn on the go and adapt to new environments without needing retraining. This adaptability means that LNNs can perform complex tasks with far fewer parameters. As a result, LNNs are better suited to handling dynamic, unpredictable situations, such as real-time decision-making in autonomous systems or robotics, where flexibility and efficiency are key.

This is why LNNs have immense potential. Same or maybe better performance (future will tell) , but far less computational power, energy, and cost. This has an obvious impact on the sustainability profile of AI, but also opens up many more deployment options and use cases. LNN-based architectures enable AI deployments on smaller edge devices—such as mobile phones, vehicles, smart-home systems, airplanes, and industrial machinery—without relying on massive, cloud-based computing resources.

Try to imagine a fully offline automotive AI system that runs efficiently on a standard PC CPU without needing specialized hardware like GPUs. Or an industrial robot that continuously adapts to new tasks and surroundings, making real-time adjustments as it learns from ongoing interactions.

The implications of Liquid Neural Networks are profound. Their ability to deliver state-of-the-art performance with fewer resources and real-time adaptability represents a significant add-on in the evolution of AI. For business leaders and CxOs interested in staying ahead in the AI race, keeping a close eye on Liquid Neural Networks evolution is not just advisable—it’s essential. This could very well be the future of sustainable, efficient, and explainable AI.

Imagine this for a future work experience: a user in procurement starts the day by asking their virtual assistant to create a purchase order.

This is an action that requires only the basic facts, ranging from vendor and quantity to item and date, with no manual data entry needed to complete the process.

Likewise, a manufacturing user asks their system to tell them what orders they are likely to miss today, and receives not just a detailed report of real-time progress against plan, but also a series of options for addressing potential problems.

These and countless other use case scenarios form the true vision for business AI in the modern work environment. This vision is already building significant momentum, even while introducing various organizational and technical challenges – and it’s only a couple of years away from transforming the everyday interaction with digital applications.

Early gains

The pace of AI and Gen AI adoption is obviously going to differ by organization, depending upon individual business use cases and perceived benefits. But to identify the tangible value underpinning these considerations is first to identify a future state, and imagine a way of working that combines human and machine components into a complete and harmonized whole.

Such thinking typically puts the focus on “quick wins” made possible by AI, including:

• Automating manual, repetitive tasks, which can extend from data entry to scheduling and report creation, thereby freeing people up to focus on more creative and complex work.
• Boosting user productivity: where individuals no longer need to access a range of systems to complete tasks and find answers, and instead rely on AI agents to do the heavy lifting – while proactively delivering insight before they even seek it.
• Streamlining business processes: where agents offer recommendations and autonomously taking actions across a range of commonly completed tasks.
• Increasing business resilience by proactively designing response plans to critical scenarios.
• Supporting complex SAP implementations: for example, supporting the project teams activities on RISE with SAP and GROW with SAP integration, working with an Augmented Software Development Life-cycle, and ensuring high data quality.

According to the ��ѻ��ý Research Institute’s report, Data-powered enterprises 2024, AI has the capacity to streamline business processes and enhance business resilience. This aligns perfectly with the potential of Gen AI to transform user experiences and create new revenue opportunities.

All told, Gen AI promises to transform the user experience in terms of the way we interact with information and back-end systems, discover insights, and find inspiration. Just as importantly, the technology is rapidly introducing new revenue opportunities and removing “skills barriers” – such as by enabling people to create complex spreadsheet analysis based on a simple query.

Bumps in the road

When the potential of AI is combined with industry and business use cases, the reasons to act become even harder to ignore. Hence the growing focus today on removing any obstacles in the way – with the headlines being:

• A lack of trust: a concern that spans ethical considerations as well as a resistance inside many organizations to actively experiment with new – and therefore unproven – technologies.
• A seeming lack of maturity: where decision-makers are waiting on the technology to become “perfect” before committing, held back by talk of AI hallucinations and output bias.
• Regulatory concerns: where frameworks such as the European Union’s AI Act 2024 aim to ensure AI systems are safe, transparent, and respectful of fundamental rights – but can impact future innovation.
• Human nature: which sees people preferring the “comfort zone” that comes with traditional ways of working.

This last point is understandable given the fact that AI brings with it a demand to change standard operating procedures. A transformation takes place in the way tasks are completed, to optimize the mix of human and artificial intelligence required at distinct touchpoints along the way.

A dynamic move forward

Overcoming these impediments is an important next step that requires the continued evangelization of AI and Gen AI from technology leaders. This is a task that ��ѻ��ý is heavily involved in, helping our customers to better understand the most suitable options for Gen AI – while also providing training and education to master the different aspects of change management.

Such support is a vital way station for any AI roadmap, as organizations seek guidance on the right approach and common pitfalls, as well as ways to introduce the necessary safeguards and appropriate ways to keep a “human in the loop.”

The good news, certainly from a technical perspective, is that Gen AI does not require major changes to existing IT environments, especially when the AI capabilities offered by SAP and global hyperscalers are taken into account. This situation might change with the advent of multi-agent AI systems, and the number of AI agents interacting autonomously – but that is a bridge most will worry about crossing when they finally reach it.

Final thoughts

Gen AI is often described as a train that is gaining speed. In this context, the key question facing organizations is when to get onboard: should they join now while advances are steady, or risk trying to gain access when the locomotive is hurtling through the station at full throttle?

What’s emerging as best practice is the idea of starting small and validating the potential of Gen AI for different use cases. This approach focuses on non-business-critical processes that can be addressed by out-of-the-box functionality available from providers like ��ѻ��ý and SAP. Once these initiatives prove their value, organizations gain the confidence to proceed with more advanced design strategy to tackle the bigger task of integrating Gen AI into the very fabric of day-to-day operations.

Ultimately, it comes down to one overriding thought: how to ensure your business doesn’t get left behind.

Watch this space for our next blog post.