Integration of AI in Cloud Solutions

Cloud AI Architecture This week I’ve been sharing insights on various aspects of AI governance, and today I want to dive deep into one key component - cloud based AI architecture. This example is designed to serve as a guide for any Data/AI leader looking to progress towards responsible AI development and robust governance.   The architecture should be built on layered principles that integrate both global and local regulatory requirements. Here’s a snapshot of what it covers:   Data Ingestion & Quality - Securely collect, cleanse, and store data with built in quality checks and compliance controls to ensure you always have reliable regulated data as the foundation.   Secure API & Service Integration - Expose AI models through secure APIs by leveraging encryption, robust authentication (OAuth, mutual TLS) and proper rate limiting protecting your models against unauthorized access.   Model Training & Deployment - Use containerized environments and automated CI/CD pipelines for scalable and secure model development. Ensure every change is traceable and reversible while continuously monitoring for bias and performance.   Monitoring, Governance & Human Oversight - Implement real time dashboards and detailed audit logs for continuous risk management. Integrate human in the loop controls for critical decision points to ensure that AI augments human intelligence rather than replacing it.   Cloud Security & Compliance - Design your infrastructure with stringent network security, dedicated VPCs, and adherence to data residency regulations. Secure your architecture with encryption, key management, and proactive monitoring.   This layered approach not only mitigates risks like adversarial attacks and data breaches but also supports rapid innovation. It’s a practical scalable blueprint that any organization can adopt to build a secure responsible AI ecosystem.   Want to advance your AI approach? Let's connect and explore possibilities.

SAP BTP Integration Suite with AI: The Next Evolution of SAP CPI SAP has enhanced its Cloud Platform Integration (CPI) capabilities under the SAP Business Technology Platform (BTP) Integration Suite, now infused with AI and automation for smarter, self-healing integrations. Key AI-Powered Features in SAP BTP Integration Suite 1. AI-Assisted Integration Flows (SAP AI Core & Joule) Smart Mapping: AI suggests field mappings between systems (e.g., SAP S/4HANA ↔ Salesforce) by learning from past integrations. Anomaly Detection: AI monitors message processing and flags unusual patterns (e.g., sudden API failures or data mismatches). Self-Healing: Automatically retries failed calls or suggests fixes (e.g., OAuth token renewal). Example: An EDI 850 (Purchase Order) from a retailer has inconsistent product codes. AI recommends corrections based on historical data before forwarding to SAP S/4HANA. 2. Generative AI for Accelerated Development (Joule + OpenAI Integration) Natural Language to Integration Flow: Describe an integration in plain text (e.g., "Sync customer data from Salesforce to SAP every hour"), and Joule generates a draft CPI flow. Auto-Generated Documentation: AI creates integration specs and test cases. Example: A developer types: "Create a real-time API that checks credit risk before approving orders." Joule proposes: A webhook trigger from SAP Commerce Cloud. A call to a credit-scoring API. A conditional router in CPI to approve/reject orders. 3. Event-Driven AI Integrations (SAP Event Mesh + AI) Smart Event Filtering: AI processes high-volume event streams (e.g., IoT sensor data) and forwards only relevant events to SAP systems. Predictive Triggers: AI predicts when to initiate integrations (e.g., auto-replenish inventory before stockouts). Example: A logistics company uses SAP Event Mesh to track shipment delays. AI analyzes weather + traffic data to reroute shipments proactively. 4. SAP Graph + AI for Context-Aware Integrations Unified Data Access: SAP Graph provides a single API endpoint for cross-SAP data (S/4HANA, SuccessFactors, Ariba). AI Adds Context: Example: When fetching a customer record, AI automatically enriches it with related sales orders and support tickets. Real-World Use Case: AI-Powered Invoice Processing Scenario: Automatically validate supplier invoices against POs and contracts. AI Extraction: Invoice arrives via SAP Document Information Extraction (DocAI). AI parses unstructured PDFs into structured data. Smart Matching: CPI calls SAP AI Core to compare invoice line items with SAP Ariba POs. AI flags discrepancies (e.g., price changes, missing items). Self-Healing Workflow: If discrepancies are minor, AI auto-approves. If major, CPI routes to a SAP Build Workflow for human review. Result: 70% faster invoice processing with fewer errors.

Amidst the #AI dominance of boardroom discussions and headlines, there is one factor, whose catalyzing importance often goes under the radar: the #cloud. Let’s take a look. There is a two-way critical interdependence between AI and the cloud: —     Cloud computing is AI’s critical infrastructure. AI is literally running on the cloud. —     AI is a tremendous added value for the cloud, enhancing its functionality (i.e. automation, data analytics, security) and scalability. It might be going against public belief, but it’s not AI driving #innovation but the synergy between AI and the cloud. Which is why building an AI-native cloud is becoming the name of the game. There is no better justification for this twist than the emphasis that leading infrastructure companies are putting on cloud native #technology. Huawei with its Cloud Native 2.0 approach is a very good example: —     Introduced as early as the end of 2020, Cloud Native 2.0 features a new technical architecture: distributed cloud, application-driven infrastructure, hybrid deployment, unified scheduling, decoupled compute-storage, automated data governance, trusted DevOps, serverless, heterogeneous integration based on soft bus, multi-modal iterative industry AI, and all-round security. —     The new architecture translates to visible benefits in enterprise digital transformation: efficient resources, agile applications, Internet of Things, ultimate experience, service intelligence, security and trustworthiness, and industry enablement. One of the most notable aspects of Huawei’s Cloud Native 2.0 #strategy is the way that it connects AI and the cloud, via a parallel bi-polar focus in 2 directions: —     AI for Cloud —     Cloud for AI Via this dual strategy, Huawei is using AI to optimize cloud infrastructure ("AI for Cloud"), while simultaneously leveraging cloud resources to enhance AI development and deployment ("Cloud for AI"). The extent to which this dual play is critical is reflected in corporate spend. According to Huawei’s Intelligent World 2030 report: —     By 2030, cloud services will account for 87% of enterprises’ application expenditure, while —     AI computing will account for 7% of a company’s total IT investment Companies of all kinds are realizing that no matter the industry they are in and their role in the value chain, they are fast becoming software companies. In the sense that their ability to effectively deploy software can be a critical make or break factor. In turn, this ability depends on adopting cloud native technologies. For one main reason: because cloud native technologies mean fast delivery, which is a critical go-to-market component. This is a play in progress. Choosing the right cloud infrastructure (and provider) is becoming one of the main decisions companies will have to make, greatly influencing their ability to make good use of AI and to innovate. Opinions: my own; Graphic sources: Panagiotis Kriaris, Huawei  #HuaweiConnect

The Future is Private AI Clouds: Who’s Leading the Pack? The world of private cloud computing is evolving fast, and we’re entering a new era—one defined by the rise of private AI clouds. Traditional, general-purpose private clouds are being surpassed by highly specialized, AI-optimized solutions that are designed to meet the unique demands of artificial intelligence workloads. This shift is being driven by unprecedented investments in AI as businesses seek the infrastructure necessary to power their next generation of innovation. Enterprises are increasingly adopting dedicated private AI clouds—prepackaged ecosystems built specifically for AI—that run within their data centers. With tailored tools and architecture, these solutions provide unparalleled control, scalability, and efficiency for AI initiatives. The demand for AI-optimized private clouds will reshape the industry, but not every player will keep up. In my view, companies like Broadcom, Dell Technologies, NVIDIA, Rackspace, and IBM are set to emerge as leaders in this space: Broadcom’s expertise in high-performance silicon creates the foundation for tomorrow’s AI infrastructure. Dell Technologies continues to lead with innovative private cloud solutions that integrate seamlessly into enterprises' AI strategies. NVIDIA is a cornerstone of AI innovation with its GPUs and end-to-end AI computing platforms, ensuring it remains a central figure in these advances. Rackspace Technology stands out by delivering enterprise-class managed services, helping businesses adopt private AI clouds with ease. IBM, a pioneer in enterprise AI with Watson and hybrid cloud expertise, is uniquely positioned to help enable AI-driven transformation. These companies are investing in the future of AI, while others risk falling behind in the coming years. Let’s face it—this new era of specialized private AI clouds demands vision, resources, and adaptability, and not every traditional cloud provider will make the cut. The question now isn’t if, but when enterprises will embrace private AI clouds as the foundation for their AI-driven growth. Those that lean into this change and partner with the right players will gain competitive advantages that will define their success in the years ahead. We’re standing at the beginning of an exciting transformation. What’s your organization doing to prepare for this shift? Let’s discuss! 💡 #CloudComputing #PrivateCloud #AI #ArtificialIntelligence #TechInnovation #FutureOfWork

Is your Cloud and Digital strategy ready for the next evolution? As business leaders strive to maximise ROI from their cloud and digital transformation efforts, a pivotal challenge has emerged: integrating Gen AI into existing strategies. But this is more than a challenge, it’s a unique opportunity to elevate your organisation. Yet, many businesses are hitting roadblocks in their cloud journey, including: 1. Data Management Challenges: As the volume of data grows, organisations struggle to manage and analyse it effectively, limiting their ability to extract actionable insights. 2. Regulatory Complexities: Banking and financial services face regulations such as DORA (Digital Operational Resilience Act), which emphasise the need for robust risk management and resilience planning. 3. Cloud Concentration Risk: Over reliance on a single cloud provider can create vulnerabilities such as potential compliance challenges or increased exposure to systemic risks across providers. 4. High Investment Costs: Initial cloud adoption demands significant financial and time commitments. However, the stakes are high, with cloud computing projected to generate a staggering $3 trillion in EBITDA by 2030. In a digital landscape where Gen AI is a game changer, the cost of inaction is steep. Organisations slow to adapt risk being outpaced by more agile competitors. How can businesses stay ahead of the curve? 1. Integrate Gen AI into Cloud Strategies: Assess current cloud initiatives to identify how Gen AI can add value. Focus on both immediate and future use cases for a sustainable strategy. Studies show that businesses that effectively integrate AI see higher productivity gains and enhanced decision making. 2. Prioritise High Value Applications: Target use cases where Gen AI can deliver the highest ROI. The scalable nature of cloud technology allows businesses to continuously adopt new features and innovations, driving better outcomes in customer support, predictive analytics and personalised services. 3. Enhance Data Governance: Establish robust data governance frameworks to ensure data quality, security and compliance. This enables organisations to leverage AI driven insights while adhering to evolving regulatory requirements like DORA, which emphasises operational resilience. 4. Adopt a Multi-Cloud Strategy: Mitigate cloud concentration risk by diversifying cloud providers, reducing dependency on a single provider and optimising performance. A multi-cloud approach ensures greater flexibility and resilience, especially for meeting regulatory expectations and handling data sovereignty requirements. By aligning cloud and digital transformation efforts with Gen AI, businesses can not only avoid falling behind but also unlock new avenues for growth and innovation. In this era of digital acceleration, embracing change isn’t optional, it’s essential. Thoughts? #Banking #AssetManagement #DigitalTransformation #GenerativeAI

🚀 SAP and Google Cloud are joining forces in a collaboration that could reshape how SAP professionals interact with AI-driven workflows. 🤔👇 This shows how AI will influence how SAP consultants work, learn, and lead projects in the years ahead. 🔄 SAP and Google Cloud are co-founders of the Agent2Agent (A2A) Interoperability Protocol, an open standard for AI agent collaboration. It is a common language that allows AI agents from different vendors to work together in enterprise environments. 🧠 SAP is positioning Joule to be the primary agent in this AI system, integrating actions across business processes. Consultants will soon be leading projects where Joule coordinates agents in cross-application processes, reducing context-switching for users. 📡 The A2A protocol creates secure, real-time cooperation in a new kind of automation where agents initiate actions with each other without needing human prompts, which could accelerate SAP S/4HANA and cloud solution implementations. 🌐 SAP’s generative AI hub now supports Gemini 2.0 Flash and Flash-Lite. These offer multimodal reasoning and can be embedded within SAP BTP applications. This gives SAP customers access to high-speed, low-latency AI services tuned for enterprise-grade performance. 🧰 With Google’s Vertex AI now accessible through ABAP, developers can call Gemini models directly from SAP applications. This gives consultants new tools to build intelligent features within their client environments. It also allows tight integration between SAP core systems and AI services without needing third-party platforms. 🎥 SAP is using Google’s Video Intelligence and Speech-to-Text APIs (RAG) to power smarter training content. That means better, more searchable knowledge resources. The structured data from video indexing includes timestamps and metadata, making retrieval precise and contextual. 📈 By time-aligning video and audio insights, SAP allows users to retrieve context-specific information with precision. This directly improves support documentation, training, and knowledge management for SAP delivery teams. Consultants can expect more intelligent help systems, where training clips respond to real-time usage scenarios. 🛡️ This is happening within SAP’s governed, business-context-rich environment: giving reassurance for clients worried about data compliance, integrity, and governance. SAP ensures that AI operates within enterprise-grade boundaries, avoiding shadow AI or uncontrolled experimentation. 🤝 Both SAP and Google are committed to AI that is open, composable, and embedded in real workflows. The focus is on use cases like supply chain automation, finance process optimisation, and HR decision support. 🔮 AI agents can support consultants in everything from approvals to analytics. Expect to see these capabilities become part of everyday delivery models. Have you already seen AI changing your role? Share your thoughts in the comments below. ⬇️ #IgniteSAP #SAPAI #SAPInnovation

As enterprise AI adoption accelerates, organizations are building robust infrastructure stacks across AWS, GCP, Azure, Oracle, Alibaba, and hybrid environments. Here’s how the AI stack is evolving:  𝗔𝗪𝗦 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 🔹 Orchestration Layers: Examples include 𝘓𝘢𝘯𝘨𝘊𝘩𝘢𝘪𝘯 & 𝘓𝘭𝘢𝘮𝘢𝘐𝘯𝘥𝘦𝘹 for retrieval and reasoning.   🔹 Vector Databases: Commonly used solutions are 𝘗𝘪𝘯𝘦𝘤𝘰𝘯𝘦, 𝘍𝘈𝘐𝘚𝘚, 𝘢𝘯𝘥 𝘞𝘦𝘢𝘷𝘪𝘢𝘵𝘦 for real-time search.   🔹 Validation & Guardrails: Popular tools such as 𝘎𝘶𝘢𝘳𝘥𝘙𝘢𝘪𝘭𝘴, 𝘙𝘦𝘣𝘶𝘧𝘧 help ensure compliance & reliability.   🔹 Scalable Stack: Some infrastructure choices include 𝘈𝘞𝘚 𝘉𝘦𝘥𝘳𝘰𝘤𝘬 (𝘮𝘰𝘥𝘦𝘭 𝘩𝘰𝘴𝘵𝘪𝘯𝘨), 𝘙𝘦𝘥𝘪𝘴 (𝘓𝘓𝘔 𝘤𝘢𝘤𝘩𝘪𝘯𝘨), 𝘢𝘯𝘥 𝘔𝘓𝘧𝘭𝘰𝘸 (𝘮𝘰𝘯𝘪𝘵𝘰𝘳𝘪𝘯𝘨).  𝗚𝗖𝗣 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 🔹 Orchestration: Examples include 𝘓𝘢𝘯𝘨𝘊𝘩𝘢𝘪𝘯, 𝘓𝘭𝘢𝘮𝘢𝘐𝘯𝘥𝘦𝘹 integrated with 𝘝𝘦𝘳𝘵𝘦𝘹 𝘈𝘐.   🔹 Vector Search: A few options are 𝘎𝘰𝘰𝘨𝘭𝘦’𝘴 𝘝𝘦𝘳𝘵𝘦𝘹 𝘈𝘐 𝘔𝘢𝘵𝘤𝘩𝘪𝘯𝘨 𝘌𝘯𝘨𝘪𝘯𝘦, 𝘍𝘈𝘐𝘚𝘚.   🔹 Security & Validation: Common tools include 𝘎𝘶𝘢𝘳𝘥𝘙𝘢𝘪𝘭𝘴, 𝘙𝘦𝘣𝘶𝘧𝘧, 𝘢𝘯𝘥 𝘎𝘊𝘗’𝘴 𝘈𝘐 𝘌𝘹𝘱𝘭𝘢𝘯𝘢𝘵𝘪𝘰𝘯𝘴.   🔹 Scalable Stack: Frequently used components include 𝘎𝘒𝘌 (𝘤𝘰𝘯𝘵𝘢𝘪𝘯𝘦𝘳 𝘰𝘳𝘤𝘩𝘦𝘴𝘵𝘳𝘢𝘵𝘪𝘰𝘯), 𝘉𝘪𝘨𝘘𝘶𝘦𝘳𝘺 (𝘢𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴), 𝘢𝘯𝘥 𝘝𝘦𝘳𝘵𝘦𝘹 𝘈𝘐 𝘗𝘪𝘱𝘦𝘭𝘪𝘯𝘦𝘴.  𝗔𝘇𝘂𝗿𝗲 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲   🔹 Orchestration: 𝘓𝘢𝘯𝘨𝘊𝘩𝘢𝘪𝘯 & 𝘓𝘭𝘢𝘮𝘢𝘐𝘯𝘥𝘦𝘹 are often used with 𝘈𝘻𝘶𝘳𝘦 𝘖𝘱𝘦𝘯𝘈𝘐 𝘚𝘦𝘳𝘷𝘪𝘤𝘦.   🔹 Vector Databases: Examples include 𝘈𝘻𝘶𝘳𝘦 𝘈𝘐 𝘚𝘦𝘢𝘳𝘤𝘩, 𝘞𝘦𝘢𝘷𝘪𝘢𝘵𝘦.   🔹 Validation & Compliance: Some widely used tools are 𝘎𝘶𝘢𝘳𝘥𝘙𝘢𝘪𝘭𝘴, 𝘙𝘦𝘣𝘶𝘧𝘧, 𝘢𝘯𝘥 𝘔𝘪𝘤𝘳𝘰𝘴𝘰𝘧𝘵’𝘴 𝘙𝘦𝘴𝘱𝘰𝘯𝘴𝘪𝘣𝘭𝘦 𝘈𝘐 𝘵𝘰𝘰𝘭𝘬𝘪𝘵.   🔹 Scalable Stack: 𝘈𝘻𝘶𝘳𝘦 𝘔𝘓, 𝘊𝘰𝘴𝘮𝘰𝘴 𝘋𝘉, 𝘢𝘯𝘥 𝘙𝘦𝘥𝘪𝘴 are common choices for scalability.  𝗢𝗿𝗮𝗰𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 (𝗢𝗖𝗜) 🔹 Orchestration: A few options include 𝘓𝘢𝘯𝘨𝘊𝘩𝘢𝘪𝘯, 𝘓𝘭𝘢𝘮𝘢𝘐𝘯𝘥𝘦𝘹 integrated with 𝘖𝘳𝘢𝘤𝘭𝘦 𝘈𝘐 𝘚𝘦𝘳𝘷𝘪𝘤𝘦𝘴.   🔹 Vector Databases: Examples include 𝘖𝘳𝘢𝘤𝘭𝘦 𝘈𝘶𝘵𝘰𝘯𝘰𝘮𝘰𝘶𝘴 𝘋𝘢𝘵𝘢𝘣𝘢𝘴𝘦 𝘸𝘪𝘵𝘩 𝘈𝘐 𝘝𝘦𝘤𝘵𝘰𝘳 𝘚𝘦𝘢𝘳𝘤𝘩.   🔹 Validation & Compliance: 𝘖𝘳𝘢𝘤𝘭𝘦 𝘈𝘐 𝘎𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘵𝘰𝘰𝘭𝘴, 𝘎𝘶𝘢𝘳𝘥𝘙𝘢𝘪𝘭𝘴, 𝘢𝘯𝘥 𝘙𝘦𝘣𝘶𝘧𝘧 are commonly used.   🔹 Scalable Stack: Some infrastructure choices include 𝘖𝘊𝘐 𝘋𝘢𝘵𝘢 𝘚𝘤𝘪𝘦𝘯𝘤𝘦, 𝘖𝘳𝘢𝘤𝘭𝘦 𝘊𝘭𝘰𝘶𝘥 𝘈𝘐 𝘪𝘯𝘧𝘳𝘢𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦 (𝘈𝘮𝘱𝘦𝘳𝘦 𝘎𝘗𝘜𝘴, 𝘕𝘝𝘐𝘋𝘐𝘈).  The Future of AI Infrastructure  The AI landscape is evolving toward multi-cloud, hybrid, and on-prem solutions to optimize cost, performance, and compliance. What does your AI stack look like for 2025? 𝘎𝘪𝘧 𝘣𝘺 𝘛𝘪𝘯𝘨𝘺𝘪 𝘓𝘪  

𝗔𝗪𝗦 𝗜𝘀 𝗤𝘂𝗶𝗲𝘁𝗹𝘆 𝗕𝗹𝗲𝗻𝗱𝗶𝗻𝗴 𝗔𝗜 𝗜𝗻𝘁𝗼 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 👇 If you're working with Cloud / AWS, you’ve probably noticed something happening lately: AI isn’t just a separate service anymore... it’s being woven into everyday cloud tools. As a cloud learner / professional you just need to understand how these updates are changing the work we do. Let me break it down 👇 🔹 Lambda: Now supports agent-based workflows You can now create AI agents inside AWS Lambda using the new Agent capabilities. This means it can call external APIs, make decisions based on responses, and Execute step-by-step plans. 🔹 CloudWatch: Smarter anomaly detection CloudWatch has added AI-based insights that automatically detect unusual spikes or drops, help explain what caused the change, and reduce the need for manual dashboard digging. 🔹 IAM: AI-generated policy suggestions When creating IAM roles or policies, AWS now offers auto-suggested permissions based on usage, it saves time and reduces the chance of misconfigured access. 🔹 S3: Data prep for AI/ML built-in S3 recently added features like object transformations for model-ready formats, and integrations with SageMaker and Bedrock. Your raw data can be cleaned, structured, and sent to models, all without leaving S3. You don’t need to shift to a new “AI role” to stay relevant, but you do need to notice what’s changing in the tools you already use. Start small, Try the new options, and understand where AI is quietly helping. 💬 Have you tried any of these new AI features in AWS? Let me know in the comments👇 ♻️ Found this helpful? Feel free to repost & share with your network. — 📥 For weekly Cloud learning tips, subscribe to my free Cloudbites newsletter: https://www.cloudbites.ai/ 📚 My AWS Learning Courses: https://zerotocloud.co/ 📹 Watch my weekly YouTube videos: https://lnkd.in/gQ8k29DE #aws #cloud #ai #genai #tech #zerotocloud #techwithlucy

What does unlocking the value of AI across the enterprise look like? Having worked closely with #CIOs, #CTOs, and business leaders on digital transformation, one thing is clear: we’re well past the experimentation phase with AI. The focus today is on extracting real business value and tracking ROI. The organizations leading the way are those that treat AI and hybrid cloud as foundational, not optional. This was powerfully reinforced in IBM Chairman and CEO Arvind Krishna’s keynote at IBM Think, where he explained why technology is no longer just a business enabler and is a core source of competitive advantage. Here are the enterprise trends that stood out to me: both in the keynote and in my own work:  ✅ AI + Hybrid Cloud = Value Engine Hybrid cloud empowers enterprises to unify unstructured data across environments, layer AI on top, and convert it into actionable insights—critical for scaling AI across the business.  ✅ From Hype to ROI We’ve moved from pilot projects to outcomes. Enterprises are focusing on integration, ROI, and speed to value.  ✅ Purpose-built > Monolithic Smaller, targeted AI models are outperforming general-purpose ones in efficiency, cost, and deployment speed.  ✅ Open and Everywhere Data is everywhere. Enterprise AI must be open, portable, and capable of delivering insight across silos.  ✅ The Untapped Opportunity With 99% of enterprise data untouched by AI, the opportunity is massive. Today, 450 billion inferencing operations happen daily, and the scale is accelerating.  ✅ IBM as Client Zero IBM is using watsonx internally to drive $3.5B in cost savings by 2025, optimizing discretionary spend and automating at scale. Leaders like Frederic Vasseur at Ferrari and Kate Johnson Lumen Technologies brought these principles to life. Lumen’s use of watsonx at the edge, enabling real-time inferencing, reducing costs, and accelerating innovation really resonated with me. 2025 is the year of Agentic AI. We are in a transformative era. The enterprises that integrate AI, hybrid cloud, and data strategy today will define the market tomorrow. It’s encouraging to see how IBM watsonx Orchestrate makes it possible to build your own AI agents in less than 5 minutes, empowering businesses to quickly integrate, innovate, and automate. This is how organizations can unlock the value of enterprise AI. For those of you who missed the keynote, here is the replay: https://obvs.ly/helen-yu7 #Think2025 #AI #Watsonx #HybridCloud #EnterpriseAI #IBMPartner Want to stay plugged into #Think2025? Subscribe to #CXOSpiceNewsletter: https://lnkd.in/gy2RJ9xg or #CXOSpiceYouTube: https://lnkd.in/gnMc-Vpj

Architecture - SAP Agent2Agent (A2A) Interoperability Big news #SAPSapphire! SAP just dropped something big. Agent2Agent (A2A) collaboration is here - powered by SAP + Google + Microsoft + Amazon Web Services (AWS). This means intelligent agents can actually talk to each other across platforms. Solving real business problems. Driving real innovation. 𝗪𝗵𝗮𝘁’𝘀 𝗰𝗼𝗼𝗹𝗲𝗿? The Agent Catalog + Agent Card now speak one language - ORD-compliant. Standardized. Scalable. Enterprise-ready. And yes - Mohawk Industries is already on it. Real use case. Real results. 𝗔2𝗔 isn't just a system. It's a comprehensive architecture that enables seamless connectivity across platforms, empowering intelligent, scalable business solutions. 𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: ✅ 𝗔𝗴𝗲𝗻𝘁 𝗟𝗮𝘆𝗲𝗿 ➞ Agents play a pivotal role in ensuring smooth communication between SAP applications and other platforms. ➞ Integrated through the 𝗝𝗼𝘂𝗹𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗴𝗲𝗻𝘁 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻, enabling smarter business decisions. ➞ Each agent is trusted, secure, and connects various applications like 𝗦𝗔𝗣 𝗕𝗗𝗖, 𝗦𝗔𝗣 𝗖𝗼𝗻𝗰𝘂𝗿, 𝗦𝗔𝗣 𝗦𝘂𝗰𝗰𝗲𝘀𝘀𝗙𝗮𝗰𝘁𝗼𝗿𝘀, 𝗮𝗻𝗱 𝗦𝗔𝗣 𝗦/4𝗛𝗔𝗡𝗔. ✅ 𝗢𝗥𝗗 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗼𝗿 & 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿 ➞ Serves as the core for managing agent connections. ➞ Facilitates smooth, seamless integration and data flow across different cloud environments. ➞ Ensures efficient communication, integration, and orchestration with platforms like 𝗔𝗪𝗦, 𝗔𝘇𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱. ✅ 𝗖𝗹𝗼𝘂𝗱 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 ➞ A unified platform for connecting 𝗔𝗪𝗦, 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗔𝘇𝘂𝗿𝗲, and 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 for better scalability and flexibility. ➞ A cloud-agnostic design that ensures your business isn't locked into one specific provider. ➞ Real-time connectivity ensures that data and services are always in sync. ✅ 𝗦𝗲𝗹𝗳 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗧𝗿𝘂𝘀𝘁 ➞ Streamlined process for agent registration, ensuring a hassle-free experience. ➞ Built-in trust protocols to ensure that data is always secure and reliable. ✅ 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 ➞ Powered by 𝗦𝗔𝗣 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗜, enabling intelligent decision-making based on real-time data insights. ➞ Helps businesses improve processes, reduce inefficiencies, and drive smarter operations. ➞ Fully integrated with other SAP applications to enhance automation and decision-making. This architecture is more than just a solution; it's a framework built for a future of seamless interoperability. 𝗔2𝗔 ensures that businesses can scale faster, innovate smarter, and connect more securely. Embrace 𝗔2𝗔 𝗜𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆 to unlock smarter connections, improved business efficiency, and secure integrations across systems. 🔗 P.S. Bookmark this to see how A2A can transform your enterprise. Save 💾 ➞ React 👍 ➞ Share ♻️   Follow Alok Kumar for all things related to SAP and business innovation