When it comes to selecting the right DXP, things can go off the rails. But don’t lose your caboose. Dan Drapeau has a two-track evaluation model that can keep you rolling – full steam ahead. In today's Guest Critic article, Dan proves why he’s a masterful conductor on the digital experience train. As he explains, the DXP competitive landscape has converged across architectural models, and evaluation approaches haven’t evolved at the same pace. Dan’s observation is that single-track processes are still chugging along – focusing on feature breadth and demo cohesion and favoring pre-composed suites. In his experience, what we really need is a more balanced approach that separates platform evaluation from ecosystem architecture. How do you leave the station? By running two structured tracks, which he outlines in detail. This helps vet not only current capabilities but also long-term durability relative to core considerations like vendor lock-in, CDP and DAM strategies, and the ever-evolving target of AI. In his post, Dan details the operational requirements, where the complexities exist, and how to synthesize results. He even digs into the critical topics of validation and stress testing, and distills it all into a handy checklist to help guide your DXP evaluation. As he says, composable and pre-composed DXPs represent architectural strategies along a spectrum, and these pointers can help keep you on track. “A structured two-track evaluation approach ensures that whichever model is selected, it is chosen for structural alignment and long-term outcomes rather than surface cohesion.” If you’re currently evaluating DXPs – or considering it in the future – this is a fantastic read from a sharp practitioner. Check it out on CMS Critic: 🔶 https://lnkd.in/e-XB3Pig #dxp #digitalexperience #digitalexperienceplatform #softwareevaluation #dam #digitalassetmanagement #cdp #customerdata #customerdataplatform #cmp #contentmarketingplatform #ai #artificialintelligence
Dan Drapeau's 2-Track DXP Evaluation Model
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🚀 Software-Defined Manufacturing (SDM) Explained Manufacturing is becoming software-driven, but most factories are still built on tightly coupled stacks where change is slow, risky, and expensive. We just published a new article introducing a practical, engineering-grade Software-Defined Manufacturing (SDM) reference architecture, designed for real factories, including brownfield environments. 👉 In this article, we explain: What Software-Defined Manufacturing really means (beyond buzzwords) Why Digital Twins & reference models are the core of SDM How to safely combine AI, optimization, and real-time control How to decouple applications from machines without breaking determinism or safety 🔗 Read the article here: https://lnkd.in/ePXfx-5b 📌 This is the first article of a series. Over the coming weeks, we’ll publish weekly deep dives, one per layer, covering design principles, pitfalls, and real-world implementation patterns. If you’re interested in: ✔️ Industrial digital transformation ✔️ IT/OT architectures ✔️ AI in manufacturing (done right) ✔️ Software-defined systems ➡️ Follow Embedia to get the next articles directly in your feed. #SoftwareDefinedManufacturing #SDM #DigitalTransformation #ManufacturingIT #OT #IndustrialAI #SystemsEngineering #ThinkSoftware Embedia.io
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🚀 Software-Defined Manufacturing (SDM) Explained Manufacturing is becoming software-driven, but most factories are still built on tightly coupled stacks where change is slow, risky, and expensive. We just published a new article introducing a practical, engineering-grade Software-Defined Manufacturing (SDM) reference architecture, designed for real factories, including brownfield environments. 👉 In this article, we explain: What Software-Defined Manufacturing really means (beyond buzzwords) Why Digital Twins & reference models are the core of SDM How to safely combine AI, optimization, and real-time control How to decouple applications from machines without breaking determinism or safety 🔗 Read the article here: https://lnkd.in/ePXfx-5b 📌 This is the first article of a series. Over the coming weeks, we’ll publish weekly deep dives, one per layer, covering design principles, pitfalls, and real-world implementation patterns. If you’re interested in: ✔️ Industrial digital transformation ✔️ IT/OT architectures ✔️ AI in manufacturing (done right) ✔️ Software-defined systems ➡️ Follow Embedia to get the next articles directly in your feed. #SoftwareDefinedManufacturing #SDM #DigitalTransformation #ManufacturingIT #OT #IndustrialAI #SystemsEngineering #ThinkSoftware Embedia.io
🚀 Software-Defined Manufacturing (SDM) Explained Manufacturing is becoming software-driven, but most factories are still built on tightly coupled stacks where change is slow, risky, and expensive. We just published a new article introducing a practical, engineering-grade Software-Defined Manufacturing (SDM) reference architecture, designed for real factories, including brownfield environments. 👉 In this article, we explain: What Software-Defined Manufacturing really means (beyond buzzwords) Why Digital Twins & reference models are the core of SDM How to safely combine AI, optimization, and real-time control How to decouple applications from machines without breaking determinism or safety 🔗 Read the article here: https://lnkd.in/ePXfx-5b 📌 This is the first article of a series. Over the coming weeks, we’ll publish weekly deep dives, one per layer, covering design principles, pitfalls, and real-world implementation patterns. If you’re interested in: ✔️ Industrial digital transformation ✔️ IT/OT architectures ✔️ AI in manufacturing (done right) ✔️ Software-defined systems ➡️ Follow Embedia to get the next articles directly in your feed. #SoftwareDefinedManufacturing #SDM #DigitalTransformation #ManufacturingIT #OT #IndustrialAI #SystemsEngineering #ThinkSoftware Embedia.io
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Software-Defined Manufacturing is moving from theory to practice, and this series does a solid job grounding SDM in real factory constraints and engineering realities. Worth reading and worth following as the deeper technical layers roll out: https://lnkd.in/ePXfx-5b Embedia.io Safouen SELMI Joseph Wehbe
🚀 Software-Defined Manufacturing (SDM) Explained Manufacturing is becoming software-driven, but most factories are still built on tightly coupled stacks where change is slow, risky, and expensive. We just published a new article introducing a practical, engineering-grade Software-Defined Manufacturing (SDM) reference architecture, designed for real factories, including brownfield environments. 👉 In this article, we explain: What Software-Defined Manufacturing really means (beyond buzzwords) Why Digital Twins & reference models are the core of SDM How to safely combine AI, optimization, and real-time control How to decouple applications from machines without breaking determinism or safety 🔗 Read the article here: https://lnkd.in/ePXfx-5b 📌 This is the first article of a series. Over the coming weeks, we’ll publish weekly deep dives, one per layer, covering design principles, pitfalls, and real-world implementation patterns. If you’re interested in: ✔️ Industrial digital transformation ✔️ IT/OT architectures ✔️ AI in manufacturing (done right) ✔️ Software-defined systems ➡️ Follow Embedia to get the next articles directly in your feed. #SoftwareDefinedManufacturing #SDM #DigitalTransformation #ManufacturingIT #OT #IndustrialAI #SystemsEngineering #ThinkSoftware Embedia.io
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🚀 Built an AI Agent using MCP (Model Context Protocol) I recently created an AI agent powered by an MCP Server & Client architecture. The MCP server exposes tools, data, and actions, while the client agent consumes them dynamically—making the AI modular, secure, and scalable. 🔹 Clear separation between intelligence and execution 🔹 Easy tool orchestration 🔹 Plug-and-play agent capabilities 🔹 Perfect for enterprise AI use cases This approach makes AI agents more maintainable and production-ready. Excited about where MCP is taking agentic systems 👀 #AI #AIAgents #MCP #LLM #Automation #AgenticAI #Engineering
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🚪 Model Context Protocol (MCP): The Most “Builder-Heavy” Tech Right Now On one side: MCP Users On the other: MCP Builders — and the line never seems to end. 💡 Why is this happening? Because MCP isn’t just another framework or SDK. It’s a new primitive for how AI systems connect to tools, data, and services. What MCP actually enables: 🔌 Standardized way for LLMs to interact with external systems 🧩 Tool & context interoperability across platforms 🏗️ Faster experimentation for AI agents and workflows 🚀 A foundation for truly extensible AI applications Why builders are rushing in: It solves a real pain: fragmented tool integrations Early adopters get architectural leverage Standards = ecosystem power Today’s builders become tomorrow’s platform owners Yes, adoption will catch up. Yes, users will come. But every meaningful tech shift starts the same way: > More builders than users. If you’re building with MCP right now, you’re not early — you’re on time. #MCP #ModelContextProtocol #AIEngineering #LLM #AgenticAI #Developers #FutureOfAI #TechEcosystem #Builders
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AI agents bringing Bigger transformation in legacy systems modernization. work like reverse engineering, documentation , artchitectural design and feasibility used to take several weeks, now these can be achieved in a few days with further validation by engineering teams in a few weeks. However the foundation information AI agents brings , it allows engineering teams to focus more on feature development rather than surface thus speeding up delivery. what is your experience ? #AIFirstengineering #AIFSD
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Exploring the Core Features of the Model Context Protocol (MCP) Building on last week's architecture overview, MCP's features make AI integrations practical and secure, addressing key challenges in model-tool connectivity. Key features include: - Standardized Schemas: consistent JSON formats for requests and responses, ensuring reliable, predictable workflows. - Secure Session Management: isolated channels with authentication and auditing for compliant, low-risk enterprise use. - Dynamic Tool Discovery: on-the-fly querying of tools and metadata, enabling adaptive prototyping. - Robust Error Handling: structured feedback for failures, supporting intelligent retries and resilient automation. - Extensibility: lightweight core with support for custom extensions, avoiding full reinventions. These features lower adoption barriers by standardizing a fragmented process and enabling scalable interoperability across AI ecosystems. MCP ensures models operate as collaborative system components rather than isolated silos, improving efficiency, reducing development costs, and helping organizations focus on innovation at scale.
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𝗠𝗮𝗻𝘆 𝗽𝗹𝗮𝗻𝘁𝘀 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗔𝗣𝗠 4.0. But the real shift is not adding sensors or building dashboards — it is building a true Asset Twin. 𝗔𝗻 𝗔𝘀𝘀𝗲��� 𝗧𝘄𝗶𝗻 𝗶𝘀 𝗻𝗼𝘁 𝗮 3𝗗 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗮 𝗺𝗮𝗰𝗵𝗶𝗻𝗲. 𝗜𝘁 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗳𝗮𝗻𝗰𝘆 𝗮𝗻𝗶𝗺𝗮𝘁𝗶𝗼𝗻. It is a structured digital model that combines: ●Live sensor data ●Engineering design data ●Failure modes and FMEA logic ●Maintenance history ●Operating context 𝗔𝗻𝗱 𝘁𝗵𝗶𝘀 𝗱𝗼𝗲𝘀 𝗻𝗼𝘁 𝗵𝗮𝗽𝗽𝗲𝗻 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗰𝗮𝗹𝗹𝘆. Building an Asset Twin requires serious data work: □Asset criticality assessment □Data mapping and cleansing □Standardized naming conventions □Ontology development (clear data relationships) □AAS (Asset Administration Shell) modeling for structured digital representation Without this foundation, AI becomes guesswork.Dashboards become noise,Predictions lose trust. The #APM market is growing #fast. But the winners are not those creating 3D visuals. They are the ones investing in structured data, ontology models, and AAS-based digital frameworks. APM 4.0 is #not about #visualizing assets. It is about giving assets a structured digital identity that can reason and support decisions. #APM40 #AssetTwin #DigitalTwin #AAS #Industry40 #SmartManufacturing
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For more of Dan's DXP insights on search (and more), check this out: https://cmscritic.com/3-things-to-look-for-in-a-dxp-as-conversational-search-changes-the-game