How to adopt AI in DAM: key questions and best practices

Today, we’ll be taking a closer look at one of the core innovations powering trusted AI experiences in Salesforce — the Einstein Trust Layer, with a specific focus on what we call "The Response Journey." As generative AI becomes more deeply integrated into CRM workflows, ensuring that responses are not just intelligent but also secure, private, and trustworthy is critical. That’s where the Einstein Trust Layer comes in. It acts as a protective framework — quietly but powerfully working behind the scenes — to safeguard data, maintain compliance, and ensure AI-generated responses remain relevant, safe, and aligned with enterprise standards. #59 #EinsteinTrustLayer #SalesforceAI #TrustedAI #ResponsibleAI #GenerativeAI #AIEthics #DataPrivacy #AIinCRM #EnterpriseAI #SecureAI #Customer360 #DataSecurity #TrustInAI #AITrustLayer #SalesforceInnovation

I met a Founder whose company spent $2M on AI tools last year. But he was confused about the ROI they got from each one? Not just ROI - but no one in his company knows what's connected to what data, and also no compliance. He solved this recently when he started using Airia - Enterprise AI Simplified which is an enterprise AI orchestration platform that brings order to this AI madness. It is your central command center for all things AI where you get: - No-code agent builder – Your teams can prototype AI assistants in minutes, not months. - Universal data connections – Seamlessly plug into your existing enterprise systems and databases. - Smart cost management – Automatically route requests to the most cost-effective models. - Built-in security & governance – Audit trails, data protection, and compliance controls from day one. - Model flexibility – Use any LLM you want. No vendor lock-in. Whether you're in finance needing risk compliance, healthcare managing sensitive data, or retail analyzing customer insights, this can help all. Enterprise AI needs to be orchestrated properly and not to be complicated. Try it out and let me know in the comments below. #AI #Enterprise #MCP #collab

🧠 What if your entire business could build itself — from text? Imagine describing your company in plain language, and watching every process, policy, and interface generate automatically. No modules. No integrations. Just one AI brain with unified memory, policy, and control — creating and evolving your entire enterprise safely and transparently. This idea is called Monocentric Relationship Management (MRM) — a conceptual framework defining the Single-AI Paradigm Beyond CRM. 📘 Read the full concept note (v2.1, Zenodo): 👉 https://lnkd.in/eshvFahs 💬 Would you trust an AI system that can build — and explain — your entire business? #AI #MRM #AgenticAI #EnterpriseArchitecture #DigitalTransformation #AIGovernance #GenerativeEnterprise #NoCodeAI

If your AI projects keep failing, you probably skipped this step: It’s not your model, your vendor, or even “lack of adoption” across your desks. Hear what I am saying: 𝐀𝐈 𝐰𝐢𝐥𝐥 𝐧𝐨𝐭 𝐟𝐢𝐱 𝐢𝐧𝐜𝐨𝐫𝐫𝐞𝐜𝐭 𝐝𝐚𝐭𝐚 𝐢𝐧𝐩𝐮𝐭𝐬. That’s why if your firm is spending millions on AI with: - No defined KPIs - No data dictionary - 10+ disconnected systems - Entire revenue lines missing from your CRM You can’t even be helped until you fix this internally. Because AI is just a mirror of your data. In other words: Technology is not the bottleneck. It’s lack of CONTEXT that makes AI useless. When your data is clean, structured, and mapped to your core KPIs…you can easily go from ingestion to production-ready AI in 30 days or less. So before you blame the messenger, first ensure your data is entered correctly into your applications. Fix that foundation…and you’ll stop expecting magic from broken data.

Your AI projects aren’t failing because your teams lack talent or vision. They are failing because your semantic layer is broken. Without a trusted, unified context for your data, even the best-designed models will produce unreliable results. That is why we built Syncari’s Agentic MDM™. It is a modern approach to enterprise data management that does more than fix bad data. It creates a governed, strategic data layer that makes AI accurate, actionable, and aligned with your business. If you want AI initiatives you can trust and a data foundation built for the next decade instead of the last, this is where to start. Read more about this topic, not gated, here: https://lnkd.in/d-Jhdt7H #AgenticMDM #MDM #Data #DataScience

🛡️ CPG Leaders: Is Your Data Ready for the AI Future? Data is the new goldmine from demand forecasting to personalized customer experience. But as AI becomes central to decision-making, can it be trusted? The Future of AI-Safe Data Governance means creating ecosystems where data is not just available, but accurate, secure, and AI-ready: ✅ Ensuring compliance and security ✅ Enabling reliable analytics ✅ Building confidence in every decision Data trust isn’t a trend, it’s a necessity for AI success #DataGovernance #AIReady #DigitalTransformation #MicrosoftPurview #AzureAI #Acuvate

🚩 The Vendor Red-Flag Test 🚩 Most AI vendors are great at talking about what their systems could do. But very few can show you what their AI actually does inside a live business environment. Here’s the problem: a slick demo doesn’t prove integration. It proves marketing. Over time, I’ve found a few questions that cut straight through the noise — questions that either make a vendor smile (because they’re ready) or squirm (because they aren’t). The Red-Flag Test: 1️⃣ “Can your AI perform actions in my live systems right now, not a mockup?” If the demo is screenshots or canned data, you’re not seeing the truth. 2️⃣ “How long will it take to connect your AI to our calendar and CRM?” If the answer is weeks or months, you’re buying integration headaches, not intelligence. 3️⃣ “What happens when we upgrade our systems, does it break?” If they talk about custom fixes instead of standards, brace yourself for endless IT bills. 4️⃣ “If we switch AI vendors next year, do we keep our integrations?” If the answer is no, you’re not buying AI, you’re buying lock-in. The beauty of these questions is that they take 60 seconds to ask but can save you six figures in wasted spend. Because in 2025, the danger isn’t that AI will underperform. It’s that vendors will overpromise , and you’ll pay for the difference. More in this article: https://lnkd.in/dNQTMzfJ #AI #EnterpriseAI #BusinessStrategy #MCP #DigitalTransformation #FutureOfWork

Kelly Perone and Gary Noonan are taking the #Dreamforce stage to explore how AI agents, real-time data, and CRM are transforming digital labor. Learn more: https://lnkd.in/dfwuh9Dv

The Hidden GTM Engine: Your Data Quality Everyone talks about AI readiness. Few talk about data readiness. Before your GTM motion can truly scale with AI, your data quality must be rock solid. Without it, every predictive model, intent signal, and personalization effort collapses under flawed inputs. The 3 Cs of data quality determine whether your GTM data works for you or against you: Completeness: Are your buyer records fully enriched with firmographic, technographic, and intent context? Consistency: Do Marketing, Sales, and RevOps operate on a single, unified truth? Currency: Is your data fresh enough to match today’s decision-makers, not last year’s? 3rd-party referential data strengthens each “C” and turns your CRM and MAP into intelligent engines rather than static repositories. 👉 Challenge: Before you chase AI outcomes, fix your data foundation. Is your GTM data clean enough to trust your next algorithmic decision? #GTM #DATA #AI #CRM #MDM #REVOPS

Imagine GenAI drafting your contracts overnight. Now imagine a misstep exposing sensitive data. That’s the reality leaders face as contract lifecycle management evolves. CLM platforms have moved from basic storage to full lifecycle solutions: drafting, performance tracking, compliance, and now AI-driven intelligence. GenAI can speed workflows, surface insights, and automate tasks but only if trust, accuracy, and data security are built in. Next-gen CLM systems are tackling these challenges: • AI trained specifically on contracts, not generic models • Strong data privacy that keeps sensitive information in-house • Multi-model AI that balances broad understanding with contract-level precision GenAI can unlock smarter, faster contract management but only when intelligence, human in the loop and security all go hand in hand. Are your CLM tools ready to deliver speed without compromising trust? I'd love to hear your thoughts. #CLM #ContractManagement #RiskManagement #AI