On August 8, we had the privilege of welcoming over 80 Yonyou partners and enterprise customers to our Hangzhou headquarters. The executive team shared actionable strategies on translating AI capabilities into tangible business value. The future of enterprise AI isn’t just about technology—it’s about partnership and practical application. Thank you to everyone who joined us! #AI# #EnterpriseAI# #AITransformation# #AlibabaCloud# #Yonyou#

AI transformation doesn’t start with experiments. It starts with leadership. At #DellTechWorld#, @MichaelDell shared a clear message: ➡️ Don’t start small. Go after the biggest processes in your business. The companies moving fastest into AI aren’t just adopting new tools—they’re rethinking how work gets done, end to end. And the fastest path forward? Find your super users. Empower them. Scale what works. 🎥 Watch the full conversation with @siliconangle & @theCUBE:

10 most common AI transformation asks we get from enterprises right now (in order of frequency): 1) AI Diagnostic / ROI Study - Identify opportunities for AI, estimate ROI by use case, prioritize investments, and create an implementation plan. 2) Agentic Workflow Automation - Build agents, automations, and integrations that execute recurring business processes. 3) Architecture & SDLC Assessment - review codebase and architecture, identify technical debt and risks, evaluate engineers AI workflow fluency and create a modernization plan. 4) Security Testing & Patching - Test AI applications and agents for vulnerabilities, data leakage, prompt injection, and misuse. 5) Security Assessment - Identify security, privacy, and compliance risks and recommend how to address them. 6) Code Modernization - Legacy tech stacks create drag that decreases speed/accuracy for agents 7) Data Engineering - Build data pipelines, integrations, migrations, search systems, and knowledge systems. 8) MCP Gateway - Provide controlled access to models, tools, and data through authentication, routing, logging, and usage controls. 9) Model fine-tuning - Post-training an LLM on a smaller, targeted dataset so it gets better at verticalized task or domain. 10) Citizen SDLC - Give non-technical employees a secure, governed process for turning AI prototypes into approved production applications. Comment below with the use case # you're most interested in (i.e. #6# = code modernization), and I'll DM you specific insights from work we've done on it.

Day one of AI Skills Fest is officially underway, kicking off a full week of learning from June 8th–12th. Start by joining a LinkedIn live session. ⬇️ Prepare for Microsoft Certification Exam AB-731: AI Transformation Leader: • 6/8 5:00 PM PDT (Americas) • 6/8 7:30 PM IST (EMEA/Asia) Prepare for Microsoft Certification Exam AI-103: Developing AI Apps and Agents on Azure • 6/8 6:30 PM PDT (Americas) • 6/8 9:00 PM IST (EMEA/Asia) Reimagine AI Collaboration for Business: 5 Practical Prompts and Workflows • 6/8 10:00 AM PDT • 6/8 8:30 AM IST And after, jump right into a playlist specially designed for your role:

Healthcare's AI transformation is accelerating, and JPM 2026 made that clear. We kicked off the week with our second annual Healthcare CEO Dinner, co-hosted by our CEO @htaneja alongside Matt Garman, CEO of @awscloud, bringing together the founders, investors, and executives leading enterprise transformation at @SummaHealth, @WellSpan, HATCo, and beyond. Throughout the week, conversations centered on a vision we're a decade into building: health assurance. That means proactive, accessible, and affordable care that shifts from treating sickness to sustaining wellness. We've assembled a portfolio of companies—@CommureOS, @Hippocraticai, @transcarent, Capital Rx, @ro, @HealthEx_io, @aidocmed, and others—architecting this future together. Applied AI is the engine of healthcare abundance. It creates capacity where there was constraint, access where there were barriers, and affordability where costs once spiraled. The scarcity that has defined healthcare for generations can finally give way to a system designed for everyone. The capital and innovation are here. What matters now is the conviction to execute and the commitment to partner in building a system that doesn't wait for people to get sick, but works tirelessly to keep them well.

37 mistakes companies make with AI transformation: 1) Not investing in your data foundation/not having a data “clean-up” strategy. Often people expect that with tools, everything gets solved. 2) Starting with “we need AI” instead of a real problem (this is true for every tech cycle ever). 3) Underresourced AI center of excellence that serves every part of the organization. Backlog builds up, employees get disenfranchised, shadow AI explodes. 4) Trying to automate the same workflow vs rethinking from scratch. Building AI add-ons to existing processes rather than rethinking processes from the ground up. 5) Thinking too big and flashy. Not considering the implications day-to-day and the value of quick, unsexy wins. 6) Over-engineering. Sometimes you dont need a full agentic system and traditional software works just fine. 7) Obsessing over cost before proving feasibility of a use case (i.e using a smaller model first before validating technical feasibility with larger models). 8) Encouraging/pushing employees to use AI without real depth. Widespread rollout with limited education/lack of training for employees. 9) Telling your people that AI won’t impact jobs. 10) Overprotecting data + spend to the point of limited experimentation from your workforce. IT/Security blocking this or slow rolling it out (which is fair but bad for the speed in which this is moving). Culture doesn’t encourage AI use. 11) Not having places to go to ask questions / knowledge share. Whether that be a skills library, shared repo, or internal AI office hours. 12) Failing to solve the last mile. Everyone’s so focused on models, but successful applied AI is a complex last mile problem: governance, data, observability, context management, people, process, etc. 13) Shipping it and call it done. Lack of discipline to go beyond the shiny demo and ensure sustained adoption that meaningfully empowers teams. 14) Slop is tolerated. 15) No governed way to build for non-technical people. No Citizen SDLC to empower SMEs to build and share production apps. 16) Assuming AI transformation is the responsibility of one person within the org. 17) Run like an IT project. No senior exec actually owns injecting AI across the business, therefore initiatives stall and leave no lasting impact. There is no clear owner. 18) CEO is not a driving force. Leadership enforcement without the leaders actually knowing how or what to enforce. 19) Not getting the buy in of the “bad guys.” Bring Legal, Finance, and IT along for the ride early. 20) Not investing in / underestimating change management. Easy to get the folks who are excited on board, but it's a long process to make others feel comfortable. 21) Not measuring baselines before any adoption. What are the metrics pre-AI tool to post AI tool? No baseline = no roi story, and thinking that all AI usage is positive ROI without measuring usage/tying it to real outcomes fails the same way. 22) Inventing new KPIs for AI instead of focusing on having AI accelerate existing functional KPIs. 23) Reducing AI to headcount and being overly stringent on ROI too early into programs. 24) Being driven by FOMO and not having the patience to treat AI transformation as the multi-year migration it actually is. 25) Being married to past purchasing mistakes and not choosing the best technology at the moment. 26) Not anticipating the complexity of getting systems to work nicely together (a kind of scope creep as the reality blows up work required). 27) Not being agile enough to change course when the landscape changes drastically. 28) Locking in to a single provider ecosystem. 29) Not providing employees access to the underlying systems needed to make AI useful to take action, not just chat. 30) Underestimating how much of an impact AI can actually have. It is both a cooler and scarier time than ever before to be an incumbent. 31) Outsourcing thinking to AI - everyone can prompt, the differentiation is how you wield the tool to multiply the work you're doing. If you have good judgement you can do a lot more. If you don't, you end up wasting a lot of tokens spinning your wheels. 32) One functional department thinking they should own AI transformation. It treats AI as a vertical solution vs. horizontal capability that’s more than just technology. 33) Executing on AI initiatives before anchoring your work in a clear strategy that’s tied to business goals, a map of key processes, understanding of your technology and data reality, and clarity around how to meet your people where they are. 34) Not solving data permissioning and RBAC considerations before rolling out agentic tools firmwide. 35) Not giving people dedicated time to experiment or carving out time in their roles for it. 36) Not understanding how a business function ACTUALLY works before trying to apply AI. In someone’s head, the process for generating some end state dashboard is simple: systems generate the data, it gets consistently transformed and warehoused, then read into the dashboard that the VP sees. In reality, it’s a complete mess. 37) Neglecting internal evals to constantly test and evaluate how new models/harnesses perform company tasks on a $ per successful task basis. What's missing?

Capital is being sucked into AI and SaaS today. This is the type of market where you let you winners run but start creating a list of sectors and companies that will ultimately benefit from the AI transformation but the market is ignoring.

Most financial institutions don’t have an AI problem. They have a workflow problem. Over the last year, we’ve spoken to banks, credit unions, and PE firms all running the same experiment: 10+ AI tools. Multiple vendors. Internal pilots everywhere. And still… no real operating leverage. The issue isn’t model quality. It’s orchestration. 1. Where exactly does AI sit in your underwriting flow? 2. Which steps should be automated vs supervised? 3. How do you connect outputs across compliance, risk, ops, and investment teams? 4. Who owns the agent once it’s live? This is the gap. And this is why we’re building Playbooks at Multimodal Playbooks are not demos. They’re not sandbox experiments. They’re not generic “AI transformation decks.” They are battle tested, workflow level blueprints designed specifically for financial institutions. Underwriting Playbook. Portfolio Monitoring Playbook. Compliance Automation Playbook. Deal Flow Intelligence Playbook. Each one maps AI agents directly onto how your teams already operate. Day one, it feels native. No chaos. No tool sprawl. No six month science projects. What changes? Time to decision drops. Manual review cycles compress. Analyst leverage increases. Cost per transaction decreases. That’s real margin impact. Most firms are still asking, “How do we use AI?” The better question is, “How do we redesign our workflows around it?” Playbooks answer that. We’re releasing soon. If you’re leading AI, ops, risk, or tech at a financial institution and you’re tired of disconnected pilots, this will matter. AI doesn’t create leverage by existing. It creates leverage when it’s embedded into how work actually gets done. That’s what Playbooks are built for. More soon.