Artificial Intelligence in Business

𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆, 𝘆𝗼𝘂 𝗳𝗶𝗿𝘀𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮 𝘀𝗼𝗹𝗶𝗱 𝗱𝗮𝘁𝗮 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗲𝗻𝗳𝗼𝗿𝗰𝗲 𝘀𝘁𝗿𝗶𝗰𝘁 𝗱𝗮𝘁𝗮 𝗵𝘆𝗴𝗶𝗲𝗻𝗲. Getting your house in order is the foundation for delivering on any AI ambition. The MIT Technology Review — based on insights from 205 C-level executives and data leaders — lays it out clearly: 𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗱𝗼 𝗻𝗼𝘁 𝗳𝗮𝗰𝗲 𝗮𝗻 𝗔𝗜 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘆 𝗳𝗮𝗰𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗿𝗶𝘀𝗸 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. Therefore, many firms are still stuck in pilots, not production. Changing that requires strong data foundations, scalable architectures, trusted partners, and a shift in how companies think about creating real value with AI. Because pilots are easy, BUT scaling AI across the enterprise is hard. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: ⬇️ 1. 95% 𝗼𝗳 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 — 𝗯𝘂𝘁 76% 𝗮𝗿𝗲 𝘀𝘁𝘂𝗰𝗸 𝗮𝘁 𝗷𝘂𝘀𝘁 1–3 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀:   ➜ The gap between ambition and execution is huge. Scaling AI across the full business will define competitive advantage over the next 24 months. 2. 𝗗𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗹𝗶𝗾𝘂𝗶𝗱𝗶𝘁𝘆 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀: ➜ Without curated, accessible, and trusted data, no AI strategy can succeed — no matter how powerful the models are. 3. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗮𝗿𝗲 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 — 𝗮𝗻𝗱 𝘁𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗴𝗼𝗼𝗱 𝘁𝗵𝗶𝗻𝗴:   ➜ 98% of executives say they would rather be safe than first. Trust, not speed, will win in the next AI wave. 4. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱, 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗜 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝘄𝗶𝗹𝗹 𝗱𝗿𝗶𝘃𝗲 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘃𝗮𝗹𝘂𝗲:  ➜ Generic generative AI (chatbots, text generation) is table stakes. True differentiation will come from custom, domain-specific applications. 5. 𝗟𝗲𝗴𝗮𝗰𝘆 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗱𝗿𝗮𝗴 𝗼𝗻 𝗔𝗜 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻𝘀:  ➜ Firms sitting on fragmented, outdated infrastructure are finding that retrofitting AI into legacy systems is often more costly than building new foundations. 6. 𝗖𝗼𝘀𝘁 𝗿𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗿𝗲 𝗵𝗶𝘁𝘁𝗶𝗻𝗴 𝗵𝗮𝗿𝗱: ➜ From GPUs to energy bills, AI is not cheap — and mid-sized companies face the biggest barriers. Smart firms are building realistic ROI models that go beyond hype. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗳𝘂𝘁𝘂𝗿𝗲-𝗿𝗲𝗮𝗱𝘆 𝗔𝗜 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗵𝗮𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗺𝗼𝗱𝗲𝗹 𝗿𝗲𝗹𝗲𝗮𝘀𝗲.   𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 — 𝗱𝗮𝘁𝗮, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗥𝗢𝗜 — 𝘁𝗼𝗱𝗮𝘆.

AI’s ability to make tasks not just cheaper, but also faster, is underrated in its importance in creating business value. For the task of writing code, AI is a game-changer. It takes so much less effort — and is so much cheaper — to write software with AI assistance than without. But beyond reducing the cost of writing software, AI is shortening the time from idea to working prototype, and the ability to test ideas faster is changing how teams explore and invent. When you can test 20 ideas per month, it dramatically changes what you can do compared to testing 1 idea per month. This is a benefit that comes from AI-enabled speed rather than AI-enabled cost reduction. That AI-enabled automation can reduce costs is well understood. For example, providing automated customer service is cheaper than operating human-staffed call centers. Many businesses are more willing to invest in growth than just in cost savings; and, when a task becomes cheaper, some businesses will do a lot more of it, thus creating growth. But another recipe for growth is underrated: Making certain tasks much faster (whether or not they also become cheaper) can create significant new value. I see this pattern across more and more businesses. Consider the following scenarios: - If a lender can approve loans in minutes using AI, rather than days waiting for a human to review them, this creates more borrowing opportunities (and also lets the lender deploy its capital faster). Even if human-in-the-loop review is needed, using AI to get the most important information to the reviewer might speed things up. - If an academic institution gives homework feedback to students in minutes (via autograding) rather than days (via human grading), the rapid feedback facilitates better learning. - If an online seller can approve purchases faster, this can lead to more sales. For example, many platforms that accept online ad purchases have an approval process that can take hours or days; if approvals can be done faster, they can earn revenue faster. This also enables customers to test ideas faster. - If a company’s sales department can prioritize leads and respond to prospective customers in minutes or hours rather than days — closer to when the customers’ buying intent first led them to contact the company — sales representatives might close more deals. Likewise, a business that can respond more quickly to requests for proposals may win more deals. I’ve written previously about looking at the tasks a company does to explore where AI can help. Many teams already do this with an eye toward making tasks cheaper, either to save costs or to do those tasks many more times. If you’re doing this exercise, consider also whether AI can significantly speed up certain tasks. One place to examine is the sequence of tasks on the path to earning revenue. If some of the steps can be sped up, perhaps this can help revenue growth. [Edited for length; full text: https://lnkd.in/gBCc2FTn ]

Missing the Agentic AI Revolution? Here's Your Roadmap to Get Started If you're not exploring Agentic AI yet, you're missing the biggest paradigm shift since the emergence of LLMs themselves. While others are still perfecting prompts, forward-thinking teams are building systems that can autonomously plan, reason, and execute complex workflows with minimal supervision. The gap between organizations leveraging truly autonomous AI and those using basic prompt-response systems is widening daily. But don't worry—getting started is more accessible than you might think. Here's a practical roadmap to implementing your first agentic AI system: 1. 𝗕𝗲𝗴𝗶𝗻 𝘄𝗶𝘁𝗵 𝗮 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲 – Choose a specific task with clear boundaries where automation would provide immediate value. Document research, competitive analysis, or data processing workflows are excellent starting points. 2. 𝗗𝗲𝘀𝗶𝗴𝗻 𝘆𝗼𝘂𝗿 𝗮𝗴𝗲𝗻𝘁'𝘀 𝘁𝗼𝗼𝗹 𝗯𝗲𝗹𝘁 – An agent's power comes from the tools it can access. Start with simple tools like web search, calculator functions, and data retrieval capabilities before adding more complex integrations. 3. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 – The ReAct (Reasoning + Acting) pattern dramatically improves reliability by having your agent think explicitly before acting. This simple structure of Thought → Action → Observation → Thought will transform your results. 4. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗺𝗲𝗺𝗼𝗿𝘆 𝘀𝘆𝘀𝘁𝗲𝗺 𝗲𝗮𝗿𝗹𝘆 – Don't overlook this critical component. Even a simple vector store to maintain context and retrieve relevant information will significantly enhance your agent's capabilities. 5. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 – LangGraph, LlamaIndex, and CrewAI provide solid foundations without reinventing the wheel. They offer battle-tested patterns for orchestration, memory management, and tool integration. The most important step? Just start building. Your first implementation doesn't need to be perfect. Begin with a minimal viable agent, collect feedback, and iterate rapidly. What specific use case would you tackle first with an autonomous agent? What's holding you back from getting started?

Last quarter, we spent $1,404,619 on AI tokens - an all-time high - and the ROI wasn’t what we expected… Most of the ROI didn’t come from “flashy AI”, it came from boring AI doing boring work at scale. Here’s where our spend went and what actually moved the needle: 1. Telling reps who to call today (and why) We’re using AI to sift through millions of signals and tell reps who to talk to today and why. The signals that we’ve found matter: Job changes (new decision makers = new opportunities), buying committee changes and intent signals (active web research and pricing page visits). The big ROI driver is helping our customers with daily prioritization so they don’t have to go fishing for actionable info. At ZoomInfo, We’ve seen a 25-33% increase in meeting quality and opp creation when AEs are sourcing using our AI tools. Win rates also jump from 16-20% to 30%. 2. Writing outreach that doesn’t sound automated We’re moving from “20 segments of 1,000” to 20,000 segments of 1. Not “VP IT at enterprise insurance” messaging… but John at State Farm, who we talked to last year, who competes with three of our customers, with context pulled in automatically. Customer ROI here ultimately comes from better response rates and higher close rates by being more relevant. Buyers care when you show you care. 3. Turning sales calls into usable data Every sales call (ours and customers) is recorded using @Chorus and becomes structured data: objection patterns, competitor mentions, deal risk, coaching moments. We’ve found the benefits of this are huge - 25-30% faster ramp time for new reps, and 10-15% larger deal sizes through better discovery and value articulation. The average rep sells more like the best rep. 4. Speeding up low-value engineering work Every engineer at Zoominfo has Intellij and VS Code w/ Cline. AI handles the unglamorous stuff: Boilerplate code, refactors, test coverage. We’ve seen ~25–30% faster execution on these routine tasks, which frees senior engineers to focus on system design and real product innovation. Our biggest lesson so far has been that if your data foundation is garbage, AI just helps you move faster in the wrong direction. You won’t get AI “working” until you have contextual customer/prospect data centralized, and you can actually build on top of it. We’re still early and we’re trying a lot of things but these have been the highest ROI drivers by a mile. If you’re testing AI in your GTM stack, drop a comment with what’s actually working for you - I’m all ears.

I curated and reviewed 16 AI strategy playbooks so you don’t have to. These strategy roadmaps offer guidance and inspiration for any tech leader driving AI, automation, or enterprise transformation. They’re practical, high-impact resources from top consulting firms, tech giants, and respected professional associations. Here’s the curated list (in alphabetical order): 1) Accenture – The Art of AI Maturity ➜ https://lnkd.in/g4kyWCNd 2) Amazon – AI/ML/GenAI Cloud Framework ➜ https://lnkd.in/gbmUAgQT 3) Bain – Transforming CX with AI ➜ https://lnkd.in/gqq-66ST 4) Bain – Winning with AI ➜ https://lnkd.in/gWk84MjS 5) Booz Allen – Securing AI ➜ https://lnkd.in/gceVreFG 6) BCG – Transforming with AI ➜ https://lnkd.in/gWtqJFuB 7) Deloitte – AI Transformation ➜ https://lnkd.in/gGNURxzq 8) Google – AI Adoption Framework ➜ https://lnkd.in/gCj2S6uF 9) IBM – CEO’s Guide to GenAI ➜ https://lnkd.in/gqDam-yS 10) McKinsey – The Executive’s AI Playbook ➜ https://lnkd.in/gFRqm2MW 11) Microsoft – CIO’s GenAI Playbook ➜ https://lnkd.in/gbJ4vwVE 12) PMI – DS/AI Project Playbook ➜ https://lnkd.in/g7_wQbRs 13) PwC – Agentic AI Playbook ➜ https://lnkd.in/gSicWfeV 14) PwC & Microsoft – Deploying AI at Scale ➜ https://lnkd.in/gwg-CBBb 15) Scaled Agile – AI-Augmented Workforce ➜ https://lnkd.in/gunGGgWJ 16) World Economic Forum – AI C‑Suite Toolkit ➜ https://lnkd.in/gh-FQT72 🔍 Three things that stood out: 1) Business-first strategy: The best firms align AI with outcomes like OKRs - not just tech stacks. 2) Track what matters: Success depends on measuring value with the right metrics, not just completing sprints and deployments. 3) Stay agile: AI evolves fast - successful organizations adapt quickly with flexible teams and tools. ♻️ If this sparked an idea, save it or pass it along. I will be sharing more on Agile & AI adoption, automation blueprints, and use cases - follow for the next drop. #Automation #Strategy #DigitalTransformation

NEWS: Okta just exposed AI's biggest blind spot. Most companies aren't ready. I sat down with Shiven Ramji, President of Auth0 at Okta, to find out what's being missed. The problem is bigger than you think: → 80% of breaches already involve compromised identity → 91% of organisations are deploying AI agents → Only 10% have a governance strategy for managing them Traditional security was built for deterministic apps. AI agents are anything but. They access sensitive data at machine speed, far faster than any human ever could. 4 things developers need to get right from day one: 1. Authentication  ↳ Verify the agent is working on behalf of who it claims 2. Human in the loop ↳ Sensitive transactions need async authorisation 3. Fine-grained permissions ↳ Ephemeral access that expires when the task is done 4. Secure connections ↳ No more static API keys scattered everywhere The future Shiv predicts: Memory will become tied to identity. Right now, your context is locked inside each platform. Eventually, you'll want to take your AI memory with you across tools. My takeaway: AI security is identity security. You can't have one without the other. The AI agent market is projected to hit $250 billion by 2034. The companies racing to deploy without solving identity will learn this the hard way. Follow me Alex Banks for daily AI highlights and insights.

This Stanford study examined how six major AI companies (Anthropic, OpenAI, Google, Meta, Microsoft, and Amazon) handle user data from chatbot conversations.  Here are the main privacy concerns. 👀 All six companies use chat data for training by default, though some allow opt-out 👀 Data retention is often indefinite, with personal information stored long-term 👀 Cross-platform data merging occurs at multi-product companies (Google, Meta, Microsoft, Amazon) 👀 Children's data is handled inconsistently, with most companies not adequately protecting minors 👀 Limited transparency in privacy policies, which are complex and hard to understand and often lack crucial details about actual practices Practical Takeaways for Acceptable Use Policy and Training for nonprofits in using generative AI: ✅ Assume anything you share will be used for training - sensitive information, uploaded files, health details, biometric data, etc. ✅ Opt out when possible - proactively disable data collection for training (Meta is the one where you cannot) ✅ Information cascades through ecosystems - your inputs can lead to inferences that affect ads, recommendations, and potentially insurance or other third parties ✅ Special concern for children's data - age verification and consent protections are inconsistent Some questions to consider in acceptable use policies and to incorporate in any training. ❓ What types of sensitive information might your nonprofit staff  share with generative AI?  ❓ Does your nonprofit currently specifically identify what is considered “sensitive information” (beyond PID) and should not be shared with GenerativeAI ? Is this incorporated into training? ❓ Are you working with children, people with health conditions, or others whose data could be particularly harmful if leaked or misused? ❓ What would be the consequences if sensitive information or strategic organizational data ended up being used to train AI models? How might this affect trust, compliance, or your mission? How is this communicated in training and policy? Across the board, the Stanford research points that developers’ privacy policies lack essential information about their practices. They recommend policymakers and developers address data privacy challenges posed by LLM-powered chatbots through comprehensive federal privacy regulation, affirmative opt-in for model training, and filtering personal information from chat inputs by default. “We need to promote innovation in privacy-preserving AI, so that user privacy isn’t an afterthought." How are you advocating for privacy-preserving AI? How are you educating your staff to navigate this challenge? https://lnkd.in/g3RmbEwD

Everyone talks about Salesforce as a CRM giant. But if you zoom out, they’ve quietly become The most dominant DISTRIBUTION LAYER for enterprise AI. Not by building models or launching flashy labs. They did what AI-native companies currently can’t: → Deep inside Fortune 500 workflows → Embedded in procurement-approved vendor lists → Installed on the desktops of 150,000+ enterprise reps While everyone is chasing or waiting for AGI, Salesforce focused on ADJACENCY. ✔ Embedding generative AI into Sales Cloud, Service Cloud, and Tableau ✔ Building industry-specific copilots tailored to how real teams sell and support ✔ Acquiring vertical intelligence via Data Cloud, Slack, and Mulesoft integrations The result? A defensible wedge that sits on top of 𝗺𝗮𝘀𝘀𝗶𝘃𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗱𝗮𝘁𝗮. And a flywheel where AI adoption directly increases platform stickiness. They’re not trying to win the model war. They’re winning the 𝘥𝘦𝘱𝘭𝘰𝘺𝘮𝘦𝘯𝘵 𝘸𝘢𝘳. And if you’re building in B2B SaaS, that’s a lesson worth stealing. 💡 #artificialintelligence #growth #strategy #startups Image Credit: Eric Flaningam

AI in 2026: What Will Actually Matter to Business Leaders In 2026, AI should be improving your business metrics significantly. If you are still not using it, you are leaving a lot of efficiency on the table. SAP’s 2026 AI outlook makes it clear that business advantage will come from where AI is placed in your business and how directly it shapes outcomes. Outlook 1: From Generic AI to Business-Specific Intelligence Enterprises are moving away from general-purpose models toward specialized AI trained on structured business data, because only domain-specific models improve forecast accuracy and execution quality. This means: Faster execution with fewer process failures. Outlook 2: Agentic AI Will Reshape Operations, Not Tools Autonomous AI agents will increasingly plan and execute multi-step tasks, because this is the only way to scale decisions without scaling headcount. Agent governance will become mandatory, because ungoverned agents create operational risk and accountability gaps. This means: Scalable automation without loss of control. Outlook 3: Intent-Driven Systems Will Replace Interface-Driven Work Natural language and intent-based interfaces will reduce dependency on complex enterprise navigation; employees express outcomes faster than they complete workflows. Sovereign and compliant AI architectures will gain importance because regulatory alignment determines where AI can be deployed safely. This means: Faster adoption with lower organizational friction. Two takeaways for legacy business leaders 1. AI returns depend on integration depth. Disconnected pilots cannot change the flow of work through your organization. 2. Data quality defines the AI ceiling. Poorly governed data limits decision confidence and caps long-term value creation. One practical tip to begin integrating AI Select one revenue-critical or cost-critical workflow, identify decisions that delay outcomes and apply AI only where it shortens decision time or removes manual dependence. If AI does not improve speed or economics, it should not be deployed. #AITransformation #EnterpriseAI #AIStrategy #DigitalTransformation #AIImplementation #TechLeadership #BusinessTransformation #AIOperations #OrganizationalChange #DataGovernance

𝗗𝗥𝗘𝗔𝗠𝗙𝗢𝗥𝗖𝗘 𝟮𝟬𝟮𝟱 - 𝗜‘𝗺 𝗶𝗻 𝗦𝗮𝗻 𝗙𝗿𝗮𝗻𝗰𝗶𝘀𝗰𝗼 𝘄𝗶𝘁𝗵 𝗦𝗮𝗹𝗲𝘀𝗳𝗼𝗿𝗰𝗲!! ☁️💙 [𝗔𝗱/𝗔𝗻𝘇𝗲𝗶𝗴𝗲] Many companies like to talk about AI painting big pictures of what might come next. Salesforce takes a different approach: they build! For more than a year now, Salesforce has been rolling out AI agents that are already running inside companies around the world. In yesterday’s keynote, 𝗠𝗮𝗿𝗰 𝗕𝗲𝗻𝗶𝗼𝗳𝗳, 𝗖𝗘𝗢 𝗼𝗳 𝗦𝗮𝗹𝗲𝘀𝗳𝗼𝗿𝗰𝗲, shared new use cases that made it easier than ever to understand how these agents really work in daily operations. 𝗕𝘂𝘁 𝗼𝗻𝗰𝗲 𝗮𝗴𝗮𝗶𝗻: 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁? Imagine opening your laptop and finding a small team of digital helpers already inside. Each one is an expert… one knows your customers very well, one your workflows, one is your data expert. They don’t just answer your questions or react to commands but fix things before you see them and make work feel more fluent, faster, personal & fun. Marc Benioff described this evolution clearly: “𝘈 𝘺𝘦𝘢𝘳 𝘢𝘨𝘰, 𝘈𝘨𝘦𝘯𝘵𝘧𝘰𝘳𝘤𝘦 𝘸𝘢𝘴 𝘢 𝘱𝘳𝘰𝘥𝘶𝘤𝘵. 𝘛𝘰𝘥𝘢𝘺, 𝘪𝘵’𝘴 𝘵𝘩𝘦 𝘱𝘭𝘢𝘵𝘧𝘰𝘳𝘮 𝘣𝘦𝘩𝘪𝘯𝘥 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨 𝘸𝘦 𝘥𝘰.” 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗳𝗼𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀: –> up to 30 % faster service resolution and 40 % lower response time in customer operations –> productivity gains between 20–35 % across early adopters –> now used by 12,000+ companies from retailers to logistics firms –> interoperability with AWS, Microsoft & OpenAI, so it fits into existing tech stacks –> built-in governance and transparency layers, critical for regulated industries 𝗟𝗲𝘁’𝘀 𝘁𝗮𝗹𝗸 𝗮𝗯𝗼𝘂𝘁 𝘀𝗼𝗺𝗲 𝗰𝗼𝗻𝗰𝗿𝗲𝘁𝗲 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀: 𝗪𝗶𝗹𝗹𝗶𝗮𝗺𝘀 𝗦𝗼𝗻𝗼𝗺𝗮 – An AI “shopping chef” that knows your taste. It connects recipes, products, and past purchases turning every visit into a personalized experience that feels more like a conversation than a normal shopping experience. 𝗙𝗲𝗱𝗘𝘅 – AI agents read and route thousands of logistics documents in seconds catching exceptions, rerouting shipments, and reducing manual work across global operations. 𝗣𝗮𝗻𝗱𝗼𝗿𝗮 – An AI assistant follows you from online to in-store. What you like in chat appears ready in the boutique creating one seamless, personalized customer journey. 𝗗𝗲𝗹𝗹 – AI agents automate supplier onboarding verifying documents, sending approvals, and cutting setup time from 60 days to under 20. Faster partnerships, faster production. Each example shows how AI can move beyond experimentation, into real outcomes & that‘s what we need more now: REAL IMPLEMENTATION! Tomorrow continues with more 𝗧𝗲𝗰𝗵 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝗮 𝗚𝗟𝗢𝗕𝗔𝗟 𝗦𝗨𝗣𝗘𝗥𝗦𝗧𝗔𝗥 I was able to meet and we all probably know more for his music than for his tech… STAY TUNED!! 💙🦾 Do you already use AI Agents in YOUR business? –> If yes, what for? –> If not, which tasks would you 𝘭𝘰𝘷𝘦 to hand over to an agent friend?