How Agentic AI can boost your business operations

Launching AI without clear processes is like giving wings to a faulty engine. According to Fast Company, 74% of employees say their data is treated like gold—but many organizations still struggle to manage it properly. As automation gains power, broken workflows get louder. You need clarity, accountability, consistency, visibility, and connectivity to make AI effective. Check out this article to see how to set the foundation before you scale AI—and why skipping this step could cost you more than you think. #AI #Operations #FutureOfWork

Launching AI without clear processes is like giving wings to a faulty engine. According to Fast Company, 74% of employees say their data is treated like gold—but many organizations still struggle to manage it properly. As automation gains power, broken workflows get louder. You need clarity, accountability, consistency, visibility, and connectivity to make AI effective. Check out this article to see how to set the foundation before you scale AI—and why skipping this step could cost you more than you think. #AI #Operations #FutureOfWork

Launching AI without clear processes is like giving wings to a faulty engine. According to Fast Company, 74% of employees say their data is treated like gold—but many organizations still struggle to manage it properly. As automation gains power, broken workflows get louder. You need clarity, accountability, consistency, visibility, and connectivity to make AI effective. Check out this article to see how to set the foundation before you scale AI—and why skipping this step could cost you more than you think. #AI #Operations #FutureOfWork

Agentic AI ≠ “just a smarter chatbot.” It’s a system that can plan, reason, act, and evaluate—continuously. This is the core lifecycle that separates Agentic AI from traditional prompt-response LLM usage: --- 1) Planning The agent breaks the goal into clear steps. It identifies what needs to be done, which tools/data to use, and the expected outcome. Example: “Search customer records → analyze patterns → draft a summary → send report.” --- 2) Reasoning The agent thinks before acting. It compares options, checks constraints, and selects the best step forward. This is where chain-of-thought, heuristics, and policy rules come into play. --- 3) Action The agent executes—not just generates text. This can be: ● API calls ● Database queries ● File retrieval ● Running workflows ● Triggering external tools (MCP, Functions, Plugins, Agents) This is where AI becomes useful, not just informative. --- 4) Evaluation (Self-Reflection) After acting, the agent checks: Did the output meet the goal? Do I need to revise, retry, or continue? Should I update memory? This feedback loop is what enables improvement over time. --- Why This Matters Traditional LLMs respond. Agentic AI adapts. This lifecycle enables: ✅ Multi-step workflows ✅ Context continuity ✅ Error correction ✅ Tool + system integration ✅ Real autonomy, not just text generation --- Short Version Plan: Understand the goal Reason: Decide the best steps Act: Execute using tools + APIs Evaluate: Check → refine → continue This is the foundation of AI systems that work in the real world. --- Question: Where do you think most teams struggle—Planning, Action, or Evaluation? #AgenticAI #LLMEngineering #AIWorkflows #SystemDesign #DistributedSystems #AutonomousAgents #RAG

Litera is making agentic AI a default feature across its core tools at no extra cost. That is a line in the sand for product strategy and for buyers. Here's my take, as a long-time operator and spectator of enterprise AI: 1. When the price tag goes to zero, AI stops being a SKU. It becomes part of the workflow. 2. The value moves to the firm’s knowledge graph, precedent, and governance. Not the model brand. 3. Copilots are not the moat. Data lineage, policy guardrails, and change management are. 4. Adoption beats invention. If AI shows up where lawyers already live - Word, Outlook, matter systems - the buyer will demand outcomes, not pilots. 5. The new KPIs are boring and powerful: time to first draft, variance to precedent, risk-adjusted cycle time, auditability. AI as a line item is starting to round to zero in workflow software. The spend shifts to clean data, permissioning, and proof. Litera’s move is a tell for where every mature category is headed. The winners will treat AI as default and compete on governance and outcomes, not SKUs.   Credit to Litera for pushing the market in this direction.

Most companies view AI as just a tool, such as another assistant, workflow, or dashboard. However, the true transformation occurs when AI reshapes the way an organization operates: Decisions are made in a matter of days rather than weeks. Context is shared across teams instead of being confined to silos. Systems admit when they do not know something instead of pretending they do. This is what we call an AI-native organization, which is faster, clearer, and more self-aware. 👉 The Future: AI-Native Organizations https://lnkd.in/ds6GEJWv

One Fortune 500 company cut development time by 90% using AI agent teams. No humans managed the workflow. The agents coordinated themselves. This isn't science fiction. It's happening now. Multi-agent AI systems are changing how we work. Instead of one AI trying to do everything, you build specialized teams. Think of it like a marketing department: 🔍 Research agent finds market trends ✍ Content agent writes blog posts 📊 Marketing agent creates social campaigns Each agent has one job. They're really good at it. CrewAI makes this possible. It's an open-source framework that lets you build these AI teams easily. The numbers speak for themselves: • 20-30% faster workflow cycles • Better accuracy through specialization • Less noise per agent I've seen teams automate entire marketing workflows. From research to content creation to distribution. All without human intervention. The key is defining clear roles. Give each agent a specific job. Let them collaborate naturally. Your AI team can work 24/7. They don't get tired. They don't make human errors. But here's the thing. You still need strategy. You still need oversight. AI agents execute. Humans direct. The future of work isn't human vs AI. It's humans with AI teams. What would you automate first with your own AI agent crew? #AgenticAI #MultiAgent #CrewAI 𝗦𝗼𝘂𝗿𝗰𝗲꞉ https://lnkd.in/ectiuJaq

In her new Forbes Technology Council article, Karen Abramson, CEO of Wolters Kluwer Corporate Performance & ESG, shares how agentic AI will reshape enterprise technology and why success depends on a new AI product mindset. Take a look. #AgenticAI #ProductLeadership #DigitalTransformation #WoltersKluwer

In her new Forbes Technology Council article, Karen Abramson, CEO of Wolters Kluwer Corporate Performance & ESG, shares how agentic AI will reshape enterprise technology and why success depends on a new AI product mindset. Take a look. #AgenticAI #ProductLeadership #DigitalTransformation #WoltersKluwer

In her new Forbes Technology Council article, Karen Abramson, CEO of Wolters Kluwer Corporate Performance & ESG, shares how agentic AI will reshape enterprise technology and why success depends on a new AI product mindset. Take a look. #AgenticAI #ProductLeadership #DigitalTransformation #WoltersKluwer