Jason Nissen’s Post

AI agents shouldn't just sound smart, they should deliver results. At @Sema4.ai, we focus on making that possible. This week, we've introduced our most capable platform release yet – built to help business and teams automate complex data and document workflows with accuracy and confidence. - Agents can now run mathematically accurate analysis at scale - Document Intelligence that structures data with near-perfect precision in seconds - Tools that let business users (not just developers) build reliable agents they can trust to handle critical workflows This is how we move enterprise AI from experimentation to execution. Learn how we're advancing the accuracy, scale, and reliability of enterprise AI: https://lnkd.in/gUmxZrYA #EnterpriseAI #AgenticAI #AIagents

The promise of enterprise AI is often bottlenecked by data silos and fragmented systems. We just solved that. Our new blog details how Egnyte's LangChain Integration bridges the gap between AI and your enterprise content. This innovation unlocks the true potential of AI by giving LLMs secure access and context, allowing them to process data across your entire repository. Read the post to see how we’re turning fragmented knowledge into trusted intelligence. https://bit.ly/3WeHCpN

AI is only as smart as the data you give it. That’s a truth many businesses overlook. If your data is locked away in unstructured files, your AI might be missing out on critical insights. 🔍 OpenText File Content Extraction helps uncover the hidden gold in your documents—making your AI smarter, faster, and more effective. 💡 Learn how to unlock the full potential of your data: https://lnkd.in/eM8FEzua

Fact: AI is only as smart as the data you give it. Want to find out on how to deliver, analyze and process information in the times of AI? Then have a look at the blog below.

One Platform. Trusted Data. Scalable AI. Our strategic partner Databricks just shared breakthrough results showing that by combining open-source models with automated prompt optimization, enterprises can now achieve state-of-the-art AI performance at 90× lower cost. For financial institutions, that changes everything. Because true AI readiness is not about having many tools it is about having one platform that connects data, governance, and AI development in a single, trusted environment. At Danske Bank, we are building exactly that foundation: Trusted, governed data products as reusable assets for every model, report, and decision A unified platform where data, analytics, and AI innovation coexist seamlessly Responsible AI that scales efficiently, transparently, and safely across the organisation This is how we simplify the data-to-AI value chain, from source to decision, and ensure that governance, trust, and efficiency reinforce each other. When everything happens on one governed platform, complexity goes down, confidence goes up, and AI becomes a business capability not a side experiment. https://lnkd.in/d7sgGvgE

Salesforce To Buy Spindle AI To Expand Agentic Analytics Capabilities: Salesforce has agreed to acquire Spindle AI, a company specializing in agentic analytics technology designed to help enterprises use artificial intelligence to model business scenarios and forecast outcomes. Spindle AI’s platform combines multi-agent systems, machine learning, and advanced data modeling to enable organizations to make faster and more accurate decisions using their data. The post Salesforce To Buy Spindle AI To Expand Agentic Analytics Capabilities appeared first on Pulse 2.0.

If data is king, why do so many AI projects fail with "perfect" data? The gap isn't more data-it's context. As a student exploring enterprise tech, I'm fascinated by this shift. Companies like Elastic and Salesforce are proving a key point. Data alone doesn't make AI work. Context engineering is the missing piece: • Right data at the right time • Business logic integration • Domain expertise built-in • Proper guardrails and metadata Elastic's CEO calls LLMs the "new enterprise operating system." But without context? They're just expensive calculators. Salesforce's Agentforce gets this. They connect AI to customer data for real context. Workday is building Data Cloud to merge people and finance data. The pattern is clear. Context beats raw data every time. For us entering the tech world, this matters. We need to think beyond algorithms. We need to understand business problems first. Then build AI that solves them. What's your take? Have you seen AI projects fail despite good data? #AI #EnterpriseAI #ContextEngineering 𝗦𝗼𝘂𝗿𝗰𝗲꞉ https://lnkd.in/g4fcWHPz

95% of enterprises have yet to see ROI from their AI projects, with most stalling in the pilot phase. The reason? Flashy demos rarely translate into scalable, production-ready systems. Infrastructure gaps, unstructured data chaos, and governance blind spots create what many are calling the “prototype-to-production cliff.” The next evolution of AI success lies in infrastructure that’s built for production, not prototypes. Read more and learn how enterprises are transforming pilot projects into real ROI and why treating inference as a native data operation is the key to making AI work profitably: https://lnkd.in/eSbKjyrk #AI #EnterpriseAI

AI Platform Shift - Redefining the Friendship Between Enterprises and Technology ! - This shift is about “AI Systems” and not just “AI Models”.  - From AI Augmented to Agentic AI and becoming more and more autonomous. - It is where these AI Systems are adaptive, continuously learn and don’t remain “static”.   - It is about AI-Based Computing where intelligence integrates with the computing stack. - It is about having Human-AI “Choreography” be it the validation, established guardrails or reinforcement learning. ===> Finally it is about being AI-“Native” Organisation: * Where you Design, Build, Operate these AI Systems * Building AI-Native Teams * Have a data governance framework that manages “AI-Oriented Data” * Having a AI System Lifecycle process ===> So as you move up the this AI Platform shift maturity 1 to “X” level -> from Foundational to Transformational - the enterprise moves from:  “We use AI Models and Tools” → “We build AI Systems”. At the centre of all of this is AI System. The orchestrator that continuously learns, reasons, acts and improves -> powered by the four building blocks and 2 cross cutting concerns Below is the what defines the AI System building blocks: -> Data & Knowledge Foundation Layer: Fuel and context for intelligence ! - Description: Every AI system begins with high-quality, well-governed, and continuously refreshed data. - Key Dimensions: * Data Pipelines for structured and unstructured * Vector Databases  * Knowledge Graphs * Data governance -> AI-Oriented Data -> Model & Reasoning Layer: Core cognition and decision engine ! - Description: This is where intelligence resides from foundation models to specialised reasoning mechanisms. - Key Dimensions: * Foundation models * Fine-tuning, distillation, and adapters * Evaluation, monitoring, and continuous improvement -> Tooling, Action & Integration: Connecting AI System to the real world ! - Description: AI Systems do not just think they act, they need to integrate and take action across systems. - Key Dimensions: * AI Agents and orchestration frameworks * APIs / MCP Servers / Agent to Agent Communication * Context management and memory systems -> AI-Native Software Engineering: How modern AI systems are built, released and evolve ! - Description: AI-Native Teams that are skilled to Design, Build and Operate AI System Lifecycle. - Key Dimensions: * AI-assisted coding, testing, CI/CD * MLOps / LLMOps / DataOps / AIOps integration * AI governance, versioning, observability -> Governance, Safety & Trust: Cross Cutting Concern across all the “4 Foundational Boxes” ! - Key Dimensions: * Privacy & Security by design * Explainability and bias monitoring * Human in the loop validation -> Learning & Evolution: A continuously learning loop ! - Key Dimensions: * Reinforcement learning from human or operational signals * Synthetic data generation * Model refresh pipelines

Navigating the AI Rush? The landscape of AI has shifted dramatically, and for professionals grounded in systems design, coding, DevOps, SRE and operational optimization, it presents a unique opening. I have been doing transformation programs since 2008 first-hand and have witnessed how business challenges evolve, and how the right mix of strategy, process, and technology can turn risk into competitive advantage. What’s happening in the AI space? AI is moving beyond experimentation and into scale and value-delivery. One survey found that 46% of executives expect to scale AI to optimize core processes rather than just pilot new initiatives. Service industry is changing: clients now expect outcomes rather than just reports. Firms are leveraging AI-enhanced delivery, and hybrid models that combine human expertise + intelligent automation are becoming standard. The market for AI and AI-consulting is growing rapidly: organizations are seeking help with strategy, implementation, governance, and integration of multimodal/agentic systems. The emphasis is also shifting toward responsible, explainable, and real-world AI less about “could we build this?” and more about “how do we use this, safely and effectively?” Where my experience fits in Systems & process change: Having delivered large scale shipping and logistics systems (e.g., building a new system for shipping-audit, cost-predictability), I know how to architect end-to-end solutions that integrate technology, data flows and business workflows. Consulting + business translation: My background consulting at firms like Infosys, Ciber and Widenet means I’m fluent in bridging business needs and technology capabilities essential when AI is no longer a lab but embedded into business. Data-driven optimization: With hands-on experience working on database queries, audit functions, shipping cost models and operational platforms, I bring the mindset and skills to deliver measurable business value (not just proofs of concept). Startup + enterprise mindset: From building a shipping software with real-time rate comparisons, AI-driven optimization & integration with carriers, to structuring corporate equity, I can engage both strategic and execution levels helping companies move from “this is interesting” to “this is delivering”. My message to organizations exploring AI If you’re looking to move from “we’re thinking about AI” to “we’re doing AI” and you need a partner who understands business flows, knows how to embed new systems, and can move projects from strategy into live operations then I’d welcome a conversation. There’s huge opportunity now in aligning AI capabilities to real business problems (not just hype), and I’d love to explore how we might collaborate. Feel free to reach out directly, or I’d be happy to connect for a brief 15-minute discussion to see if there’s alignment.