Oliver Steil’s Post

There is an interesting shift happening in enterprise AI right now. For the last couple of years, most of the conversation has focused on the models. Which model is fastest. Which model is cheapest. Which model performs best. Which company is ahead. But I think the more important battle is starting to move somewhere else. Into the workflow. The Information has been reporting on a few signals pointing in the same direction: OpenAI discounting models to gain developer traction. Anthropic’s Claude Code building strong enterprise habits. AI labs looking for proprietary workplace data. Routing platforms becoming more important. Software companies rethinking pricing and operating models around AI usage. The common thread is quite simple: The model is only one part of the product. The real enterprise AI advantage may increasingly come from: • developer behaviour • proprietary company data • workflow integration • model-routing infrastructure • distribution inside the organisation • and how deeply AI becomes embedded into everyday work Operationally, this matters. Because if most businesses eventually have access to similar AI models, then advantage will not come from access alone. It will come from how intelligently those models are used. That is a very different conversation to simply “which AI tool should we buy?” For SMEs especially, I think this is the lesson. Most businesses do not need more random AI experimentation. They need to understand where AI fits into the operating rhythm of the business. Which workflows should change? Which data should be connected? Which decisions should AI support? Which tasks should remain human-led? Which behaviours need to evolve? That is where AI starts becoming operational infrastructure rather than another tool subscription. The businesses succeeding with AI are usually not just chasing the latest model release. They are quietly redesigning how work gets done around the technology and that may become the more durable advantage. The operational model may matter as much as the AI model. Maybe more. Where do you think real AI advantage will sit: models, data, workflows, or distribution? #AI #ArtificialIntelligence #OperationalAI #WorkflowRedesign #FutureOfWork #AIAdoption #BusinessTransformation #Leadership #SME

From AI Experiments to Enterprise AI Over the past few days, we've shared our perspective on three foundational pillars of Enterprise AI: 🔹 Intelligence through Agents — bringing together fragmented enterprise data into a unified, actionable view. <https://lnkd.in/gGu5-H3F> 🔹 Execution through Orchestration — turning insights into coordinated actions across people, systems, and workflows. <https://lnkd.in/gJftg--R> 🔹 Continuity through Memory — preserving context, decisions, and organizational knowledge so AI becomes smarter over time. <https://lnkd.in/gj4wn8aA> Our biggest takeaway? Enterprise AI isn't just about deploying more models. It's about building systems that understand context, orchestrate complex processes, retain institutional knowledge, collaborate seamlessly with people, and continuously improve from every interaction. At MangoBytes AI, we're focused on helping enterprises move beyond AI pilots by building these three pillars—delivering Trust + Orchestration at Scale. What, in your view, is the biggest barrier preventing organizations from moving from AI experimentation to enterprise-wide adoption? #EnergyAI #AgenticAI #EnterpriseAI #ArtificialIntelligence #MangoBytesAI #MicrosoftAI #AIAutomation #FutureOfWork #BusinessTransformation Vijay Kasireddy Kamalakar Poonuru Gary Peterson David Martin Brett P.

What Does “Scaling AI” Actually Mean? Today, it often means: “We ran a successful pilot.” Which is a bit like saying: “We made toast once, so naturally we’re ready to run a bakery.” The reality in 2026 is more interesting. AI is moving beyond isolated experiments: >Nearly 9 in 10 organisations use AI in at least one business function. >44% say it is scaling across the enterprise. >Chatbots are becoming commonplace. >AI agents and coding agents are moving into production. >But only around 6% of organisations report the kind of performance that would make the CFO stop asking whether this is all just an expensive autocomplete subscription. And five years from now? Scaling won’t just mean more employees with access to AI. It will mean more: Compute. Data-centre capacity. Energy demand. Autonomous workflows. AI-generated software. Regulatory exposure. Decisions that need human oversight. The leadership challenge is no longer getting AI into the organisation IN 2031. It is making sure the organisation can grow around AI without becoming the least intelligent part of the system. Scale the capability. Scale the controls. And please, for the love of all things automated, scale the accountability too. #AILeadership #ResponsibleAI #AIGovernance #DigitalTransformation #FutureOfWork https://lnkd.in/g6BQD5ZV

The next step for enterprise AI isn't a better model. It's a smarter stack. Consumer AI adoption has been lightning-fast. Enterprise AI adoption has been... expensive. The real bottleneck isn't capability—it's ROI. Adoption isn't the problem. McKinsey found 88% of organizations already use AI in at least one business function. Returns are the problem: MIT's 2025 NANDA research found that 95% of generative AI pilots are producing no measurable return on the P&L, and most enterprises are still stuck in experimentation rather than scaled deployment. What's missing from most enterprise AI stacks? An intelligent model orchestration and routing layer. A control center that dynamically decides, for every task: ✅ High-consequence work → Frontier / closed models (maximum accuracy & safety) ✅ Low-consequence, high-volume work → Open-source / open-weight models (maximum cost efficiency) Without this layer, companies face a lose-lose: overspending on top-tier models for routine tasks, or risking quality by under-powering critical workflows. The architecture for profitable enterprise AI: • Data & Context: Clean, agent-ready data foundations • Hybrid Model Strategy: A blend of frontier and open models • Dynamic Routing: Real-time optimization of cost, performance, and risk • Process-Level ROI: Granular measurement tied directly to business outcomes This is the shift from "AI experimentation" to "AI as a managed business capability." At AI Clarity Labs, we help enterprise leaders design and deploy this exact stack—turning AI from an unpredictable cost center into a high-yield capability. The models are already powerful enough. The real edge belongs to the teams that learn to orchestrate them. Are you routing dynamically yet, or is your stack still running on default settings? Let's discuss below. #EnterpriseAI #AIStrategy #AIROI #ModelOrchestration #AIAgents #GenerativeAI #DigitalTransformation #AIClarityLabs

The conversation around AI has officially shifted. For the past two years, companies have been asking, "How can AI help us?" Today, the question is becoming, "How can AI do the work?" A recent article referencing Gartner's latest research highlights just how quickly agentic AI is moving into the enterprise. By the end of 2026, Gartner expects 40% of enterprise applications to embed task-specific AI agents, yet more than 40% of agentic AI projects are also expected to be cancelled by 2027. Why? Because building an AI agent isn't the hard part anymore. Turning it into measurable business value is. The companies seeing real results aren't trying to automate everything at once. They're focusing on high-value workflows, connecting AI securely to business systems, putting governance in place, and measuring outcomes instead of activity. Enterprise AI shouldn't stop at generating answers. It should execute workflows, orchestrate work across systems, and help businesses move faster with less manual effort. That requires more than a powerful model. It requires the right architecture, trusted data, secure integrations, and governance from day one. Great article: https://lnkd.in/dAdcm2ay

The conversation around AI has officially shifted. For the past two years, companies have been asking, "How can AI help us?" Today, the question is becoming, "How can AI do the work?" A recent article referencing Gartner's latest research highlights just how quickly agentic AI is moving into the enterprise. By the end of 2026, Gartner expects 40% of enterprise applications to embed task-specific AI agents, yet more than 40% of agentic AI projects are also expected to be cancelled by 2027. Why? Because building an AI agent isn't the hard part anymore. Turning it into measurable business value is. The companies seeing real results aren't trying to automate everything at once. They're focusing on high-value workflows, connecting AI securely to business systems, putting governance in place, and measuring outcomes instead of activity. Enterprise AI shouldn't stop at generating answers. It should execute workflows, orchestrate work across systems, and help businesses move faster with less manual effort. That requires more than a powerful model. It requires the right architecture, trusted data, secure integrations, and governance from day one. Great article: https://lnkd.in/dvyUSstF

Knowledge evaporates in enterprises. Not all at once. Quietly. A process gets automated, the AI performs well in month one, decently in month three, and by month six it's stagnant, while the business underneath it has already moved on. New exceptions. New edge cases. New "the way we do things now." The AI never caught up. It was never built to. This is the part of Enterprise AI nobody puts in the pitch deck, and it's the thing I see most often in the field: automation without a memory. A model gets deployed, it captures a snapshot of how the business worked on day one, and then the business keeps living while the model stands still. The knowledge that should have compounded, evaporates. Here's why it happens. Most AI Technologies are trained once, on general data, and pointed at a specific process. They were never built to learn the small, specific, high-value knowledge that only exists inside one company, the exceptions an experienced ops person spots in a second, the informal rule nobody wrote down, the pattern that only shows up in this business, this quarter. That knowledge is exactly what generic models can't see. And without a mechanism built to capture it, it never enters the system at all. This is the problem we've been solving inside ContextLab. Axiobit's experts are introducing a machine learning engine into ContextLab's architecture, built specifically to learn and continuously improve the AI processes running inside the enterprise. Not a model trained once and deployed. A learning system, paired with ContextLab's business process framework, that keeps absorbing the specialized, small-data knowledge of the business it runs inside; with a human closing the loop on every correction that matters. The result isn't AI that automates a process on day one and drifts by day ninety. It's AI that gets sharper the longer it runs inside your business; this is because the knowledge that used to evaporate is now the thing the system is built to hold onto. That's the unicity of the approach: not bigger models. A machine learning engine built to make sure enterprise knowledge never evaporates in the first place. #EnterpriseAI #MachineLearning #ContextLab #Axiobit #AI

95% of enterprise generative AI pilots show no measurable profit-and-loss impact. Yet enterprises that partner with specialists succeed roughly twice as often as those building entirely in-house. The gap isn't the model. McKinsey's 2025 research points to data readiness, integration complexity and change management: delivery problems, not model problems. So the vendor conversation needs to change. Stop judging a demo. Ask for three things instead: evaluation scores against a defined dataset, not a curated example set. Evidence of a system that has survived six months of real production traffic. And a straight answer on who owns the system after launch, with named accountability, not a support ticket queue. A partner with genuine engineering discipline answers all three directly. One that can't is asking you to fund their learning curve. What's the one question you wish more vendors could answer straight? Read more on our blog: https://lnkd.in/ejuNcz4k #GenerativeAI #EnterpriseAI #AIStrategy #DigitalTransformation #AIDevelopment

🚀 Is your business actually ready for AI transformation? The signs may be more obvious than you think - repetitive manual work, employees asking for better tools, or AI pilots that never quite made it into production. We break down 5 signs your organization may be ready to move from experimenting with AI to putting it to work. 🤖📈 👇 Read the full article and see where your organization stands. https://lnkd.in/g7wEgW4a

The State of AI in 2026: On the Road to ROI Agentic coding tools are moving from individual experimentation into deployment decisions, but employee usage alone is not evidence of enterprise ROI. As AI costs become clearer, deployment needs to be tied to measurable workforce benefits before it scales further. The question is how those benefits are being measured.