Delo Revolutionizes AI Adoption with Context

I read an interesting article in TechCrunch this week about Anthropic launching a new push into enterprise AI agents, including plugins designed specifically for Finance, Engineering and Design teams. What stood out to me, particularly given my network, was the focus on Finance and Accounting use cases. Anthropic is positioning its enterprise agents to move beyond simple chat functionality and into multi step workflows. In practice, that means AI agents that can access approved systems, pull relevant financial data, perform analysis, and generate outputs such as reports or summaries within existing business environments. For finance teams, the emphasis is on controlled deployment. The article highlights enterprise grade governance, private plugin management and administrative oversight. This is clearly aimed at larger organisations that need security, auditability and compliance wrapped around AI adoption. The broader shift here is from AI as a productivity assistant to AI as a task owning agent embedded into operational workflows. For those building or selling into finance functions, this feels significant. If AI agents can reliably support areas like financial analysis, reporting and internal knowledge retrieval, the structure of finance teams and the skills they prioritise may evolve quickly. Curious to hear how others in Finance or Accounting are thinking about AI agents right now. Are you seeing experimentation, resistance, or structured rollout plans? Full article in the comments!

🚀 From AI Experiment to AI-Native Organization in Finance Many financial institutions are piloting AI, but scaling from experiments to structural value creation is the real challenge. Dennis Joosten (Head of Banking at EPAM) outlines seven best practices to make AI truly transformative in the financial sector: 1️⃣ Balance automation with human oversight – AI accelerates workflows, but supervision ensures compliance and trust. 2️⃣ Test data maturity with AI – fragmented data and legacy systems quickly surface when scaling. 3️⃣ Redesign processes before adding AI – automation only works if workflows are optimized first. 4️⃣ Link AI to business objectives – measure impact beyond ROI, focusing on outcomes like speed, accuracy, and customer experience. 5️⃣ Deploy agentic AI for scalable value – support multi-step decision-making across client management, risk, and portfolio analysis. 6️⃣ Mitigate risks with strong governance – privacy, compliance, and data quality must be embedded in controls and monitoring. 7️⃣ Choose models that fit the work – smaller, domain-specific models often deliver more practical and sustainable value. 💡 The takeaway: Becoming AI-native isn’t about technology alone—it’s about aligning data, processes, and people into a cohesive strategy. Financial institutions that treat AI as a strategic foundation, not just a tool, will unlock sustainable transformation. 👉 How is your organization approaching the shift from AI pilots to AI-native operations? #AITransformation #FinancialServices #AINative #DataGovernance #FutureOfFinance #BusinessTransformation #DataGovernance #ProcessRedesign #AgenticAI #DigitalInnovation #FutureOfFinance #EPAM

Everyone in finance is talking about AI. But there's a conversation most executives are avoiding. AI is only as good as the data behind it ,and in my experience, most finance teams aren't there yet. Fragmented systems. Inconsistent definitions for basic things like revenue and cost. Spreadsheet workarounds that nobody wants to admit exist. When the foundation is shaky, AI doesn't generate insight. It just automates the mess. Before doubling down on AI tools, I think CFOs need to get honest about three things: - Data quality: Do your KPIs mean the same thing across every system and team? -System integration: Are finance and operations actually talking to each other, or are people still emailing exports? - Process discipline: How many manual adjustments are quietly distorting your numbers each close cycle? The real opportunity for most finance functions isn't AI itself. It's building the data foundation that makes better decisions possible, with or without AI. The companies that tackle the data problem first will be the ones who actually capture AI's value. The rest will end up with faster dashboards built on numbers nobody fully trusts. So before asking "do we need AI?" Ask: "is our data ready for it?" That's usually the harder and more important question.

It took 4 days to build something that would normally take weeks. There’s a lot of noise about AI in development right now — productivity gains, self-writing code, automated tests, even self-generated requirements. Over the past few days, I used AI to build a full ETL lineage and execution-order extraction framework — parsing package XML, resolving dependencies, mapping data flows, and computing execution chains across projects. This isn’t something we normally build as part of standard delivery. It was necessary for a specific initiative. Without AI, this would typically have been a complex effort involving several people and a multi-week timeline. Instead, it took four days, not because the problem was simpler, but because AI was used as an execution accelerator. I still had to: • define the architecture • break the problem into components • validate edge cases • challenge incorrect assumptions • tune performance when recursion exploded • ensure the result was production-ready AI didn’t replace judgment, it reduced friction. The real shift isn’t that “AI writes code.” The shift is that experienced professionals can compress delivery cycles dramatically — if they know how to guide it. After 20+ years leading complex data transformations, I see this clearly: The advantage isn’t technical syntax. The advantage is structured thinking and AI amplifies structured thinking. I’m now exploring how AI agents can support build and test automation for data platforms. It’s exciting to see how small teams can use agents to deliver enterprise-level solutions — and how this may reshape governance, redefine skill requirements, and change how we think about team size and delivery capacity. More on that soon.

Most AI initiatives never make it to production. After 50+ implementations, we've identified the exact failure patterns—and the playbook companies use to beat the odds.

The real cost of AI isn't the model — it's the integration tax. Every team building with LLMs hits the same wall: you start with one provider, then need another for embeddings, a third for image generation, and suddenly you're managing 5 API keys, 5 billing dashboards, and 5 different error-handling patterns. This "integration tax" silently eats 30-40% of engineering time on AI projects. Not building features. Not improving prompts. Just... plumbing. Here's what the most productive AI teams are doing differently in 2026: → Single gateway, multiple providers. One endpoint, one key, one billing view. Switch models with a config change, not a code rewrite. → Automatic fallback chains. When Claude is rate-limited, route to GPT. When GPT is down, route to Gemini. Zero downtime, zero pager alerts. → Cost-aware routing. Send simple tasks to cheaper models. Reserve frontier models for complex reasoning. Same API call, intelligent dispatch. → Usage analytics across all providers. Know exactly which models deliver ROI and which are burning budget on tasks a smaller model handles fine. The teams shipping fastest aren't the ones with the biggest budgets — they're the ones who eliminated the integration tax entirely. We built CrazyRouter to solve exactly this. 400+ models, one API, pay-as-you-go with no markup surprises. Stop managing providers. Start building products. 🐦 Follow on X: https://x.com/metaviiii 💬 Join Telegram: https://t.me/crzrouter 🌐 Try free: https://crazyrouter.com

Five common real-time AI failures—blind spots, weak testing, poor data, legacy integration, and missing monitoring—and clear, practical fixes to scale safely.

Many AI projects perform well as pilots but stall when it’s time to scale across products, teams or markets. https://hubs.li/Q047rPdS0 Written by Ankit Agrawal of Equifax

📝 What Building AI Systems Taught Me About SME Data Most conversations about AI in SMEs start with tools. But after building and experimenting with AI systems, I've realized something: The real constraint isn't AI. It's data reality inside SMEs . Here are a few patterns I keep seeing: 1️⃣ Data exists — but it's fragmented Financial data in one system. Customer data in another. Operations somewhere else. AI doesn't fail because of lack of data. It fails because the data is disconnected. ========================= 2️⃣ Most data is not decision-ready Reports are built for: • accounting • compliance • record-keeping Not for real-time decision-making. There's a big difference between: Having data vs Having actionable signals ========================= 3️⃣ Manual processes break intelligence flow Critical workflows still depend on: • copy-paste • manual exports • human interpretation This introduces delays, errors, and blind spots. ========================= 4️⃣ Context is missing AI models don't just need data. They need business context. What matters? What's normal? What's risky? Without this, AI produces noise — not insight. ========================= 5️⃣ The biggest gap is not technical — it's architectural Most SMEs don't need more tools. They need: • structured data flows • integrated systems • defined decision points In other words — infrastructure. ========================= Building AI systems has made one thing clear: AI is not the starting point. Architecture is. And the SMEs that understand this early will unlock disproportionate advantage. #AIForSMEs #DataInfrastructure #DecisionSystems #Automation #FounderInsights

Most AI teams in regulated industries fail at the same five things. I've started calling them TRUST. After four years of shipping AI systems in Financial Services, the failure modes are remarkably consistent. The model works and the demo impresses stakeholders. But then the project stalls, or worse, goes live and quietly degrades because nobody built the system around it with the same discipline. The gaps almost always fall into five categories: Testing: Teams test the model but not the system. Evaluation stops at accuracy metrics and never covers preprocessing, integration behaviour, or post-deployment regression. The result is that silent failures go undetected until someone does a manual audit. Risk: Governance gets treated as a gate at the end instead of a design input at the start. Teams arrive at risk review without the evidence, documentation, or shared language needed to get sign-off. Projects stall not because governance is slow, but because the engineering team didn't build for it. User workflow: The system works in isolation but breaks when it meets real operational context. Human oversight requirements, escalation paths, and feedback loops are afterthoughts rather than first-class design decisions. Spend: Cost modelling happens after launch, if it happens at all. Token costs, infrastructure scaling, and third-party API pricing are treated as finance problems instead of engineering design constraints. By the time the bill arrives, the architecture is locked in. Telemetry: If a system cannot be observed, it cannot be operated. Most teams ship with basic uptime monitoring and little else. When behaviour drifts or a component fails silently, there is no signal to catch it. Some teams have manual reviews but this adds operational overhead. Each of these is individually manageable. The challenge is that most teams address them reactively and in isolation, rather than treating them as a connected set of production concerns that need to be designed for upfront. TRUST is the framework I use to make sure none of them get missed. Testing, Risk, User workflow, Spend, Telemetry. Five pillars, each with specific questions that need answering before a system is production-ready. The diagnostic is simple: if your team cannot confidently answer questions in all five areas, you have a prototype, not a product. Which of these five is the biggest gap in your organisation? #AIEngineering #MLOps #ProductionAI #FinancialServices