Simform

Legacy modernization and AI production governance are not different but are the same infrastructure problem. At Red Hat Summit 2026, the announcement by @Microsoft and Red Hat around ARO (Azure Red Hat OpenShift) was not primarily about a new Kubernetes release. It was about a platform that runs legacy VMs and containerized AI workloads side by side, under the same identity controls, the same compliance policies, and the same operational model. There are major consequences for engineering teams running a sequential roadmap, meaning modernization in Phase 1 and AI governance in Phase 2. Because you are not running two projects. You are accumulating two separate compliance burdens. Governance-by-default is now the baseline expectation. The platform decision your team makes this quarter determines whether your AI workloads and your legacy estate share a compliance model or accumulate two. At @Simform, when we work with engineering leaders on migrations & modernizations, the constraint we keep encountering is not the migration tooling itself. It is the upstream assessment bottleneck: which workloads move first, at what refactoring cost, and in what sequence. 𝐍𝐞𝐮𝐕𝐚𝐧𝐭𝐚𝐠𝐞, our AI-powered modernization accelerator, is specifically built to compress that assessment cycle so teams can make the platform transition without the months of manual triage that typically slow it down. This does not mean modernization got easier. It means the cost of keeping it sequential just got higher. #MSPartner #MicrosoftPartner

Your first 10 AI agents worked. Your next 50 probably won't, and the reason has nothing to do with the models. The first wave of agents succeeds because informal governance holds them together. Someone knows what each agent does, roughly what it costs, what data it touches, and who owns it. That proximity creates stability. It is not a system; it is human attention acting as a system. Cross 50 agents, and that model breaks. No single person can hold the full picture anymore. Agent identity starts to blur. Ownership fragments across teams. Agents hand off work to other agents without structured contracts, so interaction volume grows faster than agent count. Enterprises end up paying for conversations no one designed, authorized, or measured. Meanwhile, access permissions quietly expand. Outputs accumulate with no traceable audit trail. And the AI spend that looked forecastable at 10 agents becomes unpredictable at 50. @Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, not because the agents fail to work, but because the operating model around them does not exist. At @Simform, we see this inflection point consistently. We built 𝐓𝐡𝐨𝐮𝐠𝐡𝐭𝐌𝐞𝐬𝐡 as a reference architecture specifically for what breaks here: agent identity, governance boundaries, evaluation, and cost attribution at fleet scale. Scaling agents is the easy part. Building the governance architecture that keeps 50 of them from compounding your risk is where the real engineering work starts.

We work with enterprises across BFSI, healthcare, retail, supply chain & more — in North America, the UK, EMEA, and beyond. 👇Read the full @MarketsandMarkets report here marketsandmarkets.com/Market-Reports… #ApplicationModernization #360Quadrant

In practice: → 70% reduction in quote generation time for a manufacturing enterprise → 30% lower manual operational overhead for a last-mile logistics platform

Simform has been recognized as a 'Pervasive Player' in the Application Modernization Services Market by @marketsmarkets on its 360Quadrants platform — one of only 35 vendors selected from 200+ evaluated globally. 🧵

Watch the full webinar here 👇 simform.com/lp/webinar-dwh…

Most organisations believe they're ready to adopt GenAI. But their data says otherwise. McKinsey found that 63% of Chief Data Officers report they don't have the right data foundation to adopt generative AI. Here's what that actually means for your organization

Before pushing for more AI-led automation in the data lifecycle, ask one question: Does your organization have clear ownership, enforceable quality standards, and enough observability to trust what is being scaled? That is where productivity becomes durable.

At @Simform, that is exactly how our data engineering teams work. Governance and quality policies embedded directly into pipelines. Not managed separately outside them. That is where productive automation and risky automation actually diverge.

Most enterprises are not struggling with a lack of data automation. They are struggling with slower, more expensive data execution as scale increases. Here is why #AIready #DataOps is the answer most teams are not ready for. 🧵

AI won't break your budget. It will expose whether you ever knew where your money was going. The question isn't whether your AI costs are growing. It's whether you can see them at the level where they actually compound.

This is engineering-led FinOps. For Azure teams, the gap between observability & actual spend governance gets closed at the infrastructure layer. Not bolted on at quarter-end. Built in from day one. That's the model we bring into agentic AI engagements at @Simform.

IDC projects AI infrastructure costs will run 30% over budget by 2027. Agent usage: 10x. Inference demand: 1000x. Cloud waste at baseline: 20 to 30%. AI doesn't create new cost problems. It amplifies the ones you haven't solved. Here's what's actually happening. 🧵