Moving analytics from a transactional database to a column store is a good decision that gets scoped as a data project and paid for as an application project. This is about the second half.
Build went from weeks to hours. The process did not move. This is the argument behind our 8 December session on what the AI-native SDLC becomes in 2027, and who runs it.
What is DevOps in software development? It's a way of working that merges software development (Dev) and IT operations (Ops) into one team with shared ownership of building, shipping, and running software.
Software development is the process of designing, building, testing, and maintaining software. Writing code is one stage of that lifecycle, not the whole job. This guide covers the SDLC, the skills and roles involved, real examples, and how AI changed the work in 2026.
AI coding tools look magical on a blank repo and break on the system that pays your salary. The gap is not model quality. It is grounding, and it is fixable.
Give every engineer an AI coding assistant and each one gets faster while the organization does not. The gap is not a tooling problem; it is a coordination problem, and orchestration closes it.
Rolling out AI coding tools isn't the same as making delivery AI-powered. A coding tool speeds up authorship; a lifecycle governs change. Here's why the difference decides whether software ships safely, and what a governed AI lifecycle actually looks like.
Most enterprise AI initiatives don't fail in the build. They fail in the gap between "the pilotworks" and "we're allowed to run it," and IDC found that only four of every 33 AI pilots ever reach production. This piece walks through the ten blockers that stop AI getting approved, from no one owning the decision to security reviews that run as open-ended investigations, and gives you the concrete move that clears each. The pattern underneath all ten is the same: approval is not a test of whether your AI is good, but whether you can prove it was controlled.
AI can help fintech teams modernise legacy systems, cut maintenance burden, and stretch scarce engineering capacity. But production fintech software touches money movement, customer data, fraud controls, and compliance, so a change that looks small in review can ripple across the business. The real question is not whether AI can change software, but what must be true before it is allowed to. This piece lays out the nine demands fintech leaders should make before AI participates in production change, from clear business intent and verified system context to human approval gates, test evidence, and accountable ownership, and shows why governed delivery not raw productivity, is the bar that matters.
The five questions security and risk teams ask before approving AI in your codebase, and a seven-point checklist to clear review the first time.
Most AI failures in companies aren’t because the model is dumb. They happen because the company’s knowledge is messy, scattered, and outdated. If your data is chaos, your AI will confidently give you wrong answers. The fix is not a better model. It’s a structured, governed knowledge base that AI can actually understand and trust.
Claude Code and similar AI coding tools genuinely make engineers faster, but speed alone doesn't guarantee better outcomes. The real variable is whether your system can absorb an increased rate of change. The same underlying problem shows up differently depending on who you are: technical leaders see loss of system coherence, business leaders see loss of delivery predictability. Most teams try to fix this with more tooling, better prompts, or better models, when what's actually missing is a governance layer that controls how changes enter the system.
Enterprise codebases will never fit cleanly into an AI context window, and the limits are structural rather than engineering problems waiting on the next model release. Scaling AI coding agents at system level requires a structured, continuously maintained representation of the system itself, exposed to the model as a queryable map and governed by lifecycle gates that prevent "almost-right" code from quietly breaking production.
When a senior engineer leaves, the context they carried walks out with them. AI tools operating on codebases without that context make dangerous assumptions. This article breaks down why system understanding is the prerequisite for AI-assisted development, and what technical leaders should do before pointing any AI tool at their code.
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