Simor Consulting
Category: AI Enablement
AI projects have a scoping problem. Teams either scope too loosely, "use AI to improve customer experience", or too tightly: "build a transformer model with 12 attention layers for intent classificati
Post-mortems are useful. Pre-mortems are cheaper. A post-mortem tells you why a project failed after the money is gone. A pre-mortem tells you why a project might fail while you can still change cours
An executive looking at an AI dashboard does not want to see loss curves. They want to know if the AI system is doing its job, whether it is trustworthy, and what happens when it is not. Translating A
The decision to centralise AI infrastructure into a platform team usually comes after a period of decentralised pain. Three product teams independently built model serving pipelines. None of them shar
Code completion gets the attention, but it is the narrowest part of what AI can do in a development workflow. Walk into any team that has shipped software for a few years and they will tell you: writi
Professional services firms sell judgment, billed by the hour or by the matter. That makes them both the biggest winners and the most cautious adopters of AI. The upside is real: every firm carries ho
If you run a small business, you have heard the AI pitch a hundred times. Most of it is aimed at enterprises with data teams, seven-figure budgets, and a CIO to translate. That framing is now out of d
Most RAG systems are evaluated with vibes. An engineer runs ten queries, eyeballs the results, and declares the system "working." Three months later, a customer reports that the system confidently ret
Most organisations have attempted some form of AI initiative. Some succeeded and delivered measurable business value. Many failed and produced results that were technically interesting but did not mov
The first Head of AI Engineering at a company inherits one of three situations. Situation one: there is no AI team, no AI infrastructure, and the mandate is to build from scratch. Situation two: there
Organisations that skip readiness assessment before investing in AI tend to discover their gaps expensively. A financial services firm spent four months building a customer churn prediction model only
Prompts are not prompts in the casual sense of suggestions or starting points. They are software. They take inputs, produce outputs, have failure modes that manifest in specific conditions, and requir
Prompt management in most AI teams starts the same way. One engineer writes a prompt, it works well enough, and the prompt gets committed to a config file. Three months later, there are forty prompts
A technology company built an impressive AI platform. They had GPU clusters, fine-tuning pipelines, evaluation frameworks, and a growing model registry. They opened access to any team that wanted to u