Know Today
👀 Keep an eye on
- Reusable AI skills become valuable when they capture reviewer corrections and edge cases around a repeatable deliverable.
- Small internal tools and prototypes are now cheap enough to manage as a backlog of reversible experiments rather than major projects.
- As generating options gets easier, selecting the right problem and defining a quality bar become the practical bottlenecks.
- Concrete work samples are likely to matter more in hiring and vendor evaluation as polished AI-written claims become easier to produce.
- Usage-reflection and wellbeing features in AI products need scrutiny of retention, access controls, and regional availability before workplace use.
🛠️ Practical workflows
| Pattern | How to use it |
|---|---|
| Start with a reviewable task | Choose a frequent deliverable with a clear expert approver, not an open-ended judgment call. |
| Log the correction | Keep the input, draft, reviewer edit, failure category, and accepted version so the workflow can improve. |
| Use reversibility as a guardrail | Move quickly on low-cost changes; require deeper review for irreversible, regulated, or expensive decisions. |
| Treat simulated feedback as rehearsal | Use an LLM to challenge framing and generate questions, then validate important assumptions with actual stakeholders. |
💭 Opinions worth testing
- Opinion: A “decision premium” may grow as implementation gets cheaper; invest in discovery, prioritisation, and evaluation rather than measuring progress by generated output alone.
- Prediction: Teams that build private feedback loops around valuable recurring work may develop a durable advantage; test this by measuring whether each review cycle reduces specific failure modes.
- Opinion: Proof of work may outweigh credentials in AI-saturated hiring; use a bounded work sample or portfolio review where practical.
⚠️ Caveats
- Reported product capability: AI usage-reflection features and their privacy boundaries require independent verification before adoption.
- The compliance-report example is anecdotal and provides no accuracy, cost, test-corpus, or escalation evidence.
- Automated stakeholder simulations can reinforce the assumptions in their prompt, so they are not evidence of user preference.
✨ Try this today
Build one narrow reviewer loop
Pick a recurring email, report, or support summary and ask an LLM to draft it from three past examples. Have a knowledgeable person edit it, then record each correction in a small spreadsheet; the expected outcome is a concrete map of where the task is actually automatable.
Run a reversible tool test
Make a tiny local page or script for one irritating internal step, such as formatting intake notes or checking a template. Test it with one intended user before adding integrations; the expected outcome is a quick signal on whether the friction is real.
Turn feedback into an evaluation set
Collect five representative inputs, including one awkward edge case, and write down what an acceptable result must contain. Re-run the same set after changing the prompt or workflow; the expected outcome is improvement you can inspect instead of a vague sense that it feels better.
Stress-test a proposal
Ask an LLM to play a skeptical user and list assumptions, objections, and missing evidence in a proposed change. Use the result to prepare interviews or a small user test, not as a substitute for either; the expected outcome is a sharper validation plan.