AI Snapshots

Short, practical lessons from using AI at work, one idea at a time. Each one ends with a takeaway you can use.

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20 snapshots

AI Snapshots· #001

Use Multiple GenAI Applications to Achieve a Goal

People are searching for the one AI tool that does everything. That's rarely how it works in practice.

The strongest results often come from combining tools rather than expecting one model to do everything.

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AI Snapshots· #002

STANDARDISE PROCESSES NOW

There's a big push to layer AI into existing processes as quickly as possible. But in most organisations, those processes aren't actually standardised.

AI cannot stabilise a process that is inconsistent before automation begins.

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AI Snapshots· #003

CONTEXT, CONTEXT, CONTEXT

A lot of the time when people say "AI isn't that good", it comes down to one thing. Lack of context.

When outputs disappoint, the missing ingredient is often relevant context.

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AI Snapshots· #004

PROMPT LIBRARIES AREN'T OPTIONAL

Prompting doesn't scale well when it lives in people's heads. Everyone has their own way, until it doesn't work.

Prompts only scale when useful approaches are stored, refined, and shared.

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AI Snapshots· #005

AI IS ONLY AS GOOD AS YOUR DATA

There's a tendency to focus on the AI itself, the model, the tool, the interface. But most of the issues show up before AI is even used.

Many AI failures begin with inconsistent or incomplete data.

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AI Snapshots· #006

MAXIMISING TOKENS DOESN'T MEAN BETTER OUTPUTS

More detail doesn't always lead to better AI responses. Better prompts aren't longer. They're clearer.

More prompt detail can dilute the result; relevance and structure matter more.

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AI Snapshots· #007

HUMAN IN THE LOOP STILL MATTERS

AI can support more decisions than ever. That doesn't mean it should be making all of them.

Human review can remain an intentional control when decisions affect people.

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AI Snapshots· #008

OUTPUT ≠ OUTCOME

AI can produce something impressive in seconds. That doesn't mean the job is done.

Polished output still needs review before it becomes useful.

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AI Snapshots· #009

THE HIDDEN COST OF “QUICK WINS”

A quick AI solution can solve today's problem while quietly creating tomorrow's.

Quick wins need ownership, reuse and consistency if they are going to scale.

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AI Snapshots· #010

AI GOVERNANCE STARTS EARLIER THAN YOU THINK

Governance isn't something to add once an AI solution is ready to go live.

Governance questions belong in design from the beginning.

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AI Snapshots· #011

PROMPTING IS A SKILL, NOT A TRICK

There isn't a magic collection of words that suddenly makes AI good at everything.

Better prompts come from clearer thinking, not secret formulas.

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AI Snapshots· #012

NOT EVERYTHING NEEDS AI

Sometimes the smartest AI decision is deciding not to use AI at all.

AI should be used where it adds value, not simply because it can.

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AI Snapshots· #013

CONTEXT SWITCHING KILLS AI VALUE

A useful AI tool can still create friction if people have to leave their work to use it.

AI is most useful when it sits inside the workflow, not beside it.

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AI Snapshots· #014

AI ADOPTION FAILS WITHOUT BEHAVIOUR CHANGE

Giving people an AI tool doesn't automatically change how they work.

AI adoption depends on new behaviours, not just access to new technology.

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AI Snapshots· #015

ONE USE CASE > TEN IDEAS

A long list of AI ideas can feel like progress. Sometimes one properly developed use case is worth far more.

One well-developed use case can teach more than a backlog of ideas.

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AI Snapshots· #016

SECURITY AND PRIVACY AREN'T OPTIONAL

The easiest way to give AI more context isn't always the right way.

Before adding context to a prompt, check whether the information belongs there.

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AI Snapshots· #017

SHARED PROMPT LIBRARIES VS PERSONAL HACKS

Some of the most useful ways of working with AI are probably sitting in someone's private notes.

Shared prompt libraries help teams reuse learning instead of repeating the same work.

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AI Snapshots· #018

AI WON'T FIX POOR REQUIREMENTS

If the problem isn't clear, adding AI won't make it clearer.

A clear requirement still has to come before an AI solution.

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AI Snapshots· #019

EXPLAINABILITY MATTERS

A convincing AI answer isn't automatically one you should rely on.

Confidence in the wording is not the same as evidence you can validate.

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AI Snapshots· #020

THE “LAST MILE” PROBLEM

Generating the answer is often the easy part. Making it useful in the real process is harder.

The value of AI depends on what happens between the output and the completed task.

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AI Snapshots

Choose a snapshot.

Use Multiple GenAI Applications to Achieve a Goal

STANDARDISE PROCESSES NOW

CONTEXT, CONTEXT, CONTEXT

PROMPT LIBRARIES AREN'T OPTIONAL

AI IS ONLY AS GOOD AS YOUR DATA

MAXIMISING TOKENS DOESN'T MEAN BETTER OUTPUTS

HUMAN IN THE LOOP STILL MATTERS

OUTPUT ≠ OUTCOME

THE HIDDEN COST OF “QUICK WINS”

AI GOVERNANCE STARTS EARLIER THAN YOU THINK

PROMPTING IS A SKILL, NOT A TRICK

NOT EVERYTHING NEEDS AI

CONTEXT SWITCHING KILLS AI VALUE

AI ADOPTION FAILS WITHOUT BEHAVIOUR CHANGE

ONE USE CASE > TEN IDEAS

SECURITY AND PRIVACY AREN'T OPTIONAL

SHARED PROMPT LIBRARIES VS PERSONAL HACKS

AI WON'T FIX POOR REQUIREMENTS

EXPLAINABILITY MATTERS

THE “LAST MILE” PROBLEM

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