🔵Developers Are 12 Months Ahead in How They Use AI. 8 practices business professionals should steal from Coding tools

👋 Welcome

Developers aren't necessarily getting more value from AI because they have better models.

They're learning a different way of working with them.

Open Claude Code, Codex, Cursor or similar coding tools today and you'll encounter concepts most office workers rarely use: Rules, Skills, Plan Mode, agents, subagents, checkpoints, sandboxes, tool access and autonomous verification.

Strip away the coding terminology and these are not software-development concepts.

They're a preview of how all knowledge workers will use AI.

Here are 8 worth stealing.

1. Stop prompting AI. Start onboarding it.

One of Cursor's strongest concepts is Rules. Instead of repeatedly telling AI how you work, you give it persistent instructions: standards, conventions, workflows, guardrails, and examples. Rules are being described as the information an agent should “always know,” similar to onboarding a teammate.

This is extremely transferable to business.

Most people currently do:

“Write an email to management. Make it concise. Don't exaggerate. Use our terminology…”

…every single time.

The more advanced pattern is:

AI onboarding document

• who I am

• what my team does

• terminology we use

• how executives like information presented

• things AI should never do

• examples of excellent outputs

• decision principles

• preferred structure

Then every task inherits that context.

This maps increasingly well to persistent instructions, project/workspace instructions, memory, custom GPTs/projects, Claude Projects, skills, etc.

2. Rules ≠ Skills — and most business users don't know the difference

A Rule is something AI should always follow.

A Skill is a repeatable process.

Example:

Rule

Never mix actual data and management assumptions without labelling them separately.

Skill

Monthly Forecast Review:

1. Compare forecast vs prior version

2. Flag material deviations

3. Investigate drivers

4. Separate one-offs from structural changes

5. Produce a management summary

6. List unresolved questions

This is more powerful than a saved prompt.

It's a reusable workflow.

Use it today

Create a folder called:

AI Skills

Add files like:

• forecast-review.md

• vendor-comparison.md

• meeting-prep.md

• research-check.md

A useful model is:

Prompt → one task

Rule → persistent behaviour

Skill → reusable workflow

Agent → AI that can execute the workflow

3. Plan first, execute second

Coding tools have a dedicated Plan Mode. For complicated work, the agent first researches the environment, asks questions, develops a detailed plan, lets the human inspect/edit it, and only then executes.

This is a fantastic business pattern.

For simple tasks, immediate answers are fine.

For difficult work, they can be dangerous.

Instead of:

Analyze why our forecast accuracy declined.

Use:

Explore → Plan → Critique → Execute → Verify

Explore
What do we know? What is missing?

Plan
What analyses should be done?

Critique
What could make this approach wrong?

Execute
Do the work.

Verify
Check whether the conclusion is actually supported.

Use it today

Start important tasks with:

Do not jump directly to the answer. First explore the information, create a plan, critique the plan, execute it, then verify the conclusion.

Sometimes better AI output comes from a better workflow, not a better prompt.

4. Give AI tools, not just information

Compare:

AI A

• brilliant model

• receives your prompt

• cannot access anything

versus

AI B

• slightly weaker model

• knows company rules

• can search SharePoint

• read CRM

• inspect dashboards

• query Snowflake

• create a PowerPoint

• send draft email

• check Calendar

AI B may be much more useful.

Think:

• company documents

• email

• calendar

• spreadsheets

• CRM

• databases

• presentations

A more realistic AI productivity formula is:

Model + Instructions + Context + Tools + Verification

Use it today

Ask yourself:

What am I manually copying into AI every week?

That is probably the next thing worth connecting.

5. AI should prove that the task is finished

Suppose you ask:

Clean this spreadsheet.

AI replies:

Done.

But what changed?

Did it remove 20 duplicates or 2,000?

Did it overwrite values?

Did it make questionable assumptions?

For important work, require evidence.

Ask AI to show:

• what changed

• what was removed

• assumptions made

• checks performed

• unresolved anomalies

• anything requiring human review

The principle is simple:

Done is not evidence.

As AI performs more work, verification becomes more important, not less.

The general concept is:

Completion ≠ verification.

Agents should provide evidence of work, not confidence.

For enterprise AI, that's huge.

6. Give agents a sandbox — not endless approval popups

Cursor encountered a fascinating problem: if AI requests permission for every action, people eventually stop properly reviewing those approvals. Cursor calls out the problem of approval fatigue and instead uses controlled/sandboxed environments where agents can operate relatively freely and only escalate when they need to cross important boundaries.

This has direct relevance for enterprises.

Companies often think AI governance means:

“Human approval before every action.”

But that can actually become bad governance.

A smarter model is:

Green zone: agent can act autonomously

• draft

• analyze

• reorganize

• search approved systems

Yellow zone: agent must show work / request approval

• contact customer

• change important records

• submit recommendation

Red zone: prohibited / privileged

• transfer money

• delete critical data

• alter access rights

That's a much better enterprise agent-control model than approve everything.

This could make a standalone newsletter.

7. Run several AIs at once instead of having one AI do everything sequentially

Multiple agents operating in parallel. Its cloud environment can run multiple agents independently, and plugins can include specialized subagents.

For normal business users, this feels surprisingly futuristic, but they can already imitate it.

Example: evaluating a €2m investment.

Instead of asking one AI:

“Should we invest?”

run:

Agent 1 — Market analyst
Estimate market attractiveness.

Agent 2 — Skeptic
Find reasons the investment will fail.

Agent 3 — Financial analyst
Challenge assumptions and economics.

Agent 4 — Customer perspective
Evaluate actual customer problem.

Then:

Agent 5 — IC chair
Read all four and synthesize the decision.

That is quite different from asking ChatGPT to “think harder.”

And it introduces one of the concepts business users will increasingly encounter:

AI orchestration.

8. When AI keeps making the same mistake, change the system — not the prompt

A coding tool gives surprisingly good advice here:

Add Rules when you notice the agent making the same mistake repeatedly.

That's almost a management philosophy.

Business users currently correct AI like this:

“No, don't use those bullet points.”

Next day:

“I said don't use those bullet points.”

Next week:

“Again…”

The agent made the mistake three times.

At that point, the problem isn't the latest output.

It's that your AI operating system has not learned the correction.

So a useful business workflow becomes:

Mistake → correction → determine whether it should become a Rule / Skill / template / validation check.

That is exactly how organizations should gradually build their internal AI operating layer.

Build context instead of repeatedly explaining yourself.

AI quality depends on more than the model.

It also depends on what the AI knows about your work.

Create a lightweight AI workspace:

AI_INSTRUCTIONS.md
How AI should work with you.

CONTEXT.md
Important business or project context.

DECISIONS.md
Important decisions already made.

/SKILLS
Reusable workflows.

/EXAMPLES
Examples of excellent outputs.

Then give AI only the relevant files for the task.

The bigger shift

AI work is moving through four stages:

1. Chat

Human works. AI helps.

2. Reusable AI

Human provides persistent instructions, context and workflows.

3. Agentic work

AI performs substantial parts of the task using tools.

4. Orchestration

Multiple agents perform work while humans govern exceptions and decisions.

That means one of the most valuable AI skills may not be prompt writing.

It may be designing:

• what AI knows

• what it should remember

• which workflows it can reuse

• what tools it can access

• where it can act

• how its work is verified

That is much more durable than collecting 50 clever prompts.

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