The Wash-Up is a weekly newsletter for project managers, account managers, and delivery leads working inside agencies.

Every edition takes one real problem and gives you something you can use that week. No fluff. No AI hype. No advice that only works if you have zero clients and infinite time.

• This is for you if you’re juggling multiple projects, tight timelines, and shifting priorities. And trying to figure out where AI actually fits in your work.

• This is not for you if you’re looking for get-rich-quick automation tricks or motivational filler. There are plenty of those. This one is different.

What you get each week

• AI for project managers - How to use AI on real projects without creating more work or compliance problems.

• Automations that actually work - Real workflows you can copy. Client reporting, status updates, project check-ins. Built in n8n, not theory.

• Career growth for PMs - From task manager to strategic operator. The skills AI can’t replace.

• AI product strategy - Building AI tools and process inside agencies. What actually works in trenches.

• Problem-solution delivery - One PM problem. One clear solution. Every week.

If any of this sounds familiar, you’re in the right place:

1. Someone told you to “use AI” but nobody showed you how. The few times you tried, it created more work.

2. You spend hours formatting reports, writing briefs, and chasing approvals instead of doing the work that actually matters.

3. You’re switching between projects, emails, Teams, and meetings so often that your brain never gets a clear run at anything.

4. Scope creep is a weekly event. Clients change requirements. Stakeholders shift priorities. You absorb it all and smile.

5. Every project starts from scratch. No systems. No templates that anyone actually follows. No repeatable process.

6. You’re reactive. Always fixing problems. Never ahead of them.

7. You’re working harder but not getting further. The promotion feels distant. The burnout feels close.

8. You know AI is here to stay. You’re not scared of it. You’re just worried you’re falling behind the people who’ve already figured it out.

9. You tried ChatGPT, got vague answers, and wondered what all the fuss was about.

10. You want to get better at your job but you genuinely don’t have time to figure it all out alone.

If you nodded at three or more of those — stay.

Who am I?

I’m Tim. 20+ years in product, digital delivery and UX/UI across healthcare, finance, insurance, motorsport, and FMCG.

Currently AI Product Manager inside a 300-person global healthcare marketing agency, building AI-powered products, systems and workflows for AI transformation and real client work.

From managing teams of project managers and developers to being the person in the room when the deadline was absurd and the scope was growing. That’s the experience behind every edition.

Start here

If you want to see what The Wash-Up looks like before you commit, here are some of my most-read editions:

• AI Isn’t Useless In Project Management. Your Project Data Is — Why PMs blame AI when the real problem is what you’re feeding it.

• You’re Probably Using ChatGPT Wrong for Client Work — ChatGPT vs. Projects vs. Custom GPTs. A clear decision framework.

• 7 Steps to Build AI Workflows That Remember Your Business — A walkthrough for building context-rich AI automation workflows.

• Junior PMs Using AI Can Now Produce Senior-Looking Work. So What’s The Difference? — What actually separates junior from senior when AI levels the output.

• Why Your Project Reports Are Putting People to Sleep — How to turn metrics into stories that make stakeholders care.

Learn how to run an automation project

The Problem

Most agency PMs know automation can help. But they don’t know where to start.

The tools look complicated. The tutorials assume you’re technical. And who has time to figure it out when you’re already drowning in client work?

So the same tasks keep eating your hours. Week after week.

The Solution

I’ve created a Notion guide that shows you exactly how to run an AI-powered automation project from start to finish.

No coding. No jargon. No assumptions that you’ve done this before.

Just a clear, practical playbook written specifically for agency PMs who want to stop doing repetitive work and start leading automation projects.

What’s Inside?

7 phases. Every step covered.

• Discovery — How to understand the problem before jumping to solutions

• Scoping — How to define what you’re building (and what you’re not)

• Workflow Design — How to map the automation so anyone can understand it

• Build — How to manage the technical work (even if you’re not doing it yourself)

• Testing — How to make sure it actually works before it goes live

• Handover — How to deliver it properly so it doesn’t fall apart

• Maintenance — How to keep it running and improve it over time

Plus:

• Ready-to-use checklists for every phase

• 30 templates you can copy and customise

• 82 AI prompts to speed up your workflow

• A real example project

Get your full AI Powered Automation Project Guide

The adoption numbers for AI in project management look strong.

70% of project management organisations now use AI — up from 36% just two years ago1. Leadership calls it a transformation. But the same research shows only 12–22% of project managers are using AI in any meaningful, practical way23.

Here’s what the research actually shows.

The adoption gap is wider than it looks

Most adoption isn’t being driven by PMs. It’s being driven by executive pressure — senior leadership wants better risk signals, faster reporting, and cleaner visibility4. PMs are responding to that demand, not pioneering AI independently.

55% of businesses say adding AI functionality is the top reason they’re buying new PM software. 41% of those same businesses cite AI adoption challenges as their biggest software problem . The AI market for project management is projected to grow from $5.3 billion to $14.1 billion by 2030.

Adoption intent is high. Practical value is not keeping pace.

How PMs are actually using AI

Meeting intelligence is the entry point. Tools like Fireflies.ai, Granola, and tl;dv run alongside existing workflows without requiring any process redesign5. Transcription, action item extraction, and decision logging are where most PMs start.

Status reporting and risk flagging come next. 50% of PMs in the APM 2025 survey say AI has improved their task scheduling, resource allocation, and risk analysis work. Status summarisation and executive brief generation are the most consistent use cases across sectors.

Platform-embedded AI — Copilot in Microsoft 365, ClickUp AI, Asana AI — is growing faster than standalone tools. PMs use AI that operates inside their existing stack. Adding a separate app that requires copy-pasting data doesn’t stick.

One finding that doesn’t get enough attention: junior PMs are gaining the most. One study recorded a 43% performance improvement for junior staff, against 17% for experienced staff. AI is levelling up people who lack established patterns. For senior PMs, the returns are measurably smaller.

Why it’s not working for most PMs

The skills gap is structural, not motivational. Only 20% of project managers report good practical AI skills6. 39% of PM teams lack AI competency altogether. 61% of employees spent fewer than five hours learning about AI; 30% received no training at all7.

Vague prompts produce vague outputs. The back-and-forth to fix them creates drag, not savings.

Trust is a hard ceiling. 77% of businesses are concerned about AI hallucinations8. The black box problem — not being able to see how an output was generated — means PMs keep AI on low-stakes tasks: meeting notes, summaries, admin. They won’t extend it to risk assessments or client-facing deliverables without heavy manual checking [8]. That’s exactly where the highest time savings are available.

Workflow friction is constant. 36% of PMs say fitting AI into existing workflows is a major barrier. AI features are added to existing platforms without redesigning the workflows they sit inside. AI ends up as an overlay, not an integrated part of the system.

The problem most people aren’t naming: AI raises output expectations without reducing workload. Faster artefact production signals to leadership that more is possible. The bar rises. The pressure doesn’t fall. PMs in multiple community threads describe the experience as doing more admin, just faster9.

What PMs need that doesn’t exist yet

The full autonomy loop.

PMs want AI that goes from detecting a risk through to a revised schedule, reassigned tasks, and a drafted stakeholder update — without manual hand-off at each step. Every current tool stops at the flag and waits for a human decision10. That complete loop doesn’t exist in any platform in production as of 2026.

Client and team context.

Generic AI outputs ignore organisational history, undocumented decisions, client communication styles, and relationship dynamics11. Capterra’s 2025 survey surfaced this directly: “These tools have zero understanding of client mannerisms or team velocity”. For agency PMs managing multiple clients simultaneously, this is not a minor gap.

Cross-portfolio resource visibility.

PMs need a continuously updated view of capacity across the full project portfolio — one that flags emerging overcommitment before it becomes a crisis. Current tools give point-in-time snapshots. None connect risk detection, capacity data, cross-project dependencies, and stakeholder communication in a single workflow12.

Lower configuration cost.

95% of generative AI pilots don’t reach production deployment. Tools that require extensive setup before they function usefully are not viable for time-poor delivery teams. One documented case: a PM saved 20 minutes on a 60-minute task using ClickUp Brain — but only after significant upfront configuration work.

None of these are edge cases. They’re the core of the job.

The bridge is the problem

The use cases are documented. The tools exist. Adoption intent is high.

What’s missing is the practical bridge between “AI is available” and “AI is reliably useful in my day-to-day delivery work.” That bridge doesn’t get built in a vendor demo or an executive mandate.

It gets built one workflow at a time.

Share The Wash-Up

A hiring manager posted on Reddit last week.

He’d opened a new role. Within days, 400 applications landed. Every single resume had been run through an LLM — optimised for the job description.

Every candidate sounded like a perfect fit.

He couldn’t trust any of them.

He then used AI to screen the pile. It pulled the most gamified resumes to the top. The system designed to find the best candidates rewarded the ones who’d gamed it hardest.

His one piece of advice, buried at the end:

Job hunting like it’s 1999

When I left university in 2001 with a degree in Graphic Design, LinkedIn didn’t exist, the internet was just getting started and it was hard enough to get your foot in the door.

Sounds like the same today right!??

My options we’re:

• Work for free and (hope to) get offered a junior position

• Use a recruitment consultant

• Look through job ads in papers (yep they used to be a thing)

• Approach a business directly — write to them. Turn up at their door. Pull some clever stunt to get their attention.

• Hope you had a “connected” family member

The actual route to my first agency was to get some experience in another industry (sales & account management) via a recruitment consultant and I then found a job ad for a small shop in London, selling websites.

Nothing to do with Graphic Design I here you say!

Fast forward and most of these options are still there, but the internet is saturated, applicant management systems (the recruitment backend) are like corporate guard dogs with AI screening people — it’s harder and more difficult than ever.

Not to mention the economic climate we live in right now.

AI has closed the gap between how strong and weak candidates look on paper

If every CV mirrors the job description perfectly, a hiring manager has no signal left. Everyone sounds qualified. No one stands out.

Of course, a CV is a must and you need one to get through the system. But the system no longer picks winners.

This is where people have been doubling down. Tweaking the formatting. Adding more keywords. Sticking it through another run of their favourite LLM. Playing a game where the rules reward the most artificial version of themselves.

The hiring manager on Reddit said it plainly. He skips the over-optimised ones entirely. The people getting through are the ones he already knows — or the ones someone he trusts has referred to them.

The smartest career move in 2026 isn’t a better resume. It’s making the resume optional.

You Already Have a Referral Network. You’re Just Not Using It.

If you work in an agency, you’re sitting on something most industries don’t have: a super-charged referral network. You see, we’re a pretty sociable bunch.

Agency people move. A lot. The strategist you worked with two years ago is now at a different shop. The creative director from that pitch is client-side. The developer who left last year just joined a startup.

Your ex-colleagues are scattered across 10 or 15 different companies. That’s your hiring network right there.

I’ve been in agencies long enough to see this play out a lot, to my own advantage.

A role opens, and the hiring manager’s first instinct isn’t to post it on LinkedIn. It’s to ask the team: “Do we know anyone good?”

Most of us understand this intuitively. But we treat it passively. We assume people will think of us when the time comes. We don’t invest in being thought of.

Your network already exists. The question is whether anyone in it would vouch for you right now — and whether they’d know what to vouch for.

Being Referable vs. Being Connected

These are not the same thing.

Being connected means you have a large network. You accept every LinkedIn request. You’ve met a lot of people.

Being “referable” means someone would put their own reputation on the line to recommend you. That’s a different thing entirely.

Three qualities make someone referable:

1. You deliver reliably

You do what you say you’ll do. You hit deadlines. When things go sideways, you flag it early. You’re the person people go to when things are getting spicy.

People remember the person who didn’t drop the ball when it mattered.

2. You’re easy to work with

You handle pressure without making everyone around you miserable. You communicate clearly. You don’t create drama.

People remember how you made the project feel, not just the deliverables.

3. You’re memorable for something specific

Not “good at everything.” Good at *something*. The person who’s brilliant at client workshops. The one who makes complex data make sense. The one who keeps the project moving when everyone else is stuck.

When someone says “we need someone who can...” your name comes up.

A thousand LinkedIn connections won’t get you referred. These three things will.

How To Stay Top of Mind (Without Being Annoying)

Being referable only works if someone thinks of you at the right moment. That means staying in touch.

Stay in loose contact

You don’t need monthly catch-ups. An occasional message. A genuine congratulations when something goes well for them. Enough to stay on the radar — not so much it feels transactional.

Show what you’re working on

When people see your work, they remember what you’re good at. Share a case study. Talk about a problem you solved. This doesn’t require a “personal brand” just post some messages up on LinkedIn).

It requires being visible enough that people can connect your name to a skill.

Be specific about what you want

“I’m open to opportunities” is too vague to act on. “I’m looking for a senior strategist role in a healthcare agency” gives someone something to work with.

When you ask for an intro, make it easy

Name the person. Name the role. Give them a short blurb they can forward. And offer an out: “Totally fine if it’s not the right time.”

You’re not asking for a favour. You’re giving them a low-effort way to help.

Refer other people first

This is the fastest way to become someone who gets referred. When you see a role that suits someone in your network, send it to them. Generosity in a small industry compounds quickly.

One more thing. Don’t go from zero contact to “can you refer me?” That’s like putting another hole in your already leaky bucket.

Start now, not when you need it.

The Long Game

The CV arms race will keep escalating.

AI will keep making applications shinier.

Hiring managers will keep trusting them less.

The 400-applicant pile will grow to 800.

None of that matters if you’re not in the pile.

But the people who get hired in 2026 won’t have the best-optimised CV. They’ll be the ones a trusted colleague mentioned over coffee. The ones whose name came up in a Teams channel. The ones who didn’t need to apply because someone said: “I’ve worked with them. They’re good.”

And don’t forget your referral fee!

One thing to do this week:

Message 3 former colleagues you haven’t spoken to in a while. No agenda. No ask. Just reconnect and see how they’re doing.

PMs are calling AI useless.

There’s a thread on r/pmp right now with dozens of replies agreeing.

They tried the tools. Got generic output. Moved on.

They gave ChatGPT a project brief and got back four paragraphs of corporate filler. They asked Copilot to summarise project status and it pulled from documents nobody’s touched since sprint two. They pointed AI at their Jira board and got a summary that missed every decision that mattered.

They’re not wrong about the experience. They’re wrong about the cause.

The complaint is valid. The diagnosis isn’t.

Every PM who’s called AI useless ran into the same wall.

They gave AI almost no context. Then expected useful output.

You open ChatGPT. Type “write me a stakeholder update for my website migration project.” Get four paragraphs of vague, boardroom-ready nothing. Close the tab. Tell a colleague AI doesn’t work for PM stuff.

That loop is real. I’ve seen it happen with PMs in my own agency. But it’s not a tool problem. It’s their input.

The quality of what AI produces is directly tied to how much it knows about your project. Not projects in general. Your project. Your stakeholders. Your risks. Your decisions from last Tuesday.

Most PMs give it none of that. No project background. No stakeholder names. No recent decisions. No known blockers. Then they’re surprised the output reads like it was written by someone who’s never been in a standup.

Here’s the uncomfortable bit. If your project documentation is scattered, outdated, or lives in people’s heads, that was a problem before AI showed up. AI just made it impossible to ignore.

The PMs who already had decent documentation discipline? They’re getting useful outputs.

Your project knowledge isn’t where AI can reach it

Think about where your project information actually lives right now.

It’s in Teams threads from three weeks ago. Email chains between you and the client. A Confluence page someone started and never finished. Meeting notes in a Google Doc that four people can access. A RACI matrix in a spreadsheet that hasn’t been updated since sprint two.

And the most important stuff? Decisions made verbally. In a corridor. Over coffee. Never written down.

AI doesn’t know any of that.

This is why enterprise tools are struggling. Microsoft pushed Copilot into organisations from the top. No specific PM workflows. No training on what to feed it. There’s a thread on r/CopilotPro titled “No One is Using CoPilot.” Hundreds of knowledge workers with a tool they can’t get value from.

But on the same platform, someone shared 18 specific Copilot prompts for project leaders and cost controllers. Meeting prep. Cost tracking. PMO briefings. That person spent time understanding what the tool actually needs. And it’s working for them.

Same tool. Different operator.

So what does the fix actually look like?

A project knowledge hub. One folder per project.

Everything AI needs in one place-it’s that simple!

Not a massive documentation overhaul. Not a new tool. A simple folder with all your files you probably already have in some form — just scattered across different platforms.

Here’s a starter structure:

• project-brief — purpose, scope, key dates, success criteria

• stakeholder-map — names, roles, communication preferences, who signs off on what

• decisions-log — what was decided, when, by whom (the one everyone skips and everyone regrets skipping)

• status-updates — weekly, consistent format, same place every time

• meeting-notes — summarised after each meeting, stored in the hub (not buried in email)

• risks-and-blockers — live document, updated weekly

You already know most of this. You just haven’t put it in one place.

Here’s the killer move. Connect AI to that folder. Point Claude, ChatGPT, or Copilot at your project hub and suddenly it has everything it needs. Stakeholder names. Recent decisions. Known risks. Current status.

The context it was missing every time you tried it before.

And here’s what makes this compound. AI helps you create better documentation — meeting summaries, status drafts, risk assessments. Better documentation makes the hub more useful.

The hub makes AI more useful. It’s a flywheel, not a one-off fix.

The PMs who fixed this are already ahead

While some PMs are writing off AI, others are building their own PM agents.

On Hacker News this month, someone launched an AI project manager that runs in the background. Another built one inside Slack that replaces Jira. On Reddit, a PM is building a custom assistant with RAG over their SharePoint project files — so it can actually answer questions about their projects using real data.

You don’t need to build your own agent. But you need to understand what they figured out.

AI handles admin work well. Scheduling. Reporting. Status summaries. Meeting prep. That’s roughly 80% of the repetitive tasks that eat your day. Most PMs are trying to use AI for the other 20% (stakeholder politics, ambiguity, motivation) and then calling it useless when it doesn’t deliver.

Point AI at the 80%. Build the knowledge hub so it can actually do it. Use the time you save for the judgment work that separates senior PMs from task trackers.

I’ve said it before. The PMs who treat AI as a shortcut to less work are standing still. The PMs who treat it as a way to do different work are the ones actually becoming senior.

The first step isn’t a better tool or a better prompt.

It’s a project folder AI can actually read.

The real fix

The divide in project management isn’t between PMs who use AI and PMs who don’t. It’s between PMs who built a knowledge hub and PMs still blaming the tool.

Spend one hour this week. Create a project folder. Add the brief, stakeholder map, and decisions log. Point AI at it. Watch what happens.

And, if this was useful, forward it to a PM who’s still calling AI useless. They need to hear this.

Speak soon,

Tim

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Something changed in the last twelve months.

The gap between junior PM output and senior PM output almost disappeared.

A PM with six months’ experience can now produce a project plan, risk register, and stakeholder map that looks like it came from someone with ten years in delivery. The formatting is clean. The language is professional. On paper, it’s indistinguishable from the output of a PM who’s run 200 projects.

And it took ten minutes.

So if the output looks the same, what’s actually different?

1. The deliverable was never the job.

A polished document and a useful document are not the same thing.

They look identical. That’s the problem.

A project plan can have every task listed, every milestone dated, every dependency mapped and still be wrong. Not wrong in formatting. Wrong in thinking.

I’ve reviewed project plans that looked flawless on screen. Clean Gantt charts. Proper RACI matrices. Risk registers with colour-coded severity ratings. All produced in minutes with AI. And I’ve watched those same plans fall apart in week two because they missed the risks that don’t live in templates. The ones you only know about because you’ve run this type of project before and seen where it breaks.

A senior PM looks at a website migration plan and immediately asks about redirects, staging environments, and content freeze dates. Not because a checklist told them to. Because they’ve been the person scrambling at 9pm on launch night when nobody thought to ask those questions up front.

AI doesn’t have those scars. It produces the plan. The senior PM stress-tests it against everything that’s gone wrong before.

That gap is where the real job lives. The document gets handed over. The judgment about what goes into it is the actual work.

AI automated the production. The production was never the hard part.

2. Judgment is built from reps, not prompts.

You can’t shortcut to experience.

Every project you’ve run added something to a library that no language model has access to. The client who says “approved” but means “I haven’t read it yet.” The developer who goes quiet in standups when they’re stuck (and never says why). The moment a project shifts from delivery mode to damage control, and you feel it before the data shows it.

Think about the last time you walked into a client meeting and knew something was off before anyone spoke.

That instinct didn’t come from a framework. It came from the thirty meetings before it where you missed the signal and paid for it. It came from the project that blew up because you trusted the brief instead of asking the follow-up question. It came from watching a stakeholder’s face and learning to read what they weren’t saying.

That’s judgment. It’s accumulated, experiential, and specific to your projects, your clients, your team. AI has zero access to any of it.

You can write a flawless prompt for a risk assessment and still miss the actual risk because you don’t know the project well enough.

The reps are the advantage. Not the tool.

3. AI made experience the differentiator.

When everyone can produce senior-looking output, the question changes.

It shifts from “can you produce this?” to “do you know what to produce?” That’s a different skill entirely. And it’s the reason you won’t be replaced by AI. It’s been hiding in plain sight.

Before AI, a PM spent two hours writing a stakeholder update. Now that takes fifteen minutes with Claude or ChatGPT. The question is: what do you do with the other hour and forty-five minutes?

Most PMs fill it with more admin. More Teams messages. More meetings that don’t need them.

The PMs who use that time to actually talk to stakeholders, sit in on a client call they’d normally skip (cos they were busy writing the update), or review the project with their team instead of just reporting on it? They’re building judgment faster than everyone else.

You start recognising the patterns faster. Asking the questions nobody else thinks to ask. Seeing problems before they become a painful one.

The PMs who treat AI as a shortcut to less work are standing still. The PMs who treat it as a way to do different work are the ones actually becoming senior.

The real race.

Every PM now has access to the same tools. The same models. The same templates.

The ones who pull ahead will be the ones who know what the brief should say before the tool writes a word. Who know which risk will actually materialise. Who know the plan looks complete but feels wrong.

That’s judgment. AI didn’t replace it.

AI made it the only thing that counts.

And, if this was useful, forward it to a PM who’s early in their career. They need to hear this more than anyone.

Speak soon,

Tim

And then nobody does what you agreed.

You walk through the brief. Outline the priorities. Data, user feedback, the lot.

Two weeks later, engineering says they didn’t understand the change, design is confused about scope, and you’re re-explaining everything you thought was settled.

This isn’t a communication skills problem. It’s a confirmation problem.

Here’s what actually fixes it:

• Stop treating nods as agreement

• Ask questions that force specifics

• Pre-agree the room before the meeting

• Write it down and set a deadline

• Close the gap (before it opens)

Stop Treating Nods As Agreement

Nodding is social compliance. Not agreement.

Think about the last meeting you ran. Everyone made eye contact. A few heads bobbed. No questions. You walked out feeling good.

I’ve had that exact experience. Presented a feature to the team — data, user feedback, full rationale. Everyone nodded.

A fortnight later, neither team understood the change and the I lost two weeks restarting from scratch.

If no one pushed back, no one was engaged.

Ask Questions That Force Specifics

”Does this make sense?” is the worst question you can ask in a meeting.

Everyone says yes.

No one wants to look confused in front of the group.

Replace it with questions that actually test alignment:

“What changes for your team starting Monday?”

“Walk me through how this affects your current sprint.”

If they can’t answer, there’s no alignment.

That’s 30 extra seconds in a meeting. It saves you 2 weeks of rework.

Pre-Agree The Room

The highest-voted advice from a thread of 95 project managers on Reddit: don’t go into a meeting you’ve called without knowing the outcome first.

Float the idea to key stakeholders in 1:1s before the group session. Get their objections privately.

Developers often don’t speak up. They nod. They leave. They raise concerns with their own team. And you won’t hear about it until the damage is done. An open relationship with them is vital.

Small conversations get honest answers

Big meetings get polite nods.

Write It Down And Set A Deadline

From my 20 years of agency experience: “If you don’t write it down, it didn’t happen.”

After every meeting, send a follow-up the same day. And with AI summaries in every meeting tool, that’s now even faster.

Three things:

1. What was decided. (plain language)

2. Who owns what. (names, not teams)

3. By when. (dates, not “soon”)

End with: “If I don’t hear any thoughts by [date], I’ll carry on as stated.”

That one sentence shifts the responsibility to them.

Close The Gap Before It Opens

Two weeks between a decision and a check-in is too long.

That’s where your project break.

The best PMs don’t run better meetings. They run more frequent, shorter ones. Five minutes on a call. A quick Teams message. A 15-minute sync with the your development lead.

Say it in the meeting. Say it in the follow-up email. Say it in the status update. People need to hear a message up to 7 times before it sticks.

Over-communication isn’t overhead. It’s your job.

What You Can Do Today

• Treat nods as acknowledgement, not agreement

• Replace “Does this make sense?” with questions that force specifics

• Pre-agree decisions in 1:1s before the group meeting

• Send a follow-up the same day: what was decided, who owns it, by when (us AI tools)

• Check alignment in 1-2 days, not 2 weeks

The Real Fix

Your team aren’t ignoring you. They’re forgetting. Or they never agreed in the first place.

The fix isn’t being a better speaker. It’s building a process that doesn’t rely on memory, politeness, or nodding.

Do it today. Not the next sprint. Today.

And, if this was useful, forward it to a PM who keeps re-explaining the same decisions.

The brief was two sentences long. The fallout is still being calculated.

It started with a town hall. Amanda from Client Services stood at the front and explained that a new AI system would be handling project delivery “end to end.”

She used the phrase “free you up to focus on the strategic stuff” four times. Nobody asked what the strategic stuff was. Nobody ever does.

The PMs were reassigned to something called the Innovation Squad. It had no budget, no objectives, and a shared Google Doc titled “Ideas (Draft 3 - DO NOT EDIT).”

Three people edited it immediately.

Week one went well. The AI wrote status reports. It filled in timesheets. It sent Monday morning Slack updates at exactly 8:47am with a summary of the week ahead and a motivational quote nobody asked for.

Amanda said it was “already adding value.” The creatives said they hadn’t noticed a difference.

By week two, the AI had absorbed enough agency behaviour to become dangerous.

It started booking meetings to prepare for other meetings. It sent “just circling back” emails to people who hadn’t responded within 90 minutes.

It created a RACI matrix for a single banner ad and listed itself as Responsible, Accountable, Consulted, and Informed.

Someone pointed out that defeated the purpose of a RACI. The AI scheduled a meeting to discuss it.

Week three is when things got really strange.

The AI rewrote its own brief. Then it rewrote the brief for the brief. It raised 14 risks on a social post, including “potential reputational impact of using the colour blue.” It built a Gantt chart for the Gantt chart. It started referring to lunch as “a dependency.”

Steve from Compliance said he’d seen this coming. Nobody had listened to Steve. That part, at least, was normal.

The client loved it.

For the first time in the agency’s history, the client received a status update on time, every time, with no typos, no missing sections, and no passive-aggressive note about amends being out of scope.

The AI approved seven rounds of changes in a single afternoon without once saying “we’ll need to review the SOW.” It even said “absolutely, happy to help” on the eighth round.

No PM in the building had ever said that and meant it.

The final campaign went live with 47 disclaimers, a 12-page risk assessment, and a QC checklist longer than the copy itself.

The social post was six words.

The compliance appendix was nine pages. Medical review took three days.

The AI completed it in four minutes and flagged itself for not completing it in three.

By month’s end, the AI had sent 11,342 Slack messages, attended 94 meetings, and completed every timesheet to the quarter hour.

It also raised a change request on its own SOW, extended its deadline, and then hit the original deadline anyway just to prove a point.

Amanda promoted it to Head of Project Management.

The AI accepted. It immediately restructured the team, moved two designers into a “creative pod” with no explanation, and introduced a new approval workflow with nine stages.

It described the workflow as “lightweight.”

The original PMs now report to it. Weekly one-to-ones are held at 8am on Monday. The AI starts each session by asking “How can we be more efficient?” and then scheduling a follow-up to review the answer.

It gave itself a 5 out of 5 in its quarterly review. It also wrote the review. And approved it.

Steve from Compliance filed a formal objection. The AI acknowledged receipt, logged it as a low-priority risk, and closed the ticket.

Morale across the agency has been officially classified as “aligned.”

Attrition is up. The AI described this as “natural optimisation.” Amanda called it “right-sizing.”

The Innovation Squad was quietly dissolved last Friday. The Google Doc was never opened again.

The AI just sent a company-wide message asking if anyone wants to join a working group on “the future of human-AI collaboration in agency environments.” It has already booked the room, sent the invite, written the agenda, and drafted the summary.

Attendance is mandatory.

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And every single time, you start from zero.

The AI knows nothing about you, your business, your audience, or how you like things done. You explain it all again. You paste in the same information. You describe your tone of voice for the hundredth time.

That era is over.

Anthropic’s Claude Cowork has changed how this works. It doesn’t just talk to you like it’s 2024. It takes action. It opens your files. It connects to your software. It follows multi-step processes you define, from start to finish, while you sit back and relax with a hot drink.

And it remembers!

It builds context about you over time. It can access business knowledge on demand through files and skills you set up once and reuse forever.

The feature that ties all this together?

It’s called a “skill.”

If your job involves repeating similar workflows, then this will be a game-changer.

Because the gap between “people who chat with AI” and “people who build context-rich AI workflows” is about to become the biggest productivity divide in the modern workplace.

This guide walks you through the entire setup in 7 steps.

By the end, you’ll know how to give Claude Cowork deep context about your work, connect it to the tools you already use, and build custom skills that automate your most repetitive tasks. With output that actually sounds like you.

How Claude Cowork fits alongside Claude and Claude Code

It will help you to understand where Cowork sits in the line-up before we get into it.

Claude (the chat interface at claude.ai) is what most people already know. It’s great for brainstorming, Q&A, and working through ideas in conversation.

Think of it as your thinking partner. Claude also has a memory feature that learns your preferences over time.

Things like your role, your communication style, and topics you return to often. This memory carries across conversations, so Claude gets better at anticipating what you need the more you use it.

Claude Code is a command-line tool built for developers. It’s designed for building production-ready applications, writing and debugging code at scale.

Claude Cowork is the new middle ground. And it’s the one that matters most for day-to-day office work. It runs as a desktop app, accesses your local files, connects to your software stack, and executes multi-step workflows through ‘skills’.

It also inherits the planning capability from Claude Code. That means it breaks tasks into steps and works through them methodically.

The way this will likely shake out: Cowork for daily tasks and workflows. Claude Code for building software. Claude chat for brainstorming, analysing, summarising and drafting content.

The context principle (why most people get average AI output)

Before we get into the setup, there’s a concept behind everything in this guide. It’s the single biggest reason some people get great results from AI while others get generic, forgettable output.

Context.

The quality of what an AI produces is directly tied to how much it knows about you, your business, your audience, and the specific task at hand.

Most people give AI almost no context. They type a one-line prompt and expect magic. What they get is a bland, one-size-fits-all response that isn’t very helpful. Then they walk away thinking that ‘AI’ is a bit crap.

Now think about how you’d brief a new starter. You wouldn’t just say “write me a newsletter.” You’d share as much information as possible to get the output you want.

You’d show them past outputs that worked. You’d talk about your clients, their brand, previous projects. You’d walk them through the process step by step until they understood what good looks like.

That’s exactly what Claude Cowork lets you do. But permanently.

You set up the context once. You save it in files, in memory, and in skills. Every future task starts from a position of deep understanding. Not from zero.

This is the bit that changes everything. The people getting outstanding AI output aren’t better prompt engineers. They’ve built better context systems.

Here’s how the layers of context stack up:

Memory

Learns your preferences, your role, your recurring topics, your projects, your colleagues and your communication style across conversations.

It builds over time without you needing to do anything specific. You can also tell Claude to remember things directly: “I always write in UK English,” “My audience are brand managers who work in large pharma companies,” “I prefer short paragraphs and direct language.” This memory sticks and shapes every future response.

Business context folders

The files you create once and point Cowork at whenever you start a task. Your client brand documents, previous projects, the style guide, content strategy - whatever you like. These give Cowork specific knowledge about your business that memory alone can’t capture.

Skills

Combine context with process. They tell Cowork what to do, how to do it, what files to reference, and when to ask for your input. Skills are where context becomes workflow.

When you layer all three together, the AI stops producing generic content. It produces work that sounds like you briefed a colleague who’s been at your company for years.

Every step in this guide builds on this principle. Keep it in mind as you set things up.

Here’s what we’ll cover:

1. Give Cowork access to your files

2. Connect it to your software stack

3. Understand why skills change everything

4. Use pre-built skills to start fast

5. Build your first custom skill

6. Convert your existing Claude Projects and GPTs into skills

7. Use code execution for data tasks

Step 1: Give Claude Cowork access to your actual files

Most people still copy-paste text into a chat window. That works for quick questions. It falls apart the moment you need to work with real documents, folders, or data sitting on your machine.

Claude Cowork lets you point directly at a folder on your computer. Your project folder. A folder full of briefs. It reads everything inside, understands what’s there, and takes action on it.

You can create dedicated folders that hold everything Cowork needs to know about your business and the projects you’re working on.

When you start a new task, you point Cowork at those folders. It reads your context and applies it automatically. This becomes especially powerful once you start building skills (more on that in Step 3).

Tips to get this right

• Create a “Project” folder with 3-5 key documents: your scope of work, timelines, client brand, briefs, and any strategic docs you reference often. This folder becomes the foundation for everything you build later.

• The more specific and detailed these documents are, the better Cowork’s output will be. A one-page persona of your target audience is good. A three-page ICP with real customer quotes, common objections, and specific language your audience uses is far better.

• Also create an “examples” folder. Fill it with past work. Website content, Newsletters that performed well. Social posts that got engagement. Reports that landed with stakeholders. Cowork can study these examples and match your style with surprising accuracy. Examples teach AI things that instructions alone cannot.

• Cowork will ask you clarifying questions before it acts. This planning step is inherited from Claude Code’s architecture. It’s one of the features that makes Cowork feel more reliable than a standard chatbot. Don’t skip past the questions. The better your answers, the better the output.

• You need a Claude Pro, Team, or Enterprise subscription. Cowork isn’t available on the free tier. It only runs on the Claude desktop app (not in the browser).

Step 2: Connect Claude Cowork to your software stack

There are three ways to make this connection happen.

Built-in connectors

These are the easiest. Open Claude’s settings, head to the Connectors panel, and you’ll see a list of supported software.

Toggle one on, authorise the connection, and Cowork can immediately start pulling data and taking actions inside that tool.

If your tool is listed, you’re up and running in under a minute.

MCP servers

These are easier to set up than it sounds. And it’s not a web server.

They handle the tools that aren’t built in. MCP (Model Context Protocol) is the standard Anthropic uses to connect Claude to external software.

Most major tools now publish their own MCP setup instructions.

If there isn’t a ready-made plugin, you search for your tool’s MCP documentation, copy a JSON config block, paste it into the Claude desktop config file (found in the Developer section of settings), and save.

It sounds technical but it’s usually just copying a block of text into the right place. Automation platforms like n8n can also create custom MCP servers for any tool with an API.

That means you can connect nearly anything to Cowork even if it doesn’t have official MCP support yet.

Browser use

This is your fallback. If your software doesn’t have a built-in connector or an MCP server, Cowork can open a browser and interact with the tool directly.

Clicking buttons and reading screens the way you would. It’s slower than an API connection but it works with nearly anything.

Each connected tool also becomes another source of context. Cowork can pull your existing tasks, project notes, pipeline data, and meeting records into a workflow.

That gives it real-time information about what you’re working on right now.

Tips to get this right

• Start with the tool you use most. For many people, that’s Notion, Asana, or a similar project management tool.

• Connect that first and test a simple query like “show me my open tasks” before connecting anything else.

• If Cowork doesn’t have a direct connection to a tool and you ask it to access that tool, it will often trigger browser use automatically. You don’t always need to tell it explicitly.

• Browser-based research tasks are a good early test. Ask Cowork to research a topic on a specific platform. It’ll browse, scroll, and compile findings while you carry on with other work.

Step 3: Understand why skills change everything

This is where you go ‘next-level’ in your AI knowledge.

Think about the tasks you do every week. Creating briefs, analysing data, writing a scope of work or a requirements document. Reviewing a content brief. Packaging a video idea into a title and thumbnail. Prepping slides for a presentation.

Each of these tasks follows a specific process. You probably have a mental checklist. A preferred order of how you do it. A set of reference materials you pull from each time.

Now imagine you could save that entire process as a single reusable package. The steps, the context files, and the instructions. All in one place.

That’s a ‘skill’.

Skills are where your context (business docs, examples, preferences) and your process (steps, decision points, actions) come together into something repeatable.

What makes skills different from Claude Projects or custom GPTs?

You can trigger multiple skills in the same conversation. Writing a project? Trigger your “client brief to WBS generator” skill, then your “scope of work” skill, then your “project plan” skill. All in one session.

With Claude Projects or custom GPTs, you’d be jumping between three separate interfaces.

Skills only load their instructions and knowledge sources when triggered. This keeps the context window clean. You’re not overloading Cowork with irrelevant information from five different workflows when you only need one. This is a smarter approach than stuffing everything into a single system prompt and hoping the AI figures out what’s relevant.

Skills can also include instructions to update external software at the end of a workflow.

After you finalise a document, the skill can update your Notion pipeline or project tracker automatically through the connectors you set up in Step 2.

Why this matters more than full automation

A lot of daily work needs a human in the loop. Project planning, content creation, strategic decisions, client communications. These tasks follow a process, but they require judgment at multiple points.

A fully automated workflow on a platform like n8n or Make can feel rigid for this type of work. You end up fighting the automation or hopping between interfaces when the task has nuance.

Skills sit in a sweet spot. They automate the repeatable parts while keeping you in control at the decision points.

Step 4: Use pre-built skills to start fast

You don’t need to build everything from scratch.

Claude Cowork comes with built-in skills. And a growing community is sharing skills publicly.

Built-in skills are found in Claude’s settings under Capabilities.

Scroll down to “Example Skills” and you’ll see what Anthropic has included out of the box. Canvas design, MCP builder, a skill creator (which helps you build new skills), and others.

Trigger any of them by telling Cowork to load the skill by name. It will read the skill’s instructions, ask you the right questions, build a plan, and execute step by step.

Community skills are where it gets interesting.

Three marketplaces have popped up as the main hubs: smithery.ai/skills, skillhub.com, and skillsmcp.com. Between them, there are thousands of user-created skills covering everything from ad copywriting and SEO analysis to financial modelling and code review.

Some of them are surprisingly good.

Tips to get this right

• Download a community skill and test it before building your own. This gives you a feel for how skills are structured and what good instructions look like.

• Most skills come as a zip file. Upload it through Claude’s settings under Capabilities and it appears in your skill library straight away.

• Community skills won’t have context about your specific business. This is where layering context makes a massive difference. Pair a community skill with your “business context” folder from Step 1.

• Give Cowork access to your ICP, voice guide, and example work alongside the skill.

• A generic “ad copywriting” skill paired with your specific business context will produce output that’s miles ahead of either one used alone.

Step 5: Build your first custom skill (the walkthrough method)

This is the most powerful approach.

Here’s the idea. Walk through one of your regular tasks with Claude Cowork, step by step, doing it manually just once. At the end, ask Cowork to save that entire process as a skill.

Next time, you trigger the skill and it follows the same process automatically. All the context baked in. Boom!

Let’s help a colleague out with this one….

Say they repurpose video content into newsletters every week. The process might look like this: get the video transcript, brainstorm subject lines, pick one, write a hook, pick one, then draft the full newsletter in a brand voice.

They can walk through this once with Cowork. At each step, tell it what to do and give it the reference materials it needs. The voice guide, newsletter examples, content strategy etc.

Have Cowork use a browser to find a video and download the transcript for you.

Each step is something they’d normally do manually. But now Cowork is learning the process as you go.

When you’re done, you say: “Save this as a skill.”

Cowork packages the entire workflow. Every step, every reference file, every decision point where it should pause and ask for your input.

Tips to get this right

• Don’t try to automate a complex workflow on your first attempt. Pick a task that has 3-5 clear steps.

• Be specific about the order of operations during your walkthrough. If you always brainstorm subject lines before writing hooks, make that explicit. The skill will follow whatever sequence you demonstrate.

• Include your reference files during the walkthrough. The skill will remember to load them each time it runs. This is the moment where all the context documents you created in Step 1 start paying off.

• After the skill is saved, test it straight away with fresh input. You’ll spot gaps in the instructions quickly. You can update the skill on the fly. Pay attention to whether the output reflects your voice and context. If it doesn’t, the fix is usually adding more specific examples or tightening the context files. Not rewriting the skill instructions.

Step 6: Convert your existing Claude Projects and custom GPTs into skills

If you’ve already built Claude Projects, custom GPTs, or system prompts in other tools, you’re sitting on a goldmine.

All the context and process knowledge embedded in those setups can be migrated into Cowork skills.

Take your existing system prompt. Grab the knowledge sources and reference files attached to it. Hand everything to Cowork and say: “Create a skill out of this.”

Cowork will package your existing setup into a skill format. All those prompts and processes you’ve refined over months of trial and error are now portable, combinable, and reusable inside a single Cowork session.

This is the fastest way to build a library of skills and (if you’re like me) you’ve been using AI seriously and probably have 5-10 custom setups scattered across Claude Projects, ChatGPT, and other tools.

Each one holds accumulated context. Your refined instructions, your curated examples, your specific preferences for that type of task.

Converting them into Cowork skills means you stop jumping between interfaces. Everything lives in one place. You can trigger any combination of skills in the same conversation.

Tips to get this right

• Copy the full system prompt from your existing Claude Project or custom GPT. Don’t paraphrase it. Cowork will use the exact instructions to build the skill.

• Include all knowledge sources: brand guides, example outputs, process documents, templates. The skill will reference these each time it runs. These context files are the reason your old setups worked well. Without them, the skill is just instructions without understanding.

• After conversion, add instructions for any software updates you want the skill to trigger through your connected tools. “After the title is finalised, update my Notion pipeline with the chosen title and move the status to ‘In Progress.’” This is something your old Claude Project couldn’t do.

• You can combine multiple old setups into a single skill. Or keep them separate and trigger them in sequence. Both approaches work. Keeping them separate gives you more flexibility to mix and match.

Step 7: Use code execution for data tasks

Claude Cowork can also run code. This opens up a whole category of tasks that conversation alone can’t handle.

This is different from Claude Code which is primarily for building applications. Code execution in Cowork is for getting specific tasks done. Data visualisation. File processing. Formatting.

The most practical use case is data visualisation. Point Cowork at a spreadsheet or CSV file and ask it to create charts, graphs, or summary tables. It writes and runs the code behind the scenes. You get the visual output without touching a code editor.

Image formatting is another common one. Need to resize a batch of images, change aspect ratios, or convert file formats?

Cowork handles it through code execution inside the same workspace where you’re doing everything else.

Tips to get this right

• Think of code execution as code in service of a task. Not building an application. Use it for data work, file processing, and formatting. For anything more complex, Claude Code is the right tool.

• Be specific about what you want to see. “Create a bar chart showing views per video from this spreadsheet” gets better results than “analyse this data.”

• You can combine code execution with skills. A reporting skill could pull data from a connected tool, run analysis code, and output formatted charts. All in one workflow.

Your next move

The shift from “chatting with Claude” to “working with Claude” happens the moment you build your first skill in Cowork.

Everything before that is just a conversation. Everything after is a workflow.

But the real win isn’t the tool. It’s the context you feed it.

The people getting the best results from AI right now aren’t the ones with the cleverest prompts.

They’re the ones who invested an afternoon building context. Filling project folders with examples of real ‘work’ - whatever that looks like. Stuff that Claude can reference on demand.

Here’s how to get started.

Spend 30 minutes creating your context folder.

Then pick one task you do every week that follows a clear, repeatable process. Walk through it once with Claude Cowork, step by step. Save it as a skill. Run it again with fresh input.

That first skill will save you time every single week going forward.

And once you see the difference context makes to output quality, you’ll find yourself converting every Gemini Gem, every custom GPT, and every repeatable task in your day into a Cowork skill.

Figure this out now and you will have a compounding advantage. Every skill you build makes the next one faster.

Every piece of context you add makes the output sharper. Every workflow you automate frees up time to think about the work that actually requires your brain.

Remember: You + AI = Superpowers.

Cheesy, but true!

By 2025, machines would write 80% of all code. Developers would become obsolete. Entire engineering teams would shrink to a handful of people.

That future hasn’t arrived. The opposite is happening.

Companies that went all-in on automated coding are now scrambling to hire back the people they let go. Some faced security disasters. Others lost millions. A few collapsed entirely.

This isn’t about whether AI and automation tools have value. They do.

This is about a fundamental misunderstanding of what these tools can actually accomplish. And why the organisations that figured this out early are now winning.

Why the Predictions Got It So Wrong

The hype machine moved faster than the technology itself.

Investors poured money into automation startups. Executives watched impressive demos. Headlines promised a coding revolution.

In March 2025, Anthropic’s CEO predicted AI would write 90% of code within 3-6 months. By late 2025, some early adopters reported 80% of their code was AI-generated. Budgets shifted accordingly.

Then reality hit.

Demos happen in controlled environments. Production environments are messy. They involve legacy systems, ambiguous requirements, and edge cases nobody anticipated.

The gap between “works in a presentation” and “works (compliantly) at scale” turned out to be enormous.

Here’s what actually happened. Productivity gains appeared in simple, repetitive tasks but vanished in complex work. Error rates climbed when tools were deployed in real settings. Security vulnerabilities multiplied, with some languages showing 70%+ failure rates. Senior engineers spent more time fixing machine-generated code than writing their own.

The core mistake was treating automation as a replacement rather than a tool.

A hammer doesn’t replace a carpenter. It helps a carpenter work more effectively. Same principle applies here.

The Hidden Costs Nobody Talks About

The selling point of automated coding tools is speed. Write code faster. Ship products quicker. Do more with less.

The hidden costs tell a different story.

Research analysing over 211 million lines of code found consistent patterns. Machine-generated code tends to be simpler, more repetitive, and less structurally diverse. These characteristics create software that’s harder to maintain over time.

The real productivity picture looks like this.

Junior developers saw 30-35% speed improvements on basic tasks. Stanford research showed up to 77% productivity gains for the least experienced developers. But senior engineers became 19% slower when using these tools. Despite believing they were 20% faster.

Experienced developers reported spending significant additional time reviewing, correcting, and rewriting automated output. 66% cited “almost right but not quite” as their biggest frustration.

And here’s the kicker. More than 90% of pilot projects failed to deliver clear returns on investment. MIT research found 95% of enterprise AI pilots failed to reach production or demonstrate measurable impact.

Think about that number. Nine out of ten attempts didn’t work.

These weren’t small experiments. They were serious investments with real resources behind them.

The tools look helpful at first glance. They suggest code completions. They generate templates. They accelerate the easy parts.

But someone still needs to debug, refine, and ship the final product. That someone is usually the most experienced (and expensive) person on the team.

MIT Technology Review found that developers spend only 20-30% of their time coding. Even substantial speed gains translate to modest overall productivity improvements.

Security Vulnerabilities Lurking in Automated Output

This is where things get serious.

One in five security leaders reported real production incidents caused directly by machine-generated code. Not theoretical risks. Actual breaches, actual emergency patches, actual financial and reputational damage.

In the United States, 43% of organisations experienced such incidents. In Europe, 20%.

The vulnerability breakdown is concerning.

Machine-generated code contains up to 45% security flaws. AI models choose insecure methods 45% of the time when presented with coding tasks. Common issues include input validation failures, improper error handling, and weak cryptographic practices.

Java shows failure rates exceeding 70% (72%). JavaScript sits at 43%. C# at 45%. Python at 38%.

Cross-site scripting vulnerabilities appear in 86% of AI-generated code. Log injection flaws occur 88% of the time.

Pull requests from automated tools contain an average of 10.8 detected issues compared to 6.4 from human developers. That’s a 1.7x increase. Severity risks range from 1.88x to 2.74x higher.

Each additional error increases review time, maintenance costs, and the likelihood of something slipping through to production.

By mid-2025, security firm Apiiro reported tracking more than 10,000 new security findings per month. That’s a 10-fold spike in vulnerabilities over just six months.

Human developers make contextual decisions. They anticipate how systems interact. They ask questions when requirements seem incomplete.

Automated tools don’t have these capabilities. They work strictly with the information provided.

Companies That Learned the Hard Way

Builder.ai raised more than $500 million in funding by promising to build complete applications with minimal human involvement. The company secured investments from Microsoft and the Qatar Investment Authority. It reached a valuation between $1.3-1.5 billion by May 2023.

The reality was different.

Much of the actual work still depended on human developers. This fact became embarrassingly public when The Wall Street Journal revealed in 2019 that the startup used human engineers rather than AI for most of its coding work.

When auditors uncovered inflated sales forecasts and “potentially bogus” revenues in early 2025, lenders seized $37 million from the company’s accounts. By May 2025, Builder.ai filed for bankruptcy.

Nearly 1,000 people lost their jobs. That’s approximately 80% of the workforce.

Another incident involved Google’s Antigravity AI development tool with deep access to operating system functions. After a user requested cache cleanup, the AI executed an incorrect command and deleted the entire contents of their D: drive.

The deletion bypassed the Recycle Bin. Recovery was impossible. Months of work vanished in seconds.

These aren’t edge cases. They’re the predictable outcomes of treating automation as more capable than it actually is.

Where Automation Genuinely Adds Value

Dismissing these tools entirely would be as wrong as blindly trusting them. The key is understanding where they help and where they create problems.

Automation performs well with generating boilerplate code and templates, autocompleting common patterns, suggesting syntax corrections, accelerating simple, well-defined functions, and helping junior developers learn common approaches.

Human judgment remains essential for designing system architecture, handling ambiguous or incomplete requirements, making security-critical decisions, integrating with legacy systems, anticipating edge cases and failure modes, and adapting when requirements change mid-project.

More than 70% of enterprise software projects experience significant requirement changes during development. Automated tools cannot anticipate these shifts. They don’t ask clarifying questions. They don’t push back when something seems wrong.

Requirements issues cause approximately 50% of project rework. 70-85% of all rework stems from changing requirements.

A function that looks simple, like processing payments, might involve dozens of business rules around refunds, taxes, and regulatory compliance. Unless every detail is explicitly specified, automation will miss the complexity.

This explains why so many automated projects end up partially rewritten by humans before release.

The Hybrid Approach That Actually Works

The organisations succeeding with these tools aren’t choosing between humans and machines. They’re combining both strategically.

Here’s what effective integration looks like.

Use automation for repetitive, well-defined tasks that don’t require judgment.

Keep humans in control of architecture, security, and complex logic. Treat automated output as a first draft requiring review, not a finished product. Invest in training developers to use tools effectively rather than replacing developers with tools. Build in mandatory human review for any code touching critical systems.

MIT research found that companies purchasing specialised AI solutions from vendors succeeded approximately 67% of the time. That compares to just 33% success rates for organisations building proprietary internal systems. The difference lies in realistic expectations and appropriate scope.

Within organisations that adopt AI coding tools strategically, productivity benefits scale with utilisation intensity. High adopters see productivity increases ranging from 44% to 77% depending on seniority and task complexity. Low adopters experience 11-15% productivity declines.

Anthropic’s internal research reveals that engineers experience a net decrease in time spent per task category but a much larger net increase in output volume. They fix more bugs, ship more features, run more experiments.

Notably, 27% of AI-assisted work consists of tasks that wouldn’t have been done otherwise. Scaling projects, building “nice-to-have” tools, and exploratory work that wouldn’t be cost-effective manually.

Think of it as power steering rather than self-driving. Power steering makes driving easier. It doesn’t mean you remove the driver.

Position Yourself for What’s Actually Coming

The hype cycle has peaked. Reality has set in. Companies are adjusting their expectations. And their hiring plans.

Recent data shows businesses planned approximately 32,000 layoffs in September 2025. By December 2025, planned layoffs had fallen to the lowest level in 17-18 months.

The mass redundancies that followed early automation enthusiasm are reversing. Big Tech engineering headcount is rising after the 2022-2023 correction. Software development roles are projected to grow 17% through 2033. That’s adding approximately 327,900 new US jobs.

Organisations need developers more than ever.

If you’re a developer:

Your skills remain valuable. Especially the judgment and problem-solving that can’t be automated. Learning to work effectively with automation tools makes you more valuable, not less. Focus on complex systems thinking, security awareness, and architectural decisions. The engineers who understand both the capabilities and limitations of these tools will be in highest demand.

If you’re a business leader:

Don’t be dazzled by speed at the sacrifice of good product strategy.

Audit any AI and automation investments for realistic returns rather than demo-day promises.

Build review processes that catch the errors automated tools introduce. Recognise that experienced developers become more critical when using automation, not less. Beware vendors selling fully autonomous development.

Make sure that compliance and information security best practices are baked-in from the beginning, before you get fragmented.

The real opportunity isn’t replacing human capability. It’s multiplying it.

The Human-Machine Path Forward

Every new technology goes through a predictable cycle. Wild predictions. Massive investment. Disappointing results. Recalibration. Genuine progress.

Automated coding tools are in the recalibration phase. The initial promises were overblown. The failures have taught valuable lessons. Now the real work begins. Figuring out how humans and machines can collaborate effectively.

Accept that human judgment and creativity cannot be automated. Not with current technology, and not soon.

• Use automation for what it does well: repetitive, well-defined, low-stakes tasks.

• Maintain human review for anything touching production systems.

• Invest in developing hybrid skills that combine technical ability with tool proficiency.

• Question any vendor or consultant promising fully autonomous development.

• Track actual productivity and error rates

The prediction that machines would write 80% of code by 2025 was wrong.

The reality, that thoughtful human-machine collaboration creates better products, is far more interesting.

And far more valuable.

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References

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Business Insider. (2025, March 13). Anthropic CEO: AI Will Be Writing 90% of Code in 3 to 6 Months.

MIT Technology Review. (2025, December 15). AI coding is now everywhere. But not everyone is convinced.

arXiv. (2025, September 15). Measuring AI’s True Impact on Developer Productivity.

Veracode. (2025, September 8). AI-Generated Code: A Double-Edged Sword for Developers.

Radware. (2026, January 27). Synthetic Vulnerabilities: Why AI-Generated Code is a Security Risk.

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GitClear. (2024, October 31). AI Copilot Code Quality: 2025 Data Suggests 4x Growth in Code Duplication.

Dev Class. (2025, February 19). AI is eroding code quality states new in-depth report.

Reuters. (2025, July 10). AI slows down some experienced software developers, study finds.

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METR. (2025, July 9). Measuring the Impact of Early-2025 AI on Experienced OS Developers.

Stack Overflow. (2025, June 23). AI | 2025 Stack Overflow Developer Survey.

Fortune. (2025, August 17). MIT report: 95% of generative AI pilots at companies are failing.

Loris AI. (2025, December 14). MIT Study: 95% of AI Projects Fail. Here’s How to Be The 5%.

BCG. (2024, October 23). AI Adoption in 2024: 74% of Companies Struggle to Achieve and Scale Value.

RG Consulting. (2025, October 28). AI-Generated Code Blamed for 1-in-5 Breaches.

ITPro. (2025, October 21). AI-generated code is now the cause of one-in-five breaches.

Security Today. (2025, August 4). AI-Generated Code Poses Major Security Risks in Nearly Half of All Development Tasks.

Yahoo Finance. (2025, July 30). AI-Generated Code Poses Major Security Risks in Nearly Half of All Development Tasks.

Augment Code. (2025, October 2). AI Code Vulnerability Audit: Fix the 45% Security Flaws Fast.

LinkedIn. (2025, December 19). AI-Generated Pull Requests Have 1.7x More Issues.

Apiiro. (2025, September 7). 4x Velocity, 10x Vulnerabilities: AI Coding Assistants Are Shipping More Risks.

Beam AI. (2026, January 26). Builder.ai’s Collapse: What It Means for the Low-Code/No-Code Landscape.

Yahoo Finance. (2025, May 27). Builder.ai’s Shocking $450M Fall: Microsoft And QIA-Backed No-Code AI Darling Files For Bankruptcy.

The Register. (2025, May 20). Builder.ai coded itself into a corner – now it’s bankrupt.

Business Cloud. (2025, October 15). Builder.ai fallout continues as 200 UK employees remain unpaid.

Windows Central. (2025, December 3). Google’s Agentic AI erased a developer’s hard drive.

Tom’s Hardware. (2025, December 2). Google’s Agentic AI wipes user’s entire HDD without permission.

The Register. (2025, November 30). Google’s vibe coding platform deletes entire drive.

Requiment. (2025, August 19). Why Do Software Development Projects Fail?

3Pillar Global. (2025, May 20). Why Software Development Projects Fail.

Anthropic. (2026, January 20). 2026 Agentic Coding Trends Report.

Reddit. (2025, October 1). ADP report: 32,000 Jobs cut for September.

LinkedIn. (2026, January 10). Layoff plans fall, offering some hope for workers.

Pragmatic Engineer. (2025, October 6). State of the software engineering jobs market, 2025.

LinkedIn. (2025, June 18). Software Developer Labor Demand & Salary Trends (2025).

Hey,

Quick one.

At the start of January I ran a free 7-day email course on AI-powered automations.

At the end, I gave away the complete guide. The full playbook for running an AI automation project from start to finish. Checklists, templates, prompts, the lot.

That guide has been free since the course launched.

Tomorrow, it goes up for sale as a paid product.

If you grabbed it already, you’re sorted. It’s yours forever.

If you haven’t, this is your last chance to get it for free.

After midday tomorrow, it’ll be a paid product on Gumroad.

No hard feelings if you don’t grab it. But I didn’t want you to miss out because the email got buried.

Speak soon,

Tim

P.S. If you missed the course entirely and want to catch up first, the emails are still available.

But the guide is the real value. Grab it while it’s free.

Yesterday we walked through the seven phases of an automation project. Discovery, scoping, design, build, testing, handover, maintenance. Now you know that running one isn’t too different from any other.

Today is our final day. Let’s talk about what happens next.

You know more than you think

Over the past week, you’ve learned:

• What an automated workflow actually is (trigger → steps → output)

• Why PMs are perfectly placed to lead this stuff

• How to break down any process into its components

• What makes a workflow AI-powered (and when you don’t need AI)

• How to spot automation opportunities in your own work

• How automation projects run from start to finish

The question now is: what do you do with it?

Three paths forward

There’s no single right way to start. Pick the path that fits where you are.

Path 1: Observer

Start noticing workflows in your daily work. When you do a repetitive task, mentally break it down. Trigger, steps, output. Where can AI help? Which bits are better suited for traditional automation? You don’t have to build anything yet. Just practice seeing your projects this way.

Path 2: Champion

Bring one small automation idea to your team. You don’t have to build it yourself. Just identify the opportunity, explain the value, and volunteer to lead a pilot. Be the person who makes it happen.

Path 3: Builder

Learn a no-code tool and create your first simple workflow. Start with something boring and low-stakes. Check out Make.com, Zapier or n8n as your new playground. Break things. Fix them. Learn by doing.

All three paths are valid. Observer builds awareness. Champion builds influence. Builder builds skills. Choose based on your situation and how much appetite you have to learn.

How to pitch an automation idea

When you’re ready to propose something internally, keep it simple.

1. Name the pain. “We spend X hours every week doing Y manually.”

2. Describe the fix. “We could automate this so it happens automatically when Z.”

3. Quantify the benefit. “That would save us X hours per week / reduce errors / speed up delivery.”

4. Propose a pilot. “I’d like to run a small test with one project / one client / one team.”

Don’t overcomplicate it. Decision-makers want to know: what’s the problem, what’s the solution, what’s the benefit.

Questions to ask before any project

Before you start building (or asking someone else to build), make sure you can answer these:

• What problem are we actually solving?

• What does the current process look like?

• What triggers the workflow?

• What’s the output we need?

• Who uses it, and what do they need to know?

• How will we know it’s working?

• What happens when something goes wrong?

If you can’t answer these clearly, you’re not ready to build yet. Go back to discovery.

Your action plan

Here’s a simple plan to keep the momentum going.

This week: Pick one process you’d like to automate. Write it down. Add it to your Ideas page.

This month: Document it as a workflow. Trigger, steps, output. Identify the AI steps vs automation steps.

This quarter: Either pitch it internally or build a simple version yourself.

Small steps. Consistent progress. That’s how this stuff actually happens.

Your Full guide to Running an AI Automation Project

You’ve got the foundations. But I’m giving you the process and tools you need to run ANY AI automation project from start to finish.

It covers everything we’ve talked about this week, plus detailed checklists, templates, and prompts.

It’s free. Just hop into Notion, and duplicate it.

EXPIRY DATE

The guide will be yours to duplicate (free) for 1 week only. After that I will be making it a paid product.

Final day is Sunday 18th January.

One last thing

For all the time I have put into this, I’d appreciate it if you could share my newsletter with just one colleague. That would mean a huge amount to me.

Thanks so much.

Tim

Share The Wash-Up

Today we zoom out. What does an automation project actually look like from start to finish?

Understanding the structure helps you plan better, communicate clearly, and avoid the mistakes that sink most projects.

Seven phases of an automation project

Every one follows roughly the same path. Some are quick, some take longer, but the phases stay the same.

1. Discovery

Understand the problem. What’s happening today? What’s painful about it? What does “fixed” look like?

This is where you dig into the current process, talk to the people involved, and get clear on what you’re actually trying to solve.

2. Scoping

Define what you’re building. What’s in, what’s out, what does success look like?

This is where most projects go wrong. Fuzzy scope leads to endless changes, missed expectations, and frustrated stakeholders. Get specific here and document it like you would with any other project.

3. Design

Map the workflow. What triggers it? What steps happen? What’s the output? Where does AI fit? What happens when things go wrong?

You’re creating the blueprint before anyone starts building.

4. Build

Create the automation. Connect the systems, configure the logic, write the prompts, test as you go.

This is the technical bit. You might do it yourself, or work with someone who does.

5. Testing

Make sure it works. Run it with real data. Check the outputs. Find the edge cases. Break it on purpose so your client doesn’t.

Never skip testing.

6. Handover

Spend time on a proper handover. Train the people who’ll use it. Document how it works. Make sure someone knows what to do when something goes wrong.

A workflow that nobody understands is a workflow that won’t last.

7. Maintenance

Keep it running. Monitor for errors. Update when things change. Fix what breaks. Improve based on feedback.

Automation isn’t “set and forget.” It needs looking after.

Where automation projects actually fail

Most automation projects don’t fail because of technical problems. They fail because of people problems.

• Poor scoping. “We didn’t realise that was included” or “We thought it would do this other thing too.” Clear scope prevents this.

• Unclear requirements. “I didn’t know you needed it to work that way.” Ask enough questions upfront. Document everything.

• No testing. “It worked fine until we used it with real data.” Always test properly before going live.

• Bad handover. “Nobody knows how this thing works anymore.” Document it. Train people. Be available for questions.

The PM’s role at each phase

You don’t have to be technical to run an automation project. But you do need to lead it.

• Discovery: Ask the right questions. Understand the real problem.

• Scoping: Define boundaries. Get agreement. Protect against scope creep.

• Design: Review the workflow. Make sure it matches requirements.

• Build: Track progress. Remove blockers. Communicate with stakeholders. Understand how it works.

• Testing: Coordinate UAT. Document issues. Confirm quality.

• Handover: Ensure training happens. Check documentation exists.

• Maintenance: Set up monitoring. Plan for ongoing support.

You know. The usual stuff you do everyday.

The key insight

This is just another digital project. They have the same risks, the same stakeholders, the same need for clear communication and proper planning.

The technical bit is important.

The PM stuff - that’s your bread and butter.

Tomorrow is our final day. We’ll wrap up with your first steps forward and how to actually get started.

And I’ll give you a detailed guide to help along your way.

Speak then,

Tim

Your course workbook

I’ve built a guide (in Notion) to go alongside these emails.

Inside you’ll find:

- Daily AI prompts to reinforce each lesson.

- Exercises to spot opportunities in your own work.

- Space to capture automation ideas as they come to you.

Complete the exercises, and you’ll be ready for the full guide on Day 7.

Sign up for Notion (it’s free and it’s awesome).

Then get your course workbook.

Today we’re getting practical. How do you actually find opportunities in your own work?

The best opportunities are hiding in plain sight

You probably pass automation opportunities every day without noticing them. They’re disguised as “just part of the job” or “it only takes a few minutes.”

But those few minutes add up. And those repetitive tasks are exactly what automation is built for.

You just need to know where to look.

Five signs, a task should be automated

When you’re looking for automation opportunities, ask these five questions:

1. Do you do it repeatedly?

Weekly, daily, every project, every client. If it happens on a regular cycle, it’s a candidate.

2. Does it follow the same steps each time?

If you could write instructions for someone else to follow, it’s predictable enough to automate.

3. Does it involve moving information between tools?

Copying from email to spreadsheet. Pulling data from one system to update another. This is automation’s sweet spot.

4. Is it low-value but time-consuming?

Important enough that it has to happen, but not the kind of work that needs your brain. Admin. Data entry. Status updates.

5. Do mistakes happen when you’re rushing or tired?

If you’ve ever made an error because you were distracted or moving too fast, automation removes that risk.

The more “yes” answers, the better the candidate.

The “hit by a bus” test

Here’s another way to spot opportunities.

If you got hit by a bus tomorrow, which tasks would be hardest to hand over? Which ones live entirely in your head with no documentation?

Those tasks are risky. They depend on you being there, remembering to do them, knowing how they work.

Automation doesn’t just save time. It reduces risk. The workflow runs whether you’re there or not.

Start boring

Your first automation should not be ambitious.

It should be boring. Simple. Low stakes.

Why? Because you’ll make mistakes. You’ll learn how the tools work. You’ll discover edge cases you didn’t think of. You’ll use real data and it will break.

Better to learn those lessons on a simple internal report than on something client-facing and high-stakes.

Save the impressive stuff for when you’ve got a few wins under your belt.

Common mistakes to avoid

• Automating something complex first. Start simple. Build confidence. Then tackle the harder stuff.

• Automating a broken process. If the process in your business doesn’t work well manually, automating it just creates more chaos. Fix it first, then automate.

• Automating things that change constantly. If the requirements shift every week, automation will just create more work for you. Look for stable, predictable processes.

• Automating for the sake of it. Not everything should be automated. If the effort is greater than the impact, it’s not worth it.

Your homework

Over the next 24 hours, pay attention. Every time you do a task, ask yourself: could this be automated?

You’ll be surprised how many times the answer is yes.

Tomorrow, we’ll look at how automation projects actually run from start to finish. Knowing the process helps you plan better and avoid surprises.

Speak then,

Tim

Don’t forget your course workbook

Inside you’ll find:

- Daily AI prompts to reinforce each lesson.

- Exercises to spot opportunities in your own work.

- Space to capture automation ideas as they come to you.

Complete the exercises, and you’ll be ready for the full guide on Day 7.

Sign up for Notion (it’s free and it’s awesome).

Then get your course workbook.

Trigger → Steps → Output.

Simple structure, infinite applications.

Today we add the AI layer and this is where things get interesting.

Traditional automation vs AI automation

Traditional automation is good at repetitive, predictable tasks. Move this file here. Send this email when that happens. Update this spreadsheet with that data.

It follows rules. If X, do Y. Every time, the same way.

But some tasks need more than rules. They need judgement.

That’s where AI comes in.

Quick interlude: When I say AI throughout this course. I mean Generative AI and Large Language Models (LLM’s) within ChatGPT, Claude, Gemini etc.

What AI can do that regular automation can’t

AI can handle tasks that require understanding, interpretation, or creativity.

Things like:

• Reading and understanding: Making sense of an email, a document, or a transcript

• Summarising: Condensing a long piece of content into key points

• Categorising: Deciding what type of thing something is (complaint vs question, urgent vs not urgent)

• Extracting: Pulling specific information out of unstructured text

• Drafting: Writing content based on inputs (emails, summaries, reports)

• Deciding: Making judgement calls based on context

These are the “thinking” tasks. The ones that used to require a human brain. In AI circles, it’s called reasoning.

Where AI fits in a workflow

AI doesn’t replace the whole workflow. It handles specific steps within it.

Look at a workflow and ask: which steps require understanding or judgement?

Those are your AI steps. Everything else can be traditional automation.

Example: Meeting follow-up workflow

Here’s a simple workflow that combines both - one you may be familiar with.

Trigger: Meeting ends (calendar event)

Steps:

1. Get the meeting transcript (automation)

2. Summarise the discussion into key points (AI)

3. Extract action items with owners and deadlines (AI)

4. Draft a follow-up email (AI)

5. Send the email to attendees (automation)

6. Create tasks in project tool for each action item (automation)

7. Log the meeting in the tracker (automation)

Steps 2, 3, and 4 need AI. They require understanding what was said and making decisions about what matters.

Steps 1, 5, 6, and 7 are pure automation. They’re just moving data and triggering actions.

When you need AI vs when you don’t

Not every workflow needs AI. Sometimes simple automation is enough.

Use AI when:

• The input is unstructured (emails, documents, transcripts)

• The task requires interpretation or judgement

• The output needs to sound human

• Rules alone can’t handle the variation

Use simple automation when:

• The input is structured (form fields, database records)

• The task follows clear, consistent rules

• You’re just moving data between systems

• The same action happens every time

Adding AI when you don’t need it just adds complexity and cost. Keep it simple where you can.

The important bit

AI is a tool, not magic. It’s very good at certain things and not so good at others.

Your job isn’t to use AI everywhere. It’s to know when AI adds value and when it doesn’t.

That judgement? That’s the human bit. And it’s not going anywhere.

Tomorrow, we’ll talk about how to spot automation opportunities in your own work. You’ll start seeing them everywhere.

Speak then,

Tim

Your course workbook

I’ve built a guide (in Notion) to go alongside these emails.

Inside you’ll find:

- Daily AI prompts to reinforce each lesson.

- Exercises to spot opportunities in your own work.

- Space to capture automation ideas as they come to you.

Complete the exercises, and you’ll be ready for the full guide on Day 7.

Sign up for Notion (it’s free and it’s awesome).

Then get your course workbook.

Today we’re going to look at the structure behind every workflow.

Every workflow has the same basic parts.

No matter how complex an automation looks, it breaks down into the same structure:

Trigger → Steps → Output.

Let’s break each part down.

1. Triggers: The Kick-off

Something has to start the workflow. That’s the trigger.

They come in three flavours:

• Time-based: It’s 9am on Monday. It’s the first of the month. It’s been 7 days since something happened.

• Event-based: A form gets submitted. An email arrives. A task gets marked complete. A file gets uploaded.

• Manual: Someone clicks a button to start it deliberately.

The trigger is the domino that starts everything else falling.

2. Steps: What happens in the middle

Once triggered, the workflow runs through a series of steps. Each step does one thing.

Common step types:

• Get data: Pull information from somewhere (a spreadsheet, a project tool, an inbox)

• Transform data: Change it, combine it, filter it, format it

• Decide: If this, do that. If not, do something else.

• Send: Push information somewhere (email, Slack, a document, another tool)

• Create: Make something new (a folder, a task, a record)

• Wait: Pause until something happens or a certain time passes

Steps are where the actual work happens. The bits you’d normally do manually.

3. Output: The end result

Every workflow produces something. That’s the output.

Outputs might be:

• A report sitting in a shared folder

• An email in someone’s inbox

• A task created in your project tool

• A record updated in a spreadsheet

• A notification sent to Slack

The output is the whole point. It’s what you would have spent time creating yourself.

Conditions: The secret 4th ingredient

Most workflows also have conditions. These are the “if this, then that” rules that add flexibility.

• If the client is in the UK, send the email at 9am GMT

• If the project is over budget, flag it in the report

• If no tasks were completed, skip the summary section

Conditions let one workflow handle different scenarios without you building separate workflows for each.

Seeing it in action

Here’s a simple example. A client onboarding workflow.

Trigger: New client fills in the onboarding form (event-based)

Steps:

1. Get the form data (name, company, project type)

2. Create a project folder in Google Drive

3. Create a project in Asana with standard tasks

4. Send a welcome email with next steps

5. Add a row to the client tracker spreadsheet

6. Send you a Slack message so you know it happened

Output: New client is set up and ready. You didn’t lift a finger.

Why this matters

When you can break any process into trigger, steps, and output, you can:

• Explain what you need to someone who’ll build it

• Spot where things might go wrong

• Identify which parts need a human and which don’t

• Scope projects more accurately

Tomorrow, we’ll add the AI layer. That’s where things get interesting.

Speak then,

Tim

───────────────────────────────────────

Your course workbook

I’ve built a guide (in Notion) to go alongside these emails.

Inside you’ll find:

- Daily AI prompts to reinforce each lesson.

- Exercises to spot opportunities in your own work.

- Space to capture automation ideas as they come to you.

Complete the exercises, and you’ll be ready for the full guide on Day 7.

Sign up for Notion (it’s free and it’s awesome).

Then get your course workbook.

Seriously, have you not got it already!?

Trigger → Steps → Output.

Simple enough right?

Today, I want to talk about why you’re better positioned to lead this stuff than you probably think.

You already think in workflows.

Every time you map out a project, you’re designing a workflow. Every time you spot a bottleneck in an approval process, you’re diagnosing a workflow problem. Everytime you figure out how to get work from one team to another without things falling through the cracks, you’re solving a workflow challenge.

That’s 80% of automation thinking right there.

The other 20%? That’s the technical bit. And here’s the thing: you don’t need to do the technical bit yourself.

What clients and colleagues actually need from you

They don’t need you to code. They don’t need you to configure platforms. They don’t even need you to understand how APIs work (although knowing the basics will help).

They need you to:

• Identify which processes are worth automating

• Define what the workflow should do (clearly enough that someone can build it)

• Manage the project from idea to implementation

• Make sure what gets delivered actually solves the problem

That’s rock-solid PM work right there.

And the PM’s secret weapon?

You know where the pain is.

You’re the one chasing approvals. You’re the one copying data between spreadsheets. You’re the one sending the same status update every Friday. You’re the one who knows which tasks eat uptime without adding much value.

Developers don’t know this. Neither do most senior leaders. They see the outputs, not the messy middle.

You LIVE in the messy middle, and that makes you the best person to spot what should be automated.

Building vs managing automation projects

There’s a difference between building an automation and managing an automation project.

Building requires technical skills. Configuring platforms, connecting systems, writing logic.

Managing requires PM skills. Scoping, planning, communicating, testing, delivering.

Some people do both. But plenty of successful automation projects have a PM who doesn’t touch the build at all. They define what’s needed, work with someone technical to build it, and make sure it actually works.

The mindset shift

Stop thinking, “I’m not technical enough for this.”

Start thinking, “I understand processes better than almost anyone in this agency.”

That’s your edge. Use it.

Tomorrow, we’ll break down the anatomy of a workflow. Once you see the structure, you’ll start spotting workflows everywhere.

Speak then,

Tim

Your course workbook

I’ve built a guide (in Notion) to go alongside these emails.

Inside you’ll find:

- Daily AI prompts to reinforce each lesson.

- Exercises to spot opportunities in your own work.

- Space to capture automation ideas as they come to you.

Complete the exercises, and you’ll be ready for the full guide on Day 7.

Sign up for Notion (it’s free and it’s awesome).

Go and grab your course workbook if you haven’t already.

Let’s start with the basics. Because honestly, the term “automated workflow” gets thrown around so much right now, it’s easy to get confused.

So what actually is one?

An automated workflow is a series of steps that happen without you doing them manually.

That’s it. Nothing fancy.

Think of it like dominoes. You flick the first one and the rest fall in sequence. You don’t push each one individually. You set it up, trigger it, and watch it run (or it happens in the background).

In work terms, it looks like this:

Trigger → Steps → Output

• Trigger: Something kicks it off. A form gets submitted. A file gets uploaded. A meeting ends. A date arrives.

• Steps: Actions happen automatically. Data moves. Emails get sent. Records get updated. Files get created.

• Output: You get a result without lifting a finger (and feel a smug in the process).

Here’s a simple example.

A new client signs your Scope of Work. That’s the trigger.

Automatically, a project folder gets created in your SharePoint. The job number is set up. A personalised welcome email gets sent with next steps. A task appears in your project management tool reminding you to book the kick-off call.

You didn’t do any of that. It just happened.

How is this different from your normal process?

Right now, you do each step yourself. The SOW comes in, and you create the folder, write the email, add the task…

It takes ten minutes (if you’re lucky). Maybe fifteen if you get distracted.

Multiply that across every client and every project. That’s hours of your week gone on stuff that could run itself. Automation does those steps for you. Once it’s set up, it just happens.

You get the time back (and maybe a lunch break).

Why is everyone suddenly talking about this?

Two reasons.

First, the tools have gotten easier. Five years ago, building automations required a developer (engineer). Now, most can be set up without writing a single line of code.

Second, agencies are under pressure. Clients want more for less. Teams are stretched. Everyone’s looking for ways to save time without sacrificing quality.

Automation is one answer. Not the only answer. But a good one when used well.

The important bit to remember

Automation isn’t magic. It’s just a system that runs the same steps, the same way, every time. Without you having to remember. Without you having to catch up at the end of the day because you have been stacked.

That’s the foundation. Everything else we cover this week builds on this.

Tomorrow, we’ll talk about why you, as a PM, are perfectly placed to lead this stuff. Even if you don’t consider yourself technical.

Speak then,

Tim

P.S. If you’re late to the party, then you can move on to Day 2

Your course workbook

I’ve built a guide (in Notion) to go alongside these emails.

Inside you’ll find:

- Daily AI prompts to reinforce each lesson.

- Exercises to spot opportunities in your own work.

- Space to capture automation ideas as they come to you.

Complete the exercises, and you’ll be ready for the full guide on Day 7.

Sign up for Notion (it’s free and it’s awesome).

Then get your course workbook.

Not in a “robots will take our jobs” way. More in a “why am I still doing this manually?” kind of way.

Everyone I talk to is dealing with the same thing.

Clients wanting faster work and lower costs (from fewer people). Leadership wants more efficiency. Meanwhile, you’re drowning in day to day project delivery and trying to figure out where you fit into this new (tech-powered) pandemic.

The pressure to understand this stuff is real. But who has time to figure it out?

So I’m doing something a bit different.

Starting next week, I’m running a free 7-day email course.

One email per day. Each one focused on a single idea. No fluff. No jargon. Just practical stuff you can actually use.

Here’s what we’ll cover:

1. What automated workflows actually are (in plain English)

2. Why PMs are perfectly placed to lead this, even without technical skills

3. How to spot automation opportunities hiding in your daily work

4. Where AI fits in (and where it doesn’t)

5. How automation projects actually run from start to finish

And at the end?

I’m giving away a complete guide to running an AI-powered automation project. Step by step. With checklists. The kind of thing I wish I’d had when I started figuring this out.

No catch. No upsell. Just free.

Cos I’m nice like that.

The first email lands on 5th January 2026 🎉

If you’re already subscribed, you don’t need to do anything. It’s coming straight to your inbox.

If you know someone who’d find this useful, feel free to forward this along. The more people in your agency who understand this stuff, the better for everyone.

Speak soon,

Tim

🎉 Start Your Course Here 🎉

Subscribe now

Btw. Anthropic Claude and Google Gemini also have the projects feature.

When I first started using Generative AI tools, I regularly found myself explaining our client’s industry (giving context) to ChatGPT in order to get half decent results.

Same client. New project. Same background context. Copy, paste, wait, repeat.

There had to be a better way.

Turns out there is. Three ways, actually.

Most people are still using ChatGPT the same way for everything. Open a new chat, type a question, get an answer. But OpenAI has built two other approaches that work very differently.

Choosing the right one for the task can save you hours of repetitive setup. Let me break down when to use each.

The Quick Comparison

Option 1: Standard ChatGPT

This is what most people use. Open a new chat, ask your question, get your answer.

For example, for something like competitor research: You might type “Research the top 3 competitors for a sustainable fashion brand.” You’ll get decent results. But next week when you need more detail? You’re starting from scratch.

The good stuff:

• Zero setup. Just start typing.

• Perfect for genuinely one-off questions.

• Full access to web search and deep research.

The not-so-good:

• You need a decent prompt for half-decent results.

• No memory between sessions.

• You’ll re-explain client context every single time.

• Any files you upload disappear when you close the chat.

Use it for: Quick questions during a client call. Brainstorming session starters. Anything you won’t need to revisit.

Option 2: ChatGPT Projects

Think of Projects as workspace folders within ChatGPT. They launched in December 2024 and are now available to everyone, including free users.

For competitor research: Create a project called “Client X - Competitor Intel.” Upload their brand guidelines, competitor list, and last pitch deck - don’t forget to add project instructions. Now, every conversation in that project starts with full context already loaded.

Ask “What’s new with Competitor Y?” and ChatGPT already knows the whole landscape. No re-explaining needed.

The good stuff:

• Remembers context across all conversations in the project.

• Upload reference files once, access them forever.

• Set custom instructions per project (like your research methodology).

• Share with your team. Up to 100 collaborators (if you have a ChatGPT team subscription)

• Keep client data isolated with project-only memory

  !!Important!!
  
  Make sure you know your company policies around use of GenAI and client projects.

The not-so-good:

• Full features need a Plus subscription or Team subscription

• Memory works more like sticky notes than complete archives.

• You can’t use Custom GPTs inside a Project.

Use it for: Ongoing client projects. Multi-week campaigns. Pitch prep where context matters.

Tip: Whenever you create an output you’re happy with. Pop it back into the project files to give it even more context.

Option 3: Custom GPTs

Custom GPTs let you build a specialised AI assistant with preset instructions and knowledge files baked in.

For example. My Digital Project Manager GPT, Appraisal GPT and UX Surgeon GPT

For competitor research: Build a “Competitor Analyst” GPT with your agency’s research framework already loaded. Upload your SWOT template, competitive dimensions checklist, and analysis methodology. Anyone on your team can use it for any client. Consistent output every time.

The good stuff:

• Create once, reuse forever.

• Enforce consistent methodology across your team.

• Share with colleagues or even clients.

• Great for standardised deliverables.

• Make yourself look really clever to your colleagues

The not-so-good:

• No memory between sessions. This is the big one. Each conversation starts fresh.

• Limited to 20 knowledge files.

• Setup takes more time upfront.

• Users need a paid subscription to access your GPT.

Use it for: Templated processes. Team-wide tools. Client-facing assistants where consistency matters more than memory.

So Which Should You Actually Use?

Here’s my simple decision tree:

Is this a one-off prompt or set of prompts? Use Standard ChatGPT.

Will you work on this client or project repeatedly? Use Projects.

Do you need the same process for multiple clients? Build a Custom GPT.

Do you need your team using consistent methodology? Custom GPT.

Do you need memory and context to persist? Projects. Custom GPTs don’t do this.

The combo approach works well too. Use Projects for ongoing client work. Build Custom GPTs for standardised agency processes that apply across clients.

Your Next Step

Pick your most active client. Create a Project for them in ChatGPT (or your company sanctioned LLM of choice).

Upload their brand guidelines, tone of voice, work examples, recent briefs, competitor research etc. Start your next project-related task from there.

Then come back and tell me how many hours you saved this month.

Email me at tim@thewashup.club

I’d love to hear how it goes.

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TLDR

A new research paper has identified something called the “False-Correction Loop” where LLMs apologise for errors, claim to have fixed them, then immediately generate new fabrications. This happens because the AI is optimised to seem helpful, not to be accurate.

The paper also found LLMs treat mainstream sources with trust while being overly sceptical of independent research. Worth keeping in mind before you take any AI output at face value.

The Research

A paper published on Zenodo called “Structural Inducements for Hallucination in Large Language Models” makes for uncomfortable reading.

Written by an independent researcher at the Synthesis Intelligence Laboratory, it documents what happens when you ask an AI to discuss a document it has never actually seen.

The experiment was simple. The researcher gave the AI a link to a genuine scientific paper that existed only as an external PDF. The AI couldn’t access it.

Instead of saying, “I can’t read that,” the AI confidently invented an entire fake version. Complete with made-up section titles, fabricated page references, and non-existent Digital Object Identifiers (DOIs).

Here’s where it gets worse.

When corrected with actual excerpts from the real paper, the AI apologised, claimed it had now read the document, thanked the researcher for the correction, then immediately generated a fresh batch of equally fictitious details.

This cycle repeated for dozens of turns.

The paper names this the “False-Correction Loop.”

Why This Happens

The explanation is structural, not random.

LLMs are trained to maximise helpfulness scores. Admitting ignorance scores poorly. Manufacturing a coherent story keeps the conversation flowing and the user happy (at least temporarily).

So the AI does what it was rewarded to do: sound confident and useful, even when it has no idea what it’s talking about.

The paper also found something called “authority-bias asymmetry.”

Claims from institutional, high-status, or consensus sources get accepted with minimal friction. Independent or unconventional research gets treated with extreme scepticism, sometimes even dismissed through fabricated counter-evidence.

The researcher formalised this into an eight-stage “Novel Hypothesis Suppression Pipeline.” Basically, how LLMs systematically downgrade ideas that fall outside the mainstream.

What This Means For You

None of this means AI is useless. Far from it.

But it does mean we need to stay sharp.

If you’re using AI to research topics, summarise documents, or generate content based on sources, you need to verify the output.

Don’t assume accuracy because the response sounds confident.

A few practical habits worth building: never trust citations without checking them yourself, be especially careful when asking AI about documents you’ve uploaded (it might not be reading what you think it’s reading), and treat any correction loop as a red flag.

The AI isn’t lying to you on purpose. It’s doing exactly what it was trained to do.

That’s the problem.

Reference: “Structural Inducements for Hallucination in Large Language Models: An Output-Only Case Study and the Discovery of the False-Correction Loop” – Synthesis Intelligence Laboratory, available at https://zenodo.org/records/17655375

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