Adam Danyal, 86-90 Paul Street, London (2026)

17/06/2026

AI adoption does not fail because people are difficult.

Most AI rollouts have the same hidden problem.
Leaders buy the tool, announce the vision, run the training, and wait.
Then behavior barely changes.
The issue is not always resistance.
It is usually design.

People do not adopt AI in a vacuum.
They adopt what feels safe.
They adopt what makes the work easier.
They adopt what protects their identity.
They adopt what fits the workflow they already live inside.

That is why every team has adoption personas.
Not job titles.
Behavior patterns.
Once you see them, you can design around them.

The Prompt Passenger uses AI before thinking.
They outsource the first draft, the judgment, and sometimes the whole brainstorm.
The fix is not a ban.
Ask for a point of view before AI use.
Make the hypothesis first, then pressure-test it.

The Quiet Resister nods in the meeting and avoids the tool.
They are waiting for the initiative to fade.
Start with one annoying, low-risk task they already hate.
Confidence grows faster from relief than from hype.

The Shadow Power User is already using AI privately.
They may be afraid their workflow will be judged, copied, or handed off.
Make useful workflows visible.
Reward repeatable systems, not secret heroics.

The De-Skilled Expert worries that AI makes hard-earned judgment less valuable.
That fear is rational.
Show how AI amplifies expertise.
Position it as a junior analyst, not a replacement brain.

The Governance Ghost appears when nobody knows what is allowed.
Teams either freeze or freestyle.
Clarify what is allowed, risky, and off-limits.
Define access, review points, and never-touch zones.

The Workflow Orphan uses AI as a side activity.
Outputs get copied into Slack, docs, spreadsheets, and tabs.
Put AI inside the actual process.
Connect it to roles, handoffs, review points, and decisions.

The mistake is thinking adoption means enthusiasm.
It does not.
Adoption means the work changed.
Not the slide deck.
Not the town hall.
The actual work.

If your AI rollout is stuck, do not ask only:
“Why are people not using AI?”
Ask:
“What have we designed around AI?”

The tool is not the transformation.
The design around the tool is.
Which persona is slowing your team down right now?

17/06/2026

AI does not rescue a broken process. It accelerates it.

The fastest way to waste money on AI is to automate the wrong thing beautifully.

I see this pattern in founder conversations all the time.
A team says, “we need an AI agent.”
Or, “can AI handle this workflow?”
Or, “we want to automate this process.”

Then the real problem appears.

The workflow is not mapped.
The customer data lives in 5 places.
The team is using a spreadsheet as a CRM.
Nobody owns the handoff between sales, ops, and delivery.
The SOP exists, but only in someone’s head.

That is not an AI problem.
That is a foundation problem.

AI-first sounds fast because it starts with the shiny layer.
It lets you demo a chatbot, an agent, or an automation quickly.
But if the business underneath is messy, the output gets messy too.

Messy data becomes faster messy data.
Unclear process becomes automated confusion.
Poor ownership becomes a faster-moving bottleneck.

The better order is less exciting, but much safer.

Foundations first means you clean the process before you automate it.
You map the workflow before you ask AI to run it.
You decide who owns each handoff before the handoff is delegated.
You organize the data before the model touches it.
You define what should stay human before the machine speeds it up.

Then you layer in AI.

That is when AI becomes leverage instead of decoration.
The goal is not to “use AI.”
The goal is to build a business that runs better because of AI.

Here is the quick diagnostic I would use this week.

Pick one workflow you want to automate.
Write down every input, owner, decision, system, handoff, and exception.
If that map is unclear, do not start with an agent.
Start with the foundation.

Structure first.
AI second.

The companies that win will not be the ones with the fanciest demos.
They will be the ones whose operations can actually carry the speed.

What process in your business would become dangerous if AI made it faster?

17/06/2026

Most AI programs do not fail because the model was weak.

They fail because nobody made the boring ownership calls first.

I keep seeing the same AI failure in different costumes.
The tools change.
The dashboards change.
The consultants change.
The failure pattern stays painfully familiar.

One team starts data-first.
They define the foundation.
They decide who owns the data layer.
They agree what the dashboard means.
They write down who gets called when the pipeline breaks.
That work looks slow until the plane actually flies.

Another team starts AI-first.
They buy the tool.
They launch the pilot.
They ask the model to fly before the runway exists.
Then month 14 arrives and everyone is surprised that the plane is burning.

The policy route looks safer.
It usually is not.
Version 1 becomes version 4.
Version 4 becomes version 7.
The binder gets longer while the work keeps happening somewhere else.
If nobody reads it, owns it, or changes behaviour because of it, it is theatre.

Then there is Tom.
Every company has one.
Tom knows the spreadsheet.
Tom knows the exception.
Tom knows why Monday breaks.
So every AI workflow quietly depends on Tom until Tom goes on holiday.

That is not operational resilience.
That is hidden fragility with a friendly face.

The practical fix is not glamorous.
Name the owner.
Define the source of truth.
Write the decision rule.
Decide who can change it.
Decide what happens when it fails.
Then build the AI on top of that.

Before you approve another pilot, ask three questions.
Who owns this workflow when the demo ends?
Which data definition can the team trust?
What breaks if the one person who understands it is offline?
Those answers will tell you more than the vendor deck.

That is the work leaders want to skip.
It is also the work that decides whether AI scales.

Better models help.
Better ownership compounds.
Which part of your AI program still depends on Tom?

17/06/2026

Claude is becoming a small toolkit, not a single destination.

So the better question is not:
Which Claude is best?

The better question is:
Where does the work live?

If the work lives in your head, use Claude chat.
Good for strategy, writing, planning, synthesis, hiring questions, and decision prep.

If the work repeats, use Projects.
Good for brand voice, sales material, operating docs, research briefs, and recurring analysis.

If the work lives on webpages, use Claude for Chrome.
Good for page review, competitor checks, sourcing, summarizing, and browser-based workflows.

If the work lives in company files, use connected work context.
Good for reports, policies, notes, decks, and internal knowledge.

If the work lives in a repo, use Claude Code.
Good for implementation, refactors, tests, debugging, and codebase-aware changes.

If the work needs a repeatable method, use Skills.
Good for turning a strong workflow into something the team can run again.

If the work needs scheduled attention, use routines only after the judgment is clear.
Good for reminders, checks, and stable recurring tasks.
Bad for decisions that need new context every time.

That is the surface decision.
Then choose the model.

Sonnet is the daily driver.
Use it for most serious work.

Opus is the deep-thinking reserve.
Use it when the task has ambiguity, stakes, or messy reasoning.

Do not make people memorize all of this.
Turn it into a routing note for the team.

Page task → Chrome.
Repo task → Claude Code.
Repeat task → Project.
Company context task → connected files.
Reusable method → Skill.
Hard reasoning → Opus.
Normal ex*****on → Sonnet.

The risk is not that people choose the imperfect Claude once.
The risk is that the whole team keeps doing copy-paste work because nobody designed the route.

A small routing standard can save hours.
It also makes AI use easier to coach, audit, and improve weekly.

Where does your team still use plain chat when a better Claude surface exists?

16/06/2026

A crowded AI stack is not a strategy. It is usually a subscription problem waiting to happen.

This is how I would read an AI Tool Value Map.

Not as a ranking to worship.
As a renewal conversation.

Tier 1 tools should be close to expensive work.
They touch code, product, research, customer workflows, or core operations.
That is why tools like Cursor, Claude Code, Perplexity, v0, Lovable, Framer, and Windsurf deserve serious attention.
They can sit directly inside production loops.

Tier 2 tools can still matter.
Gamma can speed up first-pass decks.
Notion AI can help internal knowledge work.
Copilot can help where Microsoft 365 is already the operating layer.
Gemini can help where Google Workspace is the operating layer.
ElevenLabs can help when voice is part of the workflow.
Julius can help teams explore data faster.
Replit can help prototypes get moving.

Tier 3 tools are not automatically useless.
They may just be too far from a weekly workflow.
Or they may create output that still needs heavy cleanup.
Or nobody owns the use case.

Here is the board-level question:
Would we notice if this tool disappeared for 30 days?

If yes, it may be infrastructure.
If no, it may be theatre.

Run the stack review like this.

Keep: the tool saves a repeated step every week.
Keep: the tool improves quality after review.
Keep: the tool reduces handoff time.
Keep: the tool connects to the files, code, data, or pages where work happens.
Keep: the tool has a named owner and a clear use case.

Question: the tool is loved by 1 person but invisible to the workflow.
Question: the tool creates impressive demos but no shipped output.
Question: the tool overlaps with another subscription.
Question: the tool handles sensitive data without a clear policy.

Cut: the tool adds another tab but removes no work.
Cut: the tool has no before-and-after examples.
Cut: the tool is renewed because nobody wants to check usage.

That is the discipline founders need in 2026.
Not fewer tools for the sake of being strict.
Better tools because the work deserves clarity.

Which tool would your team fight to keep, and which one would nobody miss?

16/06/2026

Agentic AI gets easier when you stop naming the hype and start naming the pattern.

There are 9 useful patterns to know.

Prompt chaining is the assembly line.
Use it when the work has stages: research, outline, draft, edit, final check.

Parallelization is the search party.
Use it when many independent checks can run at once: competitors, test cases, content angles, lead research.

Orchestrator-worker is the project manager.
Use it when one system should plan the work and assign smaller tasks.

Evaluator-optimizer is the editor.
Use it when quality matters enough to create, critique, and improve against a rubric.

Routing is the switchboard.
Use it when different inputs need different paths, such as support tickets, sales leads, or internal requests.

Autonomous workflow is the machine.
Use it only when the rules are clear, the actions are bounded, and the downside is controlled.

Human-in-the-loop is the safety gate.
Use it for legal, finance, hiring, customer promises, medical content, and code deployment.

Reflection is the second look.
Use it when the first answer may miss assumptions, edge cases, or weak evidence.

Multi-agent debate is the pressure test.
Use it when a decision benefits from opposing views: buyer, operator, CFO, engineer, customer.

The mistake is trying to build “an agent” before choosing the pattern.

That creates demos.
It does not create dependable workflows.

A better build sequence:
Name the task.
Choose the pattern.
Define the inputs.
Define the stopping point.
Add the review rule.
Measure the before-and-after result.

One example:
Customer complaint comes in.
Router classifies it.
Retriever pulls account context.
Drafting agent proposes a response.
Evaluator checks policy and tone.
Human approves anything above a risk threshold.

That is agentic work you can inspect.
Not magic.
Not a black box.
A workflow with visible joints and owners.

The future advantage is not saying “we use agents.”
The advantage is knowing which pattern fits which job.

Which workflow in your company needs a pattern before it needs another tool?

14/06/2026

The people most confident about replacing teams with AI are often the furthest from the work.

AI looks very different from the demo room than it does at the desk.

The demo-room version sounds like this:
ChatGPT wrote 300% of the code.
Claude can replace Johnny by Friday.
Gemini will make the whole team twice as productive.
One impressive prompt becomes the proof point.
One clean slide becomes the operating plan.
One viral clip becomes the budget argument.

The daily-user version sounds different:
ChatGPT hallucinated again.
Claude gave a strong first draft, then missed the edge case.
Perplexity found sources, but one source was not relevant.
Copilot helped with Excel, then needed a human to check the formula.
Gamma made the deck faster, but the story still needed judgment.
Cursor sped up the build, but someone still had to review the diff.

That is not pessimism.
That is operating experience.

01 Adoption metric: count useful shipped workflows, not token burn.
02 Quality check: compare before and after work, not login volume.
03 Workflow test: ask whether AI removed a step or added review debt.
04 Tool choice: use Claude for structured thinking, not every tiny task.
05 Tool choice: use ChatGPT for fast ideation, not final truth.
06 Tool choice: use Perplexity when sources matter, then verify the sources.
07 Tool choice: use Copilot when work already lives inside Microsoft 365.
08 Management check: ask operators where AI breaks before expanding the rollout.
09 Budget check: fund the 3 workflows people repeat every week.
10 Risk check: watch for confident outputs that create hidden rework.

The real gap is not belief in AI.
The real gap is contact with reality.

Leaders who only watch demos overestimate speed.
Teams who use AI every day learn where the leverage is.
They also learn where the tool needs constraints, context, review, and patience.

That is where the advantage starts.
Not in louder AI enthusiasm.
Not in bigger adoption dashboards.
In boring, repeated workflow improvement.

Use AI daily.
Measure real output.
Listen to the people doing the work.

The question is simple:
Are you rewarding AI activity, or AI results?

13/06/2026

Most AI agent projects fail because teams confuse tools with architecture.

Agents are moving from demo magic to operating system decisions.
If you lead a team, the vocabulary matters.
It tells you what can scale.
It tells you what will break.
It tells you where governance must sit.

Here are the 6 terms worth knowing before the next vendor call.

1. Model Context Protocol is the connection layer.
MCP defines how an AI model requests context from tools, databases, and services.
Without MCP-style standards, every integration becomes a custom bridge.

2. Single-agent architecture is the simple starting point.
One agent receives a user request, decides the next step, calls tools, and uses memory.
This works for clear tasks like research summaries, CRM updates, or support triage.

The limit is scale.
One agent can become a bottleneck when work needs several roles at once.

3. Skills are reusable actions.
A skill might run web search, execute code, analyze data, draft an email, or query a product catalog.
The better the skill boundary, the easier it is to test and improve the agent.

4. Multi-agent architecture divides the work.
A manager agent can route tasks to research, analysis, and ex*****on agents.
That pattern helps when the job needs specialization, review, or parallel progress.

The risk is coordination overhead.
More agents do not automatically mean better output.
You need clear handoffs, shared memory, and a visible failure path.

5. Agentic RAG adds decision-making to retrieval.
Classic RAG fetches documents for a model.
Agentic RAG decides what to search, where to search, and how to use the result.

That difference matters for live business data.
The agent can retrieve, reason, check gaps, and produce a more current answer.

6. Memory gives the system continuity.
Short-term memory keeps the current task coherent.
Long-term memory stores preferences, prior decisions, and useful history.
Vector stores and knowledge bases make that memory searchable.

The leadership question is simple.
Which memories should the agent keep?
Which memories should expire?
Which memories should never be stored?

These terms are not academic.
They are design choices.
They shape cost, reliability, security, and trust.

If your team is building with agents, start with the architecture.
Then choose the tools.
Which of these 6 terms is still fuzzy in your organization?

13/06/2026

Most Claude users are still treating it like a blank chat box.

The 2026 Claude setup is less about clever prompts and more about systems.

Open Claude.

Type a request.

Wait for output.

That is the old workflow.

The better workflow starts before the prompt.

1. Claude Cowork turns Claude into a desktop operator.

Use it when Claude needs actual files, browser work, or multi-step output.

Create one parent folder with ABOUT ME, OUTPUTS, and TEMPLATES.

Add about-me.md, my-company.md, and anti-ai-style.md before the first task.

Diagnostic check: if Claude guesses your context, your setup is incomplete.

The useful prompt is simple:

Read my folder. Ask questions before you start. If anything is unclear, do not guess.

2. Claude Projects turn repeat work into a loaded workspace.

Use one Project for one deliverable, not a mixed dumping ground.

Upload 3 things only: best example output, background docs, and the team template.

Before your team uses it, test tone, structure, and level of detail.

If the first draft feels off, fix instructions before inviting anyone else.

3. Claude Skills turn repeated instructions into reusable behavior.

A useful Skill needs 3 parts: trigger word, role, and rules.

Build the first Skill around the task you repeat most often.

Narrow beats broad because Claude needs a clear job, not a vague personality.

If you give the same instruction 3+ times, turn it into a Skill.

4. Claude Code is for builders.

Use it when Claude needs to read a codebase, run tests, edit files, or ship changes.

Run /init before serious work.

Describe the problem, not the fix.

Use /compact around the halfway point so context does not collapse late.

The quick rule is practical.

Same task every week means Project.

Same instruction 3+ times means Skill.

Actual files mean Cowork.

Software build means Code.

Most teams do not need more AI experiments.

They need cleaner Claude operating systems.

One folder.

One Project.

One Skill.

One build workflow.

Which part of your Claude setup is still just a blank chat box?

Adam Danyal

17/06/2026

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