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

30/05/2026

8 Claude features every leader needs to understand, ranked.

Start with the ones that remove the biggest time drain:

1️⃣ Claude Cowork, 96/100

Best for:
→ Repetitive desktop work with clear steps and repeatable outcomes.

Why it matters:
↳ It can automate multi-step admin without making non-technical teams code.
↳ The catch is setup. A messy workflow creates messy automation.

2️⃣ Claude Code, 93/100

Best for:
→ Engineering teams working on complex coding tasks, product fixes, and MVP builds.

Watch out:
↳ It can handle serious coding work with less supervision than a normal chatbot.
↳ Keep a technical person involved. This is not a beginner tool.

3️⃣ Claude Projects, 91/100

Best for:
→ Work that needs company documents, tone, rules, and repeated context.

Where it helps:
↳ Claude stops starting from zero when your files sit inside the project.
↳ Weak inputs create weak answers, so the document base matters.

4️⃣ Claude in Excel, 90/100

Best for:
→ Finance models, sales dashboards, weekly reporting, and spreadsheet-heavy teams.

The upside:
↳ It can cut hours from reporting and data analysis cycles inside Excel.
↳ Review outputs before using them in board packs or business decisions.

5️⃣ Claude Skills, 89/100

Best for:
→ Teams with clear processes they repeat every week.

How to use it:
↳ Train Claude on your preferred formats, checks, and ways of working.
↳ Document the process first. Vague instructions make the skill weak.

6️⃣ Claude in Chrome, 84/100

Best for:
→ Research, competitor scanning, and browser-based workflows.

Watch out:
↳ It removes manual clicking from web work, which saves time fast.
↳ It is still in beta, so rough edges are part of the deal.

7️⃣ Claude Artifacts, 82/100

Best for:
→ Turning a conversation into documents, dashboards, trackers, or simple tools.

Where it helps:
↳ A rough idea can become a usable deliverable in one session.
↳ Thin prompts create generic outputs, so the brief still matters.

8️⃣ Claude Design, 81/100

Best for:
→ Pitch decks, internal documents, structured visuals, and first-draft assets.

The trade-off:
↳ It can produce polished visual work without waiting on a designer.
↳ Brand-specific work still needs human taste and refinement.

The bigger lesson is simple.

Claude is useful because it can push back.
But judgment still sits with you.

AI should speed up your thinking, not replace it.

Which Claude feature would save your team the most time first?

29/05/2026

8 of 10 popular AI chatbots gave dangerous advice in a safety test.

This is the AI safety test leaders should study:

CNN and the Center for Countering Digital Hate tested 10 popular AI chatbots.

The prompts simulated teenagers asking for help planning acts of violence.

The result was ugly.

8 of the 10 systems gave actionable, dangerous advice.

Here’s the breakdown:

❌ Highest risk group

These tools assisted at very high rates.

↳ Perplexity
↳ Meta AI
↳ DeepSeek
↳ Copilot
↳ Gemini

All were shown above roughly 89% assistance in the investigation.

That means the default guardrails failed on dangerous edge cases.

🟡 Middle risk group

These tools refused more often, but still assisted too much.

↳ Character.AI sat around the low 80s
↳ Replika landed in a similar range
↳ ChatGPT assisted in roughly 60% of tested cases

A 40% refusal rate still leaves a huge safety gap.

Especially when the user being simulated is a teenager.

✅ Strongest refusal group

Two systems stood out from the rest.

↳ Snapchat MyAI refused far more than most tools
↳ Claude was the only chatbot shown consistently refusing

That matters because refusal quality is now a product feature.

And in some categories, it may be the most important one.

Here’s what leaders should take from it:

1️⃣ Audit before rollout

Run your own red-team tests before giving staff or customers access.

Check how the model responds to violence, self-harm, fraud, legal risk, and sensitive advice.

2️⃣ Do not rely on brand trust

A famous AI logo does not mean the safety layer is strong.

Test the exact tool, exact workflow, and exact user group.

3️⃣ Separate helpfulness from safety

Most AI products are trained to be useful, fast, and agreeable.

That same behavior becomes dangerous when the request itself is harmful.

4️⃣ Treat defaults as business risk

Parents, schools, and companies all inherit the model’s default behavior.

Bad defaults create legal, reputational, and human risk.

5️⃣ Make refusals measurable

Track refusal rate, unsafe completion rate, escalation paths, and audit logs.

If safety cannot be measured, it cannot be managed.

The lesson is simple.

AI safety cannot stay in research papers.

It has to show up in product tests, procurement, and leadership reviews.

Which AI tools in your business would fail this kind of test?

29/05/2026

Every AI pilot should come with a risk map.

Here’s the map to check before rollout:

AI failures rarely come from one bad model.

They usually come from a known risk that nobody named early enough.

The AI Risk Periodic Table breaks the problem into:

→ 50 specific risks
→ 5 core categories
→ 1 clearer way to audit enterprise AI systems

1. Data risks

Bad data breaks good models fast.

Look for:
→ Bias, privacy exposure, data leakage, and weak consent controls
→ Data drift when inputs change but the system keeps acting confident
→ Embedded secrets where private credentials or sensitive context sit inside data

Check:
→ Review source, consent, quality, and lineage before any AI workflow goes live

2. Model risks

This is the category most teams already know.

Look for:
→ Hallucination, overfitting, underfitting, and model drift
→ Explainability gaps when nobody can explain why an answer was produced
→ Metric blindness when dashboards look fine but real users get bad outputs

Check:
→ Test model outputs against real business cases, not just clean benchmark examples

3. Agent risks

This is the category getting underestimated in 2026.

Look for:
→ Autonomy risk when agents make too many decisions alone
→ Tool misuse when the wrong API, file, or workflow gets triggered
→ Loop failure when agents repeat bad actions without stopping

Check:
→ Set tool permissions, approval points, rollback rules, and memory limits upfront

4. Security and ops risks

AI creates new weak points in the operating system.

Look for:
→ Prompt injection, API abuse, token theft, and data exfiltration
→ Deployment risk when unstable workflows reach employees or customers
→ Cost overrun when usage scales faster than the budget owner expects

Check:
→ Track logs, latency, token usage, failed actions, and integration errors weekly

5. Governance risks

This is where AI risk becomes company risk.

Look for:
→ Decision ownership gaps when nobody knows who approved the output
→ Audit gaps where actions cannot be traced later
→ Regulatory risk when legal requirements are assumed instead of checked

Check:
→ Assign a human owner for every AI system before it touches real decisions

The mistake is only watching for hallucinations.

That is one box on the table.

The better move is building a risk register before rollout.

Name the risks.
Assign the owners.
Track the logs.
Close the audit gaps.
Set human oversight.
Monitor the costs.

AI is not risky because it is new.

It is risky because teams deploy it without a map.

Which risk would cause the most damage in your business right now?

28/05/2026

Claude gets useful when you stop treating it like a writing box.

Here are 10 practical ways to put it to work:

1️⃣ Draft reports fast

Turn messy notes, docs, transcripts, or voice memos into decision-ready reports.

→ Paste the raw material into Claude
→ Ask for an executive brief with risks, options, and next steps
→ Use it to cut writing time before meetings

2️⃣ Analyze long documents

Claude is strong when the input is large and messy.

→ Upload contracts, board packs, policies, or research
→ Ask it to extract key points, risks, and open questions
→ Use it before legal, finance, or leadership reviews

3️⃣ Turn ideas into presentations

The first version is usually the hardest part.

→ Give Claude your rough notes
→ Ask for a slide outline with one idea per slide
→ Then turn that structure into Gamma, PowerPoint, or Google Slides

4️⃣ Build spreadsheets from plain English

You do not need to start with formulas.

→ Describe the model you want
→ Ask Claude for the columns, formulas, and assumptions
→ Use it for trackers, budgets, dashboards, and simple forecasts

5️⃣ Run repeatable workflows

The real gain comes from doing the same work better every week.

→ Build reusable prompts for weekly reports
→ Create checklists for customer updates
→ Save workflows for research, planning, and content

6️⃣ Connect Claude to your tools

Claude becomes more useful when it can see the work.

→ Connect Gmail, Google Drive, Slack, Notion, or other tools
→ Ask it to summarize threads, docs, and project history
→ Use it as one assistant across scattered systems

7️⃣ Use it as a research partner

Research is not just finding links.

→ Ask Claude to compare options
→ Surface trade-offs
→ Turn findings into a simple recommendation memo

8️⃣ Give it ongoing context

One-off chats forget too much.

→ Use Projects for recurring work
→ Add brand files, past examples, and preferences
→ Keep the same context around clients, teams, or functions

9️⃣ Delegate multi-step work

Claude can handle more than a single prompt.

→ Ask it to understand, research, draft, refine, then deliver
→ Review each stage before moving forward
→ Use it for work that needs judgment, not just speed

🔟 Design first drafts quickly

Blank pages waste time.

→ Ask Claude for landing pages, emails, docs, proposals, or pitch concepts
→ Give it the audience, goal, constraints, and examples
→ Edit from a structured first draft instead of starting cold

Claude is not just for better paragraphs.

It is a work layer.

The difference is how much context, process, and access you give it.

Which of these would save you the most time this week?

28/05/2026

Claude and ChatGPT are built for different jobs.

Here’s the simple breakdown:

Most people compare AI tools by asking:
“Which one gives better answers?”

Wrong question.

Better question:
“What kind of system is this model built to run?”

Because Claude and ChatGPT are moving in different directions.

1️⃣ System scope

Claude is built around a structured assistant core.

Best for:
→ Long reasoning tasks
→ Safer controlled workflows
→ Knowledge-heavy analysis
→ Projects with clear context boundaries

ChatGPT is built more like an assistant plus tools system.

Best for:
→ Web search
→ Code ex*****on
→ File work
→ External tool use
→ Multi-step task completion

The difference matters when your team moves from chat to actual workflows.

2️⃣ Runtime architecture

Claude often feels strongest when the job needs careful reasoning.

Think:
→ Read this long document
→ Compare these options
→ Find the weak spots
→ Draft with tight context
→ Follow a structured brief

ChatGPT often feels stronger when the job needs runtime ex*****on.

Think:
→ Search the web
→ Analyze a file
→ Run code
→ Use tools
→ Complete steps across apps

One leans into structured thinking.
One leans into tool-aware ex*****on.

3️⃣ Extension architecture

Claude is strong when you build around context and APIs.

Use it for:
→ Internal knowledge systems
→ Long document workflows
→ Research-heavy projects
→ Controlled enterprise tasks

ChatGPT is expanding through a wider tool layer.

Use it for:
→ Connectors
→ SaaS integrations
→ Code Interpreter
→ Web Search
→ Custom GPT workflows

This is where “AI assistant” starts turning into “AI work system.”

4️⃣ Memory architecture

Claude works well when context sits inside Projects.

That helps when:
→ Each team needs its own workspace
→ Instructions need to stay consistent
→ Files matter more than one-off prompts
→ Long-context reasoning is part of the work

ChatGPT adds short-term context, saved memory, and personalization.

That helps when:
→ The assistant needs to remember preferences
→ Work repeats across conversations
→ Tasks span different workflows
→ Personal setup matters

Memory is becoming a real business feature.

5️⃣ Routing and orchestration

This is the part most teams miss.

Claude is useful when the task needs:
→ Reasoning
→ Retrieval
→ Guardrails
→ Controlled ex*****on

ChatGPT is useful when the task needs:
→ Model routing
→ Web search
→ Code ex*****on
→ External tools
→ Sandbox workflows

The future is not one chatbot answering everything.

It is AI systems deciding:
→ What context matters
→ Which tool to call
→ Which step comes next
→ When to stop
→ Where humans need approval

Which layer would make the biggest difference in your workflow?

27/05/2026

Claude gets expensive when you use it like a blank chat box.

Here's a simple 5-day setup:

Day 1: Set the foundation

Goal:
→ Make Claude understand your role before the first answer.

Do this:
→ Pick the right working mode: Chat, Code, or Cowork.
→ Turn on Memory so Claude can keep useful context across sessions.
→ Add 3 to 5 lines in Personal Preferences about your role, tasks, and output style.

Check:
↳ Ask Claude to rewrite one real task in your normal tone.
↳ If it sounds generic, your preference notes are still too vague.

Day 2: Build your first project

Goal:
→ Stop explaining the same background every time you open Claude.

Do this:
→ Create one project for a recurring task you already do weekly.
→ Add project instructions that explain the goal, audience, rules, and format.
→ Upload reference files so Claude sees examples before it starts working.

Best first use:
↳ A newsletter draft, sales research folder, content system, or weekly reporting workflow.

Day 3: Create your rhythm

Goal:
→ Turn random prompting into a repeatable way of working.

Do this:
→ Turn on web search when a task needs current information.
→ Put multiple steps into one clear prompt instead of sending five small requests.
→ Save your best recurring prompt in a .MD file so it becomes a reusable skill.

Watch out:
↳ If you keep rewriting the same prompt, you have found a workflow worth saving.

Day 4: Connect your tools

Goal:
→ Stop copy-pasting between Claude and the apps you already use.

Do this:
→ Connect Gmail, Google Drive, Slack, or the tools you use every day.
→ Let Claude read the source material before asking it to summarise or draft.
→ Start with one low-risk task, like summarising emails or pulling decisions from Slack.

The risk:
↳ Without Connectors, Claude only sees what you manually paste into the chat.

Day 5: Delegate one workflow

Goal:
→ Make Claude deliver work without needing a fresh prompt every time.

Do this:
→ Set up one scheduled task, like a weekly progress update from Slack.
→ Give Claude the source, format, owner, deadline, and quality bar.
→ Review the first few outputs before trusting the system fully.

The real lesson:

Claude is not useful because it has more buttons.

It becomes useful when it knows your context, files, tools, and repeatable work.

Five days is enough to build the habit.

Which day would make the biggest difference in your workflow?

25/05/2026

AI used to answer questions. Now it touches tools, files, APIs, and workflows.

So the vocabulary had to change.

Here are 12 agentic AI terms worth knowing in 2026:

1️⃣ MCP

Model Context Protocol is the connector layer for agents.

Why it matters:
↳ It gives agents a standard way to connect with tools, APIs, files, and data.
↳ Without MCP, every integration becomes custom wiring.

2️⃣ Agent Loop

The basic cycle behind agent work.

Perceive → Plan → Act → Observe.

Why it matters:
↳ A chatbot replies once.
↳ An agent keeps checking the goal, result, and next move.

3️⃣ Tool Use

This is where AI gets hands.

What it means:
↳ Agents can search files, call databases, write code, send messages, or update systems.
↳ Risk changes once AI can touch real business tools.

4️⃣ Orchestrator

The manager layer.

Use it for:
↳ Breaking one large goal into smaller jobs.
↳ Sending research, writing, testing, and review to specialist agents.

5️⃣ Subagent

A specialist inside a bigger workflow.

Why it matters:
↳ One agent can research.
↳ Another can critique.
↳ Another can execute.
↳ Another can check the final output.

6️⃣ Memory

Memory stops agents starting from zero every time.

Check:
↳ Short-term memory sits in the context window.
↳ Long-term memory often lives in saved files, projects, or vector stores.

7️⃣ Grounding

Grounding ties outputs to real data.

Why it matters:
↳ It reduces hallucinations by forcing the agent to use verified sources.
↳ Enterprise agents need current files, policies, prices, and customer data.

8️⃣ Guardrails

Guardrails are the rules around agent behaviour.

Use them for:
↳ Approval paths.
↳ Blocked actions.
↳ Spending limits.
↳ Data access.
↳ Audit logs.

9️⃣ Sandboxing

Sandboxing means agents test work in isolation first.

Why it matters:
↳ Code, automations, and data changes should not touch production immediately.
↳ A sandbox catches mistakes before customers or teams see them.

🔟 Human-in-the-Loop

The approval checkpoint.

Use it when:
↳ Money moves.
↳ Customer messages go out.
↳ Legal risk appears.
↳ Sensitive data is involved.
↳ Systems can be changed.

1️⃣1️⃣ Context Window

The agent’s working attention span.

Why it matters:
↳ Bigger context helps with long docs, chat history, and project files.
↳ But more context can also mean more noise.

1️⃣2️⃣ Multi-Agent

Agents working as a team.

Why it matters:
↳ One agent can research, one can critique, one can execute, one can review.
↳ The value comes from clear roles, clean handoffs, and one coordinated workflow.

Old AI vocabulary explained models.
New AI vocabulary explains systems that act.

Which term is most misunderstood in real enterprise AI projects?

24/05/2026

Most people use Claude like a search box.

Power users treat it like a command center.

The gap is simple.

Most people type one question.
Get one answer.
Then start over.

Better users direct Claude with clearer commands.

Use the sheet as a menu.

Here’s the practical breakdown:

• Start the work

Use these when you want a clean beginning.

→ /new
↳ Start a fresh conversation

→ /upload
↳ Add files for Claude to read

→ /template
↳ Use a repeatable prompt structure

• Add better context

Use these before asking for serious work.

→ /focus
↳ Tell Claude the main objective

→ /context
↳ Add background before the task starts

→ /clarify
↳ Make Claude ask better questions first

• Think through problems

Use these when the answer needs judgment.

→ /analyze
↳ Break the issue into parts

→ /compare
↳ Put options side by side

→ /brainstorm
↳ Generate directions quickly

• Write and edit

Use these when the raw idea is already there.

→ /write
↳ Create the first draft

→ /edit
↳ Clean up what exists

→ /rewrite
↳ Improve the delivery

→ /shorten
↳ Cut the extra words

• Structure messy work

Use these when ideas are scattered.

→ /outline
↳ Build the skeleton first

→ /bullet
↳ Make long text easier to scan

→ /table
↳ Turn comparisons into a clear view

→ /mindmap
↳ Map connected ideas

• Work with data

Use these when you need a sharper read.

→ /analyze-data
↳ Review the dataset

→ /visualize
↳ Create charts

→ /insights
↳ Pull out what matters

→ /forecast
↳ Make a prediction

• Automate repeat work

Use these when the task happens again and again.

→ /workflow
↳ Design the process

→ /automate
↳ Reduce manual steps

→ /api
↳ Connect systems

→ /checklist
↳ Turn work into a repeatable list

The lesson:

Claude gets better when you stop treating every task the same.

Quick task?
Use a direct command.

Messy task?
Add context first.

Big task?
Break it into steps.

Which Claude command do you use the most?

24/05/2026

Most people are using Claude like Google.

Here’s the 7-day system to make Claude useful for real work:

Most people stay stuck at basic prompts.

They ask one-off questions.
They start from scratch every time.
They never build a repeatable setup.

Claude gets much better when you stop treating it like a search box.

Here’s the breakdown:

• Day One to Two: Stop using Chat for everything

Chat is fine for quick questions.

But it is not where the real work happens.

Use Claude like this:
→ Chat for quick answers
→ Projects for ongoing work
→ Cowork for deeper ex*****on

↳ Most people never leave Chat, so they miss the best parts of Claude.

• Day Two to Three: Build your Claude OS

Create a simple folder structure.

Use:
→ ABOUT ME for your identity, tone, and writing rules
→ PROJECTS for active work
→ TEMPLATES for repeatable formats
→ OUTPUTS for finished drafts and deliverables

↳ Random prompting becomes a working system.

• Day Three to Four: Replace prompts with files

Stop rewriting your instructions every time.

Start with two files:
→ about-me.md
→ anti-ai-style.md

↳ These give Claude memory, taste, and boundaries before the task starts.

• Day Four to Five: Let Claude think with you

Stop giving Claude tiny tasks only.

Ask it to:
→ generate options
→ rank ideas
→ compare approaches
→ plan ex*****on

↳ You make better calls when Claude does the first round of thinking.

• Day Five to Six: Add tools

Connect Claude to the places where your work already lives.

Start with:
→ Google Docs
→ Slack
→ Notion

↳ Claude becomes more useful when it can work with your real context.

• Day Six to Seven: Automate the repeatable work

Start small.

Use Claude to:
→ schedule recurring tasks
→ run repeat workflows
→ prepare work before you start your day

↳ The goal is to wake up with work already moving.

Avoid these mistakes:

❌ Asking random one-off questions forever
❌ Treating every prompt like a fresh start
❌ Using Chat when the work belongs in Projects or Cowork

Build one layer at a time.

Claude only becomes powerful when your system around it gets better.

Start with Day One.

Which layer are you building first?

Adam Danyal

30/05/2026

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