Let’s be honest: AI and data can feel overwhelming. 
I’m here to make them feel useful, human and possible.

I turn AI and data from buzzwords into capabilities your organisation can actually use.  

Whether you’re starting from scratch or knee-deep in a tricky program, I help you get clarity and momentum.

What I do:

1. Help you set your AI ambition and turn it into practical strategy.
2. Build AI & data governance that’s actually usable.
3. Get teams confident so they actually want to use AI and data.
4. Fix or launch programs so they deliver results, not just dashboards.

My background blends McKinsey consulting, senior leadership roles and strong technical foundations, which means I’m comfortable in boardrooms, war rooms and everything in between.

If you’d like someone who’s pragmatic, creative and energising to work with, let’s chat.
Reach me at hello@cambiq.com

Everyone wants AI magic.
Few want to invest in the plumbing.

We live in a moment where the promise of AI
is louder than the reality of data.

I see it every week with teams.

The excitement is real. The budgets are flowing. The pilots look impressive.
But when you lift the lid a different story shows up.

Data that lives in twelve places. 
Metrics that mean one thing in finance and another in operations. 
Ownership that sounds like “we think it sits with them”. 
Governance that feels optional.

And still we keep sprinting toward AI, hoping it will smooth over the cracks.
It never does. It makes them more expensive.

Here is the part no one markets:
AI built on weak data creates more rework, more delays and more organisational friction than leaders expect.

If you want real value, start with the boring foundation:
→ Clear definitions 
→ Reliable data 
→ Agreed owners 
→ Confidence in the outputs

When these are in place AI finally becomes what everyone wants it to be: simple 
scalable 
repeatable.

So here is my question for every exec team: 
What is the data truth you have been quietly avoiding?

♻️ Repost to help someone get their data AI-ready.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Clare Kitching

Transform your AI & data ambition into action | xQuantumBlack, xMcKinsey | Global top 100 Innovators in Data & Analytics ’24 | Founder & Director, Cambiq

Overlooking good data.
Overpromising on AI.

Agentic AI is moving faster than most teams can learn it.
Use this reading path to shape your 2026 roadmap:

Every report below is from the last six months, and together they form a clean & current path from “What are agents?” to “What will my organisation look like in 2026?”

Take a read:

1️⃣ QuantumBlack / McKinsey – The state of AI in 2025
→ Start here for a data-driven snapshot of how far enterprises have actually gone with AI and agents.
🔗 https://lnkd.in/gMHgSVys

2️⃣ AWS – The rise of autonomous agents
→ Ground yourself in what agentic systems actually are and the main use cases.
🔗 https://lnkd.in/gDBDqh9D

3️⃣ PwC – Agentic AI – the new frontier in GenAI
→ A clean C suite lens. Risks, decisions, moves.
🔗 https://lnkd.in/gwgXUDJi

4️⃣ McKinsey – The Agentic AI Opportunity 
→ A five minute shareable framing of the size of the shift.
🔗 https://lnkd.in/gx-i4Ykb

5️⃣ Accenture – Six key insights for C-suite Executives to maximise the ROI of Agentic AI
→ The part most leaders skip. What drives financial value.
🔗 https://lnkd.in/gZh4FvfP

6️⃣ Bain – Building the Foundation for Agentic AI
→ Get practical on the underlying data, technology and platform foundations  to make any of this real.
🔗 https://lnkd.in/g4sTnHB4

7️⃣ IBM – Agentic AI’s strategic ascent
→ Broadens into operating model, governance and enterprise mechanics.
🔗 https://lnkd.in/g2BY4uXx

8️⃣ BCG – The Emerging Agentic Enterprise
→ What a “machines that manage themselves” future starts to look like.
🔗 https://lnkd.in/gTAvW3a2

9️⃣ McKinsey – The Agentic Organization
→ A deeper dive on org design and ways of working in an agentic era.
🔗 https://lnkd.in/grCJChZe

🔟 Deloitte – Agentic enterprise 2028
Finish with a 2028 horizon-scan to stress test your 2026 roadmap.
🔗 https://lnkd.in/g6BEY-qe

If you want to walk into Q4 reviews and January planning with a clear point of view, this is the reading path that will actually get you there.

Anything you’d add to this reading path?

♻️ Repost to help someone with their agentic AI strategy.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Agentic AI Strategy Pack: 10 must reads from the last 6 months

Behind every confident decision
is invisible governance doing the heavy lifting.

Data governance often gets dismissed as red tape. 
In reality, it’s the backbone of trust, speed, and better decisions.

What people think it is:
→ Restricting access to data
→ Slowing projects with rules
→ Complicated policies no one reads

What it’s actually about:
→ Making data accessible and usable (not buried in systems only one person understands)
→ Building trust in data quality (so your dashboards stop arguing)
→ Protecting sensitive information without drowning in jargon
→ Creating shared definitions (“customer” means the same thing everywhere)
→ Balancing compliance with delivery (rules and results)
→ Enabling faster, more confident decisions (“which number is right?” solved)
→ Turning policies into real outcomes (less paperwork, more wins)

At its core, governance is about making ‘yes’ possible safely, consistently, and with confidence.

👉 What do you think is the most overlooked aspect of data governance?
♻️ Repost to help data governance gets the recognition it deserves.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

What people think data governance is about vs. what it is actually about.

The fastest way to understand AI governance?
Stop thinking about data. Start thinking about decisions.

There's a lot of noise around governance in the age of AI.
And leaders cut through some of that noise by understanding the split between inputs and outcomes.

Data governance protects the inputs.
It’s about accuracy, privacy, lineage, access and compliance.
It answers: Can we trust the data going into our systems?

AI governance protects the outcomes.
It deals with fairness, explainability, drift and human oversight.
It answers: Can we trust the decisions coming out?

Both matter.
Both are essential.
But they serve different purposes.

What I’ve seen inside organisations this year is that AI governance doesn’t sit neatly with one owner.
Legal, risk, product, ethics, engineering and the business all hold a piece.

That’s normal, because the outcomes affect so many parts of the organisation.

The companies making real progress connect the two:
They build strong data governance first. Clean foundations mean AI systems fail predictably, not catastrophically.

Then they layer AI governance on top as the mechanism that gives business leaders confidence to actually use what's been built.

Have a look at the two side by side to see where each plays a role.

What's your experience? Where does AI governance live in your organisation, and is it working?

♻️ Repost to help someone get their governance layers set.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Data governance vs. AI governance: Inputs vs. outcomes

Every project starts with hope.
Every timeline ends with a sigh.

Why? Because AI & data projects are the perfect storm of:
→ Optimism bias (a.k.a. the planning fallacy)
→ Fuzzy scope (“we think we want this…”)
→ Unknowns (it’s the first time for everyone)
→ Data, security & ethics curveballs

And this is where the frustration really sets in.

I’ve been there. 
The neat plan, the polished estimate, the stakeholder nods… 
only for reality to unravel in predictable ways.

You imagine a smooth run to delivery.

Reality looks more like:
→ The “clean” data is chaos.
→ Compliance shows up after 3 months.
→ Pilots uncover 20 edge cases you never scoped.

System works, but business team has no idea how to use it.

You start with a project plan.
You finish with a war story.

And somehow… both were inevitable.

But next time you’ll try to
→ Plan with plenty of buffer for the knowns and unknowns
→ Scope what’s in as well as what’s out
→ Adapt again, keeping everyone close as you do

What’s the most unexpected twist you’ve seen in a project?

♻️ Repost to help someone get their project back on track.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

I’m an energetic and optimistic executive and I thrive in three key areas:
1.	Building bridges across an enterprise to create meaningful change.
2.	Bringing clarity, structure, and direction to complex, ambiguous challenges.
3.	Sparking innovation and inspiring creativity to drive growth.

Let me share a bit more about how I lead in these spaces:

Collaboration across an enterprise has been central to unlocking value from data and AI. I take pride in rallying teams from marketing, legal, risk, and technology to co-create solutions that deliver results. One highlight was leading the creation of the Wesfarmers shared data asset—an achievement that underscores what’s possible when we unite diverse perspectives toward a common goal.

Navigating ambiguity and building a strategy and roadmap for success have been at the heart of many of my roles. Whether it’s shaping how data is used and shared for enterprise reporting at Treasury Wine Estates or leading the charge in managing AI risk at The Lottery Corporation, I’ve guided teams to find a path forward where none previously existed. My McKinsey consulting background has been an invaluable springboard for crafting strategy and driving execution in uncharted territory.

Inspiring growth and fostering a culture of learning are passions I bring to every team. I thrive on creating opportunities for collective learning—whether it’s through colleague-led workshops, hackathons, or interactive pilots. Seeing individuals and teams evolve and grow is what fuels me, and I believe the best innovation comes when people feel empowered and supported to stretch their capabilities.

Robert K.

AI isn’t a one-size-fits-all solution.
This confusion is quietly costing businesses millions.

Executives keep asking, “How do we use AI?” 
But everyone in the room is picturing something different.

Some imagine predictive models. 
Some imagine ChatGPT. 
Some imagine automated workflows. 
And they don’t realise they are talking past each other.

AI is not one thing.
It’s many things, and here are three flavours to know right now.

1. Traditional AI helps you decide. 
2. Generative AI helps you think and communicate. 
3. Agentic AI helps you act.

My view is that until companies name & understand these differences, they’ll keep approving the wrong investments, overlooking solid opportunities and wondering why impact is slow.

When leaders finally see the landscape, the conversation changes from tools to outcomes. Instead of “We need AI”, it becomes: 
→ Do we need clearer predictions? 
→ How can we create content and insights more quickly? 
→ Or how do we benefit from tasks automated end to end?

That’s when real progress starts.
If you were explaining AI to a non technical exec team, what’s the first distinction you’d make?

♻️ Repost to help teams agree on what they mean by AI.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

3 Roles of AI Every Leader Should Understand

CEO: “Why aren’t we using AI yet?”
Data team: “We’ve been using it for years.”

Both are right. 
Both are talking about completely different things.

The exec sees ChatGPT writing marketing copy.
The data scientist sees a churn prediction model in production.

The difference matters, because each brings new opportunities, new data needs, and new risks.

AI isn’t one thing.
It’s a spectrum:
From predicting → to creating → to acting.

Before investing in the next AI pilot:
→ Start by naming which AI you're discussing: Traditional, Generative, or Agentic.
→ Then ask: where are we today, and where should we invest next?

Because when everyone understands what kind of AI you’re actually doing, expectations align and investment decisions make sense.

Check out the infographic below: “Different Types of AI Explained to a Non-Tech Exec.”

Five minutes now could save you months of misaligned pilots and budget debates.

How does your organisation currently perceive AI?

♻️ Repost to help teams agree on what they mean by AI.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

How to explain different types of AI to a non-tech exec

Bad meetings are the hidden tax
that leaders pretend doesn’t exist.

Thank you to my friend Dan Galletta for this infographic - give him a follow!

Your AI and transformation projects already juggle uncertainty, tight timelines and big expectations. 
But nothing slows them more than unclear, looping or directionless meetings.

I’ve watched projects lose months to the same patterns:
→ No purpose 
→ No owner 
→ No decision 
→ “Let’s pick this up again next week”

The truth is uncomfortable.
Many meetings exist because someone forwarded an invite, not because there's a real decision to make.

The best-run meetings I've experienced had non-negotiables: a defined purpose, a decision owner, and clear roles. No exceptions.

When you run meetings that way, teams feel it. 
Energy lifts. 
Pace improves. 
People leave clearer, not heavier.

High quality meetings are really about respect. 
Respect for people’s time. 
Respect for the work. 
Respect for the decisions that matter.

Imagine what your projects could achieve with fewer, sharper, more respectful meetings.

What’s one meeting habit you wish your organisation would change?

♻️ Repost to help someone reduce their meeting tax.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

The biggest lie about AI? 
That it’s one technology.

People talk about “AI” like it’s just one technology.
One system. One capability. One silver bullet.

In reality, AI is a toolbox.
And just like any toolbox, using the wrong tool gives you a very expensive mess.

Here’s the simple version executives need:

Machine Learning
↳ Finds patterns in data so you can make smarter decisions.
↳ Think: spotting customer trends or predicting equipment failure.

Natural Language Processing
↳ Lets software read and write like a human.
↳ Think: chatbots that actually help customers.

Generative AI
↳ Creates new content.
↳ Think: drafting documents, creative ideas, first pass analysis.

Computer Vision
↳ Understands images.
↳ Think: monitoring shelves, defects, safety incidents.

Predictive Analytics
↳ Forecasts outcomes so you can plan with less guesswork.
↳ Think: finance forecast and demand planning.

AI Agents
↳ Do tasks for you end to end.
↳ Think: running routine processes automatically.

Agentic AI
↳ Makes decisions, takes actions and coordinates tools toward a goal.
↳ Think: autonomous operations, complex workflow orchestration.

This is why AI feels confusing.

People expect a magic wand, but what they actually get is a multipurpose toolkit that still requires judgement, structure and good leadership.

Its value comes from leaders who know which tool to use, when to use it, and why it matters.

The companies winning with AI know it’s not magic.
They’re using AI to build better decision making systems.

Which part of AI are people still confusing most in your circles?

♻️ Repost to help someone unpack their AI toolkit.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

What AI can feel like vs.
What AI actually is

Some companies make AI look effortless.
Others are exhausted.
And it’s not luck.

The heartbreak of AI isn’t failure. It's the wasted potential you can see coming from a mile away.

I walk into some organisations and the energy is electric. AI is helping people work smarter, faster, and with more confidence. 

Then I walk into others and everything feels heavy, stuck, uphill.

And the difference has nothing to do with the tool.

The places that win put in the groundwork long before the platform goes live.
→ Clear measures of impact 
→ Workflows redesigned around humans 
→ Data foundations that stop chaos 
→ People feeling safe, not replaceable 
→ Humans and AI building on each other’s strengths

When these pieces are missing, the tech never stands a chance.

Exec teams have more influence over this than they realise. 
Get the foundations right and momentum builds fast. 
Ignore them and frustration becomes the norm.

What foundation do you think leaders skip most often?

♻️ Repost to help a team meet their AI ambitions.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

What people think AI adoption is about vs. What AI adoption actually takes

Every company I’ve worked with has confessed the same thing:
“Our data is a mess.”

And here’s the funny part… 
They still think they’re the only ones.

Most companies are messy on the inside. 
Most companies carry legacy decisions, old systems, and data that has lived too many lives.
And yet… that’s still where every AI journey begins.

Every AI ambition begins with a data decision. 
Not a perfect dataset. 
Not a shiny platform. 
A decision to start cleaning, shaping and using what you already have.

The transformation comes from small, consistent steps: 
→ Identifying the critical fields. 
→ Fixing the definitions that cause arguments. 
→ Agreeing on what “good enough” looks like so the work can actually begin.

You won’t have the polished robot version of your data on day one. 
But have the courage to look at the messy version and take ownership of it.

Start somewhere. 
The finish line only appears once you do.

What’s been your biggest challenge with messy data, and how did you tackle it?

♻️ Repost to help someone turn their messy data into polished outcomes.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Start Somewhere: Every AI ambition begins with a data decision

You can’t build an AI powered future
when the middle is trapped between big expectations 
and broken foundations.

The execs paint an exciting AI future.
People get energised.
The slides look incredible.

Then the middle gets told to “make it happen”
with tools they barely understand
and systems that barely hold together.

No shared priorities.
No real training.
Leaders encouraging AI use, but not using the tools themselves.

And underneath it all sits the stuff nobody wants to own:
decades of tech debt, tangled systems, and data scattered everywhere.

This is where the AI strategy quietly collapses.

Not because people don’t care.
But because the foundations were never part of the plan.

If leaders want real AI impact, they:
→ treat the foundations as part of the AI program, not an afterthought
→ make the path clear
→ equip people with the skills to use the tools
→ role model AI use in their own work

It’s not glamorous.
But it’s what works.

If you’re honest about your organisation, where does the story break?

♻️ Repost to help someone take a look at how AI lands across their org.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Hopeful AI vision meets the messy middle and a reality check.

2026 will be a defining year for AI advantage.
Here’s what the leaders are doing now so you can map your next moves.

If you’re deciding where AI fits in your business next year, 
learn from those already doing it.

These resources give you a practical sequence:
Understand where AI makes a difference → get specific on use cases → prioritise for impact

Start the new year with clarity and momentum.

1️⃣ Understand where AI has made a difference in 2025:

1/ McKinsey / QuantumBlack – The state of AI in 2025 
→ Your reality check. What enterprises are actually doing today.
🔗 https://lnkd.in/gMHgSVys

2/ Capgemini - Harnessing the value of AI
→ How organisations are moving from pilots to scaled gen AI use cases
🔗 https://lnkd.in/gB7fF2za

3/ Google - ROI from AI 2025
→ Shows how firms using AI agents see real payoff
🔗 https://lnkd.in/gh4N7pf4

2️⃣ Get inspiration from these lists that show use cases that work:
1/ Google – Real-World GenAI Cases / 1001 Use Cases
🔗 https://lnkd.in/gGAuCFx7
PLUS: Google – 101 GenAI cases with architectural blueprints
🔗 https://lnkd.in/gmYrKNee

2/ Amazon – GenAI Customer Stories / 230 Use Cases
🔗 https://lnkd.in/g-RgmxBN

3/ Microsoft – AI Customer Stories / 1000 Use Cases
🔗 https://lnkd.in/gb-RnCKV

4/ PwC – Applied AI Compass / 200 Use Cases
🔗 https://lnkd.in/g75ZsSUX

5/ SAP – Business AI per department / 200 Use Cases
🔗 https://lnkd.in/gvh4GhqJ

3️⃣Prioritise your use cases:
1/ Gartner – AI Opportunity Radar
Great for setting your AI ambition.
🔗 https://lnkd.in/gG_tsSqB

2/ Google – Value, Feasibility & Actionability
Bridges data readiness and business impact.
🔗 https://lnkd.in/ggkJnTm9

3/ Capgemini – Potential vs Ease Matrix
Perfect for workshops.
🔗 https://lnkd.in/gPZx8jYe

4/ Deloitte – Impact, Ease & Differentiability
🔗 Highlights where AI creates unique value.
https://lnkd.in/g_7iD4Cs [chapter 1]

What excites you the most about AI in 2026?

♻️ Repost to show someone the art of the possible with AI.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

I used to think great presentations were about sleek slides and perfect fonts.
Then I joined McKinsey.

Turns out, the real magic is in structure, clarity and flow.

The longer I work in data and AI, the clearer it’s become:
If people don’t understand your work it might as well not exist.

You can spend weeks building a brilliant model or dashboard.
But if the story isn’t clear, people won’t see the impact or fund the next idea.

Presentation is the bridge between your work and its influence.

Here’s how to build that bridge:

Structure for decision-making
→ Start with your answer. “We recommend X because of Y.”
→ Executives want clarity, not suspense.

Simplify for impact
→ A few sharp charts beat a 30-slide deck full of noise.
→ Less content = more persuasion.

Design for dialogue
→ Expect interruptions.
→ Know your material so well you can pause, pivot, and continue seamlessly.

End with an ask
→ Always finish with a clear next step.

The best sign you nailed it?
Someone says: “Got it. What are our next steps?”

Because no matter how smart the work is,
if people can’t follow the story, they won’t follow the idea.

What’s the best presentation tip you’ve ever been given?

♻️ Repost to help someone get their idea into action.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Here’s the advice boards are getting on AI.
Relevant whether you sit in the boardroom or answer to it.

McKinsey, BCG, AICD and similar reports released over the last quarter are shaping how boards think about AI.

Even if you're not on a board, they still matter.
They influence the questions boards ask management, often before management has had time to build real context.

If you're on a board, they matter even more.
The message is consistent and increasingly explicit.
Directors are expected to lift their AI fluency.
This isn't something to delegate.

The sequence is clear:
Understanding obligations → setting direction and demanding value → using AI at the board table.

1/ KPMG Australia – Boardroom view on GenAI adoption
A grounded view of what's happening in boardrooms, with a practical checklist of questions boards should be asking.
🔗https://lnkd.in/gqk36hXv

2/ Institute of Directors (UK) – AI Governance in the Boardroom
Principle-led, practical guidance on overseeing AI strategy, ethics and risk at board level.
🔗https://lnkd.in/g-in7Cir

3/ McKinsey – The AI reckoning: How boards can evolve
A clear argument for why AI is reshaping competitive advantage and governance expectations, and why directors must lift their own AI literacy.
🔗https://lnkd.in/geG-Xtfz

4/ BCG – Targets Over Tools: The Mandate for AI Transformation
Pushes boards to move management away from experimentation and towards measurable outcomes. Strong on value creation over shiny toys.
🔗https://lnkd.in/gqaW4wCt

5/ Australian Institute of Company Directors – AI use by directors and boards: Early insights
Practical guidance on how boards and directors can start using AI in their day-to-day work now, not in theory.
🔗https://lnkd.in/guXwqJ4A

6/ EY – What is the agenda for reimagining more strategic, future-looking boards?
A broader perspective on board evolution, with useful insights on how AI can support better governance and decision-making.
🔗https://lnkd.in/g-TxhiHx

The quiet message running underneath all of this:
AI fluency is becoming part of baseline good governance.

How prepared do you think most boards really are for that shift?

💁‍♀️ If you want help lifting AI capability at board level, get in touch.

♻️ Repost to help board come to grips with AI.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.
👉 I’m building somewhere to go deeper, sign up here: https://lnkd.in/giQ3b6Fi

The AI Board Pack: 6 guides from the last 3 months

Your team wants to learn AI but most don’t know where to start.
Here’s everything you need to give them direction.

If you lead people, you are already feeling the shift.
By 2026, AI fluency will sit beside email and Excel as a basic skill.
Your team will expect guidance, not hype.

The good news is you do not need a paid course. 
Some of the best training comes from Google, Microsoft, IBM, Anthropic and OpenAI. 
Free, self paced and trusted.

Here is a simple starter list you can share with your team.

General overview of AI

1. Google AI Essentials: 
→ Helps people across roles and industries get AI skills to boost their productivity 
https://lnkd.in/gJz_zN8S

2. Generative AI for Everyone: 
→ Learn how generative AI works, and how to use it in your life and at work
https://lnkd.in/gv9XyTVR

3. IBM AI for Everyone: 
→ Introduction to core AI, ethics, and hands-on use of Gen AI tools.
https://lnkd.in/g8GAYM4M

4. General prompting essentials: 
→ Learn from Google experts how to use AI effectively by writing clear and specific prompts.
https://lnkd.in/gZgSxW37

5. Generative AI Leader
→ Get a broad overview of Gen AI as well as how to leverage it to transform work
https://lnkd.in/g-wEa5NY

6. Elements of AI by The University of Helsinki
→ Learn what AI is, what it can (and can’t) do AI. The courses combine theory with practical exercises
https://lnkd.in/g7J-kECe

For ChatGPT users
7. Prompting for ChatGPT: 
→ Learn how to chat with ChatGPT to give you the best results
https://lnkd.in/gduXrQft

8. Further resources for ChatGPT at work:
→ Find content to harness AI as a partner in your daily work
https://lnkd.in/gMj2h3nS

For Copilot users
9. Get started with Microsoft 365 Copilot: 
→ Explore how Microsoft 365 Copilot can streamline communication and power up your content creation.
https://lnkd.in/gQhP4EXd

10. Microsoft 365 Copilot Personal Productivity for all: 
→ Learn which Microsoft 365 Copilot tool to use for which task and when to switch
https://lnkd.in/g-Fh5hH2

11. Want more Copilot? Browse here:
https://lnkd.in/g7SNaBTf

For Claude users
12. Claude for Work:
→ Learn to us Claude across your organisation & max team productivity
https://lnkd.in/gzbwWpMv

For Gemini users
13. Google Skills Hub
→ Build AI skills for tomorrow
https://lnkd.in/gQkfhTVE

Teams want to learn, but they don't want noise, they need a simple, trusted list like this gives them the confidence to begin.

What will you point your team to first?

♻️ Repost to help a team learn AI.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Intro to AI: Essential Courses to get your team started

Every leader I work with wants AI to deliver real impact. 
But most don’t realise how much the outcome depends on them.

Not the tool. 
Not the model. 
Not the vendor.

Leaders shape whether AI becomes a breakthrough or a burden.

When AI feels harder than it should, it’s usually because of three fault lines:

1️⃣ People decide whether AI lands
Trust is everything.
When employees feel threatened or confused, momentum dies.
When leaders use AI in their own workflow, teams follow.

2️⃣ Clarity creates value
If you can’t name the decision AI will improve, you don’t have a real use case.
Most pilots fail quietly because they never shift how the business actually works.

3️⃣ Foundations make scaling possible
Weak data and broken processes don’t disappear with AI.
AI just makes the cracks louder.

The good news is: none of this is beyond a leader’s control.
Small choices made early change everything later.

What is one behaviour you think makes AI adoption easier for everyone else?

♻️ Repost to help a team meet their AI ambitions.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

There's a quiet assumption in some workplaces that AI is a “tech thing”.
It isn’t.

AI is a confidence thing.
A courage thing.
And now a career thing.

But right now, too many brilliant individuals are sitting on the sidelines because they feel they need to know everything before they begin.

My friend Georgie Hubbard has just written a book, The Bold Move, that flips that script.
It was great to catch up with her this week and see once again her energy and passion for helping women pivot and succeed in their careers.

She’s done it for over a decade and her enthusiasm hasn’t waned.

It really reminded me that the future belongs to the people who are willing to learn out loud.
To experiment.
To ask for help.
To back themselves even when they feel unsure.

We've heard it before, but it needs saying again:
AI isn't here to replace your creativity, empathy or judgement.
It’s here to amplify them.

If more people felt confident using these tools, imagine the products we would build.
Imagine the workplaces we would redesign.
Imagine the conversations we would shape.

You don't need to be an expert.
You just need to start.

What has been your biggest barrier to experimenting with AI?

6 old career rules no longer relevant in the age of AI

Some people whisper sweet nothings to AI. 
I prefer sharp one-liners that get results.

Last week I ran a training session on prompting. 
We'd barely started when someone asked if they could share a trick… 
and suddenly, the room came alive.

Faces lit up. 
People jumped in. 
Ideas flew fast.

One person said, "Always ask for 'world-class' output." 
Another added, "I tell it I'll be fired if this is wrong - makes it double-check everything."

Simple one-liners.

But they turned good prompts into great ones.

Because the best prompts skip the fancy phrasing and focus on clear thinking and what actually works.

Prompting is part art, part science. 
So I pulled together the top 18 one-liners and broke down why each one works. 

👇 Check them out below.
💬 What's your go-to trick to turn AI output from average to exceptional?

♻️ Repost to help someone hack their prompts from meh to magic. 
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Everyone's chasing AI strategy.
Most organisations can't even get the basics right.

You can buy models. 

You can’t buy:
→ Trusted data that people will actually use. 
→ Stakeholders who show up prepared. 
→ Leaders who back experiments instead of asking for guarantees. 
→ Teams who can explain the why, not just ship dashboards. 
→ Decisions tied to evidence instead of hierarchy. 
→ Solving problems aligned with business goals. 
→ A culture where the business pulls data instead of avoiding it.

These aren't fantasies.
They're what keep you from regulatory trouble, confused boards, and endless rework.

If you’ve ever spent your week:
→ Re-explaining the same data issue. 
→ Fixing the ‘regular’ report. 
→ Justifying why the AI pilot is stuck.

You know exactly why the basics matter more than the model.

So raise your standards.

Ask better questions. 
Invest in the leaders who want to learn. 
Push back on the trade offs that kill capability before it has a chance.

This isn’t being difficult. This is how you build a sane data culture that scales.

What’s one fundamental your organisation struggles with?

♻️ Repost to help a data team go from good to great.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

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