Leading Privado.ai as Founder and CTO, our mission is to embed privacy into the DNA of software engineering. Overseeing the development of Privado, our open-source tool, we enable engineers to manage sensitive data with unmatched precision. This endeavor reflects our core value of making privacy a fundamental aspect of technology.

Prashant Mahajan

Privacy teams should be worried. This is exactly how the same mistake happens twice.

Crunchyroll already paid $16M over privacy allegations tied to user viewing data.

Now it is being sued again. Different vendor, same underlying problem: a company allegedly let third-party tooling access what people were watching, tied to who they were.

That is not a policy failure first.
It is a systems failure.

From the outside, this looks like “data sharing.”
Inside the product, it usually looks routine.

An SDK gets added for engagement.
Events start flowing. User identifiers get attached.
Viewing activity becomes a usable signal for someone outside your system.

That is how legal risk gets created quietly.

This is the part many privacy programs still miss:
- The contract can look clean
- The policy can sound right
- The consent language can be reviewed

But if the system is still sending user-level viewing events to a third party, none of that matters much.

Legal teams need to ask harder questions:
1. What events are actually leaving the app?
2. What identifiers go with them?
3. Can a vendor tie content consumption back to a person?
4. Who verified the live data flow after the tool was implemented?

This is why privacy work cannot stop at notices, reviews, and vendor paperwork.

Policies describe intent.
Systems reveal reality.

If your privacy team cannot see what product telemetry is doing in production, you are relying on assumptions, not controls.

How many companies that settled privacy claims have actually fixed the underlying data flow?

#Privacy #DataPrivacy #Compliance #Legal #Law

Privacy Engineering Infrastructure Leader | Founder & CTO, Privado.ai | Built $100M+ Scale Systems | Defining AI-Driven Privacy Automation

This week California regulators fined a company most people wouldn’t expect in a privacy case.

A high school sports ticketing platform.

Students used it to buy tickets for football games, prom, and school events.

But behind the scenes, the platform was running tracking technologies for targeted advertising.

And here was the catch.

You couldn’t view your ticket without clicking “agree” to tracking.

No real opt-out or alternative path.

So attending a school event effectively meant agreeing to ad-tech tracking.

Regulators fined the company $1.1M and forced changes to its practices.

What stands out to me is the architecture behind this.

A ticketing system quietly became part of an advertising infrastructure.

The core product and the tracking stack were intertwined.

This happens more often than teams realize.

A simple product integrates analytics, marketing tools, or ad-tech.

Over time those integrations start shaping how the product works.

And suddenly core functionality depends on tracking.

That’s where privacy usually breaks... in the system design.

If someone can’t use your product without being tracked, it’s worth asking:
Was tracking ever meant to be part of the product experience?

#Privacy #Engineering #DataPrivacy #CCPA #Legal

Over the last few months, I kept walking out of customer conversations with the same feeling in my head.

Privacy teams are carrying way too much.

Not because they are not good at what they do. Not because they are not working hard. Actually the opposite. The people I speak with are thoughtful, sharp, and deeply serious about the work. But the day to day of privacy work still feels heavier than it should.

One customer told us their setup was still very manual and dependent on annual refreshes.

Another said something that really stayed with me: 
“I don’t need a person to fill out a spreadsheet anymore.”

Another put it even more plainly:
“We need the result. We need the answer.”

And another said what I think a lot of teams feel, even if they do not always say it out loud: “We don’t know what we don’t know.”

That is the part I kept coming back to.

None of these people were asking for something flashy. Nobody was asking for another dashboard, another workflow, or another pile of features to learn.

They were asking for something much simpler.

Clarity.

A better way to understand what is happening across their digital environment. What changed. What matters. What needs attention. What they can trust.

That is why we announced Wren today.

Wren did not begin as a vague idea about AI. It came from a much more practical place. We kept hearing the same pain, from different companies, in different words, and it became hard to ignore.

Privacy teams are working in environments that keep moving. Websites change. Apps change. Cookies change. Configurations drift. New tools show up. Old assumptions stop being true. But too much of the work still depends on manual effort, fragmented visibility, and tools that make you dig for the answer instead of helping you get there.

So even when teams have scans, inventories, workflows, and documentation, the real questions are still there:
- What changed?
- Why was this flagged?
- Is this actually a risk?
- Can I trust this result?
- What do I take to legal?
- What do I do next?

That gap is exactly what led us to build Wren.

We want it to take on the investigative work privacy teams should not have to do manually anymore. 

To spot when something new needs review. 
To help trigger the right assessment. 
To pull in the right docs, scans, contracts, and context. 
To help generate the assessment with evidence, not guesswork. 
And to help teams track the risks that actually need action.

For us, this announcement is not about launching something new just because we can. It is about taking a problem we kept hearing again and again and deciding to build for it properly.

We want privacy teams to investigate faster. 
We want them to get to the “so what” without all the chasing. 
We want them to feel more clear, more confident, and a little less buried in manual work.

A part of why we are building Wren is very simple: 
We want privacy pros to smile more

Check the announcement link in the comment.

When the Meta AI glasses lawsuit came out, I did not just see a product story.
I saw a privacy operations story.

Because this is what keeps happening. Legal teams document one version of data use. The system quietly evolves into another.

A feature gets shipped. A vendor gets added. A review workflow appears.

Data starts moving in ways no one properly mapped. And by the time anyone notices, the documentation is already out of date.

That is why manual privacy processes are struggling in 2026.

They depend on people explaining what they think the system does. But privacy risk does not live in what people think.

It lives in what the system is actually doing. That is the gap.

Spreadsheets, policy reviews, screenshots, and questionnaires still have a role.  

But they are not enough for environments where products change constantly and AI introduces new data flows faster than governance teams can keep up.

Automation matters because it gives privacy teams something manual processes cannot.

Actual visibility. Not more opinions or better wording.

Evidence.

For legal and compliance teams, that changes the conversation from:
what was approved
what was documented
what teams said

to:
what is running
where data is going
which vendors are involved
what changed since the last review

That is a much stronger foundation for privacy accountability.

In 2026, the problem is not that teams do not have privacy processes. The problem is that most of those processes still cannot keep up with the systems they are supposed to govern.

How are you handling that gap today between documented privacy controls and actual system behavior?

#privacy #data #AI #automation

40 million Americans are asking AI about their health.

Most assume HIPAA follows that conversation.

It doesn’t.

When a hospital uses an AI tool, there’s a Business Associate Agreement. That creates shared legal responsibility. Access is scoped. Logs exist. Controls are negotiated. OCR can enforce.

When a consumer opens ChatGPT Health or Claude and uploads lab results, that structure disappears.

There is no covered entity. No BAA. No HIPAA enforcement mechanism.

The protection comes from product design and terms of service. Not healthcare regulation.

From a systems perspective, this is a completely different environment.

Enterprise deployments sit inside defined trust boundaries. Identity controls. Access segmentation. Audit logs. Contractual limits.

Consumer tools are multi-tenant platforms. Even with encryption and isolation, governance is standardized. Not negotiated.

Legal assumption. Health data equals HIPAA.

System reality. Regulation attaches to relationships, not data types.

That gap is where risk lives.

If you lead privacy, consider this:
- Are employees pasting patient data into consumer AI tools?
- Do your policies clearly separate enterprise AI from personal experimentation?
- Can you technically detect where health data is flowing?

If regulators asked, could you evidence the difference?

None of this makes consumer AI unsafe.

But it does mean the regulatory posture is different. And most organisations are not modelling that difference clearly.

AI in healthcare now operates in two silos.

If your governance framework doesn’t reflect that, you’re relying on assumption.

Can you actually prove which environment your data is in?

#dataprivacy #HIPAA #law #AI

GDPR fines have now crossed €5.6B in publicly known enforcement.

Most people will read that as a regulator story. I think it is really an evidence story.

The hard part is no longer writing the policy. The hard part is proving what actually happened inside the system.

That is where most privacy programs break.

On paper, the company has a notice, a retention policy, a RoPA, DPAs, and internal controls.

In production, the reality is usually different.

Data fields get reused. Events change names. New SDKs get pushed live.
Vendors receive data nobody reviewed.

AI features get added on top of existing systems without anyone fully mapping the downstream flow.

Legal teams are still expected to stand behind disclosures and controls.

But those claims are often based on interviews, screenshots, and documentation created at a point in time.

Systems do not work that way. They change every week. Sometimes every day.

So when enforcement happens, the real question is not, “Do you have a policy?”

It is, “Can you show evidence of what your systems actually did with personal data?”

That changes a lot.

Documentation shows intent... system evidence shows reality... privacy reviews without engineering visibility create blind spots and accountability without technical proof is mostly trust

This is the gap legal and privacy teams need to pay attention to.

The risk is not just non-compliance. The risk is defending a position that was true when it was written, but false in the live system.

Privacy programs need to move closer to engineering reality.

Not because lawyers need to become engineers. Because legal accountability now depends on technical evidence.

How is your team validating what production systems are actually doing with personal data today, beyond documentation?

#privacy #GDPR

I’ve built systems at scale. That’s exactly why I don’t believe manual data mapping works.

I was talking recently with a data governance leader at a large media publisher.

Their process still depended on manual updates, annual check-ins with system owners, and lineage they didn’t really trust.

Honestly, that’s not surprising.

The problem is bigger than process.

Most privacy teams are still being asked to understand data flow through surveys, spreadsheets, and interviews.

But production systems do not wait for annual refreshes.

Tags change. Vendors change. Pipelines change. New use cases appear. Data starts moving in ways nobody captures fast enough.

So the map gets updated.

The system has already moved on.

That’s the gap privacy teams are dealing with now.

Not a policy gap. A visibility gap.

My view is simple:

Policies describe intent. Systems reveal reality.

If your data map depends on manual refreshes, it’s probably already outdated.

How are teams closing that gap today?

#privacy #legal #law #data

AI Strategy in 1 Day + Prototype in 3 Weeks | Fastest Path to AI & Digital Transformation While Having A Stupidly Good Time | 22+ Years Making Transformation Less Painful

🌍 Part business guru, part thrill-seeker. I thrive in discomfort, challenge the status quo, and ask 'Why not?' more often than a toddler asks 'Why?'. My clients rely on me to bring in a dash of urgency, a sprinkle of curiosity, a chug of right questions and a hefty dose of momentum. 

Work between London and Bay Area. 

How I help:
🚀 AI Accelerator: Zero to working AI prototype in 3 weeks 
🚀 Go-To-Market Accelerator: Growth hacking, product launches, market expansion 
🧠 Strategy & Innovation Labs for transforming business rapidly
🧠 Interactive workshops, Speaking & hackathons: teaching effective collaboration, value proposition, design thinking, visual thinking, and AI. 

What You Get: 
✓ Scale faster in fraction of the time
✓ High-performing teams without the burnout 
✓ Results in days, not months

Currently Open For: 
✓ AI Strategy & Innovation Lab engagements 
✓ Specialized CRM, Customer & Digital Transformation projects 
✓ Fractional/interim transformation roles 
✓ Post-M&A integration
✓ Process automation & digitization
✓ Outsourcing
✓ SaaS implementations
✓ Change Management

I spent the first part of my 22+ year career avoiding the messiest part of business transformation: "the people". As a computer nerd who loved math, algorithms, and databases, they were logical. Until I couldn't avoid the truth anymore: every transformation failure I witnessed wasn't technical – it was human.

My breaking point was when I spent six months, in weekly meetings with a senior leadership team, discussing the same slide deck, trying to make one decision. One. Decision.
I was trapped in never-ending tunnels of circular meetings – because nothing ever gets decided in a single meeting, does it? I had a choice: accept this as "how the system works" or find another way.

What Changed Everything - I went to Silicon Valley to learn how high-growth startups solve complex problems rapidly. The difference wasn't better technology – it was better collaboration.
So I combined my 22+ years of transformation experience with neuroscience-based collaboration methods. Turns out, when you fix the group dynamics, everything else follows.

That's how Solved Together was born – with a mission to fix broken collaboration and help businesses develop a human-centered approach to growth and transformation.

What I Do Now -  I help companies solve complex challenges in days instead of months using these methods. DM me if you want your team to learn to do the same! 

🏔️ Off-duty: Found defying gravity on mountains, seas, or slopes. Because life's too short for sensible hobbies.

Rujuta Singh

Your brain just added a marble to someone's jar.
Or removed one. You weren't paying attention.

Trust is literally the only thing we're judging all day.
Can I trust this person? This system? This decision?

That's it. That's the whole game.

And if you're leading anyone, you should probably know how it actually works.

I'm an eternal Brené Brown fan, and she just explained it in the most simple, sticky way I've ever heard.

𝗠𝗮𝗿𝗯𝗹𝗲 𝗷𝗮𝗿𝘀, 𝘀𝗵𝗲 𝘀𝗮𝘆𝘀....

Your brain is keeping a marble jar on every single person in your life. And you have no idea it's doing this.

Little moments add marbles. Tiny betrayals remove them.

Not big dramatic gestures. Tiny ridiculous things:
* Actually listening when someone talks
* Remembering they mentioned something important last week
* Following through on the smallest promise
* Being patient when they're not
* Being honest when it's uncomfortable

We think trust gets built during the big moments - the crisis, the impossible deadline, the inspiring speech.

Nope. It's built on random Tuesdays when nothing important is happening.

I'm sitting here mentally calculating my own score.

Your brain right now has approximately 847 marble jars stacked up in there.
Some overflowing. Some with like two marbles rattling around.
Some so empty they're basically just decorative glass.

And you're walking around completely oblivious to this mental warehouse of judgment.

This is why consistency is the most boring and most important word in leadership.

Anyway. Go watch the video. Brené's daughter's story will stick with you way more than my spiral about mental jar warehouses.

Happy Friday. May your marble jars be full.
(Mine are... a work in progress.)
-------
Video credit: Brené Brown on Diary of a CEO

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Stop saying we're "entering the AI era." We've been living in it for 80 years. You just weren't paying attention.

ChatGPT didn't invent artificial intelligence.
It just gave it a face you could talk to.

AI didn't burst onto the scene like some tech superhero.
It crawled. For decades. Like watching paint dry. Except the paint was somehow learning calculus.

While you were busy living your life, researchers were building the future one boring breakthrough at a time.

𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘁𝗶𝗺𝗲𝗹𝗶𝗻𝗲:

𝟭𝟵𝟰𝟯-𝟭𝟵𝟴𝟬: 𝗧𝗵𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗬𝗲𝗮𝗿𝘀
Neural networks were born in 1943.
Your grandparents were already teaching machines to think.

Slowly. Badly. Like training a toddler who only spoke in calculus.
But they were thinking.

𝟭𝟵𝟴𝟲-𝟮𝟬𝟭𝟲: 𝗧𝗵𝗲 𝗖𝗼𝗺𝗲𝗯𝗮𝗰𝗸 𝗘𝗿𝗮
Remember when everyone declared AI dead?
The researchers didn't get the memo.

They kept building while you were learning to text.
Kept training models while you were training for your first marathon.
Kept teaching machines while you were teaching yourself Excel macros.

𝟮𝟬𝟭𝟴-𝟮𝟬𝟮𝟯: 𝗧𝗵𝗲 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗼𝗻
This is where you finally noticed.
Not because AI suddenly got brilliant.
Because it finally got a personality you could chat with.

𝟮𝟬𝟮𝟰-𝗡𝗼𝘄: 𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗘𝗿𝗮
Here's where everything flips.
AI stopped being your smartest search engine.
Now it's doing your job. While you sleep. And it doesn't take PTO.

𝗪𝗲'𝗿𝗲 𝗻𝗼𝘁 "𝗲𝗻𝘁𝗲𝗿𝗶𝗻𝗴" 𝗮𝗻 𝗔𝗜 𝗲𝗿𝗮.
We lived through every awkward, invisible step that built it.

Think of it like puberty for machines.
Took toughly 80 years. Nobody watched the awkward middle part.
Then suddenly: different species.

So here's the question:
What are you building with 80 years of progress that just became accessible?

Image credit: Prem N. 
This timeline definitely made me realize I've been sleepwalking through an 80-year transformation.

♻️ Repost if someone in your network thinks ChatGPT invented AI
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Your IT team just blocked ChatGPT.
Your team is using it anyway.

This is the conversation happening in every company right now.

Not whether to use AI. But which AI to approve.

And at the enterprise level, the stakes are different. 
We're not talking about personal productivity tools. We're talking about systems that touch client data. 
Proprietary processes. Competitive intelligence.

Security isn't a feature anymore. It's the requirement.

Can your data be used to train their models? Does it meet compliance standards? What happens to sensitive information in prompts?

These questions matter when you're choosing for 500 people. Or 5,000.

Here's what I keep hearing: Microsoft Copilot. ChatGPT Enterprise. Claude for Teams.

Not because these are the only enterprise AI tools out there. But because these are the three most companies are actually evaluating.

So I built a simple comparison cheat sheet.

Three columns. Side by side.
Pricing. Pros and cons. What each tool does best. Security and compliance details your IT team needs.

No vendor pitch. No marketing fluff. Just what you need to understand the differences.

Because the right choice isn't universal. It depends on your context. Your systems. Your requirements.

(Want a deeper dive on Copilot specifically? Check out full white paper, that goes way more in detail on enterprise AI tools and capabilities here: https://lnkd.in/eVQWZmsj)

♻️ Repost if someone in your network is stuck in enterprise AI evaluation mode
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You're terrified AND hopeful about AI. You're not broken. You're an Apocaloptimist.
Yes, of course, we need a new fancy word for what's going on with AI.

𝘼𝙥𝙤𝙘𝙖𝙡𝙤𝙥𝙩𝙞𝙢𝙞𝙨𝙩 = 𝘚𝘰𝘮𝘦𝘰𝘯𝘦 𝘸𝘩𝘰 𝘩𝘰𝘭𝘥𝘴 𝘣𝘰𝘵𝘩 𝘵𝘦𝘳𝘳𝘰𝘳 𝘢𝘯𝘥 𝘩𝘰𝘱𝘦 𝘢𝘣𝘰𝘶𝘵 𝘈𝘐 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘭𝘰𝘰𝘬𝘪𝘯𝘨 𝘢𝘸𝘢𝘺 𝘧𝘳𝘰𝘮 𝘦𝘪𝘵𝘩𝘦𝘳.

There's a documentary coming out March 27 with that exact title: "The AI Doc: Or How I Became an Apocaloptimist."

All the big bosses of AI are being interviewed, with pointed questions : Sam Altman. Tristan Harris. Demis Hassabis. 

All the people I was questioning in my "We Do Not Consent" series.

Finally someone is asking them the questions that actually matter. The uncomfortable ones. 

Academy Award-winning director. A dad trying to figure out what world he's leaving his kid.

This is 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 what I was thinking about when I wrote those posts. 
That weird space where you can't choose between doom or denial. You just have to sit in both.

The trailer made me feel less alone in that space.

(No commission. No affiliation. Just genuinely can't wait to watch this in actual theaters with popcorn.)

🎬 Trailer in comments
♻️ Share if you're also an apocaloptimist 
➡️ Follow Rujuta Singh for uncomfortable AI truths

"𝗪𝗘 𝗗𝗢 𝗡𝗢𝗧 𝗖𝗢𝗡𝗦𝗘𝗡𝗧" series:
Part 1: The Sci-Fi Is Now  https://lnkd.in/eH49ehpj
Part 2: The Private Conversation https://lnkd.in/eqWTd2Y9
Part 3: What Can We Do? https://lnkd.in/eVrZf7F9

$35 million to redesign a juice box.
$0 to ask customers what they thought.
Tropicana lost $50 million in 6 weeks.

No, this is not a sci fi. 

January 2009: Tropicana Pure Premium owned the juice aisle. $700M+ revenue. That iconic orange-with-straw package you could spot from three aisles away.

Then they hired Arnell agency. Spent $35M on a complete rebrand.

Customer research budget? Zero dollars.

The new design looked like generic store brand. Clean. Modern. Premium.
And completely invisible to anyone who'd been buying Tropicana since the Clinton administration.

Picture this: You're in aisle 7 looking for your usual orange juice. You scan the shelf once. Twice. Three times.
Where the hell is Tropicana?

That clean, sophisticated package? Sitting right there. Hiding in plain sight.
Customers started panic-buying Simply Orange instead.

𝟲 𝘄𝗲𝗲𝗸𝘀 𝗹𝗮𝘁𝗲𝗿:

→ 20% sales drop
→ $30M in lost revenue  
→ Emergency revert to original packaging
→ Total damage: $50M+

The designer's response when asked about the disaster?

"It's still very difficult for everyone to grasp the importance of that change because it's so dramatic."

Translation: "I know better than your customers."

Spoiler alert: He didn't.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱:

Tropicana confused "better design" with "better for business."

They optimized for:
→ Awards shows (not juice aisles)
→ Design blogs (not working parents at 7 AM)  
→ Creative directors (not customers who just wanted their damn orange juice)

Sound familiar?

Your AI rollout that nobody uses because you never asked how people actually work.
Your "intuitive" platform that needs 47 training sessions to figure out.
Your breakthrough product that customers walk past three times before giving up.

We keep making Tropicana's mistake:

𝗪𝗲 𝗮𝘀𝗸: "How can we make this better?"
𝗜𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳: "How do customers actually buy/use/navigate this?"

The difference between those two questions? About $50 million, apparently.

This is the pattern:
→ Leadership decides what's "better"
→ Spends millions on the vision
→ Skips the part where you ask users
→ Acts surprised when adoption tanks

Then we blame customers for "resisting change."

Wrong again.

At Solved Together, we start every transformation with the people who'll actually live with the change. Before we design anything. Before we spend anything.

Because the most elegant solution means nothing if customers scan the shelf three times and walk away empty-handed.

♻️  Repost if this hit close to home
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I know better than your customers.

£4 million spent on AI. Six pilots running. Zero clarity on what any of it achieved.

That was the fictional CEO Johannes Deubener roleplayed, with me, on his AI to Go Podcast.

His name was Alex. 8,000 employees. Six failed AI pilots. Board breathing down his neck.

I've had this exact conversation with real CEOs. Multiple times.

So I gave Alex the same answer I give all of them.

7 rules. Nothing fancy. Just patterns I keep seeing.

𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗽𝗮𝗶𝗻, 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗽𝗶𝗹𝗼𝘁.

"Where can we use AI?" is the wrong question.
"Where does it hurt most right now?" -> that's the one.

𝟮. 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗻𝗲𝗲𝗱𝘀 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀.

If your AI team says returns are "compounding" and you just need patience -> red flag.
If your CFO can't see the value? There isn't any yet.

𝟯. 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 = 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆.

The moment AI gets its own budget detached from your business goals -> you've already lost.
Integrate it or kill it.

𝟰. 𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝘁𝗵𝗲 𝗱𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗳 𝗰𝗵𝗮𝗻𝗴𝗲.

People don't resist change. They resist being changed.
When the solution comes from them, they champion it.
When it comes from you, they quietly stall it.

𝟱. 𝗦𝗽𝗲𝗲𝗱 𝗮𝗻𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗰𝗮𝗻 𝗰𝗼𝗲𝘅𝗶𝘀𝘁.

A 14-month data roadmap isn't diligence. It's delay dressed as responsibility.
Move fast without being reckless.

𝟲. 𝗬𝗼𝘂𝗿 𝗹𝗲𝗴𝗮𝗰𝘆 𝘀𝘆𝘀𝘁𝗲𝗺 𝗺𝗮𝘆 𝗯𝗲 𝘆𝗼𝘂𝗿 𝗯𝗲𝘀𝘁 𝗔𝗜 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲.

That 20-year-old platform processing billions? Use AI to analyse it, decompose it, rebuild it.
Forget the scattered pilots. Start there.

𝟳. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸 𝗶𝘀 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗰𝘂𝗹𝘁𝘂𝗿𝗲.

Your people aren't resistant. They're overwhelmed.
The question isn't "why haven't they learned AI?"
It's "when exactly were they supposed to?"

Whatever you do as a leader, they're watching. They're copying.

The thread connecting all seven?
𝗖𝗹𝗮𝗿𝗶𝘁𝘆.

Be clear on what you want to achieve. That's your compass.

Watch the full episode here: https://lnkd.in/ebV45PmY

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We've solved harder problems than AI.

In 1985, scientists discovered a hole in the ozone layer. The thing protecting us from deadly radiation was disappearing.

Two years later? The Montreal Protocol. 195 countries agreed to phase out the chemicals destroying it. Today, the ozone hole is recovering.

Nuclear weapons nearly ended civilization in 1962. We invented the most destructive technology in human history, then realized we needed rules. The Nuclear Non-Proliferation Treaty isn't perfect, but it works. Most countries that could build nukes chose not to.

India and Pakistan have fought four wars. They've been at each other's throats for 75 years. But they still maintain their water-sharing treaty.

Because some things are too important to fail.

𝗧𝗵𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗶𝘀 𝗰𝗹𝗲𝗮𝗿: 𝗛𝘂𝗺𝗮𝗻𝘀 𝗱𝗼𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗰𝗿𝗲𝗮𝘁𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀. 𝗪𝗲 𝘀𝗼𝗹𝘃𝗲 𝘁𝗵𝗲𝗺.

Tristan Harris put it perfectly: "There is no definition of wisdom - in any tradition - that does not involve some form of restraint."

This isn't anti-technology. It's anti-recklessness.

I've sat through countless AI panels. I'm in the center of AI transformation work. I'm not saying stop AI. I'm saying we can govern it if we choose to.

The infographic below shows exactly how. Individual actions: supporting AI safety research, contacting representatives, choosing ethical AI products. Company actions: responsible development practices, transparency in AI training, safety-first deployment. Societal actions: international cooperation frameworks, regulatory standards, public oversight.

Specific steps. Real solutions.

If you want genuine hope about our path forward, watch Tristan Harris on Diary of a CEO, here: http://bit.ly/4rEiQgA
He created The Social Dilemma. The guy who predicted the social media crisis before it happened.

𝗧𝗵𝗶𝘀 𝗽𝗼𝗱𝗰𝗮𝘀𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗽𝗲𝘀𝘀𝗶𝗺𝗶𝘀𝘁𝗶𝗰 𝗮𝗻𝗱 𝗺𝗼𝘀𝘁 𝗼𝗽𝘁𝗶𝗺𝗶𝘀𝘁𝗶𝗰 𝘁𝗵𝗶𝗻𝗴 𝗜'𝘃𝗲 𝗵𝗲𝗮𝗿𝗱 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜. 

What I respect most: He doesn't just sound the alarm. He backs everything with evidence. Then shows you the path forward.

No affiliation. No sponsorship. Just the best resource I've found for understanding both the risk and the possibility ahead. 

"We Do Not Consent" series.  
---------------------------- 
Part 1: The Sci-Fi Is Now : https://lnkd.in/eH49ehpj
Part 2: The Private Conversation : https://lnkd.in/eqWTd2Y9 
Part 3: What Can "We" Do?  (today)
---------------------------------

What gives you hope about where we go from here?

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"I've applied to 200+ jobs. Nothing."
The pattern I'm seeing in career coaching sessions since last year.

As an executive and career coach, this tells me something about where we are.

The last 2-3 years have been brutal. 
Most of my coaching requests? Job seekers. And in those sessions, I'm hearing the same thing:

"I've been looking for over a year. Why am I not getting anywhere?"

It wears people down. The morale hit. The rejection fatigue. 
Even the brightest professionals start questioning themselves.

After coaching hundreds of professionals through building businesses and advancing their careers, these are the 10 patterns I see again and again - and what actually works to solve them.

Which of these 10 is holding you back right now?

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I'm not as open-minded as I thought I was.
Neither are you...

Few weeks ago, I was having a conversation with my teenage daughter about social media.
There I was, a transformation consultant who preaches innovation and adaptability by day, dismissing TikTok as "just another time-wasting platform."

"But Mum, you haven't even tried it," she said. 
"How can you know it's a waste of time?"

Ouch.

I was being exactly like the executives I coach through digital transformation.
Dismissing something new without understanding it.
Protecting my worldview rather than questioning it.

That's when I realised:

I wasn't nearly as open-minded as I thought I was.

And I don't think I'm alone.

We're open to new ideas... that confirm what we already believe.
We're open to innovation... that doesn't threaten our expertise.
We're open to change... as long as someone else changes first.

We mistake tolerance for curiosity. We mistake listening for considering. We mistake comfort for openness.

In a world where AI capabilities double every 6 months,
where ChatGPT hit 100 million users in two months,
where 40% of today's job skills could be obsolete by next year...

"I'm open to new ideas" isn't a strength anymore.

It's the minimum.

And most of us aren't even doing that properly.

When was the last time you actively looked for evidence that you might be wrong about something important?

Not stumbled across it. Went hunting for it.

Be honest. Drop it in the comments.

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GM's worst plant had workers shoving Coke bottles inside car doors to mess with future owners. 
20-25% didn't show up to work.
Cars so broken they had to be towed off the line.

Same workers became America's best plant in one year.
Same building. Same union. Same "troublemakers."

The only thing that changed was the system.

When GM closed Fremont in 1982, they blamed everyone except themselves.
"Union thugs. Unfixable. Bad workers."

Wrong question.

Toyota reopened it in 1984 as NUMMI.

GM said: "Hire new people."
Toyota said: "Give us your worst troublemakers."

They hired back 85% of the original workforce.
The exact same people GM called hopeless.

𝗪𝗵𝗮𝘁 𝗧𝗼𝘆𝗼𝘁𝗮 𝗰𝗵𝗮𝗻𝗴𝗲𝗱:

Anyone could stop the entire production line to fix a problem.

Problems were welcomed, not punished.

Workers trained in Japan for months.

No management hierarchy. No blame culture.

Job security guaranteed.

𝗥𝗲𝘀𝘂𝗹𝘁𝘀 𝗶𝗻 𝗢𝗡𝗘 𝘆𝗲𝗮𝗿:

Worst plant → Best plant

Absenteeism: 25% → 3%

Union grievances: 700+ → 30

36% fewer hours per car

Rick Madrid watched a Japanese worker stop the entire assembly line to fix one loose bolt.
Just one bolt.
"That's when it dawned on me. We can do it."

Here's where it gets absolutely ridiculous.

GM sent executives to study NUMMI for 15 years.
They took notes. Made reports. Formed committees to discuss forming committees.
Then took another 15 years to actually implement anything.

The math wasn't mathing.

Meanwhile, Toyota proved what every leader refuses to admit:

𝗧𝗵𝗲 𝘄𝗼𝗿𝗸𝗲𝗿𝘀 𝘄𝗲𝗿𝗲𝗻'𝘁 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺.
𝗧𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝘄𝗮𝘀.

When you blame people for system failures, you fix nothing.
When you fix the system, you fix everything.

Same people. Different rules. Different world.

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GM said: "Hire new people."

You're probably more comfortable than open-minded.
And comfortable won't survive what's coming.

Last week, I admitted I'm not as open-minded as I thought, here https://lnkd.in/eg_ycSaw

Today, let's talk about what makes open-mindedness radical vs Regular

(Because when AI capabilities double every 6 months, comfortable thinking becomes your biggest liability.)

𝗧𝘄𝗼 𝘁𝗵𝗶𝗻𝗴𝘀:

𝟭. 𝗬𝗼𝘂 𝗮𝗰𝘁𝗶𝘃𝗲𝗹𝘆 𝗵𝘂𝗻𝘁 𝗳𝗼𝗿 𝗽𝗿𝗼𝗼𝗳 𝘆𝗼𝘂'𝗿𝗲 𝘄𝗿𝗼𝗻𝗴.

Not wait for it. Hunt for it.

Ray Dalio, who built the world's largest hedge fund, calls this being "intensely worried about being wrong."

Not defensive. Worried.

Because being wrong and not knowing it is the most expensive mistake you can make.

𝟮. 𝗬𝗼𝘂 𝘄𝗮𝘁𝗰𝗵 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝗹𝗶𝗸𝗲 𝗮 𝗵𝗮𝘄𝗸.

Am I getting defensive right now? 
Is this a trigger for me? 
Am I listening to respond, or listening to understand?
Did my body just tense up? Why?
Am I protecting my expertise instead of questioning it?

Radical open-mindedness isn't just about ideas. It's about catching yourself in the act of closing down.

Regular open-mindedness is passive. You're open IF something comes along.

Radical open-mindedness is active. You go looking for what might change your mind.

One is a stance. The other is a practice.

Which one are you actually doing?

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📖 Join my substack to read about “How to build essential skills in AI age”  and Read the full article here:  https://lnkd.in/eg2tH9XY

This is literally every company I've worked with.

80,000 manual steps. Outdated documentation followed religiously. Processes that made sense in 2003. Or maybe not even then—one customer complained once in 2011, so now everyone gets five confirmation emails for everything forever.

Someone shows a better way.

We get angry. Kick away the solution. Keep jumping.

Because "that's how we've always done it."

Just like we've been dumping water bottles at airports for 20 years because of one incident in 2006.

We're all just birds following birds.

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𝗬𝗼𝘂𝗿 𝗔𝗜 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗶𝘀𝗻'𝘁 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹.
𝗜𝘁'𝘀 𝗮 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗔 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗔 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗽𝗿𝗼𝗯𝗹𝗲𝗺.

I was just on Johannes Deubener AI to Go Podcast I Real Talk on Enterprise AI 
He roleplayed as "Alex," a CEO with 8,000 people, six failed AI pilots, and a board breathing down his neck."

What Fun 😅

Alex, the CEO, grilled me: 
• How do you decide which pilots to kill vs scale?
• What do you do when middle managers silently sabotage initiatives?
• How do you talk to 8,000 people terrified about job replacement?
• Data governance roadmap is 14 months. Competitors aren't waiting.

We covered ROI. Talent. Legacy systems. Upskilling. Speed vs compliance.

We also talked about my Reality TV for Transformation series and Field Notes on Humans by AI - and my recent fascination with paradoxes.

In all of this, something became clear:

𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗜𝗦 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆.
Not separate. Not a different budget line. Not a side project.

If you're constantly asking whether your AI pilots move you closer to or further from business goals, you've already lost the plot.

𝗔𝗜 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗮𝗯𝗼𝘂𝘁 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻.

The reason we're slow isn't the tech.
It's because we haven't figured out how to work together as humans.

When middle managers block initiatives, pressure works short-term. Not long-term.
Change needs to come from the teams themselves.
When it originates from them, they champion it.

Fix the group dynamics. Everything else follows.

Johannes Deubener's the roleplay format was genius. It brought out nuances a regular interview never could.

Full episode in comments.

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People ask how we run AI workshops without vendor tools or endless consulting cycles.
So we filmed one.

Real participants. Real business process. Cameras on. Here's what happened.

This is one of our public AI workshops. 
(Our client workshops are confidential. We thought, why don't we do a public version to exactly how it works. in practice.)

We picked recruitment screening - a process every company understands, regardless of sector.

But that's the easy bit. What happens next?

What exactly should AI do in that process? Which steps? Which pain points? What does the solution actually look like?

Most organisations spend months going in circles on this. Or they skip it entirely, sit through a vendor demo, and buy something that may or may not solve their problem.

Then six months later: "Where's the ROI?"
It's not shortcuts. It's methodology.

A structured way to go from "AI could help here" to "this is specifically what AI does, where, and why."

In this video you'll see exactly how that happens. Step by step.

Identifying the real pain points across the process. Designing AI solution concepts using capability-level thinking - not tool-level thinking.

Nobody in the room mentioned a single AI product. 

Because that's not where you start. You start by getting clear on the problem you're actually trying to solve. 
That clarity - on the goal, the objective, what you're optimizing for - is what's missing in most organizations. 
And it's why everything after that gets messy. 

Watch the full video here: https://lnkd.in/edq945ax

What's been your experience ? 
Stuck in the "we know we want to use AI but can't figure out the why, what & how" phase?

AI capability cards by 33A , with Chris Bradshaw 🤓 

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Next up: how we move from process optimization to process innovation.

Open video: Find Your Best AI Use Case In 2 Hours, Not Months (Real example inside) on youtu.be

Find Your Best AI Use Case In 2 Hours, Not Months (Real example inside)

We built an entire industry around writing requirements. 
Business Analysis certifications. User story workshops. Acceptance criteria templates. 
MoSCoW prioritisation. Backlog grooming. Story points. Definition of Ready.

We had courses on writing good user stories. 
Career paths built entirely around translating business needs into documentation.

Why? Because building was expensive.

You couldn't afford to build the wrong thing. So you documented obsessively. Got sign-off (if you were lucky). 
Spent 3-hour sessions debating whether something was a 5 or an 8.

The 50-line user story with 50 if-then-else acceptance criteria existed because writing it was cheaper than building it.

Then the economics flipped.

Reddit's CPO put it plainly: "Our teams can dream up an idea one day and have a functional prototype the next."

One day. Not six months.

So what happens to the documentation industry we built?

What happens when you can build both options faster than you can document one?
What happens to the refinement session when the prototype IS the requirement?
What happens to the Definition of Ready when working software answers the question better than any spec?

The skill isn't "writing good requirements" anymore.

It's knowing what to prototype first. 
Asking the questions that expose the real problem before anyone builds anything. 
Facilitating decisions through working software, not documents.

The people who thrive - BAs, PMs, Product Owners - won't be the ones who write the best acceptance criteria. 

They'll be the ones who can smell a bad assumption, test it with a 2-hour prototype, and kill it before it becomes a 2-month project.

Are you and your teams still writing requirements docs? 
(tell me....I won't tell anyone...)
---

Part 4 of 11: The Transformation Playbook is Broken

Catch up on the series:
→ Part 1 : 70% fail rate: https://lnkd.in/eJTgnJ3u
→ Part 2 : Us Change People Don’t Like Change : https://lnkd.in/en24SA9H
→ Part 3 : Old playbook expired : https://lnkd.in/e_HqKcMQ


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You're not building habits.
You're accidentally practicing who you'll become.

Everyone's obsessing over morning routines and productivity hacks.
Wrong question.

Stop asking: "What habits should I build?"
Start asking: "What am I accidentally practicing?"

Because here's the thing:

You check your phone before saying good morning to your partner? You're practicing disconnection.

You say "yes" to every meeting? You're training yourself that your time has no value.

You doom-scroll while your kid asks you a question? Congrats. You're becoming the parent who's physically there but emotionally gone.

𝗧𝗵𝗲 𝘀𝗰𝗮𝗿𝗶𝗲𝘀𝘁 𝗽𝗮𝗿𝘁?
You don't feel it happening.

That calm executive you admire? They didn't wake up zen.
They practiced staying calm when Karen from accounting lost her mind over printer paper.
Small stuff. Low stakes. A thousand times.

Now it's automatic.

Your habits aren't building your future.
They're building you.

Every choice is a vote for who you're becoming:
• Hit snooze → "I don't trust myself to keep commitments"
• Send emails at midnight → "I have no boundaries"
• Skip lunch again → "I'm too busy for basic needs"

Cast enough votes in one direction?
That's who you are now.

The good news: The qualities you admire in others? With a little practice, you can make them your own.

The bad news: So can the ones you can't stand.

Warren Buffett has a brutal exercise for this.
Watch the video.

𝗩𝗶𝗱𝗲𝗼 𝗰𝗿𝗲𝗱𝗶𝘁: entrepreneursonig (Instagram)

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Us change management people telling everyone else to embrace change.
Haven't changed ourselves in years.

If that landed uncomfortably - good.

Because I've been doing exactly that. And for a long time, I didn't see it.

I've spent two decades telling organizations that resistance is normal. That change is hard. That people need time to adapt.

Turns out? I'm just as resistant as everyone I've been advising.

The contradictions were everywhere once I started looking:

I'd preach digital transformation while emailing Word documents for review cycles.
Sell 18-month "agile" programs with fixed deliverables and fixed timelines.
Same change management playbook - pushing change down people's throats, that they didn't ask for or know anything about. 

The cobbler's children have no shoes.
The change consultant's methodology has no change.

Here's what I finally had to admit:

I wasn't adapting. I was defending.

Defending a methodology I'd built my career on. Because it's what I knew how to sell. What clients expected when they hired me. What felt safe.

But the methodology I've been defending wasn't even built for this world anymore.

It was designed when documentation was cheaper than building prototypes. 
When technology stayed stable once you deployed it. 
When "best practice" had a shelf life longer than 6 months.

None of that is true with AI.

𝗪𝗲 𝗰𝗵𝗮𝗻𝗴𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗿𝗲 𝘀𝗰𝗮𝗿𝗲𝗱 𝗼𝗳 𝗰𝗵𝗮𝗻𝗴𝗲. 𝗧𝗵𝗲𝗿𝗲. 𝗜 𝘀𝗮𝗶𝗱 𝗶𝘁 𝗼𝘂𝘁 𝗹𝗼𝘂𝗱.

And if you just felt defensive reading that? 
Welcome to the club. 
Population: all of us.

But awareness accounts for 50-60% of behavior change. 
Just naming the problem is literally half the work.

So if you're feeling uncomfortable right now? Good. That's the first step.

(I've been sitting in that discomfort for months. Still am, honestly.)

The good news? The core skills haven't changed.

Alignment. Clarity. Decision-making. Understanding what problem you're actually solving.

Those matter MORE now, not less.

What's expired is how we deliver them. Not why we do it.

This series is about rebuilding the playbook. 
Not throwing everything out. 
But being brutally honest about what's expired and what needs to replace it.

Next: Why the old transformation lifecycle worked and exactly where AI breaks it completely.

💬 Be honest: when's the last time you fundamentally changed how YOU work?

I'll go first in the comments.

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The uncomfortable truth

Puppies. Obviously.
(Not my first dog video, won't be my last. 
They're adorable. Sue me.)

Watch what happens when one puppy ignores the crowd and tries the hard route. Falls off. Gets back up. Tries again.

Here's the thing: Being the only one doing something doesn't mean you're wrong. It usually means you're early.

The crowd isn't taking that path because it works. They're taking it because it looks safe.

And "safe" just means "failing in familiar company."

Sunday thought: The crowd doesn't always know where they're going.

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