AI Strategy Pioneer & Innovation Catalyst | Transforming businesses with AI-driven strategies and digital innovation

Most AI investments fail — and not because of the technology.

They fail because we keep deploying sophisticated AI to unprepared humans. Then we measure "adoption" and wonder why the ROI never shows up.

I lead AI Capability globally at Capgemini Invent. My team and I are building what I call Human-AI Chemistry — the hard work of making sure people and systems are actually ready for each other before we hit deploy.

What does that mean in practice?
→ Stop renting tools. Start building Engines that compound value.
→ Stop measuring adoption. Start measuring Cognitive Load, Stress, and Trust.
→ Stop chasing collaboration. Start engineering fusion.

This isn't theory. We're testing it, breaking it, and scaling what works — inside Capgemini and with clients who are done pretending.

33,000+ people follow along. 
If you're serious about making AI actually work, welcome.

Bora Ger

2026 won't be the year of AI scale.
It will be the year we learn that scale requires chemistry.

Enterprises will pour billions into AI in 2026.

Most of it will fund expensive shelfware.
The culprit? We're measuring the wrong thing.

Wharton just released their Wave 3 GenAI report:

88% of leaders expect budget increases.
80% expect payoff within 2-3 years.
Enterprise adoption is accelerating.

Their prescription for 2026 success?

"Training, hiring, and rollout approaches are key human capital aspects that need to be addressed."

That's it. 
That's the human-side strategy.

Meanwhile:

→ 95% of AI pilots fail to reach production (MIT)
→ 42% of companies abandoned most AI initiatives this year (S&P Global)
→ 75% of employees lack confidence using AI tools
→ 63% cite human factors as their primary implementation challenge

We keep measuring the technology. We keep ignoring the connection.

Here's what I've learned building AI capability at scale:

The problem isn't adoption --> It's readiness.
The gap isn't training hours --> It's cognitive load.
The failure isn't the model --> It's the friction.

High-performing Human-AI systems share four traits:

𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 that delivers accurate outputs.
𝗧𝗿𝘂𝘀𝘁 that's calibrated—neither blind nor absent.
𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗟𝗼𝗮𝗱 that releases mental bandwidth, not consumes it.
𝗦𝘁𝗿𝗲𝘀𝘀 that stays below the abandonment threshold.

When these align, value compounds.
When they don't, you get shelfware with a budget line.

Measure what matters.

Global AI Capability Lead | Creator of the Human-AI Chemistry Index | Chief AI Strategist @ Global AI Lab: Turning Future Signals into Sovereign Assets

𝗧𝗵𝗲 𝗔𝗜 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗘𝗰𝗼𝗻𝗼𝗺𝘆: 𝗬𝗼𝘂 𝗕𝘂𝗶𝗹𝗱 𝗜𝘁, 𝗧𝗵𝗲𝘆 𝗖𝗼𝗻𝘀𝘂𝗺𝗲 𝗜𝘁

Three numbers that should terrify every content creator:

• 79% CTR drop when AI Overviews appear
• 69% of searches now end without a single click
• 1% of users click links inside AI summaries

The traffic-based internet is dying. 
And AI is writing the obituary.

𝗧𝗛𝗥𝗘𝗘 𝗖𝗔𝗦𝗘 𝗦𝗧𝗨𝗗𝗜𝗘𝗦 𝗧𝗛𝗔𝗧 𝗦𝗛𝗢𝗪 𝗧𝗛𝗘 𝗗𝗔𝗠𝗔𝗚𝗘

1. 𝗦𝘁𝗮𝗰𝗸 𝗢𝘃𝗲𝗿𝗳𝗹𝗼𝘄
Built the knowledge base that trained every coding AI.
Now developers get answers from ChatGPT instead of visiting the site.
Monthly questions are back to 2008 levels.
15 years of community growth erased.

2. 𝗧𝗮𝗶𝗹𝘄𝗶𝗻𝗱 𝗖𝗦𝗦
Became embedded in AI coding tools with 75 million developers downloading it monthly.
But those developers never see Tailwind's website, docs, or paid products.
Revenue down 80%.
Three of four engineers gone.

3. 𝗖𝗹𝗲𝗮𝗻 𝗘𝗮𝘁𝗶𝗻𝗴 𝗞𝗶𝘁𝗰𝗵𝗲𝗻
Spent years perfecting recipes Google now summarizes in search results.
Users get the ingredient list and instructions without clicking through.
80% of traffic vanished.
Ten employees became zero.

𝗧𝗛𝗘 𝗕𝗨𝗦𝗜𝗡𝗘𝗦𝗦 𝗠𝗢𝗗𝗘𝗟 𝗛𝗔𝗦 𝗙𝗟𝗜𝗣𝗣𝗘𝗗

𝗢𝗹𝗱 𝗠𝗼𝗱𝗲𝗹:
Create content → Attract visitors → Monetize attention

𝗡𝗲𝘄 𝗠𝗼𝗱𝗲𝗹:
Create content → AI extracts value → Users never arrive

Your content still gets consumed.
Your value still gets delivered.
Your business just doesn't benefit.

𝗧𝗛𝗜𝗦 𝗜𝗦𝗡'𝗧 𝗔 𝗧𝗘𝗖𝗛 𝗨𝗣𝗗𝗔𝗧𝗘

It's a fundamental restructuring of value flow on the internet.

If your business depends on humans clicking through to your site, you're no longer selling to customers.

You're supplying training data to competitors.

Thanks Robin Jose for the inspiration.

𝗡𝗩𝗜𝗗𝗜𝗔 𝗷𝘂𝘀𝘁 𝗽𝗿𝗼𝘃𝗲𝗱 𝗘𝗹𝗼𝗻 𝗿𝗶𝗴𝗵𝘁.

And every car company is about to feel it.

The Alpamayo announcement at CES wasn't just another self-driving upgrade.
It's the moment autonomy started exposing its "thought process" in ways humans can understand.

Old approach: "If pedestrian detected, then brake."
Alpamayo approach: "That person looks distracted, checking their phone, approaching the crosswalk at an unusual angle. 
I should slow down and prepare for unpredictable movement."

𝗧𝗵𝗮𝘁'𝘀 𝗻𝗼𝘁 𝗵𝗶𝗱𝗱𝗲𝗻 𝗰𝗼𝗱𝗲. 𝗧𝗵𝗮𝘁'𝘀 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗶𝗻𝘀𝗽𝗲𝗰𝘁.

Mercedes-Benz CLA will ship with this technology to handle the complex safety scenarios that break traditional rule-based systems.

𝗛𝗘𝗥𝗘'𝗦 𝗪𝗛𝗬 𝗧𝗛𝗜𝗦 𝗩𝗔𝗟𝗜𝗗𝗔𝗧𝗘𝗦 𝗧𝗘𝗦𝗟𝗔

Vision-heavy, end-to-end learning was always the right bet.
While competitors added more sensors and hand-crafted logic, Tesla doubled down on teaching cars to see and behave like human drivers.

NVIDIA just made Tesla's approach the industry standard.

Autonomy doesn't fail at 80% or 90% accuracy.
It fails at 99%.

That last 1% - the weird edge cases no engineer anticipated - is where reasoning beats rules.

And where human-AI interaction becomes critical.

If the model can explain why it's slowing, swerving, or yielding, humans can debug and improve instead of guessing at a black box.

𝗕𝗨𝗧 𝗡𝗩𝗜𝗗𝗜𝗔'𝗦 𝗥𝗘𝗔𝗟 𝗠𝗔𝗦𝗧𝗘𝗥𝗦𝗧𝗥𝗢𝗞𝗘

They opened Alpamayo as a reasoning stack others can build on - while keeping the best performance bound to their platform.

This is Google's Android playbook for autonomy.

Democratize the software patterns, own the compute underneath.
Every car manufacturer can use the reasoning stack, but the fastest path runs on NVIDIA's hardware and cloud.

While AMD scrambles with Ryzen AI 400 and Samsung promises "AI Living," NVIDIA just positioned itself as the default brain for autonomous vehicles.

𝗣𝗛𝗬𝗦𝗜𝗖𝗔𝗟 𝗔𝗜 𝗜𝗦 𝗧𝗛𝗘 𝗡𝗘𝗪 𝗙𝗥𝗢𝗡𝗧𝗜𝗘𝗥

CES made this clear:
→ Hyundai's humanoid robots moving from showcase to factory floors
→ NVIDIA's Vera Rubin platform scaling beyond vehicles into general robotics
→ Samsung's AI-everywhere vision wiring intelligence into every home device

Your environment is about to get smart.
Not just connected - but filled with systems whose internal logic is increasingly exposed and shaped by humans in real time.

Ray Dalio calls this an AI bubble, but bubbles don't come with hard safety cases and robot fleets operating under physics.

Physical AI is where abstraction ends and interaction with the real world becomes the bottleneck.

Software can scale infinitely.
Physical intelligence has to work in three dimensions, with consequences.

𝗧𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗶𝘀𝗻'𝘁 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗰𝗼𝗺𝗶𝗻𝗴. 

𝗜𝘁'𝘀 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝘆𝗼𝘂'𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗳𝗼𝗿 𝗮 𝘄𝗼𝗿𝗹𝗱 𝘄𝗵𝗲𝗿𝗲 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝘀 𝗶𝘁𝘀𝗲𝗹𝗳 𝗯𝗲𝗳𝗼𝗿𝗲 𝗶𝘁 𝗮𝗰𝘁𝘀.

𝗚𝗼𝗼𝗴𝗹𝗲'𝘀 𝗺𝗼𝗻𝘁𝗵𝘀 𝗼𝗳 𝘄𝗼𝗿𝗸 𝗷𝘂𝘀𝘁 𝗴𝗼𝘁 𝗿𝗲𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝗱 𝗶𝗻 𝗼𝗻𝗲 𝗵𝗼𝘂𝗿.

Google Principal Engineer Jaana Dogan dropped this reality check yesterday:

Anthropic's Claude Code generated a distributed agent orchestration system in 60 minutes.

The same problem Google had been working on since last year.

Not perfect. 
But comparable.

And that's before the feedback loop.

Boris Cherny, Claude Code's creator, revealed the multiplier: 
Enable the tool to self-check its work.
That iterative process doubles or triples output quality.

Think about it. 
An AI writing code, then reviewing its own code, then improving it. 
Automatically.

𝗧𝗵𝗶𝘀 𝗶𝘀𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗮 𝗰𝗼𝗼𝗹 𝗱𝗲𝗺𝗼. 𝗜𝘁'𝘀 𝗮 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝘀𝗵𝗶𝗳𝘁.

Your engineering capacity just expanded beyond traditional project planning assumptions.

Complex system architecture that previously required months of senior engineer time? Now takes days.
Proof-of-concept development that consumed entire quarters? Done in weeks.
Technical debt that's been sitting in your backlog for years? Finally addressable.

𝗧𝗵𝗲 𝗻𝗲𝘄 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸 𝗶𝘀𝗻'𝘁 𝗰𝗼𝗱𝗶𝗻𝗴 𝘀𝗽𝗲𝗲𝗱.

It's knowing what problems are worth solving.
It's architectural decision-making.
It's understanding how systems integrate with your business logic.

Your best engineers won't be replaced by AI. 
They'll be freed from the grunt work to focus on what actually drives value: strategy, optimization, and solving the edge cases that matter.

That's human-AI chemistry working beautifully in action. 

But this only works if you recalibrate what you expect from your teams.

Stop planning like it's 2023. 
Start asking for solutions that seemed impossible six months ago.

𝗠𝗧𝗩 𝗷𝘂𝘀𝘁 𝘀𝗵𝘂𝘁 𝗱𝗼𝘄𝗻 𝗶𝘁𝘀 𝗴𝗹𝗼𝗯𝗮𝗹 𝗺𝘂𝘀𝗶𝗰 𝗰𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝗼𝗻 𝟯𝟭.𝟭𝟮.𝟮𝟬𝟮𝟱.

Their final song? "Video Killed the Radio Star" by The Buggles.

The exact same song that launched MTV on August 1st, 1981.

Technology doesn't just evolve - it cannibalizes itself with poetic precision.

𝗧𝗛𝗘 𝗜𝗥𝗢𝗡𝗬 𝗜𝗦 𝗘𝗩𝗘𝗥𝗬𝗧𝗛𝗜𝗡𝗚

MTV didn't just broadcast music videos. 
It created the template for how each generation of media kills its predecessor while birthing its own replacement.

Video killed radio. 
Streaming killed broadcast. 
Algorithms killed curation.
Each convinced it would be different. 
Each eventually consuming its own relevance.

It's the end of shared cultural gatekeeping and the beginning of personalized cultural isolation.

𝗪𝗛𝗘𝗡 𝗘𝗩𝗘𝗥𝗬𝗢𝗡𝗘 𝗪𝗔𝗧𝗖𝗛𝗘𝗗 𝗧𝗛𝗘 𝗦𝗔𝗠𝗘 𝗖𝗛𝗔𝗡𝗡𝗘𝗟

MTV didn't just show music videos. 
It decided which stories an entire generation experienced together. 

The platform amplified voices that radio systematically ignored.
 It created visual representation for artists who looked different, came from different places, told different stories.

They were curatorial choices with cultural consequence.

𝗙𝗥𝗢𝗠 𝗖𝗨𝗥𝗔𝗧𝗜𝗢𝗡 𝗧𝗢 𝗔𝗟𝗚𝗢𝗥𝗜𝗧𝗛𝗠

Now your next song is determined by data points from your last hundred plays. 
Engagement metrics optimize for retention, not revelation.

Personalized feeds give you more of what you already consume, less of what might challenge or change you. 
Art becomes data. 
Discovery becomes prediction. 
Cultural moments become individual consumption patterns.

We gained infinite choice and lost serendipitous discovery.
We got perfect personalization and lost shared cultural experiences.
We optimized for individual satisfaction and lost collective cultural evolution.

The algorithm doesn't care if you see someone who looks like you succeed. It cares if you stay engaged. There's a difference between giving people what they want and showing them what they need to see.

𝗧𝗛𝗘 𝗥𝗘𝗔𝗟 𝗟𝗢𝗦𝗦

MTV's shutdown isn't about nostalgia for music videos.

It's about the fundamental shift from broadcast culture to algorithmic culture. 
From curators who took creative risks to systems that minimize them. 
From platforms that shaped cultural taste to feeds that reflect it back without challenge.

The Buggles predicted their own obsolescence. 
"Video Killed the Radio Star" was both celebration and eulogy.

But what kills the video? 
Data does. 

And what kills human curation? 
Algorithmic optimization does.

Technology doesn't just change how we consume culture. 
It determines whose culture gets consumed, whose stories get amplified, whose art gets discovered.

We're not just changing how we find music. 
We're changing how culture finds us.

Over 9 + years of experience in Operations, Transition and Service Delivery has made me extremenly strong to drive any process and churn out results. A strong leader who is blended with People Management and Business Management which give me the confidence and the ability to choose the right expertise to do the right task to deliver results beyond expetations. Currently Enhancing and learning in depth and making myself more stronger in Process management & getting exposed to different situations to learn & handle those situations which inturn would make me a great business decision maker. Goal is to head a business unit where people, process and reveune are the key targets and would want to implement my decision to see all the 3 parameters at its best.

Specialties: Recruitment Process Outsourcing,Operations Management, Client Management, People Management, Sourcing Strategies, Process re-engineering.

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Bora Ger

Suresh Sukumaran (PMP)®

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Suresh Sukumaran (PMP)®

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