Amazon Web Services (AWS)

Go to Market expert for AI & ML AWS services in EMEA. Responsible for providing business and technical expertise to help our customers succeed. This involves leading the Go to Market for Generative AI, scaling activities with other stakeholders and shaping the evolution of our AI Services and Products.

Principal Go to Market Generative AI

Responsible for building business and technical relationships with large enterprises and operate as their trusted advisor, ensuring they get the most out of the cloud at every stage of their journey in adopting Machine Learning across their organisation.

Principal MLOps EMEA

Vodafone

The Analytics manager will lead the analytics strategy within Vodafone Group across all the countries in order to:

Contribute to the creation, delivery and evolution of the insight strategy to maximize the benefit of significant investment in data and business intelligence tools.
Co-lead the development and delivery of a commercial roadmap of data products and services.
Manage the definition and delivery of actionable insight for both Business-As-Usual activities and ad-hoc/project based initiatives.

General Manager AI

Vodafone Business

Lead the creation of advanced analytics products that provides actionable business insights to Vodafone and its customers.
Identify opportunities to generate value internally and external customers through the use of predictive models with large and varied datasets.
Optimise use of existing big data technology.
Contribute to the definition of the data monetisation strategy for enterprise customers.

Manager Big Data & Advanced Analytics

Responsible for building, maintaining and managing sound relationships with the customers through our team of Partners and Solution Selling Program.

Manager Partners & Solutions

Vodacom

To help position Vodacom Business as a Mobile Solution Partner in solving service delivery issues for corporate customers.  Achievement of sales objectives by identifying, creating and pursuing newrevenue growth opportunities, within targeted customers.

Business Development Manager

Sales support professionals work in the administration side of the business. My job includes handling correspondence, reviewing and distributing sales related documents, creating new sales opportunities, examine client accounts, prepare and distribute financial and sales reports to advise team members. They also assist sales teams with new ideas and suggestions to achieve their targets.

Sales Support Specialist

Responsible for the management of the sales and relationship with strategic customers.
To engage in a variety of tasks including project management, coordination, strategic planning, relationship management, negotiation, leadership and innovative development of opportunities. keeping record of transaction of sale and purchase goods.

key Account Manager

To lead the development of sales propositions, researching sales markets, creating sales plans that match specific market needs with Upstream’s solutions. As well, responsible of building up existing sales collateral, to present innovative and add value commercial proposals.

Pre-Sales Specialist

Thales

Junior Programmer & Analyst

🤖 Generative AI Lead @ AWS ☁️ | Startup Advisor | Public Speaker

One of my favorite quotes that I strive to live by every day is, "To give anything less than your best is to sacrifice the gift." In addition to this guiding principle, I have a daily ritual that I cherish - rising early in the morning to run a few miles, setting the tone for an energised and productive day ahead."

With a strong technical foundation, I have over 15 years of extensive experience in sales and business development. Throughout my career, I've successfully led cross-functional teams and in recent years, I've honed my expertise in Artificial Intelligence and Machine Learning. In the ever-evolving landscape of AI and ML, organisations are grappling with the need to accelerate their journey. To overcome that, I work as the AI/ML Go to Market EMEA Lead at AWS, where I assist customers around the world in harnessing the full potential of Artificial Intelligence.

I am passionate about actively engaging in discussions surrounding these topics and connecting with fellow professionals in the field to exchange knowledge and experiences

I am a proud parent of two amazing children, whom I consider to be among the most wonderful accomplishments in this world

MCP is probably the first protocol in tech history with more builders than users… or at least that’s how it feels.

Since Anthropic launched MCP last November, the ecosystem exploded:

🔹6,070+ servers live today (up from ~100 at launch 🤯)
🔹2,000–3,000 active devs building and forking like there’s no tomorrow
🔹50,000–75,000 users actually… using them.

Yes, you read that right. For every 25 users, there’s a builder.
A protocol where supply is building faster than demand.

The irony?
👉 The top 10 servers hoard almost half of the attention.
👉 The top 10% swallow 88% of all stars.
👉 Most of the other thousands sit there like a ghost town.

Of course, enterprises are jumping in but users? They’re mostly flocking to a handful of “quality over quantity” servers like GitHub and so on…

MCP has become the new JS framework of AI: everyone builds, only a few survive, and adoption lags behind the hype.

But make no mistake: despite the meme-worthy builder-to-user ratio, MCP is maturing fast.
Once security and governance catch up, it won’t just be a dev playground, I t’ll be the enterprise standard.


#ai #mcp #humor

Eduardo Ordax

🤖 Generative AI Lead @ AWS ☁️ (150k+) | Startup Advisor | Public Speaker | AI Outsider | Founder Thinkfluencer AI

🚨 At DEFCON, Microsoft’s Copilot Studio agents got absolutely wrecked.

Researchers hijacked them with a few clever prompts and… boom:

– Full CRM records dumped
– Private tools exposed
– Actions executed without human oversight

Yes, the “autonomous agents” everyone is rushing to deploy turned into data exfiltration machines — zero governance, zero brakes.

The irony? We keep selling “no human in the loop” as a feature, when in practice it’s a hacker’s dream. One vulnerability = full system compromise.

This time it was Salesforce records, billing info, and internal comms. Next time? Who knows.

Enterprises, here’s the memo: autonomous AI without serious security is not innovation, it’s malpractice.

We’re widening the gap between capability and security and hackers are the only ones winning.

Autonomous is cool… until your agent is working for someone else.

(@Michael Bargury on X)

#ai #agents #security

graphical user interface, text, application

Vibe coding was supposed to kill software developers.
Reality check: it’s creating more jobs than ever.

Introducing the hottest new role in tech: 𝗩𝗶𝗯𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 𝗖𝗹𝗲𝗮𝗻𝘂𝗽 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁™.

Because when your AI-generated “final_final_v12.py” breaks prod at 3 AM… guess who’s called in? Not the vibe coder.

So no, developers aren’t going anywhere.
They’re just evolving into janitors of AI’s creative mess.

#ai #vibecoding

🤖 Generative AI Lead @ AWS ☁️ (150k+) | Startup Advisor | Public Speaker | AI Outsider

🔥 The daily stand-up… aka the meeting nobody asked for.

I talk to developers every day and the answer is always the same: the worst part of the job isn’t the fixing bugs, it’s the meetings.

Agile was supposed to fix this. Instead, it feels like we just invented new ways to waste time.

Funny thing? AI has actually boosted developer productivity… only for all that time to get killed by endless “what did you do yesterday?” rituals.

But hey—if the scrum master is Vito Corleone himself… with that tone, that leadership, that temperance… I’m convinced this project will definitely succeed.

Otherwise, you know the other option left… 🪦

#agile #ai

This 2’ video is pure magic…. Sorry, I mean maths!

How LLMs Really Work Behind the Scenes (No Magic, Just Math) 🚀

Ever wondered what’s actually happening inside GPT, Claude, or LLaMA when you type a question? Here’s the “movie” playing in the background:

1️⃣ Words become tokens and then numbers
Your text is split into tokens (words or subwords). Each token is turned into a high-dimensional vector—thousands of numbers that capture meaning.

2️⃣ Order matters (positional encoding)
Transformers don’t know word order by default, so we add positional signals telling the model if a token is first, last, or somewhere in between.

3️⃣ The “attention” magic
Every token looks at every other token to figure out what’s relevant. This self-attention step is like having all words in a sentence talk to each other, regardless of distance.
And with multi-head attention, the model does this several times in parallel—spotting grammar in one head, tone in another, and meaning in another.

4️⃣ Feedforward thinking
After attention, each token’s vector passes through a mini-neural network (MLP) that adds new knowledge and transforms it further.

5️⃣ Residuals & normalization
To keep learning stable, the input and output of each block are added together (residuals) and normalized. Think of it as “memory foam” for data—retaining what matters and smoothing the rest.

6️⃣ Layer upon layer
Powerful LLMs stack this process dozens of times.
▪️Early layers: capture basic word meanings
▪️Middle layers: detect relationships and patterns
▪️Final layers: combine everything into deep context

7️⃣ Output generation
At the end, vectors are turned into probabilities for the next token using a softmax. The model picks the most likely one… then repeats until your sentence is complete.

So next time ChatGPT gives you an answer—it’s not guessing. It’s running a massive, highly-orchestrated information exchange at lightning speed!

Link to the video in the comments! Kudos to 3Blue1Brown

#ai #tranformers

👩‍🔬👨‍🔬 If you think data scientists just train ML models… you clearly have never met one.

The myth:
📊 A curated dataset magically appears
🏋️ They train an XGBoost model
👨‍🔧 Someone else deploys, monitors, and maintains it

The reality:
➡️ Data scientists play AI engineer + ML engineer + Data engineer + DevOps + Architect all at once
➡️ They juggle 157 versions of the same notebook no one will ever put in prod
➡️ And “training models” is maybe 1% of their job

Honestly, sometimes I think we should stop calling them data scientists. The role never was what recruiters sold. It’s chaos. Hard, messy, unscalable chaos.

That’s why I’m excited about tools like Zerve — a proper IDE built for data scientists. Forget the “generic AI-first IDEs” like Cursor or Windsurf. Zerve is agentic by design and finally gives data scientists the workflows they deserve.

👉 Free community tier available: https://lnkd.in/d-vhtwnZ

#ai #genai #datascience

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In my opinion the best video you’ll ever see about Large Language Models.  1.5M views in less than a year!!!!!

Not very fancy slides or visuals and the sound quality is not perfect (you can fix it easily with ElevenLabs), but come on, this guy is not a hyper nor you are watching a movie. This is a guy from Stanford University who really knows what he is taking about.

Summer is over so if you want to stay up to date with AI, I recommend to book 2-3 hours to see it, engage with it and make some notes and revisit the important topics. You can also do some cool stuff like uploading the video into Humata so anyone can talk to the video and get some follow up questions with references and timestamps. Also I sent the transcript into Notebook LM to listen it during my morning runs, it’s quite good!

This is probably the best way you can spend your 2-3 spare hours per week.

🎥 Link to the video: https://lnkd.in/d-JwAmyM

#ai #stanford #llm #genai

🤯 Why do LLMs still hallucinate?

OpenAI just published an interesting paper that digs into one of the most stubborn problems in LLMs: hallucinations.

👉 The core insight?
It’s not just about model size or reasoning. It’s about incentives.
Today’s benchmarks reward models for guessing instead of admitting uncertainty.

Think of it like a multiple-choice test:
- Leave it blank → 0 points.
- Take a wild guess → maybe you score.

No wonder models “prefer” to hallucinate rather than say I don’t know.

Some key takeaways from the paper:
🔸Accuracy ≠ reliability. A model can look more accurate but hallucinate way more often.
🔸Errors are worse than abstentions. Saying “I don’t know” should count as a good outcome.
🔸Hallucinations aren’t random glitches. They come directly from how models are trained and evaluated.
🔸Fix the scoreboard, fix the behavior. If evals penalize confident errors more than uncertainty, models will learn to be humble.

✌️My 2 cents: hallucinations can be both a feature and an issue.
Just like many of the greatest artists in history created genius from states of mental distortion, hallucinations in AI can fuel creativity and unexpected catastrophic ideas. The question is not whether they’re good or bad, but how to handle them based on your use case.

🔗 Link to the blog: https://lnkd.in/dtj2SWsK
🔗 Link to the paper: https://lnkd.in/dK3mSZre

🚀 Agentic AI is exploding, but which framework should you bet on?

If you’ve tried building AI agents, you know the options are multiplying: LangGraph, LangChain, Autogen, CrewAI, Make.com, n8n… but they’re not interchangeable. Here’s how to make sense of the chaos:

🦜🔄 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 
Think enterprise-grade orchestration. Graph-based, stateful, long-running workflows with loops, branching, and persistent memory. Perfect when your agent system needs durability + complexity.

🦜🔗 LangChain
The OG. Great for chaining prompts, tools, and RAG. If you just need a chatbot, simple agent, or MVP, start here.

🟥🟦🟩🟨 AutogenAI (Microsoft)
Built for multi-agent collaboration. If you want agents to negotiate, coordinate, and tackle big tasks together, this is your go-to.

🤖 CrewAI 
Lightweight and flexible. Assemble “crews” of role-specific agents quickly, while keeping granular control. Fast deployments, minimal dependencies.

🥢 Make
Visual, no-code automation for business users. Connect AI to CRMs, reports, SaaS tools—without writing a single line of code.

🟣🔄🟤 n8n 
Open-source, node-based automation. Great for RAG-powered workflows and deep data integrations with a visual touch.

💡 Bottom line:
▪️Want enterprise complexity? → LangGraph
▪️Need fast AI app prototyping? → LangChain
▪️Building collaboratively? → Autogen or CrewAI
▪️Prefer drag-and-drop ? → Make.com or n8n

The right choice depends on your workflow complexity, control needs, and dev resources. Agentic AI is not one-size-fits-all!

#ai #genai #agents #agentic #framework

I don’t know why the entire AI community on LinkedIn is not talking about this today.

Yesterday, the folks at Thinking Machines published their first blog post:

 👉 “Defeating Nondeterminism in LLM Inference.”

It tackles one of the most overlooked issues in AI systems: reproducibility.

We all know LLMs feel “random”, ask the same question twice, get two different answers. But what’s shocking is that even at temperature 0, when outputs should be deterministic, still behave nondeterministically.

Why?
 Most people blame GPU concurrency + floating point quirks. But Horace’s post shows the real culprit:
 🔹 Lack of batch invariance in inference kernels.
 🔹 Meaning: your answer can change depending on how many other users are hitting the server at the same time.

The blog goes deep into:
- Why operations like RMSNorm, Matmul, and Attention break determinism.
- How to re-engineer kernels to achieve true batch-invariance.
- Proof that with these fixes, you can get bitwise-identical outputs across runs.
- And why this unlocks things like true on-policy RL for training, something previously impossible.

This is a game-changer. Reliable AI research, reproducible enterprise apps, and trustworthy scientific results all depend on fixing nondeterminism.

📌 Read it here: https://lnkd.in/d4Bq_Qj5

My 2 cents: This is one of those “boring infrastructure” breakthroughs that quietly redefines what’s possible. Everyone building with LLMs should pay attention.

#ai #genai #research

𝗛𝗼𝘁 𝘁𝗮𝗸𝗲: 𝗡𝗼𝘁 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗻𝗲𝗲𝗱𝘀 𝗮𝗻 𝗟𝗟𝗠, 𝘀𝗼𝗿𝗿𝘆 𝗳𝗼𝗿 𝗯𝗲𝗶𝗻𝗴 𝘀𝗼 𝗰𝗹𝗲𝗮𝗿!

Yes, LLMs can write poems about your dog, generate fake images with Elon Musk or be your free therapist. But guess what? XGBoost is still the real workhorse of machine learning.

For years (and still today), data scientists:
→ get the data
→ clean it
→ train a model
→ test it
→ deploy it
→ monitor and retrain

Boring? Maybe. Valuable? Absolutely.

The problem? It’s not “sexy” anymore. Everyone wants to brag about their Agentic AI apps instead of solving problems with tools that actually work.

Here’s the truth:
✅ If traditional ML solves the problem → use it.
✅ With XGBoost you control the pipeline end-to-end: explainability, reliability, monitoring.
❌ With LLMs you get hype but also hallucinations, and high bills per token.

So, before jumping into another “AI-first” experiment, make sure your team has the tools to do the basics right. That's why Zerve AI is so powerful, it boosts data scientists teams productivity to the next level!

Sexy doesn’t always mean valuable. And XGBoost is living proof.

You can try it here 👉 https://lnkd.in/d-vhtwnZ

#ai #datascience #ml

🚨 LangChain dropped probably the most useful and condensed manual for building AI agents!

Building agents is lot of fun till you have to productionize them at scale. 

The truth is most of the companies struggle when it comes to real, production-ready agents. That’s why this guide will help you to move from experimentation into real production Agentic AI systems at scale.

🛠️ Whether you're automating email workflows or building multi-agent systems, this 6-step framework is the clearest path from idea to impact I’ve seen:

1️⃣ Define the Job
 If a smart intern can’t do it, don’t expect an agent to. Start with real examples, not AI fantasies.

2️⃣ Write the SOP
 Can’t describe it step by step for a human? The model won’t figure it out either.

3️⃣ Build the MVP Prompt
 Focus on one decision. Nail the reasoning. Skip the full pipeline — prove it works first.

4️⃣ Connect the Pipes
 Pull in real-world data (emails, calendars, docs). Agents need context to think.

5️⃣ Test Like Crazy
Use LangSmith to trace failures, measure accuracy, and catch blind spots early.

6️⃣ Ship & Learn
Deploy fast. Monitor real usage. Iterate relentlessly.
📩 Example? An email agent that triages messages, checks calendars, and drafts replies — built from this exact playbook.

🔁 And here’s the key takeaway:
Building agents isn’t about getting them to run, it’s about building something useful, reliable, and aligned with how people actually work.

If you're serious about AI agents, read this guide now:
 👉 https://lnkd.in/d8phGwVW

LangChain isn’t just building tools, they’re setting the blueprint.

#ai #agents #genai

𝗔𝗜 𝘄𝗶𝗹𝗹 𝗻𝗲𝘃𝗲𝗿 𝘀𝘂𝗿𝗽𝗮𝘀𝘀 𝗵𝘂𝗺𝗮𝗻 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲, 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗶𝗻 𝗮 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗹𝗲𝘃𝗲𝗹 👇 

I only come into the office with a laptop

I used to bring a backpack but then people can tell when you’re coming into work and when you’re leaving.
Now I just walk in with my laptop (and maybe a water bottle too). That way people think I’m just coming out of a meeting even if I come into office at 11 AM. 

Sometimes I leave home early too around 2 PM walking out with my laptop and people think I must be walking somewhere. Truth is I’m walking to the parking lot to leave.

It’s all about data….

CEO: our data is too slow

Data Engineer understood: Kafka + Spark Streaming

What CEO meant: reschedule that daily report from 11:00am to 7:30am

Kudos to Shachar Meir  

#data #ai

👀 16 Must-Read 𝗔𝗜 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸𝘀

“Culture eats strategy for breakfast”… true.
 But if you want to win in AI, you still need a strategy.

The companies that are leading today spent years building a data strategy, and now they’re building a real AI strategy on top of it.

Because adopting AI without a clear problem to solve or a vision of where you want to go? That’s just hype.

Some facts worth noticing:
▪️ 60% of orgs already have a Chief AI Officer.
▪️ 45% of budgets for 2025 put GenAI as the top priority.
▪️  90% are laying foundations for Agentic AI.

If you really want to play in this game, stop chasing shiny demos. Start building your AI strategy.....TODAY.

👉 These playbooks are some of the best resources to get started.
1) Accenture – The Art of AI Maturity
➜ https://lnkd.in/g4kyWCNd

2) Amazon – AI/ML/GenAI Cloud Framework
➜ https://lnkd.in/gbmUAgQT

3) Bain – Transforming CX with AI
➜ https://lnkd.in/gqq-66ST

4) Bain – Winning with AI
➜ https://lnkd.in/gWk84MjS

5) Booz Allen – Securing AI
➜ https://lnkd.in/gceVreFG

6) BCG – Transforming with AI
➜ https://lnkd.in/gWtqJFuB

7) Deloitte – AI Transformation
➜ https://lnkd.in/gGNURxzq

8) Google – AI Adoption Framework
➜ https://lnkd.in/gCj2S6uF

9) IBM – CEO's Guide to GenAI
➜ https://lnkd.in/gqDam-yS

10) McKinsey – The Executive's AI Playbook
➜ https://lnkd.in/gFRqm2MW

11) Microsoft – CIO's GenAI Playbook
➜ https://lnkd.in/gbJ4vwVE

12) PMI – DS/AI Project Playbook
➜ https://lnkd.in/g7_wQbRs

13) PwC – Agentic AI Playbook
➜ https://lnkd.in/gSicWfeV

14) PwC & Microsoft – Deploying AI at Scale
➜ https://lnkd.in/gwg-CBBb

15) Scaled Agile – AI-Augmented Workforce
➜ https://lnkd.in/gunGGgWJ

16) World Economic Forum – AI C‑Suite Toolkit
➜ https://lnkd.in/gh-FQT72

#ai #strategy

My very serious understanding of web dev history (and beyond with AI) 😂

For everyone shouting “web devs are cooked” … “the internet is dead” … “search engines won’t exist in 2 years”…

Relax. You’ve been saying the same thing every time a new framework, library, or shiny toy dropped.

This timeline is probably the most accurate (and painful) representation of reality 👇

Web dev isn’t dead. The internet isn’t dead. But we can kill CSS, that’s fine.

#ai #www

“𝟳𝟬% 𝗼𝗳 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗔𝗴𝗲𝗻𝘁𝘀.”
Yep. You read that right.

We live in the golden age of agent washing.
Everyone slaps “AI Agent” on their deck when in reality, most are just glorified workflows with some AI sprinkles.

📊 Research shows:
🔹 15% → True agentic AI (real autonomy, adaptive reasoning, learning).
🔸 35% → AI-enhanced workflows (basically automation + a little LLM dust).
🔹 30% → Traditional automation rebranded (a.k.a. scripts in disguise).
🔸 20% → Hybrids (somewhere in between, usually the only thing working in production).

And yet, the market is projected to explode from $7B to $93B by 2032.
Because hype sells better than honesty.

The kicker?
Carnegie Mellon tested leading “AI agents” running a fake company.
Best result? Claude with a 24% success rate.
Average cost per task? $6 and 30 steps.
One agent even renamed a colleague just to “match the expected name.” 🤦

So why do “boring” workflows still dominate?
◾️ 90%+ success on repetitive tasks
◾️ Easier to integrate into enterprise systems
◾️ Cheaper and more predictable

💡 The truth: The winners today aren’t going “full agent.” They’re going hybrid:
🔺 Automation for the stable stuff
🔺 AI for pattern recognition
🔺 Agents for niche, bounded cases
🔺 Humans for the actual thinking

👉 Before you buy into the hype, ask:
Is this an agent… or just a dressed-up workflow?

#ai #agentic

AI engineers, ML engineers, data scientists, vibe coders—anyone building AI agents—this one’s for you.

A senior Google engineer just released a 400-page FREE book (on Google Docs for review) covering Agentic Design Patterns.

The table of contents alone looks like a complete playbook for modern agent systems:
🔹 Advanced prompt techniques
🔹 Multi-agent patterns
🔹 Tool use & MCP
🔹 And much more

If you’re serious about building with agents, do yourself a favor and bookmark this. It’s one of the most comprehensive resources I’ve seen.

Link in the comments 💬

#ai #agents

How money works:

1. OpenAI signs $300B GPU deal with Oracle
2. Larry gains $100B (no GPUs shipped)
3. Larry invests in OpenAI’s $1T round
4. Sam uses $300B to pay Oracle
5. Oracle stock pumps again
6. Larry makes another $100B
7. Larry invests in OpenAI

Flywheel go brrr.

#ai #openai

🚀 𝗚𝗲𝗻𝗔𝗜 𝗶𝘀 𝗻𝗼𝘁 𝗵𝘆𝗽𝗲 𝗮𝗻𝘆𝗺𝗼𝗿𝗲. 𝗜𝘁’𝘀 𝗥𝗢𝗜.

Google just released a global report on the business impact of Generative AI, and the numbers speak louder than any keynote.

🔑 Key findings

🔸74% of enterprises using GenAI report ROI in the first year.
🔸86% of those seeing revenue growth report +6% or more in annual revenue.
🔸84% can move from idea → production in under 6 months, cutting innovation cycles drastically.
🔸Productivity is skyrocketing: 45% of orgs say employee output has at least doubled.
🔸Better experiences: 85% report higher engagement & satisfaction thanks to GenAI.
🔸Security, too: 56% improved their ability to detect threats and reduce response time.

But the real differentiator? 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽.

👉 Organizations with strong C-suite sponsorship are 91% more likely to report major revenue gains.

💡 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆 𝗶𝘀 𝗰𝗹𝗲𝗮𝗿: 𝗚𝗲𝗻𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗮 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗽𝗹𝗮𝘆—𝗶𝘁’𝘀 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗲𝗻𝗴𝗶𝗻𝗲. The winners are not the ones “experimenting,” but those embedding GenAI into strategy, operations, and culture.

📌 Full report here 👇

Are you measuring the ROI of GenAI in your organization yet—or still stuck in “pilot purgatory”?

#ai #google #genai

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