Director | AI Hub Lead | FAAS

Capgemini Invent

Chief of Staff to Global Head of the Gen AI Accelerator

Senior Manager | Analytics & AI

Managing Consultant | Insight Driven Enterprise

Senior Consultant | Customer Experience & Analytics

CoreLogic

Credit Risk Manager

NatWest Group

Senior Data Analyst

I help leaders and consultants understand AI and use it well.

The best transformations happen when we focus on people first, and let tech follow.

When you:
↳ Make the complex, simple
↳ Lead with empathy and understanding
↳ Build bridges between tech and business
Teams align. Progress feels possible. Work gets done.

Behind every successful AI project, there's always a human story worth telling.

Throughout my career, I've seen the same issue: 
Strong tech, but no connection to the people it’s meant to serve.
That’s where things stall.

I believe that people, not algorithms,
are the heart of any meaningful change.

The role of AI is simple: make work easier, faster, smarter.

But it only works when people feel confident using it.

That’s what I focus on:
↳ Helping leaders cut through the noise
↳ Supporting consultants as they grow
↳ Making sure tech and business speak the same language

After all, isn't AI just a tool to help us humans achieve more? 

If any of this matters to you, I’d love to connect.

Best way to connect: Comment on my posts or DM

AI is evolving fast.

But the most powerful tool in your company? 
Still your people.

As machines get better at being machines...
We need to get better at being human.

The most successful AI strategies do one thing well:
They AMPLIFY what humans do best.

Here are five human strengths worth protecting, 
and how AI can help:

1️⃣ Empathy & Emotional Intelligence
↳ AI handles the data. Humans build the trust.
🧠 A bank shifted advisors from spreadsheets to client conversations, and saw better retention and upsell.

2️⃣ Physical Presence & Human Connection
↳ AI can guide decisions. It can’t sit beside someone in a hard moment.
🏥 Healthcare teams using AI for diagnosis while nurses focus on care.

3️⃣ Judgment & Ethics
↳ AI can spot patterns. It can’t weigh values.
⚖️ Legal teams use AI to review documents, but keep humans involved for decisions that carry nuance.

4️⃣ Creative Imagination
↳ AI can suggest. Innovation still needs human perspective.
🧩 In manufacturing, AI helped optimise designs, but breakthrough ideas came from the team.

5️⃣ Vision & Leadership
↳ AI can predict trends. Only people can move others to act.
🚀 Tech companies use AI to spot signals, but rely on humans to rally teams around a compelling vision.

What fails:
• Automating tasks without rethinking the process
• Treating AI as a direct replacement for humans
• Focusing on efficiency without considering effectiveness

What works:
• Spot tasks humans find draining - let AI handle those
• Redesign workflows for human-AI collaboration
• Measure both performance AND human satisfaction

MIT calls this the EPOCH framework:
Empathy, Presence, Opinion, Creativity, Hope.
(link to the research paper in the comments)

So, next time you're implementing AI, start by asking:
"What makes your people exceptional, 
and how can AI amplify that?"

Which human strength do you think 
AI should support, not replace? 👇

Eng. Raffaele Tiberio✔

♻️ Repost to help teams build better AI strategies.
➕ Follow (Yousif Hussain) for more.

Yousif Hussain

Data & AI Advisory | EY Middle East | AI Hub Lead

If not AI Agents, then which AI solution fits your needs?

Let's compare the most popular ones to find the best fit...

1️⃣ LLM (Large Language Model)
↳ Text generation from prompts, no external data.
↳ Simple and fast, but limited in accuracy or depth.
💡 Best for: chatbots, drafting text, basic Q&A.

2️⃣ RAG (Retrieval-Augmented Generation)
↳ Adds real-time knowledge retrieval to LLMs.
↳ More accurate, but depends heavily on data quality.
💡 Best for: customer support, research, knowledge-heavy tasks.

3️⃣ AI Agent
↳ Plans, reasons, and uses tools to complete tasks.
↳ Works well for clear, step-by-step workflows.
💡 Best for: report writing, research assistance, workflow automation.

4️⃣ Agentic AI
↳ Multiple agents coordinate, share memory & divide work.
↳ Suited to complex, ongoing problems.
💡 Best for: large projects, multi-user or multi-step processes.

📌 The progression looks like this:
LLM ➝ RAG ➝ AI Agents ➝ Agentic AI.

Each stage adds new capability, 
it doesn’t replace the previous one.

Modern systems often combine more than one approach, 
depending on the use case.

⚠️ A note of caution
Yes, you get more capability as you move up.
But you also add more complexity, more risk, 
and less direct human control.

→ LLMs are simple and safe, but limited
→ RAG adds precision, but is only as good as the data
→ AI Agents bring autonomy, which means letting go of some oversight
→ Agentic AI scales further, but makes outcomes harder to predict

Visual Credit: Rakesh Gohel (AI Agents Expert)

👉 The rule of thumb: only move up when the need is clear

Sometimes the best results come from a simpler system done well, rather than chasing something fancy for its own sake.

If you had to choose, would you focus on simpler AI done well, or push ahead into advanced systems? 👇

Eng. Raffaele Tiberio✔

♻️ Repost to help others choose the right AI for the job.
➕ Follow (Yousif Hussain) for more.

400 million people speak Arabic.
 
But most AI models reflect Western cultural values.
 
That gap matters. Because across the Arab world, daily interactions are built on trust, respect, and cultural context.
 
Imagine you’re rolling out AI customer service.
The system learns “best practice”:
 
Apologise briefly, offer a discount,
and move to quick resolution.
 
↳ In Germany, that works. Direct and efficient. Customer satisfied.
↳ In Japan, a brief apology feels dismissive. People expect a deeper acknowledgement.
↳ In the UAE, a discount can feel like charity. Recognition and respect carry more weight.
 
Same system. Same situation. 
Completely different outcomes.
 
And this is inevitable.
 
AI models absorb patterns from their training data.
Most of that data comes from billions of English pages.
 
Which means unless they’re trained thoughtfully,
they miss the diversity of human interaction.
 
That’s why in MENA, Arabic-first AI matters.
And why we’re seeing major investment in models across the region:
 
↳ JAIS (UAE) with strong Arabic benchmarks and longer context
↳ Falcon-Arabic (UAE) trained fully on Arabic data
↳ ALLaM by Humain (Saudi) launched with SDAIA, tuned to Saudi norms
↳ Fanar (Qatar) multimodal, with Islamic and recency tools
↳ Nile-Chat (Egypt) the first dialect model for Egyptian Arabic
 
AI needs to go beyond words.
It needs to capture how culture shapes meaning.
 
That is what Arabic-first models are built for.
 
For anyone building AI in the region, a few qns worth asking:
↳ What cultural assumptions sit inside the models we use
↳ How do we test cultural awareness as well as tech performance
↳ Who in our teams makes sure this is part of the design
 
The companies that take this seriously
will build trust and adoption.
 
The ones that don’t will end up with tools that look fine
on paper but fail when people actually use them.

What cultural gaps have you noticed in global AI tools?

Amr Ali | Mohamed Nidhal SAFTA | Eng. Raffaele Tiberio✔ | Marco Biaggi | Badih Nahas | Mohamed Kamal ElSawy  | Hamza Halaweh
 
♻️ Repost to show why culture matters in AI
➕ Follow (Yousif Hussain) for more

No alternative text description for this image

Taplio

Yousif Hussain's Linkedin Analytics

's Best Posts (last 30 days)

Want to drive more opportunities from LinkedIn?

Taplio

.css-1jgreyo{background-image:linear-gradient(284deg, rgba(39,43,72,1) 0%, rgba(33,47,183,1) 100%);color:transparent;-webkit-background-clip:text;background-clip:text;}Yousif Hussain's Linkedin Analytics

's Best Posts .css-qqfgvy{font-size:var(--chakra-fontSizes-md);}(last 30 days)

Want to drive more opportunities from LinkedIn?

Yousif Hussain's Linkedin Analytics

's Best Posts (last 30 days)