Aesara Partners 

At Aesara Partners, our mission is simple: enable you to be the best version of yourself. We offer a range of focused services that unlock the potential of people, teams and cultures to maximise business performance. 

Collectively our Partners and Associates have decades of experience both in executive coaching and as board level leaders themselves working in multiple sectors across global markets.

Partner & Executive Coach

Doshisha University

Professor of Strategic Management, Global MBA Program

The CFO Coach K.K.

The CFO Coach K.K. is a boutique executive coaching firm specializing in developing High-Performance​ Financial Leaders and Teams.

Founder & Executive Coach

Kansai Gaidai University

•	Awarded President's Global Education Award (Teacher of the Year).

Professor of Leadership & Management

Benesse Holdings, Inc. (USD 4.5B; Tokyo Stock Exchange Code: 9783)

•	Member of the Board of Directors for Berlitz Corporation; Benesse Corporation; and Benesse Style Care.  Chairman of Audit Committee for Berlitz Corporation. Member of Benesse Holdings Pension Committee.
•	Global CFO covering all 40+ Benesse group companies
•	Championed and supported multiple cash generation initiatives including a CFO led "Save to Grow" initiative to improve Company Financial performance and to identify significant savings for business reinvestment
•	Enhanced Finance bench strength and hired a high potential team to start financial business consulting
•	Implemented several process and capability best practices to move group companies towards global standards

Global Chief Financial Officer & Corporate Senior Vice President

McCann Worldgroup Holdings

• Overall Responsibility for Operations (Finance, IT, Legal, and Administration - 10 legal entities) 
• Successfully turned around long term loss making lead agency by delivering double digit margin improvements within two years
• Delivered double digit margin improvements for all six MWG Group companies 
• Built a new Finance team and moved from Scorekeeping to Business Partnership and improved business performance
• Major projects within last two years include: 
  - Shared Service center creation/migration
  - New Financial System Migration
  - Acquisition & Divestiture
  - Business Turnaround
  - Retirement Plan consolidation/change
  - Office relocation/rent reductions

Group Chief Financial Officer, Executive Vice President, and Member of The Board

Becton, Dickinson and Company

• Led Finance, Procurement, General Affairs, and Rebates teams (43 staff) to deliver multi-million dollar cash and profit performance improvement for first year and beyond. Exceeded all budget expectations.
• Engaged Japan Management Team and Global Finance Leadership Team on company objectives to deliver best outcome (Finance Transformation initiative, Sales effectiveness improvement, Key Account Management, Business Continuity plan, and strategy/selection of backup distribution center).  Led process for 2012-16 Business Plan development
• Developed and executed Finance department strategy and vision to move from scorekeeping to business partnership. Implemented job rotation program and hired several high potential employees to improve bench strength.
• Championed multiple Finance and Procurement initiatives including definition and implementation of new 401k Retirement Plan; divestiture of non-core business; change of primary banks (reduced bank fees by over 30%); vendor reduction process, and new travel expense management system 
• Taught New Manager Essentials program and selected as leadership trainer for PEPFAR program in South Africa to teach Leadership and Change Management to African Health Ministry officials

Finance and Administration Director

Avon Products Inc.

•	Implementation project leader for Avon Sales Leadership Framework (ASLF) – Highest priority project for the Company to adapt to a new Sales Model.  Completed project implementation in record timing.  Awarded regional award as part of ASLF Team. Only person in Asia recognized for both Top-line and Bottom-line contributions in the same year.
•	Defined and executed new vision and mission and led Sales Support department to complete over 40 initiatives (within a six-month period) as part of Team’s new journey towards field motivational excellence. Incorporated new processes to deliver high impact incentives, and to create Sales energy and momentum for sales turn-around.    
•	Achieved highest employee engagement scores with Sales Support Department significantly exceeding country results in over 95% of categories.  Previously had also led Finance department to highest employee engagement scores.
•	Global Project leader for a high potential, multi-functional, multi-national team to study and recommend actions to improve Global Sales Incentive management/spending process.
•	Oversaw execution of a variety of Company and Public/Media events ranging from Sales Conferences to Philanthropic events including Annual Awards to Women, and Pink Ribbon Campaign support.

Executive Director and Member of the Board

•	Responsible for Financial policy making/implementation and reporting for Avon Japan as a public company (including statutory/public, and Board Meetings). All year-end financial documents audited with no comments/exceptions.
•	Defined and executed new department Vision and Mission, and recruited, trained and developed a completely new Finance team (80% new hires within a six month period) to move from scorekeeping to business partnership. Finance team formed core of country high potential list.
•	Consistently over achieved profit performance commitment under adverse conditions of sales decline by aggressively managing strategic business needs with cost cutting; Delivered zero overhead growth during all years; Japan winner of regional awards for best cost management performance, and for boldest transformation strategy.
•	Provided leadership and support for critical decisions and strategy development as Japan Leadership Team Member.  Actively led change management process within company.
•	Consistently achieved best internal control results in Asia-Pacific/Avon over a period of eight audits.  Japan one of only two countries with highest audit rating. Outstanding performance generated support to remove Japan from list of High Coverage countries.
• Co-developed strategic/financial business case, and obtained approval for number of campaign reduction in 2006 making Avon Japan only country ever in Avon to undergo campaign reduction. 
• Independently analyzed and provided strategic direction for Plant study which challenged and reversed previous recommendation by outside consultants on plant closure.
• Provided leadership in several restructuring initiatives by providing an achievable plan to achieve 12% people related cost reduction.  Played lead role in this study representing Finance, Supply Chain and HR 
• Consistently achieved highest performance rating and nominations to several high potential programs
• Elected (and re-elected) to Board of Directors

Executive Director, CFO and Member of the Board of Directors

Delphi

• Prepared Asia-Pacific regional messages / presentations for Delphi Strategy Board, and for Delphi Board of Directors.
• Supported development and implementation of critical Asia-Pacific strategies.  Performed competitive intelligence and other special studies to identify risks / competitive threats and outlined regional / corporate response.
• Provided Miller Heiman strategic selling process training to Asia-Pacific personnel to improve value propositions and increase sales effectiveness.

Director Asia-Pacific Planning

• Management responsibilities for Finance, HR, General Affairs, and Six-Sigma.
• Active member of the Japan Strategy Board and Japan management team.  
• Six-Sigma Deployment Champion for Delphi Japan.

Delphi Japan – CFO & Administration Director

Country Responsibilities:
• Managed all finance functions including accounting, budgeting / forecasting and treasury.
• Lead development of cross-functional and cross-divisional Internal Control Review Board.
• Lead and supported strategic initiatives including venture development support, site enhancement, competitive analysis, transfer pricing, and cost reduction studies.
• Represented Japan in Asia-Pacific Financial Task Team.

Regional Responsibilities:
• Coached country CFO and staff to implement common financial reporting processes.
• Supervised and provided training for engineering / commercial / plant / site analysts.  Coordinated regional financial training programs for the sector in the Asia-Pacific region.
• Coordinated the regional budget, business plan, and capital management process.
• Supported development of common financial practices among ventures including:  Loan monitoring, F/X and cash management, compliance with accounting principles, report submission, etc. Provided regional oversight of venture risk exposure.

Asia-Pacific Senior Finance Mgr.

Executive Coach | MBA Professor | Ex-Global CFO

I help senior leaders master complexity, elevate strategic impact, and lead with authentic authority.

I bring over 25 years of C-level and boardroom experience as a former Global CFO for a $5B publicly traded company, combined with a deep commitment to human-centered leadership development.

Today, I coach global executives, CEOs, and leadership teams — specializing in executive transitions, strategic leadership, high-performance teams, and Asian leadership development.

I am also an MBA Professor of Leadership and Strategic Management, blending research-driven frameworks with boardroom-tested insights to develop transformational leaders.

Based in Kyoto, fluent in English, Urdu, and Japanese.

Connect with me: arif@thecfocoach.jp

Website: www.thecfocoach.jp (please sign up on my mailing list to receive future announcements and newsletters)

Arif Iqball

Hey! 👋

I'm guessing you're here because you're worried about AI taking your job. Or maybe your business is struggling to keep up with all the changes.

I get it. I was there too.

15 years ago, I was working in manufacturing when everything started changing fast. Robots, digital systems, new ways of working. I had two choices: get left behind or figure out how to win both today AND tomorrow.

I chose to master what I call "Dual Transformation."

Helping people and businesses: 
1. **Thrive RIGHT NOW** - Fix what's broken, improve what's working
2. **Win in the FUTURE** - Get ready for AI, automation, and what's coming next

Since then, I've helped companies like Nestle, Pfizer, Sanofi and others save over $30M+ annually while generating millions in new revenue. Not just future planning, real results, happening today.

 **What I do for PROFESSIONALS:**
• Make you irreplaceable in your current job
• Build skills that matter both now and in 10 years  
• Turn AI from threat into your competitive advantage
• Create career security through smart positioning

**What I do for BUSINESSES:**
• **Business Model Innovation**: Re-engineer how you operate to win now and compete in the future
• **Operational Excellence**: I've helped companies cut production time by 47% and save $20M annually
• **Digital Transformation**: Real AI and automation that actually works (not just buzzwords)
• **Revenue Growth**: Generated $10M+ in new revenue streams through smart technology adoption
• **Process Improvement**: Saved one client $10M annually by fixing how they actually work

🌍 **Global Impact:**
I've lived and worked across multiple continents and helped drive over $1B in economic impact. I speak French, Spanish, & Mandarin because transformation is a global challenge.

**My expertise areas:**
Future of Work | Industry 4.0 | AI Integration | Digital Transformation | Operational Excellence | Business Model Innovation

The secret? You don't have to choose between being good today OR ready for tomorrow. You can be both.

Most companies try to either optimize current operations OR prepare for the future. I help you re-engineer your entire approach to do both simultaneously.

🔔 **Follow for daily insights** on thriving now while building your future.

Got questions about your career or business transformation? Message me. I read every single one and actually respond.

Let's build your dual transformation together! 

P.S. - Whether you're worried about your job or your company's future, remember: the best time to prepare for tomorrow is while you're winning today.

Gabriel Millien

You can’t outsmart bad data.
Not with agents.
Not with GenAI.
Not even with humanoids that salsa and write poetry.

If your data is trash, your AI will just make…
💩 but faster.
💩 but with a unicorn emoji.
💩 at scale.

Everyone’s chasing copilots, agents, and AGI.
But skip the “boring” stuff like data quality?
And your AI becomes a very expensive fiction writer.

Here’s what most don’t realize:

🔹 Machine Learning needs labeled, trustworthy data
🔹 Generative AI needs high-context, human-like data
🔹 Agentic AI needs clean, structured, real-time data
🔹 Superintelligence? Still needs a foundation it can trust

What to do instead:

→ Map your messy sources
→ Define “good enough” for each use case
→ Align teams on what “clean data” actually means
→ Govern it like your product depends on it (because it does)

AI doesn’t fail because the models are broken.
It fails because the data is.

📌 Save this if you’re building or cleaning up after AI.
🔁 Repost to save a team from making a $500k data mistake.
🧭 Follow Gabriel Millien for practical, no-hype AI strategy and insights.

Visual credit: Eduardo Ordax

I help you thrive with AI (not despite it) while making your business unstoppable | $100M+ proven results | Nestle • Pfizer • UL • Sanofi | Digital Transformation | Follow for daily insights on thriving in the AI age

Your executives approved the AI budget.
The project died in month 3.
Here's why.

What keeps happening:

Month 1: Team picks the model. Everyone's excited.

Month 2: Data scientists ask for clean customer data.

Three departments claim they own it. None actually do.

Month 3: Project stalls arguing about whose data is "right."

The AI wasn't the problem. The data foundation was missing.

This framework shows what you need:

CENTER → Data Products Datasets you can trust and reuse Without it: 
Can't train models on data nobody believes in

LAYER 2 → Data Management Quality controls that actually run Without it: Garbage data = garbage predictions

LAYER 3 → Data Governance Clear ownership of each dataset Without it: 
Can't fix data issues if nobody's responsible

LAYER 4 → Data Operating Model Everyone knows their role Without it: 
Chaos across departments kills projects

The pitch that gets funded:

Don't say: "We need better data governance for AI."

Say: "The customer AI project died because Marketing says 50K customers, Sales says 48K, Support says 52K.

Three systems, three definitions, nobody knows which is real.

Every future AI project hits this same wall.

Here's what we build once to fix it for all projects..."

Show this framework.

Before the next AI project:

Walk leadership through these four layers.

Then ask: "Do we fund another AI project that stalls, or build the foundation once?"

Save this diagram.

Show it before the AI budget gets approved.

Not after the project fails.

💬 Which AI project died because of data at your company?

📌 Keep this for your next AI planning meeting.

→ Repost to help someone avoid the AI project graveyard.
→ Follow Gabriel Millien for what actually makes AI work.

Image credit: Andreas Horn

The biggest CAREER risk in 2026?
Thinking AI doesn’t apply to you.

In 2026, AI isn’t a skill gap.
It’s a survival gap.

The hard truth?
Most professionals don’t know where to start.
They drown in theory.
Or they skip the basics 
Both paths fail.

This roadmap shows a better way ⬇️

𝗟𝗲𝘁'𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻:

1️⃣ Understand AI – Learn the difference between Machine Learning, Deep Learning, and Generative AI.
2️⃣ Master the basics – Probability, statistics, algebra. The math is the foundation.
3️⃣ Know the models – GPT, Llama, Gemini. Learn how they work, not just how to use them.
4️⃣ Build with the right tools – Python, LangChain, Vector DBs. Today’s must-haves.
5️⃣ Train models yourself – Collect data, prepare it, test it, deploy it. No black boxes.
6️⃣ Build AI agents – Automate real workflows, add human checks.
7️⃣ Explore Computer Vision – GANs, Midjourney, DALL·E. AI is more than chatbots.
8️⃣ Learn from the best – Kaggle, Nvidia, DeepLearning AI. Trusted places to practice and grow.

𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗼 𝘀𝘁𝗮𝗿𝘁 ⬇️

✅ Pick one area from the roadmap (don’t try to learn it all at once).
✅ Block 5 hours a week for learning, treat it like a meeting you can’t skip.
✅ Choose one free resource (Kaggle, DeepLearning AI, Nvidia) and stick with it until you see progress.
✅ Build something small even a simple AI workflow beats endless theory.
✅ Share what you learn. Teaching others cements your knowledge.

The best part?
Most of this is free.
The only cost is your time.

📌 Save this roadmap + action plan.
♻️ Repost to help someone who feels behind.
👤 Follow Gabriel Millien for more AI + transformation insights.

You don’t need a degree to master AI.
Here are 8 must-follow YouTube channels that will teach you more than most university programs: 👇

1️⃣ Two Minute Papers – Makes complex AI breakthroughs easy to understand
🔗 https://lnkd.in/d2MWB7a9

2️⃣ DeepLearning.AI – Learn real AI skills from world-class instructors
🔗 https://lnkd.in/dKxtcCCU

3️⃣ Lex Fridman – Conversations with the thinkers shaping the future of AI
🔗 https://lnkd.in/dyZe9v8m

4️⃣ 3Blue1Brown – Turns math and machine learning into visual stories
🔗 https://lnkd.in/dfEuTf_r

5️⃣ Andrej Karpathy – Learn from one of the best at explaining how AI really works
🔗 https://lnkd.in/dFjV3j4d

6️⃣ Sentdex – Practical, hands-on tutorials for building real AI projects
🔗 https://lnkd.in/dXX3xCTE

7️⃣ Matt Wolfe – Breaks down new tools and trends in a simple, engaging way
🔗 https://lnkd.in/d798mz9h

8️⃣ Google AI Channel – Explore cutting-edge experiments and research from Google
🔗 https://lnkd.in/dcFDps-3

Start with one channel.
Stay curious.
Small lessons each day add up faster than you think.

💾 Save this post for your AI learning roadmap

🔁 Repost to help others start their journey
➕ Follow Gabriel Millien for practical AI and leadership insights

Image Credit: ByteByteGo

The Modern Software Stack 
Explained Simply (And Why It Matters More Than Ever)

Every digital product we use 
from banking apps to AI tools, is built on a layered software stack.
Most people only see the interface.
But the real work, the real intelligence, and the real reliability come from what happens underneath.

Here’s the simple breakdown 👇

1️⃣ Presentation Layer (UI/UX)

This is the part you see: the screens, buttons, and layouts.
React, Swift, Angular, Flutter, everything that shapes the experience.

2️⃣ Edge & Delivery (Optional, but powerful)

These tools make apps feel fast from anywhere:
Cloudflare, Fastly, Akamai.

3️⃣ Integration Layer (API)

This is how systems talk to each other behind the scenes.
APIs, GraphQL, gRPC, the “communication layer” of modern software.

4️⃣ Messaging & Async Processing

How apps handle tasks in the background without slowing down.
Kafka, RabbitMQ, SQS, the engines of scale.

5️⃣ Business Logic Layer

Where the actual decision-making happens.
Django, Spring, Express, the rules and workflows that bring the product to life.

6️⃣ Data Access Layer

The tools that let software read and write data efficiently.

7️⃣ Data Storage Layer

Where information lives.
Postgres, MySQL, MongoDB, BigQuery, Snowflake, the backbone of every product.

8️⃣ Analytics & ML (Optional, but increasingly essential)

This is where companies learn from their data.
And it’s where AI models plug into the stack to deliver insights and automation.

9️⃣ Infrastructure Layer (Hosting + Runtime)

The foundation everything runs on.
AWS, Azure, GCP, Docker, Kubernetes, the global power grid of the internet.

Why this matters

If you understand the modern software stack, you understand how companies:
• build digital products
• support AI features
• scale reliably
• manage data
• and stay competitive

This is the blueprint behind every top tech company 
and now the foundation for how AI systems integrate into real workflows.

The more familiar you are with the stack, the better you can use AI, build with AI, and make smart decisions in an AI-driven world.

🔁 Repost to help more people understand how modern software really works.
➕ Follow Gabriel Millien for clear, simple explanations on AI, LLMs, modern stacks, and the future of intelligent systems.

Most teams don’t fail at building AI agents.

They fail when those agents collide with real processes, real incentives, and real accountability.

This roadmap gets something right that most AI discussions miss:

Scaling AI agents isn’t a prompt problem.
It’s an operating model problem.

Before tools or frameworks matter, a few things must exist. This is the actual sequence that holds up inside enterprises:

The components that determine whether AI agents scale or stall:

→ Pick an LLM
Model choice matters, but only after you know what decisions the agent is expected to make and what it’s allowed to touch.

→ Write system instructions
Clear rules, formats, and boundaries. Reusability beats cleverness every time.

→ Define agent logic
How the agent reasons, pauses, and hands off work. Complexity too early creates fragility later.

→ Add memory (short and long term)
Context improves performance. Uncontrolled memory creates risk. Design this deliberately.

→ Connect tools and APIs
Access to the real world must be constrained, observable, and reversible.

→ Assign a specific job
One outcome. One owner. “Be helpful” is not a role.

→ Build multi-agent systems only when coordination is required
Most failures here are handoff failures, not intelligence failures.

→ Add monitoring and feedback
If you can’t see errors, latency, and tool failures, you don’t have reliability. You have luck.

→ Test, version, and optimize
Prompts, logic, and behaviors are software. Treat them that way or expect drift.

→ Deploy and scale
Production is about containment, rollback, and cost control. Demos are irrelevant at this stage.

A few truths that tend to separate progress from noise:

• Agents fail more often from unclear ownership than weak reasoning
• Scale breaks at interfaces, not at models
• Adding agents increases complexity faster than capability if design is sloppy
• Most “AI issues” are actually process issues wearing new clothes

At enterprise scale, an AI agent is not a feature.

It’s a digital worker.

And digital workers need the same things humans do:
– clear responsibility
– boundaries
– feedback
– performance signals
– accountability when something breaks

If your agents look impressive in demos but struggle in production, the gap is rarely technical.

It’s structural.

📌 Save if AI agents are moving onto your enterprise roadmap

🔁 Repost to help teams move beyond prototype theater
👤 Follow Gabriel Millien for execution-level insight on Enterprise AI and transformation

Image credit: Greg Coquillo

If you want ChatGPT to perform like a top-tier strategist 
not a generic assistant, your prompts need structure, clarity, and intention.

The people achieving breakthrough results with AI aren’t writing “better prompts.”
They’re using prompt frameworks 
the backbone of expert-level LLM reasoning.

Here are 6 high-performance frameworks every AI-driven creator, strategist, or operator should master 👇

1️⃣ RACE: Role • Action • Context • Expectation

Gives the model identity, direction, background, and output formatting.
Perfect for predictable, high-quality execution.

2️⃣ RISE: Role • Identify • Steps • Expectation

A powerful structure for analysis and decision-making.
Helps ChatGPT surface root causes, outline steps, and propose actionable improvements.

3️⃣ STAR: Situation • Task • Action • Result

Great for summaries, rewrites, explanations, and scenario reasoning.
Produces clear, outcome-driven narratives — exactly how LLMs think best.

4️⃣ SOAP: Subject • Objective • Action • Plan

Ideal for strategy, operations, workflows, and systems design.
Transforms vague goals into structured, step-by-step plans.

5️⃣ CLEAR: Context • Learn • Evaluate • Action • Review

Designed for onboarding, training, and process documentation.
Helps the model understand the environment, define success, and continuously refine.

6️⃣ GROW: Goal • Reality • Options • Will

A leadership and coaching classic.
Helps ChatGPT establish goals, assess constraints, explore pathways, and lock in next steps.

Why this matters

AI performance no longer comes from “trying harder” 
it comes from thinking in systems.

These frameworks transform ChatGPT from:
• reactive → strategic
• generic → context-aware
• linear → multistep reasoning
• shallow → expert-level depth

If you want to lead in the era of AI agents, LLM orchestration, and structured prompting,
you must upgrade your prompt architecture, not just your prompts.

🔁 Repost to help more people level up their prompting.
➕ Follow Gabriel Millien for deep-insight content on LLM frameworks, AI agents, workflows, and advanced prompt engineering.

Infographic credit: Adam Danyal

Top AI Agent Frameworks You Should Know (Explained Simply)

AI agents are becoming the new operating layer for how work gets done.
But most people don’t understand the frameworks behind them
or why they’re important.

Here’s the simple breakdown 👇

1️⃣ LangChain: the original building toolkit

• Chains prompts, tools, memory, and steps into workflows
• Great for structured, multi-step automations

2️⃣ AutoGen: conversation-driven agents

• Uses multi-agent conversations to solve tasks
• Supports human-in-the-loop and code execution

3️⃣ CrewAI: role-based agent teams

• Assigns agents roles like Writer, Editor, or Analyst
• Designed for collaborative, multi-agent workflows

4️⃣ LlamaIndex: data-connected agents

• Loads, parses, and indexes your data sources
• Gives agents structured access to information

5️⃣ Semantic Kernel: enterprise-grade orchestration

• Combines templates, plugins, memory, and AI services
• Built for scalable, reliable enterprise automation

Why these frameworks matter

These frameworks are powering the next wave of:
• agentic workflows
• automated research
• content + analysis pipelines
• data-connected assistants
• enterprise AI operations
• multi-agent collaboration

Understanding them now puts you ahead of where most teams will be in the next 6–12 months.

🔁 Repost to help more people understand the AI agent landscape
➕ Follow Gabriel Millien for clear, practical insights on LLMs, agents, and the new wave of intelligent systems

cc: ByteByteGo

The biggest risk in AI isn’t what the model does.
It’s not knowing who answers for it.

That’s what AI governance is really about.

Most organizations don’t struggle with AI because the technology fails.
They struggle because accountability, ownership, and escalation were never designed upfront.

When something goes wrong, three questions surface immediately:

• Why are we using AI here?
• Who owns the outcome if it breaks, biases, or misleads?
• How do we know it’s still behaving as intended?

This playbook gets a critical point right:
governance is not a policy document. 
It’s an operating discipline.

What leaders should act on now:

• If you can’t list where AI is used, you can’t manage risk.
• If ownership isn’t explicit, accountability doesn’t exist.
• If fairness, safety, and transparency aren’t operationalized, they won’t hold under scrutiny.
• If policies don’t evolve, they quietly become liabilities.
• If governance only focuses on control, competitors will move faster than you.

Strong AI governance does two things at once:
it protects trust and creates room to scale with confidence.

The goal isn’t perfect prediction.
It’s knowing who decides, who escalates, and who answers when assumptions break.

That’s how AI becomes a strategic advantage leaders can defend in the boardroom, not a risk they explain after the fact.

🔁 Repost to help other leaders govern AI with confidence
👤 Follow Gabriel Millien for clear, executive-level insights on AI, governance, and transformation

AI Governance Playbook

Learning Generative AI doesn’t fail because it’s complex.
Here’s the order that actually works.

1️⃣ Get the mental model right
Generative AI isn’t software that follows rules.
It’s pattern-based reasoning guided by context.

2️⃣ Be clear on what it can and can’t do
Strong at synthesis, drafting, and pattern recognition.
Weak at truth, judgment, and accountability.

3️⃣ Understand what shapes the output
Context, constraints, and examples matter more than clever wording.

4️⃣ Structure the work before prompting
Clear inputs. Clear formats. Clear decisions.
This is where usefulness begins.

5️⃣ Tie AI to real workflows
Summaries, analysis, drafting, decision support.
If it doesn’t map to work, it won’t stick.

6️⃣ Learn how AI uses external information
Not memory. Retrieval.
This is how accuracy improves.

7️⃣ Understand agents conceptually
Planning, memory, tool use.
Think workflow movement, not autonomy hype.

8️⃣ Know where systems break
Hallucinations. Bias. Cost creep.
Every system fails somewhere.

9️⃣ Put guardrails in place before scale
Data exposure, safety, review loops.
This is a leadership responsibility.

🔟 Measure value, not novelty
Speed. Quality. Cost. Decision clarity.
If value isn’t visible, adoption dies.

The mistake is jumping ahead.
The leverage is stacking these steps in order.

That’s how Generative AI stops feeling overwhelming
and starts becoming operational.

———

🔁 Repost to help others upskill without getting lost
👤 Follow Gabriel Millien for clear thinking on AI, execution, and operating models

CC: Rocky Bhatia

Most AI initiatives aren’t failing because the technology isn’t ready.

They’re failing because AI is being forced into operating models that were never designed to support it.

This report puts language to what many executives are already experiencing:
The real constraint isn’t AI capability.
It’s whether the organization is willing to redesign how work flows, how decisions happen, and who owns outcomes.

A few signals worth paying attention to:

→ Only 11% of companies have AI agents in production
→ AI spend still favors tools over operating models and people
→ Inference costs collapsed, yet enterprise AI bills keep climbing
→ Many “agentic AI” efforts are automating workflows that were broken long before AI arrived

That pattern matters.

AI doesn’t stall because it’s immature.
It stalls because most organizations try to fit it into structures built for a different era.

What shows up in teams that are making real progress:

1️⃣ They redesign processes before introducing AI
They look at the full value stream, not isolated tasks.
The question isn’t “Where can we add AI?”
It’s “How should this work if humans and systems share responsibility for results?”

2️⃣ They manage AI agents like a workforce
Clear ownership. Performance expectations. Cost visibility. Governance.
Without those, scale stays theoretical.

3️⃣ They get practical about infrastructure
Cloud where variability matters.
On-prem where predictability and cost control matter.
Edge where latency and physical environments matter.
This becomes a finance and operating conversation very quickly.

4️⃣ They change how technology leadership operates
Less time maintaining systems.
More time orchestrating how people, data, and AI work together.
Titles may not change yet. Responsibilities already are.

If you sit on an executive team, this is the real question underneath the AI discussion:

Are we using AI to make our current model slightly faster
or are we willing to rethink the model before constraints harden?

Because once AI is scaled, the operating model is much harder to unwind.

📌 Save if AI is now part of your enterprise roadmap

🔁 Repost if this helps leaders move past pilot theater
👤 Follow Gabriel Millien for execution-level perspectives on Enterprise AI and transformation

2026 Tech Trends You Should Pay Attention to

If you want to understand the real AI wave
not the hype, headlines, or fear, start here. 👇

8 TED Talks that do more than explain AI…
they show where intelligence, leadership, and work are going.

This isn’t about tools.
It is about how we think, build, and lead in a world with intelligent machines.

Here are the themes that stood out to me:

🔹 AI is shifting from tool → thinking partner (collaboration is the edge)
🔹 Leaders who wait rarely “catch up later” (the gap compounds)
🔹 Intelligence is not enough, judgment and ethics matter more
🔹 It is not humans vs AI, it is humans with AI vs humans without it
🔹 The advantage goes to people who learn fast, not people who think they already know

🎥 8 must-watch TED Talks on AI

1. Sam Altman, ChatGPT, AI Agents and Superintelligence
https://lnkd.in/ejmzstmv
Where foundation models and agency are heading.

2. Eric Schmidt, The AI Revolution Is Underhyped
https://lnkd.in/e9QRdf64
Why most leaders still underestimate what is coming.

3. Tristan Harris, AI as Our Ultimate Test
https://lnkd.in/emXPwscR
What AI reveals about values, incentives, and responsibility.

4. Greg Brockman, Inside the Future of ChatGPT
https://lnkd.in/edEdAQzC
How these systems learn, reason, and evolve in real time.

5. Yejin Choi, Why AI Is Smart and Shockingly Stupid
https://lnkd.in/eNr98Ymx
Brilliance without common sense and why judgment matters.

6. Eliezer Yudkowsky, Will Superintelligent AI End the World?
https://lnkd.in/eu2cswY7
The hardest questions on safety and alignment.

7. Blaise Agüera y Arcas, Rethinking Minds in the Age of AI
https://lnkd.in/eKp9rtzm
Humans and AI as collaborators, not competitors.

8. Demis Hassabis, How AI Is Unlocking Science
https://lnkd.in/eF8rPVSD
AI accelerating discovery in biology, physics, and medicine.

AI is not the end of human advantage.
It is the beginning of a new demand for human wisdom, judgment, and courage.

Which talk are you starting with? 👇
Save this list for your learning path.

🔁 Repost to help more people go beyond headlines
👥 Follow Gabriel Millien for clear, human-centered leadership in the age of AI

Carousel credit: Chris Donnelly

8 Must Watch TED Talks on AI

This might be the most important AI video you’ll see in 2026.

Your data.
Your voice.
Your face.

All can now be copied in seconds.

And it’s already happening quietly, at scale.

AI doesn’t need your password.
It learns from what you post.

Every photo. Every video. Every story.
Training systems to look, sound, and reason like you.

The uncomfortable truth?
Once it learns you, it can simulate you.

Fear isn’t the answer.
Awareness is.

Here’s a practical AI safety playbook 👇

For everyday users
→ Pause before you post. Your digital shadow compounds
→ Blur children’s faces and avoid real-time location tags
→ Don’t publish long, clean voice samples
→ Add watermarks or on-screen text to original content

For business users
→ Keep sensitive work off public AI tools
→ Share frameworks, not personal identifiers
→ Train teams on deepfakes and data misuse
→ Diversify platforms. Don’t anchor identity to one feed

AI isn’t waiting for consent.
It’s already learning from us.

The real question isn’t if you’ll be copied.
It’s which version of you gets learned.

🔁 Repost to help someone protect their digital self
👁️‍🗨️ Follow Gabriel Millien for grounded, real-world AI insight

Most AI transformations don’t fail because of the AI model.
They stall because leadership treats AI like a tech project instead of an operating system.

This blueprint reinforces something I see repeatedly inside large organizations:
AI scales when leaders change how decisions, data, and accountability actually flow.

If you’re a leader and want this to create real value, start here:

1️⃣ Reframe ownership
AI should not live in IT.
Ask yourself: Which business leader owns the outcome of this use case?
If there isn’t a clear owner, pause the work.

2️⃣ Pick one workflow, not ten ideas
Choose a visible, frustrating process that shows up regularly.
Not innovation theater.
Something measurable like cycle time, error rates, or handoffs.

3️⃣ Use your data before buying new tools
Your advantage isn’t the model.
It’s your proprietary data, business context, and real decisions embedded in daily work.

4️⃣ Design trust into the pilot
If accuracy, governance, or compliance are “Phase 2,” scaling will stall.
Build confidence from day one so expansion is possible.

5️⃣ Set one metric that defines success
Not adoption. Not excitement.
One business outcome everyone agrees matters.

AI transformation isn’t about moving faster.
It’s about operating differently.

Leaders who get this right don’t talk about AI more.
They quietly redesign how work actually gets done.

If this helped you think more clearly about AI and leadership:
🔁 Repost to help another leader
👤 Follow Gabriel Millien for practical AI transformation insights

The Executive Blueprint for AI Transformation

Generative AI Isn’t One Thing
It’s a Set of Model Types That Create in Different Ways

When I advise teams on AI strategy, the same pattern keeps showing up:

Most people use generative AI every day,
but very few understand which model type is behind the output
or why that architecture matters.

And it matters more than most realize.
Because the model you choose determines accuracy, cost, reliability, and what the system can actually do.

Generative AI isn’t magic.
It’s math expressed through different design approaches.

Here are the five major model types shaping today’s GenAI landscape:

1- Diffusion Models

Begin with randomness and gradually shape it into an image.
Used in visual design, image generation, and synthetic imagery.

2- GANs

A “creator” network and a “critic” network compete to produce realistic outputs.
Used for deepfakes, synthetic faces, and high-fidelity image creation.

3- Variational Autoencoders (VAEs)

Compress information, then rebuild new variations from it.
Used in anomaly detection, reconstruction, and controlled creativity.

4- Autoregressive Models

Generate text, audio, or pixels step by step, predicting what comes next.
Used in writing tools, music generation, and forecasting.

5- Transformers

Understand relationships across entire sequences.
Power most modern AI systems for text, code, reasoning, and multimodal tasks.

The deeper insight

Each model type learns differently
and that learning style shapes its strengths:

• Diffusion → best for imagery
• GANs → best for realism
• VAEs → best for structure and detection
• Autoregressive → best for sequences
• Transformers → best for context and reasoning

Understanding the architecture behind a tool helps you see:

• what it’s good at

• where it will fail

• how consistent it will be

• how costly it may be

• and how it fits into your workflow or strategy

This is the foundation of mature, informed AI adoption.

A question worth asking

Do you know which model type powers the AI tools you rely on and why it’s the right fit for your workflow?

Most teams don’t.
That’s where their AI strategy begins to break down.

🔁 Repost if this clarified how generative AI actually works.
⭐ Follow Gabriel Millien for more clear, practical insights on AI models, AI agents, and enterprise AI strategy. 

Infographic credit: Shalini Goyal. Give her a follow!

AI skills aren’t about collecting tools.
They’re about changing how work actually gets done.

If you want to stay relevant into 2026
these are the 15 skills that actually pay off over time:

1) Prompt Engineering
Turn intent into clear instructions AI can follow.

2) AI Workflow Automation
Remove repeat work so things run without you.

3) Vibe Coding
Build simple tools without overbuilding.

4) AI Personal Assistants
Hand off execution while keeping control.

5) RAG Systems (Data)
Get trustworthy answers from your own files.

6) AI Image Creation
Explain ideas visually when words slow you down.

7) AI Video Creation
Create content without cameras or editing teams.

8) Multimodal Fluency
Work across text, visuals, and data in one flow.

9) AI Search Optimization
Stay visible as search shifts to AI.

10) AI-Assisted Writing
Think, draft, and edit faster.

11) Presentation Design
Turn insight into alignment and decisions.

12) AI Data Analytics
See patterns without living in spreadsheets.

13) AI Governance & Safety
Use AI confidently without creating risk.

14) AI Voice & Music
Scale communication without extra effort.

15) Model Fine-Tuning
Adapt AI to your real needs.

What most people miss:

• Prompting is basic. Systems create leverage.
• Automation saves time. Integration changes outcomes.
• Tools don’t future-proof careers. Judgment does.
• Governance isn’t optional. It’s what makes AI stick.

What actually matters in the long run
is knowing how work really flows and where AI fits.

Use this as a map, not a checklist.

🔁 Repost to help others stay relevant
👤 Follow Gabriel Millien for clear, practical insights on AI, work, and execution

Image credit: Adam Danyal

Everyone wants AI automation.

Almost nobody understands the sequence that makes it work.

This roadmap matters not because of the tools,
but because it mirrors how real systems are built, break, and mature.

Here’s what most people never explain:

Day 1 – AI Automation Fundamentals
Before you automate, decide what must stay human.
Automation without judgment creates faster chaos.

Day 2 – Automation Thinking
If you can’t reduce work into triggers, inputs, decisions, and outputs, no tool will save you.

Day 3 – APIs & Webhooks
This is where builders separate from button-clickers.
Integration literacy compounds.

Day 4 – No-Code Platforms
No-code isn’t “easy.”
It’s abstracted complexity. Ignore the limits and you’ll pay later.

Day 5 – Build Your First Automation
Shipping something imperfect teaches more than weeks of diagrams.

Day 6 – Data Handling
Every automation is a data system.
Bad data doesn’t fail quietly. It spreads.

Day 7 – Logic & Error Handling
Production systems assume failure.
Hope is not a retry strategy.

Day 8 – AI Model Basics
Knowing why a model answers matters more than knowing how to prompt it.

Day 9 – Using AI Inside Automations
AI is an ingredient, not the workflow.
Structure beats creativity when reliability matters.

Day 10 – Prompt Design for Automation
Prompts are interfaces.
Constraints outperform clever phrasing.

Day 11 – Text-Based Task Automation
This is where quiet leverage lives: summaries, routing, enrichment, follow-ups.

Day 12 – Knowledge Automation (RAG)
RAG isn’t about smarter answers.
It’s about safer ones.

Day 13 – AI Agents Basics
Agents don’t replace thinking.
They expose weak design instantly.

Day 14 – Business Use Case Automation
If it doesn’t move cost, speed, or risk, it’s a science experiment.

Day 15 – Sales & Marketing Automation
Personalization only works when the data is honest.

Day 16 – Operations Automation
Operations is where automation earns trust or gets shut down.

Day 17 – Monitoring & Optimization
If you’re not measuring latency, failure, and cost, you’re guessing.

Day 18 – Build & Ship Your System
Shipping is the skill.
Everything else is preparation.

The deeper truth:

AI automation isn’t a tool problem.
It’s a systems maturity problem.

People who struggle aren’t missing software.
They’re missing sequence, structure, and ownership.

🔁 Repost to help others avoid the common automation traps
👤 Follow Gabriel Millien for systems-level AI insights

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