Greg Coquillo

Product Leader @AWS | Startup Investor | 2X Linkedin Top Voice for AI, Data Science, Tech, and Innovation | Quantum Computing & Web 3.0 | I build software that scales AI/ML Network infrastructure

Product leader with a passion for solving business use cases such as Growth, Go-to-Market, and Price Optimization. I owned P&L for commodity-based products, and leveraged AI to increase the average customer contract values across business industries. As a Product Leader at Amazon, I own a roadmap that drives the adoption of AI products through the Media Automation charter, which supports Private Brands’ YOY growth. I work with Data Scientists, Software Engineers and Business Intelligence Engineers to leverage Machine Learning and Big Data Analytics to design predictive models that enhance business decision-making and performance to accelerate growth, reduce Time to Market of products, monitor cross-geo compliance and import requirements, and ensure customer safety.

I am always curious and eager to learn how new technology affects our daily lives, and I love to support communities on their quest to adopt Artificial Intelligence across industries. Feel free to contact me if you have Data Science projects that you’d like to be showcased on this platform for visibility.

🌐I am a Global Business Leader with 12 years of experience and skills including Product Management, P&L management, Global Networking, EBITDA Optimization, Pricing Management, Margin Management, Product Management, Program Management, Operations and Supply Chain Management, Lean Manufacturing, Six Sigma, and Continuous Improvement.

🧘🏽‍♂️Soft Skills include: Communication, Collaboration, Lateral Thinking, Adaptability, Inspiration, Innovation, Empathy, Listening, Diplomacy, Organization, Responsibility, Leadership...

🎬🎥🎞 I really enjoy watching movies, and if I could redo it all over, I’d be a Sci-Fi script writer so I can be bounded by the limits of imagination.

(Opinions are my own)

Product Management, Media Automation

Owner of the AI vision and roadmap for Private Brand’s Compliance Division. Driving adoption of new technology, allowing my customers to scale their processes and increase productivity. Core responsibilities include:

-Building cross-functional teams consisting of Data Scientists, Software Engineers, Data Engineers, Product Managers and Program Managers, to plan and implement sophisticated AI projects
-Manage the New Initiative Intake Process
-Gather Business requirement artifacts
-Communicate roadmap definition and prioritization
-Coordinate User Acceptance Testing
-Manage Launch Announcements
-Prepare and communicate annual planning artifacts

AI Agents are task-specific, autonomous systems that integrate large language models with structured tools, APIs, and real-time data sources. 

They operate across domains such as cybersecurity, supply chain logistics, and healthcare by executing workflows that traditionally required human-in-the-loop decision making. 

These agents leverage vector databases, retrieval-augmented generation, and fine-tuned embeddings to enable contextual reasoning and dynamic response generation. 

As orchestration frameworks mature, multi-agent systems are increasingly capable of handling end-to-end processes like demand forecasting, patient triage, and adaptive tutoring with minimal supervision.

The below chart shows just how broad their impact is:

1.🔹 IT & Security : Phishing filters, threat detection, patch suggestions
2.🔹Healthcare : Patient alerts, medical chatbots, symptom matching
3.🔹 Education : Flashcards, concept explainers, AI tutors
4.🔹 Sales & Marketing : Lead scoring, campaign ideas, email outreach
5.🔹Logistics : Fleet tracking, demand forecasting, inventory updates
6.🔹Manufacturing : Predictive maintenance, robotic control, energy monitoring
7.🔹 Research : Academic writing, data cleaning, topic expansion
8.🔹 Customer Support : FAQ bots, emotion detection, chat summaries
9.🔹 Smart Environments : Digital twins, voice commands, access control
10.🔹Ops Automation : Shift scheduling, system alerts, order tracking

What used require significant manual effort, now takes a few smart agents.

I believe it’s a great time to start exploring and experimenting in this space…

If you’re learning AI automation without a roadmap, you’re guaranteed to get overwhelmed.

People usually “learn AI automation” by jumping straight into tools… and then wonder why nothing works consistently.
Real automation requires structure - thinking, logic, testing, and a gradual build-up of skills.

This 18-day roadmap breaks down the exact sequence to go from zero → confidently building automations with AI, APIs, tools, and no-code platforms.

Here’s the full breakdown, day by day:

Day 1 - AI Automation Fundamentals
Learn what automation really means, how it differs from AI and agents, and see real examples.

Day 2 - Automation Thinking
Break work into steps, triggers, and outcomes - the mindset behind every good automation.

Day 3 - APIs & Webhooks Basics
Understand how apps communicate and how events trigger workflows.

Day 4 - No-Code Automation Platforms
Explore Zapier, Make, n8n - and how no-code tools actually run workflows.

Day 5 - Build Your First Automation
Create a simple trigger-action workflow and connect two apps.

Day 6 - Data Handling
Pass data between steps, map fields, and work with text, numbers, and dates.

Day 7 - Logic & Error Handling
Add filters, conditional logic, retries, and fallbacks to keep automations reliable.

Day 8 - AI Model Basics
Learn prompts vs system instructions, tokens, limits, and LLM behavior.

Day 9 - Using AI Inside Automations
Insert AI steps into workflows and parse structured AI outputs.

Day 10 - Prompt Design for Automation
Write consistent prompts and reduce hallucinations with JSON outputs.

Day 11 - Text-Based Task Automation
Automate email replies, summaries, CRM updates, and document tasks.

Day 12 - Knowledge Automation (RAG Basics)
Connect AI to internal documents and fetch accurate answers from real data.

Day 13 - AI Agents Basics
Understand agent planning, tools, and identify use cases for agents.

Day 14 - Business Use Case Automation
Automate lead qualification, ticket routing, and internal processes.

Day 15 - Sales & Marketing Automation
Personalize outreach, repurpose content, and automate follow-ups.

Day 16 - Operations Automation
Manage approvals, notifications, and repetitive operational tasks.

Day 17 - Monitoring & Optimization
Track workflow success, cut costs, and improve performance.

Day 18 - Build & Ship Your System
Design, test, document, and finalize a complete end-to-end automation.

You don’t master AI automation by learning tools, you master it by learning systems thinking, data flow, and structured execution.

Follow this roadmap, and you’ll build automations that are reliable, scalable, and business-ready.

Data ➝ AI ➝ Value.
That’s still what companies think AI Looks like

But in the real world…

AI inside companies looks nothing like that.

Because value doesn’t come from “building a model”.

Value comes from building a working system around the model.

This guide explains what AI actually looks like in companies 👇

It’s a full production chain, not a single step:

1) Data
Selection, sourcing, synthesis - getting usable data is already 50% of the work.

2) Data Science
Exploration → cleaning → normalization → feature engineering → scaling
Then modeling: selection → training → evaluation → tuning

3) Value (Operationalizing)
Real value starts only after:
registration → deployment → monitoring → retraining

So ML is not “AI work”.

It’s data engineering + modeling + MLOps, all together.

What GenAI-enabled AI actually looks like

GenAI doesn’t remove complexity.

It shifts complexity from “models” to workflows + control.

The real GenAI path is:

- Understand the decision process
What decisions are being made today? Where does judgment happen?

- Workflow experimentation
Iterate with prompts, APIs, tools, retrieval, automation, coding — until it works reliably.

- Guardrails
Policies, safety filters, access control, cost limits, prompt rules, grounding.

- Monitoring
Track quality, drift, failure modes, user behavior, data leakage, hallucinations.

- Human in the loop
Because the highest-value workflows still need supervision, approvals, and feedback.

The takeaway?

AI isn’t a “feature.”

AI is an operating system change.

And companies that understand this early…

ship faster, safer, and with real ROI.

If you're building enterprise AI, stop chasing “Data → AI → Value”.

Start building: Workflows → Guardrails → Monitoring → Value.

#AI

Knowing RAG is easy. Measuring it is the real skill.

Everyone can plug in a vector DB and call it a RAG system.
But only a few can actually evaluate whether the system retrieves the right knowledge, avoids hallucinations, and answers with confidence.

That’s where retrieval metrics matter, they turn guesswork into engineering.

Here’s a simple breakdown of the 20 metrics that actually show whether your RAG system is good… or just lucky.

1. Precision@K - Checks how many retrieved chunks are truly relevant.
2. Recall@K - Measures whether all important chunks were found.
3. Hit Rate - Confirms if at least one correct chunk appears.
4. MRR - Rewards results where the correct chunk ranks high.
5. NDCG@K - Evaluates ranking quality, not just correctness.
6. Top-1 Accuracy - Tests whether the first chunk is the best one.
7. Query Coverage - Checks if retrieval answers the entire question.
8. Evidence Sufficiency - Asks: “Is this enough to answer fully?”
9. Context Relevance Score - Measures alignment between query meaning and chunk content.
10. Chunk Redundancy Rate - Tracks repeated or duplicate chunks.
11. Chunk Diversity Score - Shows whether results come from varied, reliable sources.
12. Source Coverage - Checks if retrieval pulls from the right documents.
13. Citation Accuracy - Confirms the final answer cites real, correct evidence.
14. Groundedness Score - Tests how strongly the answer stays rooted in retrieved context.
15. Faithfulness - Ensures the model doesn’t distort the evidence.
16. Hallucination Rate - Measures unsupported claims the model invents.
17. Retrieval Latency - How fast chunks reach the model.
18. Token Efficiency - How much useful context fits into the token limit.
19. Freshness - Checks if the retrieved info is up-to-date.
20. Retrieval Failure Rate - Tracks how often retrieval returns irrelevant or misleading chunks.

Anyone can build RAG.
Only skilled teams can measure retrieval, and that’s what decides whether your system scales or breaks.

Which metric do you think is the most underrated right now?

Single agents are powerful.
But multi-agent systems?
That’s where AI starts behaving like a real team.

As AI moves from “answering questions” to doing actual work, the real unlock isn’t a bigger model, it’s how multiple agents collaborate.

Different patterns fit different types of tasks, and choosing the right one can be the difference between a smart system and a chaotic one.

Here are 8 multi-agent collaboration patterns every builder should understand:

1. Manager–Worker Pattern
One agent breaks down the task, others execute the pieces.
Great for large, structured work like research + writing + QA.

2. Specialist Agents (Role-Based Team)
Each agent has a defined role - researcher, analyzer, writer.
Best when accuracy and expertise matter.

3. Planner–Executor Pattern
A planner decides the steps; an executor runs tools and actions.
Ideal for automation tasks, workflows, and system integrations.

4. Critic / Reviewer Agent
One agent generates, another fixes mistakes.
Useful for RAG responses, code checks, compliance-sensitive tasks.

5. Debate Pattern
Agents challenge each other with alternative solutions until the best idea wins.
Good for strategy, architecture, and trade-off decisions.

6. Voting / Consensus Pattern
Multiple agents propose answers, the system picks the best one.
Great for open-ended, ambiguous, or high-risk queries.

7. Swarm / Parallel Exploration
Many agents explore multiple sources at once.
Perfect for fast research and time-sensitive retrieval.

8. Tool Router / Dispatcher Pattern
A router agent decides which tool or agent should handle the request.
Works best in enterprise setups with many databases, APIs, and systems.

AI isn’t becoming powerful because models are getting bigger.
It’s becoming powerful because agents are learning to work together.

This is what turns AI experiments into real products.

Teams get excited about the model… 
but the model is only one slice of the stack.

Real AI products work because every layer around it is engineered with intent.

This breakdown shows what a modern, production-grade AI product stack actually looks like: 

1. Application Layer
The user-facing shell - authentication, UI, APIs, monitoring, analytics. This is where reliability, speed, and trust are felt long before AI runs.

2. Agentic AI Layer
The intelligence engine - reasoning, retrieval, tool use, orchestration, and memory. This layer decides whether your system simply answers… or actually acts.

3. GenAI Ops Layer
The guardrails - tracing, evaluation, experiments, and quality checks. This is how you keep an AI system stable as it learns and evolves in production.

4. Tools & Services Layer
The essentials - model APIs, data warehouses, and external integrations. These services give your AI the inputs, structure, and capabilities it needs to function.

5. Infrastructure Layer
The backbone - compute, storage, GPUs, networking, and security. If this layer is weak, nothing above it scales safely.

AI products don’t succeed because of one great model.
They succeed because every layer, from UI to GPUs - works together as one system.

RAG is not just “upload PDFs + chat with them.”

But that’s like calling every vehicle a “car.”

Because in real AI systems, there are 3 completely different architectures hiding behind the same word:

Naive RAG vs Agentic RAG vs Agentic Memory.

And if you don’t know the difference, you’ll build the wrong system… and blame the LLM later.

Here’s what’s actually happening : 

1. Naive RAG (Basic Retrieval)
Naive RAG is the most common setup: documents get indexed once, stored in a vector database, and whenever a user asks a question, the system fetches the closest chunks and sends them to the LLM. It works well for simple document Q&A, but the accuracy completely depends on retrieval quality  - if the wrong chunk is retrieved, hallucinations become inevitable.

2. Agentic RAG (Tool-based Retrieval + Reasoning)
Agentic RAG is not a fixed pipeline - it’s a decision-making system. Instead of blindly retrieving from a single database, an agent dynamically decides which sources to search, which tools to use, whether the answer needs verification, and when to retry retrieval. This is what enables enterprise-grade search across documents, CRMs, knowledge bases, and even the web - but it also introduces more complexity and cost.

3. Agentic Memory (Long-term Learning Loop)
Agentic Memory is not about pulling knowledge from documents - it’s about building continuity. The system stores useful signals from past interactions (preferences, decisions, repeated tasks, user context) and retrieves them later to give better responses over time. This turns the assistant into something that evolves with usage but if memory isn’t governed properly, it can quickly become noisy, irrelevant, or even risky.

The easiest way to remember it:

Naive RAG = answers from stored docs
Agentic RAG = answers using tools + multiple sources
Agentic Memory = answers that improve over time

If you’re building agents in 2026…
understanding this difference is not optional, it’s architecture.

Susan Walsh - The Classification Guru

Fixer of dirty data ★ Spend data classification, supplier normalisation, and taxonomies ★ DataIQ100 x 2 ★DataIQ Data Champion 2022★ Author "Between the Spreadsheets" ★ Creator of COAT ★ TEDx - say NO to NO ★ DataVenger

I’m Susan Walsh, also known as The Classification Guru, a globally recognised expert in spend data classification, cleaning, and transformation. For over a decade, I’ve helped organisations fix dirty, unreliable data and turn it into insight they can actually trust.

In 2017, I founded The Classification Guru Ltd, rebuilding my career from the ground up after coming back from bankruptcy after my first business failed. What started as a solo consultancy has grown into a specialist business supporting 100+ organisations globally, particularly in procurement, sales, and marketing, where clean data underpins reporting, automation, and AI.

I work with mid-sized and enterprise organisations, consultancies, and technology providers that rely on complex, high-volume data but struggle with inconsistency, duplication, and poor structure. My clients include procurement, finance, sales, and marketing teams who need data they can stand behind, whether that’s for spend analysis, supplier management, CRM reporting, forecasting, compliance, or AI initiatives.

Keeping a business alive and growing for almost nine years hasn’t been about shortcuts or hype. It’s been built through real-world delivery, practical problem-solving, and a refusal to accept “good enough” data as acceptable. Along the way, I’ve developed frameworks and tools to help organisations embed sustainable data quality, including the COAT framework (Consistent, Organised, Accurate, Trustworthy) and Samification, a self-service supplier normalisation platform launched in 2024 to make clean, structured data accessible at scale.

I’m also an active contributor to the wider data and business community. I’m a TEDx speaker, international conference speaker, and recognised thought leader in data quality and classification. I’ve authored three books, including Between the Spreadsheets 1st and 2nd editions, and the upcoming Optimizing Sales and Marketing. I teach globally via platforms such as Pluralsight, O’Reilly, and the Analytics Institute. Through consistent, practical thought leadership, I’ve grown an engaged LinkedIn audience of 43,000+ professionals.

Alongside my commercial work, I founded the Women Who Speak LinkedIn group, a free initiative connecting women speakers with event organisers to improve visibility and representation. I hope my journey shows that you don’t have to fit a mould to build a credible business, lead in your field, or create meaningful impact.

My mission is simple: help organisations move from spreadsheet chaos to clean, organised, trustworthy data that actually works.

Founder/MD (Mistress of Data)

I love improving the quality of procurement spend data, giving teams clarity so they can make better business decisions and cost savings.

Why not let me take the pain out of laborious data issues and make your life easier?

Whether it's a small or large data set, the accuracy can be improved by identifying classification errors, conflicts or gaps. I can provide a data verification service to efficiently find and correct these in a fraction of the time it would take you or a team member.

And a crucial part of having accurate data is also working with a comprehensive taxonomy, something that can be easily overlooked. I can provide a taxonomy development service to ensure you have all the vital areas covered for the greatest visibility - job done!

If you struggle with the time, resources or willingness to tackle your data, why not get in touch to see how I can make your life easier.

Who does quiet, easing back into work in January? Not me!

Just had a wonderful evening up in London meeting Adita Karkera, Ph.D. and lots of fabulous ladies from Women In Data™️

Gonna need a lie in tomorrow now 🤣🤣

Thanks Shubhangi Goyal for organising and Samir Sharma for being an honorary woman in data! 

The Classification Guru Ltd Samification

Susan Walsh

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Ok 2026, I may not be ready, but let’s do this!

When you work for yourself there’s very few h&s measures in place, like a warm workplace.

I woke up to -4 outside and not much different inside, even with the heating on, so whoever gets on call with me today will find me like this. #sorrynotsorry

The Classification Guru Ltd Samification

It's baaaack!  That's right, lipsyncsunday is back in 2026, and this is the energy I'm bringing into 2026 - bad vibes we tell them bye bye!

And not just that, after years of resisting, I've now set up a TikTok account @classificationguru so give it a follow if you want even more crazy, and of course some great data tips, eventually...

What energy are you bringing into 2026?  

Oh and good luck in your first week back, take it easy!

The Classification Guru Ltd Samification

I asked ChatGPT one question: "Based on our interactions, how could I be leveraging you more?"

Because I want to work smarter, not harder this year.

And the answer wasn't, “write more posts faster," it was slightly unexpected - AI works best before things are polished, not after.

Which basically means that even with AI, I'm still a control freak, not delegating until the last minute, so that's something I'll be working on this year.

Here’s the recommendations I'll be taking forward:

 • Using it as a thinking partner, not just a writing tool
 • Pressure-testing ideas before they’re fully formed
 • Acting as an editor with opinions, not just a spellchecker
 • Repurposing one solid idea into multiple formats
 • Being a built-in “so what?” detector
 • Exploring bolder positioning without committing to it publicly
 • Preparing for talks, panels, and conversations earlier, not at the last minute

The biggest shift for me? Bringing AI in when things are messy, not when they’re “ready”. Drafts, fragments, half-formed opinions… that’s where the real leverage is.

So tell me, where in your workflow could AI take more of the heavy lifting, so you stay in your zone of genius? 

Why not try this prompt or a variation and let me know!

The Classification Guru Ltd Samification

Have you met the Dirty Data Villains?

Every dataset has them.
Some are obvious. Some hide in plain sight.
Together, they’re responsible for broken reports, failed joins, messy dashboards, and far too many “why doesn’t this match?” moments.

Here’s the current Dirty Data Villains lineup 👇

🟡 Rogue Punctuation
Commas, apostrophes, periods, and symbols turning up where they don’t belong. Breaking imports, splitting values, and causing errors that are frustratingly hard to spot.

⬜ Sneaky Spaces
Invisible troublemakers lurking at the beginning or end of values. The reason your “exact matches” aren’t exact and your duplicates refuse to reveal themselves.

👯 Duplicate Dan
The master of cloning. Same customer, same supplier, same invoice… entered twice (or more), often with just enough variation to avoid detection.

🔤 Typo Tracey
One wrong letter is all she needs. Misspellings, swapped characters, missing letters — causing failed lookups, bounced emails, and embarrassing mistakes.

📅 Format Fred
The chaos king of inconsistency. Dates, phone numbers, and text fields all formatted differently across systems, making automation and reporting painful.

💥 Why they matter
These villains don’t just make data messy; they undermine trust. And without trust, data can’t be relied on for decisions, insights, or AI.

The good news?
Once you can name them, you can spot them.
And once you can spot them, you can clean them.

More villains may be lurking… but for now, this is your most wanted list.  And if you need some help identifying them, ask me about our new data diagnostic service, more details coming soon!

Which Dirty Data Villain causes you the most pain?  And who would you add?

📘 Two books
📉 Big discounts
📊 Cleaner, calmer, more trustworthy data

Get more Susan for less!

🟣 Between the Spreadsheets from Facet Publishing – 20% off using NEW20
🔴 Optimizing Sales & Marketing Data from Kogan Page Publishing – 40% off with SALE40

What you’ll actually learn:

💡How to spot dirty data before it wrecks reporting
💡Practical ways to fix duplicates, inconsistencies, and naming chaos
💡Why “the tech will fix it” usually doesn’t
💡How to build data you can trust for decisions, automation, and AI
💡Real-world lessons from projects, not fluffy theory

If you’re wrestling with messy data, unreliable dashboards, or a CRM that feels held together with hope and pivot tables, these will save you time, frustration, and a few awkward meetings.

Built on real client work. Powered by the COAT framework. Zero nonsense.

Grab them while the discounts are live!

Shoutout to some fellow authors Jordan Morrow Tiankai Feng Nicola Askham Caroline Carruthers Kristen Kehrer Roberto Maranca Albert Bellamy Keith McCabe (MCIPS) Samir Sharma Dr. Joe Perez

The Classification Guru Ltd Samification

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Susan Walsh - The Classification Guru

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The Battle

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Greg Coquillo's LinkedIn Performance

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