Andreas Welsch

Andreas Welsch is an internationally recognized top 10 Agentic AI leader and one of today’s most sought-after thought leaders and keynote speakers for business and technology audiences. With over two decades of global leadership experience, previously as Vice President at SAP, Andreas has helped Fortune 500 companies cut through AI hype to deliver measurable business outcomes.

He is the Founder and Chief Human Agentic AI Officer of AI consulting firm Intelligence Briefing, the best-selling author of the AI Leadership Handbook and The HUMAN Agentic AI Edge, and a leading voice on AI strategy, adoption, and transformation. 

At the heart of Andreas’s work lies a clear value proposition: he helps business leaders understand how to apply AI responsibly, effectively, and profitably by addressing the questions that decision-makers face:

• How do we turn AI into business value, not just experimentation?
• How can we prepare our workforce for AI-driven transformation?
• How can we empower our teams to use AI without creating AI workslop?
• What practical steps can we take to avoid ethical, operational, or financial pitfalls?

Andreas’s keynotes and workshops are known for their clarity, strategic relevance, and deep insight. Drawing from more than two decades in the software industry, he offers real-world frameworks and practical steps tailored to leadership audiences. He introduces leaders to AI agents, leading hybrid teams of humans and AI, and AI-driven business models, explaining not only what’s next, but what matters most and why.

During his tenure at SAP, Andreas accelerated the integration of AI across enterprise applications and positioned SAP as an AI leader. He brings first-hand experience and a global perspective to every business and has been named a LinkedIn Top Voice, Thinkers360's Top 10 Thought Leader in AI, Generative AI, and Agentic AI, The Futurum Group's Top 14 AI Influencer, PEX Network’s Top 30 AI Leader, and SwissCognitive’s Top 50 Global AI Ambassador.

His work has appeared in CIO.com, VentureBeat, InformationWeek, and the American Management Association. He serves on the Editorial Board of the Journal of AI, Robotics and Workplace Automation and as an adjunct professor at West Chester University of Pennsylvania.

For leaders looking for clarity, action, and strategy, Andreas delivers a transformative experience beyond the hype. Follow his “What’s the BUZZ?” podcast and newsletter, “The AI MEMO”, and work with Andreas to turn hype into outcome for your business.

Vice President & Head of Marketing & Solution Management — Artificial Intelligence

Defined GTM and product marketing for Artificial Intelligence (AI) across SAP’s product portfolio, incl. AI Business Services on SAP Business Technology Platform in the context of embedded AI capabilities and intelligent process automation via Robotic Process Automation (RPA) and low-code/ no-code offering. Led global team of product marketers and solution managers

Ranked by Favikon as:
↳ #1 AI Creator on LinkedIn India 
↳ Top 10 AI Creators on LinkedIn Globally 

Featured in Forbes and Wired

For 14 years, I've been obsessed with a single question: how do we teach machines to truly see and understand our world?

This quest began at Qualcomm. My 3D reconstruction technology landed in Forbes, and the object tracker I co-designed was used in millions of mobile devices. When someone casually used my work in their palm, I glimpsed what's possible when research meets reality.

At Intel Labs, I immersed myself in creating AI systems that bridge the gap between theoretical potential and practical performance—systems that could deliver sophisticated intelligence within real-world constraints. Today at Valeo, I lead a team of AI experts developing computer vision for autonomous vehicles. We're teaching cars to go beyond simple object detection toward true scene understanding. Each breakthrough is a step toward fundamentally transforming how people and goods move worldwide. We are reimagining transportation.

Since 2019, I've guided teams at NASA's Frontier Development Lab as their Machine Learning Lead. We've tackled fundamental questions about our Sun and understanding it better. Our breakthroughs have since been published in conferences like NeurIPS. 

Along the way, I saw a problem I couldn't ignore.

I watched brilliant scientists—experts in physics and natural phenomena—blocked from leveraging AI. Not by ability. But by clarity. Incredible AI research remained locked behind jargon. 

There had to be a better way to get the message across. So, Gradient Ascent was born. In it, I break down complex AI concepts for engineers, product leaders, CEOs, and investors making critical decisions shaping our future. Their response has been humbling.

Outside the lab, I'm an award-winning nature photographer and enjoy illustration and music. These pursuits fuel my creative approach to problem-solving.

✍️ Subscribe to my newsletter for insights that transform technical AI jargon into actionable understanding: newsletter.artofsaience.com

———
Note: All opinions are my own and do not reflect the opinions of my employer

Sairam Sundaresan

Stanford just made a $200,000 AI education free.
The same lessons that built Silicon Valley are now on YouTube.

📚 Here's your map to go beyond prompting and start building real AI systems:

1️⃣ CS221 – Artificial Intelligence
Foundations of AI: search, reasoning, planning, decision-making
🔗 https://lnkd.in/gM9ndUCf

2️⃣ CS229 – Machine Learning (Andrew Ng)
Supervised/unsupervised learning + deep dive into algorithms
🔗 https://lnkd.in/gDc9siUC

3️⃣ CS230 – Deep Learning (Andrew Ng)
Neural nets, CNNs, RNNs, and best practices for deep models
🔗 https://lnkd.in/guaavVdk

4️⃣ CS231n – Deep Learning for Computer Vision
Neural nets, CNNs, Generative Models, and VLMs
🔗 https://lnkd.in/gVJZ-xMq

5️⃣ CS229M – Machine Learning Theory
The math and proofs behind ML - for those who want to really understand
🔗 https://lnkd.in/gFBCyuCU

6️⃣ CS234 – Reinforcement Learning
MDPs, Q-learning, policy gradients, and agents that learn by doing
🔗 https://lnkd.in/gVvz87Hc

7️⃣ CS224U – Natural Language Understanding
Semantic analysis, dialogue systems, language meaning
🔗 https://lnkd.in/gRsWMfA6

8️⃣ CS224N – NLP with Deep Learning
Transformers, embeddings, and modern NLP architectures
🔗 https://lnkd.in/gQdFAZXM

♾️ New to AI? Start here:
 https://lnkd.in/gTQyc_fi

🎯 Why this curriculum matters:
• Not just tools, real understanding
• Theory and application from world-class professors
• Build instead of just using
• Go deeper than 95% of "AI experts"

You're one click away from learning real AI.
Which course are you starting with?

♻️ Repost to help someone learn AI the right way
➕ Follow me, Sairam, for AI engineering that works.

Join 25k+ learners from Google, Meta, Netflix, and other top tech companies and level up your AI skills for free at Gradient Ascent: https://lnkd.in/gZbZAeQW

AI Engineering Leader | Author of AI for the Rest of Us | I help engineers land AI roles and companies build valuable products

Most ML courses skip production. This one starts there.
Learn to deploy without regular 3 a.m. calls.

This free MLOps course is:
↳ Built on production-ready tools
↳ Has zero setup complexity
↳ Actually teaches deployment

Why this matters:
🔸 Some engineers can fine-tune models
🔸 Fewer can actually deploy them
🔸 Even fewer know how to monitor them

The course covers the full deployment pipeline in 10 practical lectures:

1️⃣ MLOps Fundamentals
2️⃣ Databricks Development
3️⃣ MLflow Basics
4️⃣ Model Logging & Registry
5️⃣ Serving Architectures
6️⃣ Endpoint Deployment
7️⃣ Asset Bundles
8️⃣ CI/CD Strategies
9️⃣ Monitoring Basics
🔟 Lakehouse Monitoring

The real value:
↳ Uses tools companies actually use themselves
↳ Hands-on notebooks you can follow for free
↳ Current and practical 

Stop watching tutorials.
Start deploying models.

Thanks to Maria Vechtomova and Başak Tuğçe Eskili for creating this!

🔗  Course link: https://lnkd.in/gBTKaP_r

♻️ Repost to help someone learn AI the right way
➕ Follow me, Sairam, for AI engineering that works

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Want to go deeper? Gradient Ascent has you covered. Join 25k+ readers from Google, Meta, Netflix, and over 160+ countries worldwide: https://lnkd.in/gZbZAeQW

Want to learn the basics first? Check this out: https://lnkd.in/giV_N6KU

The tech landscape just shifted. Again. 
Here’s a resource stack to future-proof your career:

📹 Videos:
1. LLM Introduction: https://lnkd.in/gN8sv7Q5
2. LLMs from Scratch: https://lnkd.in/gJt-SQj2
3. Agentic AI Overview (Stanford): https://lnkd.in/gk4GKdxa
4. Building and Evaluating Agents: https://lnkd.in/ghBiVjdF
5. Building Effective Agents: https://lnkd.in/g_m78sid
6. Building Agents with MCP: https://lnkd.in/gAzBzr3W
7. Building an Agent from Scratch: https://lnkd.in/g9GR9b9F
8. Philo Agents: https://lnkd.in/gnxRq9n9

📚Books:
1. AI for the Rest of Us: https://lnkd.in/gTQyc_fi
2. Understanding Deep Learning: https://lnkd.in/g4xgvu_q
3. Building an LLM from Scratch: https://lnkd.in/g2YGbnWS
4. The LLM Engineering Handbook: https://lnkd.in/gWUT2EXe
5. AI Engineering: https://lnkd.in/gpfQSMCQ

🗂️ Repos
1. GenAI Agents: https://lnkd.in/gvfAc-9H
2. Microsoft's AI Agents for Beginners: https://lnkd.in/gK8MiVfv
3. Prompt Engineering Guide: https://lnkd.in/gJjGbxQr
4. AI Agent Papers: https://lnkd.in/gfKQ82Fs

🗺️ Guides
1. Google's Agent Whitepaper: https://lnkd.in/gFvCfbSN
2. Google's Agent Companion: https://lnkd.in/gfmCrgAH
3. Building Effective Agents by Anthropic: https://lnkd.in/gRWKANS4.
4. Claude Code Best Agentic Coding practices: https://lnkd.in/gs99zyCf
5. OpenAI's Practical Guide to Building Agents: https://lnkd.in/guRfXsFK

📜 Papers
1. ReAct: https://lnkd.in/gRBH3ZRq
2. Generative Agents: https://lnkd.in/gsDCUsWm.
3. Toolformer: https://lnkd.in/gyzrege6
4. Chain-of-Thought Prompting: https://lnkd.in/gaK5CXzD.
5. Tree of Thoughts: https://lnkd.in/gRJdv_iU.
6. Reflexion: https://lnkd.in/gGFMgjUj
7. Retrieval-Augmented Generation Survey: https://lnkd.in/gGUqkkyR.

🧑‍🏫 Courses:
1. HuggingFace's Agent Course: https://lnkd.in/gmTftTXV
2. MCP with Anthropic: https://lnkd.in/geffcwdq
3. Building Vector Databases with Pinecone: https://lnkd.in/gCS4sd7Y
4. Vector Databases from Embeddings to Apps: https://lnkd.in/gm9HR6_2
5. Agent Memory: https://lnkd.in/gNFpC542
6. Building and Evaluating RAG apps: https://lnkd.in/g2qC9-mh
7. Building Browser Agents: https://lnkd.in/gsMmCifQ
8. LLMOps: https://lnkd.in/g7bHU37w
9. Evaluating AI Agents: https://lnkd.in/gHJtwF5s
10. Computer Use with Anthropic: https://lnkd.in/gMUWg7Fa
11. Multi-Agent Use: https://lnkd.in/gU9DY9kj
12. Improving LLM Accuracy: https://lnkd.in/gsE-4FvY
13. Agent Design Patterns: https://lnkd.in/gzKvx5A4
14. Multi Agent Systems: https://lnkd.in/gUayts9s

📩 Newsletters
1. Gradient Ascent: https://lnkd.in/gZbZAeQW
2. DecodingML by Paul: https://lnkd.in/gpZPgk7J
3. Deep (Learning) Focus by Cameron: https://lnkd.in/gTUNcUVE
4. NeoSage by Shivani: https://blog.neosage.io/
5. Jam with AI by Shirin and Shantanu: https://lnkd.in/gQXJzuV8
6. Data Hustle by Sai: https://lnkd.in/gZpdTTYD

Don’t fear the agents replacing everyone else.
Learn to build them and own the advantage.

♻️ Repost to help someone master AI
➕ Follow me, Sairam, for AI engineering that works

Starting GenAI in 2026? 
These 25 are the only resources I trust.

They cover everything you need to learn, build, and stay sharp.

9️⃣ Repositories
🔸 Neural Networks: Zero to Hero: 
https://lnkd.in/gEzFebK7
🔸 Awesome-computer-vision: 
https://lnkd.in/g7jZNFHX
🔸 Hands-on-LLMs: 
https://lnkd.in/g7HcxTZz
🔸 Prompt-Engineering Guide: 
https://lnkd.in/gJjGbxQr
🔸 Awesome-generative-ai: 
https://lnkd.in/g_tmrqTi
🔸 AI Agents for Beginners: 
https://lnkd.in/gK8MiVfv
🔸 Advanced RAG Techniques: 
https://lnkd.in/g2ZHwZ3w
🔸 Gen-AI Agents: 
https://lnkd.in/gkMZs-Ks
🔸 Made with ML: 
https://lnkd.in/gER7Stdw

7️⃣ Courses
🔸 MIT 6.S191 - Intro to Deep Learning: 
https://lnkd.in/gs5Wqzj5
🔸 Neural Networks: Zero to Hero: 
https://lnkd.in/gYzeJMTc
🔸 Stanford CS336 - Language Models:
https://lnkd.in/gMFFjPX4
🔸 UMich Deep Learning for CV: 
https://lnkd.in/g_eGdiWe
🔸 Stanford CS236 - Generative AI: 
https://lnkd.in/gb9Tx33e
🔸 Stanford MLSys: 
https://lnkd.in/gP3UDqwn
🔸 Berkeley LLM Agents: 
https://lnkd.in/g5ytvtH7

5️⃣ Essential Books
🔸 AI for the Rest of Us: 
https://lnkd.in/giV_N6KU
🔸 Understanding Deep Learning - Prince: 
https://lnkd.in/g4xgvu_q
🔸 Building an LLM from Scratch - Raschka: 
https://lnkd.in/g2YGbnWS
🔸 The LLM Engineering Handbook - Iusztin & Labonne:
https://lnkd.in/gWUT2EXe
🔸 AI Engineering - Chip Huyen: 
https://lnkd.in/gpfQSMCQ
🔸 Hands-on GenAI - Sanerviero, Cuenca, Passos & Whitaker:
https://lnkd.in/gSRgvcKA

3️⃣ Guides
🔸 Google's Agent Whitepaper: 
https://lnkd.in/gFvCfbSN
🔸 Building Effective Agents by Anthropic: 
https://lnkd.in/gRWKANS4.
🔸 OpenAI's Practical Guide to Building Agents: 
https://lnkd.in/guRfXsFK

1️⃣ Newsletter
🔸 Gradient Ascent: 
https://lnkd.in/gZbZAeQW

Master the ideas.
Build with clarity.

♻️ Repost this to save someone 100+ hours of research
➕ Follow me, Sairam, for AI engineering that works

11 YouTube channels. Master AI. Zero tuition.
Learn from the best without gatekeepers.

Here’s the truth:
You don’t need a $120K degree to learn cutting-edge ML.
YouTube is the classroom of the world if you know where to look.

Here are 11 channels worth your time:
🔸 Andrej Karpathy: from fundamentals to deep dives
https://lnkd.in/gcyMKQJB
🔸 AI Coffee Break with Letitia: bite-sized ML concepts for your coffee breaks
https://lnkd.in/gE73wXbE
🔸 Umar Jamil: builds ML + LLM techniques from scratch
https://lnkd.in/gmbrMj7D
🔸 Simon Oz: low-level ML, excellent for fundamentals
https://lnkd.in/gNDkAm7M
🔸 3blue1brown: the OG of visual explanations
https://lnkd.in/gByiG2By
🔸 GPU Mode: deep dives into GPU architecture + ML workloads
https://lnkd.in/gR_tJ-f9
🔸 AI Jason: beautifully explained AI experiments + design
https://lnkd.in/gCkyPvUw
🔸 Yannic Kilcher: research papers explained without losing depth
https://lnkd.in/gACCpvhS
🔸 Artem Kirsanov: neuroscience + ML with stunning visuals
https://lnkd.in/gUF-_J83
🔸 Aleksa Gordić: practical AI code walkthroughs
https://lnkd.in/gf_TAZkK
🔸 Art of Saience: break into the AI field without the hype
https://lnkd.in/gF3ypnTP

🔸 Bonus: 
Master the basics here: https://lnkd.in/gTQyc_fi

That’s your roadmap to mastering AI without the tuition bill.

♻️ Repost to help someone learn AI the right way
➕ Follow me, Sairam, for AI engineering that works

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Want to go deeper? Gradient Ascent has you covered. Join 25k+ readers from Google, Meta, Netflix, and over 160+ countries worldwide: https://lnkd.in/gZbZAeQW

Interviewing for AI roles in 2026?
This 6-step roadmap is the difference between another rejection and your first offer.

Save this. Share it. Start your prep today.

1️⃣ Coding & Data Structures
↳The first filter in every interview: fluency in code and algorithms.

🔸Python best practices: idioms, typing, testing, profiling
🔸Core DS & Algos: arrays, trees, graphs, recursion, DP
🔸Big-O analysis, trade-offs, optimization

📚Resources: 
Effective Python - https://lnkd.in/g9j5AHkx
NeetCode 150 - https://lnkd.in/gwPGw-53 

2️⃣ Math & Classical ML
↳Show you understand what powers every ML system.

🔸Linear algebra and calculus basics
🔸Probability, statistics, and core distributions
🔸Regression, k-NN, trees, forests, XGBoost
🔸Bias-variance, regularization, cross-validation

📚Resources: 
3Blue1Brown - https://lnkd.in/gn9WR-aE
Andrew Ng ML - https://lnkd.in/gAC6iSWN
Fundamentals of AI - https://lnkd.in/giV_N6KU
Hands-On ML (Geron) - https://lnkd.in/gMMd6Ezu

3️⃣ Deep Learning Foundations
↳Prove you can build, train, and debug models.

🔸PyTorch fluency
🔸Key architectures: CNNs, RNNs, transformers
🔸Training and stabilization tricks
🔸Evaluation & debugging: metrics and guardrails

📚Resources: 
DL Specialization - https://lnkd.in/ggYzFu-E 
PyTorch Blitz - https://lnkd.in/gN_cUA7h
Understanding Deep Learning (Prince) - https://lnkd.in/g4xgvu_q

4️⃣ Specialize in NLP, CV, or RL
↳Go deep in one major area:

🔸NLP: Preprocessing, embeddings, architectures, PEFT, prompting, RAG, evals
🔸Computer Vision: image formation, architectures, generative models, multimodality
🔸Reinforcement Learning: MDPs, policy/value, DQN, PPO, evaluation protocols

📚Resources: 
Stanford CS224n - https://lnkd.in/gg-94eKj
Stanford CS231n - https://lnkd.in/gaGWv7ev
Sutton & Barto - https://lnkd.in/gC4HJ3Hd
David Silver's RL Lectures - https://lnkd.in/gxrXm5FM

5️⃣ ML System Design & MLOps
↳Demonstrate you can ship and scale production ML.

🔸ML lifecycle and infrastructure
🔸Serving patterns, scalability, observability
🔸Monitoring, deployment, CI/CD

📚Resources: 
Full-Stack Deep Learning - https://lnkd.in/g5XGChfH
Stanford CS329S - https://lnkd.in/gzGhDGgq 
AI Engineering - https://lnkd.in/gp-WeZte

6️⃣ Practice & Soft Skills
↳Pair technical excellence with on-the-spot performance.

🔸Timed DS/Algo drills
🔸ML Q&A, system design flow
🔸STAR stories: leadership, conflict, ambiguity, impact

📚Resources: 
interviewing. io 
Hello Interview

If I were interviewing in 2026, this is the only plan I’d follow.

What’s the one AI interview topic you dread most?

♻️ Repost to help someone land their dream AI job.
➕ Follow me, Sairam, for proven AI interview and career strategies

Join 25k+ learners from Google, Meta, Netflix, and other top tech companies and level up your AI skills for free at Gradient Ascent: https://lnkd.in/gZbZAeQW

LLMs forget what they learn.
Stanford found a way to make them remember.

🧩 The Problem
LLMs forget.
They over-compress.
They lose nuance on each rewrite.

Agentic Context Engineering (ACE) fixes this.

It turns static prompts into memory systems.
The key: use delta updates instead of rewrites.

🔸 1. Modular Context Playbooks
↳ Stores strategies, not just plain text
↳ Includes metadata like rules and examples
✅ Examples:
• Reject requests with missing headers
• Summarize logic before answering
• Parse finance data using XBRL tags

🔸 2. Agentic Adaptation Loop
↳ Generate → Reflect → Curate → Update
↳ Each step improves the context
✅ Prompts evolve and persist over time

🔸 3. Delta Updates That Scale
↳ Replace rewrites with small changes
↳ Add new insights, keep what works
✅ Improves precision without bloat

🔸 4. Grow-and-Refine Cycles
↳ Remove noise and merge similar entries
↳ Reinforce high-value strategies
✅ Keeps context clean and effective

🔸 5. Benchmark Results
↳ +10.6% on reasoning benchmarks
↳ +8.6% on financial task accuracy
↳ ~86% lower latency than rewrites
✅ No retraining or new model weights

Static prompts decay over time.
ACE shows adaptive context keeps agents sharp.
A real path to long-term memory in LLMs.

♻️ Repost to help more engineers build smarter agents
➕ Follow me, Sairam, for AI systems that scale in practice
---
Join 25k+ learners from Google, Meta, Netflix, and other top tech companies and level up your AI skills for free at Gradient Ascent.

Agentic Context Engineering

Fine-tuning isn't limited to big labs.
It's how smart teams make average models exceptional.

Fine-tuning becomes essential when you have:
🔹 Proprietary data (health, finance, legal)
🔹 Precise output formats or templates
🔹 Underrepresented languages or dialects
🔹 Domain-specific programming syntax
🔹 Offline or on-prem compliance needs
🔹 Prompt bloat hitting context limits
🔹 Plans to swap large models for smaller ones
🔹 Desire for long-term control and behavior drift

Fine-tuning doesn’t just teach facts.
It teaches behavior.
It shapes how a model listens, responds, and refuses.

🎯 From raw LM to aligned assistant:

✅ Base LMs are next-token predictors
🔸 Trained on large web corpora
🔸 They mimic, not reason or obey
🔸 Prompts can trigger tone, not intent

✅ Supervised Fine-Tuning (SFT) adds structure
🔸 Use curated prompt-response pairs
🔸 Compute loss only on assistant tokens
🔸 Keep clean roles: system, user, assistant
🔸 LoRA and QLoRA cut hardware costs
🔸 Mix data: tools, safety, reasoning, refusals

✅ Preference optimization adds values
🔸 Collect pairwise outputs (x, y⁺, y⁻)
🔸 Human or model picks the better one
🔸 Train the model to prefer y⁺ consistently

✅ Two main alignment paths
🔸 RLHF: train a reward model, optimize with PPO
🔸 Add KL penalty to stay grounded
🔸 DPO: skip RL loop, use supervised loss on pairs
🔸 More stable and easier to scale

✅ Faster alternatives
🔸 RLAIF: model as annotator
🔸 Constitutional AI: self-critique and rewrite
🔸 Rejection sampling: keep best, retrain

✅ Alignment at inference time
🔸 System prompts narrow behavior
🔸 Control decoding with temp and top-p
🔸 Use guardrails, routing, and JSON outputs

✅ Safety is designed, not guessed
🔸 Add refusals and safe completions
🔸 Reward harmless and truthful replies
🔸 Classifiers filter toxic or private data

✅ Under the hood: same transformer core
🔸 Fine-tuning shifts probability mass
🔸 The valleys of low loss lead to safe behavior
🔸 Obedience is engineered, not emergent

Good models don’t just predict.
They follow, adapt, and refuse when needed.

Alignment isn’t luck.
It’s a design choice.

♻️ Repost to help teams build models that follow intent
➕ Follow me, Sairam, for practical AI engineering and design.
---
Join 25k+ learners from Google, Meta, Netflix, and other top tech companies and level up your AI skills for free at Gradient Ascent.

taplio

The Battle

Andreas Welsch

Sairam Sundaresan

Followers

Posts

Engagements

Likes

Performance ScoreSairam Sundaresan's LinkedIn Performance

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taplio

The Battle

Andreas Welsch

Sairam Sundaresan

Followers

Posts

Engagements

Likes

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);}Sairam Sundaresan's LinkedIn Performance

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Performance ScoreSairam Sundaresan's LinkedIn Performance