Andreas Welsch

Intelligence Briefing

● Advised business & technology leaders on defining their business’s AI strategy, organization, idea pipeline, and multiplier community; consulted on product/ go-to-market strategy, and pricing;  customers: AIxBlock, SavorHealth, Sema.
● Authored best-selling "AI Leadership Handbook—A Practical Guide to Turning Technology Hype into Business Outcome" and courses on Agentic AI for business leaders with LinkedIn; 30,000+ learners.
● Inspired and educated audiences of up to 10,000 attendees on using AI in business through keynotes, panel discussions, and trainings; customers: Amazon, Astra Zeneca, Generative AI Week, Germany’s Employment Agency, Packt Publishing, PMI, Potter Electric Signal, Similarweb, Wizly, WOW AI.
● Provided expert perspective to private equity firms and investors on enterprise AI market & technologies.
● Supported influencer campaigns with AWS, Domo, IBM, monday.com, Rossum, Salesforce.

Founder & Chief AI Strategist

Provide expert perspective to industry analyses and news stories (See details below).

Senior Industry Analyst

Keynote Speaker

LinkedIn

Trained 30,000+ business leaders on Agentic AI. Courses recorded in English & German and localized into the world’s most-spoken languages incl. Spanish and Mandarin.

Developed and recorded online courses for LinkedIn Learning for Business Leaders: 
● Agentic AI: Challenges and Opportunities for Leadership
● AI Agents: Preparing Your Organization for Change as a Business Leader
● Mitigating AI Business Risk: A Guide for Senior Leaders and Executives
● Currently working on two new courses for leaders and professionals and guiding their use of AI agents (stay tuned…)

Course Instructor

Maven

Developed an online course to teach product managers, product marketers/ GTM leaders, and founders the foundations of monetizing Agentic AI solutions—and how to set up their commercial models.

Prophets of AI

Prophets of AI is the leading resource for engagement with the world's most celebrated experts in artificial intelligence and robotics.

Andreas Welsch serves as an indispensable interpreter of modern technology for the business world, untangling the complexities of AI. In an age where hype can be blinding, he advocates for a measured, outcome-driven approach. Recognizing that not all AI projects realize their full potential, he underscores the necessity of looking beyond the buzz and narrative constructs, positioning AI as a profoundly human topic. To him, the essence of AI lies not in technology but in its profound implications for humanity, a theme he masterfully articulates in his pursuit of transforming AI hype into tangible outcomes.

Speakers Associates Ltd

Bots & People

● Advise leadership team on strategic opportunities in the U.S. market and on emerging content topics

● Develop and deliver hybrid learning courses for leading organizations to enable their early adopters on Generative AI. Large enterprise clients in Retail, Biopharma, etc.

Strategic Advisor & Instructor

academy4.ai

● Developed and delivered module on AI Leadership (based on the AI Leadership Handbook) as part of the “Master Management for AI” program.

● Engaged with learners in monthly AMA meetings to offer additional value and deepening of learning concepts.

West Chester University of Pennsylvania

● Taught “Introduction to Management Information Systems” and "Production and Operations Management" to cohorts of up to 95 undergraduate students, spanning modules such as Artificial Intelligence, Enterprise Resource Planning, Customer Relationship Management, Databases, Cybersecurity, Ethics, and more. Instructor rating exceeded department average.

● Developing hands-on course “AI Applications in the Workplace” to prepare the next generation of leaders with the foundation of Generative AI and AI assistants.

● Contributed to defining West Chester University’s Generative AI Responsible Use policy.

● Received 4 teaching awards, recognizing outstanding contributions to bringing AI into the classroom.

Adjunct Professor

Accelerated market position in AI through end-to-end responsibility, contributing to +36% in market cap:
● Established global product marketing unit to drive vision, messaging & positioning, launches, and third-party events for Business AI and AI copilot, Joule, across 7 product CMOs, analyst/ public relations.
● Spearheaded partner marketing, including negotiating joint announcements, press releases, and launches with senior executives at Apple, AWS, Google, IBM, Meta, Microsoft, Mistral, and NVIDIA.
● Conveyed value proposition of AI capabilities as a company spokesperson in dozens of media and analyst interviews; demonstrated traction by securing 7 public reference videos and 123 customer logos within the first 6 months (exceeded goal by 23%).
● Defined commercial model for Generative AI capabilities to double total addressable market & revenue.

Vice President & Head of Product Marketing  — Artificial Intelligence

Built and led global AI Center of Excellence (CoE) of 55 product managers, data scientists, developers, business analysts, and 3 managers to embed AI capabilities into flagship products:
● Partnered with product teams and customers to create portfolio of AI use cases; delivered 3 AI use cases and proved feasibility of 9 AI pilots and secured €2.3MM board-level funding; customers: Bayer, Trumpf.
● Collaborated with senior executives across engineering units to establish governance program and resolve common challenges for implementing AI; grew product roadmap from 5 to 19 AI capabilities.
● Built multiplier network to accelerate AI literacy for 20% of workforce via formal training, technology enablement, and hands-on workshops. Attendee rating of 4.5 out of 5 points on relevance and value.

Vice President & Head of AI Center of Excellence, SAP S/4HANA Product Management

Led customer adoption of AI solutions from discovery to reference, contributing to $8.1MM revenue, leading a team of GTM experts:
● Identified high-value AI scenarios in Fortune 100 businesses by conducting stakeholder interviews and developing innovation proposals; examples: Merck & Co., ExxonMobil.
● Won first deal in region for early-stage product by demonstrating customer-first mindset during sales cycle and implementation, resulting in go-live within 4 months.
● Advised senior stakeholders in 83 innovation projects with Fortune 500 customers by connecting technology portfolio to customers’ business outcomes, directly supporting $2MM pipeline.

Head of Go-to-Market, Artificial Intelligence

Prepared Chief Technology Officer for strategic customer meetings and programs with key stakeholders:
● Represented CTO and aligned priorities with executives, customers, partners, analysts, and board, leading to effective execution of strategic meetings and follow-ups.
● Generated input for corporate AI strategy by gathering customer feedback on AI & digital transformation of their businesses during site visits; customers: Walmart, ExxonMobil.

Program Manager, Office of the Chief Technology Officer

Newtown Square, PA (Oct 2010 – Sep 2016) | Markdorf, Germany (Aug 2001 – Sep 2010)
● Developed and implemented governance model for $17M service transition program to external provider, leading to adoption of 4 additional services within 6 months of project start.
● Coordinated service level reviews with team of 23 professionals in 5 countries, resulting in implementation of corrective actions with supplier and in ensuring go-live on time.
● Optimized support processes for business-critical systems with IT staff in North America and Asia, resulting in 12% increase of user satisfaction through training and standardized procedures.

Project Manager, IT Process Automation

Andreas Welsch is an internationally recognized top 10 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—most recently as Vice President at SAP—Andreas has helped Fortune 500 companies cut through AI hype to deliver measurable business outcomes.

He is the Founder of AI consulting firm Intelligence Briefing, the best-selling author of the AI Leadership Handbook, 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?
• 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 the growing landscape of 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, DataIQ's Data & AI Leader of the Year, 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, and InformationWeek. He serves on the Editorial Board of the Journal of AI, Robotics and Workplace Automation and as an adjunct professor at West Chester University.

Whether advising boardrooms or delivering compelling mainstage keynotes, Andreas empowers organizations to lead with confidence in the era of AI. For leaders looking for clarity, action, and strategy—not hype—Andreas delivers a transformative experience.

Join his private community “AI Leadership Circle” and follow his “What’s the BUZZ?” live stream & 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

Gradient Ascent

Gradient Ascent is the go-to AI newsletter trusted by thousands of researchers, engineers, and managers from top tech firms like Google, Meta, Microsoft, and Amazon, as well as top universities and research labs from over 125 countries worldwide.

Author

Valeo

• Lead a team of 15+ engineers in developing cutting-edge perception R&D solutions for autonomous vehicles.
• Drive innovation, resulting in research papers, patents and delivering scalable solutions to a global network of automobile OEMs.
• Mentor and empower my engineers, fostering a culture of growth, innovation, and technical excellence.

Perception & AI R&D Manager

Intel Corporation

Deep Learning algorithm research and development specializing in Computer Vision, NLP, and Multimodal models.

Previously: Deep Learning algorithm research and development for the NNP line of AI processors

AI/ML Research Scientist

Frontier Development Lab

Mentor and ML lead for the 4pi EUV Solar team

Machine Learning Lead / Mentor

Mentor and ML lead for the Solar Wind project in the NASA FDL 2021 Program

Mentor and ML lead for the Starspots project in the NASA FDL 2020 Program

Qualcomm

Worked in an R&D team, focusing primarily on the use of Computer vision and machine learning in multimedia applications. Focused on Face Pose and Gaze Detection, Synthetic Data Generation, Gesture Recognition, 3D Reconstruction, Object Tracking and development of Qualcomm's FastCV Library

Senior Machine Learning Engineer

University of Michigan

Research on understanding the structure of the environment to enable an intelligent agent to navigate and interact with it.

GSRA

Worked as an intern in the Multimedia R&D team. Focused on developing an algorithm to recognize hand gestures in real time.

Interim Engineering Intern

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 23k+ 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

6 books. 6 courses. 6 months.
Ship real AI without the wait or the tuition.

I've spent over 15 years in AI research and engineering.
Read hundreds of papers. Tested countless courses.
Shipped AI products to production.

Here's the truth:
You can get 90% of the practical AI knowledge you need without spending $120K on a degree.

So, I put together a focused 6-month roadmap for you.
Each month, you'll read 1 book and complete 1 course.
Just 60 minutes/day.

Here’s your roadmap:

🔸 Month 1: Python 

📘 Book: Effective Python
✅ Automate repetitive tasks
✅ Write clean, idiomatic code
✅ Build scripts that save hours

🎥 Course: Intro to Python (Harvard)
✅ Core Python skills
✅ Solve real coding problems
✅ Build confidence through practice

🔸 Month 2: Practical Deep Learning

📘 Book: Hands on ML 
✅ Learn core ML (not just DL)
✅ Build neural nets
✅ Go from beginner to builder

🎥 Course: FastAI's Practical Deep Learning
✅ Train real models fast
✅ Solve real-world tasks
✅ Minimal code, big results

🔸 Month 3: ML System Design

📘 Book: Designing ML Systems
✅ Collecting data and choosing metrics
✅ Monitor and address issues
✅ Developing responsible systems

🎥 Course: ML in Production (Coursera)
✅ Project scoping & data needs
✅ Modeling strategies
✅ Deployment requirements

🔸 Month 4: MLOps & Deployment

📘 Book: ML Design Patterns
✅ Build reproducible pipelines
✅ Apply proven design patterns
✅ Work back from the problem

🎥 Course: Made with ML
✅ Learn MLOps first principles
✅ Easily scale ML workloads
✅ Go from dev to prod + CI/CD

🔸 Month 5: AI at Scale

📘 Book: AI Engineering
✅ Understand modern AI systems
✅ Learn to develop AI applications
✅ Explore prompting, RAG & agents

🎥 Course: Stanford MLSys Seminars
✅ Real-world AI infrastructure
✅ Proven deployment patterns
✅ Lessons from top AI teams

🔸 Month 6: LLMs in Production

📘 Book: Building LLMs for Production
✅ Design LLM systems end-to-end
✅ Build RAG pipelines reliably
✅ Monitor and maintain in prod

🎥 Course: The LLM Course
✅ Work with LLM APIs
✅ Advanced RAG & Agents
✅ Inference Optimization & Deployment

That’s your 6-month roadmap to AI mastery.
All it takes? Just 60 minutes/day of consistent learning.

Tag someone who’s learning AI.
This roadmap will save them months.
Or better yet, take the 6-month challenge together.

♻️ Repost and share this with them.
➕ Follow me, Sairam, to learn AI the right way.
-----
Want to go deeper? Gradient Ascent has you covered. Join 23k+ readers from Google, Meta, Netflix, and over 154+ countries worldwide.

P.S: It's ok if you can't finish this in 6 months. The point is to build a habit and get started. You've got this!

Forget $30k bootcamps.
IBM’s 34 free videos will build your AI foundation.

📚 Here's what's covered in this playlist:

1️⃣ Core AI Architectures
→ CNNs, GANs, Transformers, LSTMs, Autoencoders
→ How each works and when to use them

2️⃣ Language Models
→ LLMs, Zero Shot Reasoning
→ NLP vs NLU vs NLG
→ Build RAG systems with clarity

3️⃣ Training & Optimization
→ Backpropagation, gradient descent
→ Overfitting vs underfitting
→ Includes federated learning for privacy-aware systems

4️⃣ ML Fundamentals
→ Supervised vs unsupervised learning
→ Time series, Monte Carlo
→ Core patterns to solve real-world ML problems

5️⃣ MLOps
→ PyTorch inference, embedded + edge AI
→ Deploy and scale AI systems

6️⃣ Agentic AI
→ AI Agents, LRMs, LLM as a Judge
→ Covers AI bias & trust

🎯 Why this matters:
• Debug ML issues with confidence
• Build real AI, not just prompts
• Deploy models that work

🎥 Watch the full IBM AI playlist (34 videos):
🔗 https://lnkd.in/gwypBAMy

♻️ Repost to help someone learn AI the right way.
➕ Follow me, Sairam, for AI engineering that works.
-----
Want to go deeper? Gradient Ascent has you covered. Join 24k+ readers from Google, Meta, Netflix, and over 160+ countries worldwide: https://lnkd.in/gZbZAeQW

The easiest way to learn the basics of AI is this: https://lnkd.in/gTQyc_fi

Everyone’s building "agents" in 2025
But most are still just wrappers
No memory. No plan. Just prompts

That’s not an agent. 
That’s a chatbot.

The real frontier? 

Feedback loops. 
Orchestration. 
Dynamic systems.

Here's how the landscape of agents has evolved:

🔸 1. Stateless Agents
↳ Fast, schema-bound, API-based
⚙️ Tech: Foundation models, function-calling, structured prompting
📊 Metrics: Latency, schema fidelity
🛑 Risks: Prompt brittleness, semantic drift
🛡️ Fix: Canonical prompts, schema validators, adversarial tests
💡 Examples: GPT-4 Turbo API, Claude Instant (short-context mode), OpenRouter, Ollama

🔸 2. Stateful, Context-Aware Agents
↳ Knows what came before. Personalizes and adapts
⚙️ Tech: Long-context LLMs, memory modules, retrieval caches
📊 Metrics: Recall accuracy, personalization fidelity
🛑 Risks: Memory drift, context corruption
🛡️ Fix: Memory pruning, embedding recalibration
💡 Examples: ChatGPT with Memory, LangChain Memory, MemGPT (research)

🔸 3. Planners
↳ Sequences multi-step tasks across tools or APIs
⚙️ Tech: Multi-step chains, policy-gradient agents, symbolic hybrids
📊 Metrics: Plan efficiency, task completion
🛑 Risks: Tool misuse, loops, misaligned goals
🛡️ Fix: Reward audits, hierarchical goal validation
💡 Examples: AutoGPT, CAMEL agents, OpenAI o1 reasoning-model family, Graph-of-Thoughts

🔸 4. Learners
↳ Adapts over time. Improves with feedback
⚙️ Tech: Meta-learning, online adaptation, RLHF
📊 Metrics: Reward slope, adaptation speed
🛑 Risks: Catastrophic forgetting, instability
🛡️ Fix: Replay buffers, regularized retraining
💡 Examples: LoRA fine-tuning from feedback, AlphaZero, DPO-tuned chat models

🔸 5. Orchestrators
↳ Coordinates multiple agents or tools
⚙️ Tech: Multi-agent systems, cognitive loops, self-reflective LLMs
📊 Metrics: Coordination efficiency, system stability
🛑 Risks: Agent conflict, runaway optimization
🛡️ Fix: Alignment governors, coordination protocols
💡 Examples: Microsoft AutoGen, CrewAI (open framework), LangGraph, OpenDevin

Most teams never get past stage 2.

The easy part is prompts and APIs.

The hard part is this:
→ Memory that doesn't drift
→ Plans that adapt
→ Systems that coordinate

If you can build that, you won't just use prompts and RAG.
You will design behavior.

This is where engineering becomes architecture.

Which stage are your agents in?

♻️ Repost to help more engineers build smarter AI agents
➕ Follow me, Sairam, for practical AI engineering and system design

taplio

The Battle

Andreas Welsch

Sairam Sundaresan

Followers

Posts

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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