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

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

Building Agentic AI systems beyond connecting APIs or LLMs is complicated, but not impossible.

This architecture lays the foundation for how AI agents think, communicate, and improve, covering everything from testing and observability to deployment and memory management.

Here’s a breakdown of the key layers and components that make up a scalable Agentic AI Architecture :

1.🔸Decomposition
Break down complex systems by domain (e.g., Coding Agent, Data Agent), by cognitive capability (Reasoning, Planning, Execution), or by agent role (Planner, Executor, Memory Manager, Communicator).

2.🔸Communication
Enable message passing between agents using inter-agent protocols or A2A (Agent-to-Agent) orchestration.
Support both single-agent and multi-agent setups for small or distributed workflows.

3.🔸Deployment
Deploy agents in containerized or serverless environments using Docker or Modal.
Support orchestrators like CrewAI or AutoGen for collective intelligence in multi-agent workflows.

4.🔸Data & Discovery
Integrate knowledge bases (like vector databases for RAG), memory stores (FAISS, Redis, Pinecone), and APIs for dynamic data access.
Context is passed using Model Context Protocol (MCP) for structured and real-time reasoning.

5.🔸Testing & Observability
Validate workflows end-to-end, test reasoning logic, and evaluate performance under real conditions.
Monitor using Weights & Biases, LangFuse, and track metrics like latency and task success rate.

6.🔸UI & Style
Provide intuitive feedback loops through visualization layers, dashboards, and self-reflective modes.
Enable collaborative, proactive, and goal-driven reasoning among multiple agents.

7.🔸Security
Protect access with token-based authorization and data encryption.
Include Trust Layers for human-in-the-loop validation and Policy Enforcement for safe execution.

8.🔸Cross-Cutting Concerns
Handle configuration, secrets, and environment management.
Support flexible frameworks like LangChain, AutoGen, or CrewAI for runtime execution and modular design.

Agentic AI is the future of automation - where AI doesn’t just assist but collaborates and learns.

Save this post to understand the architecture that powers the next generation of AI systems

#AgenticAI

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AI agents are great at working alone. However, they can also be effective when operating within networks of collaboration.

The real power of AI lies in how these agents communicate, coordinate, and complement each other’s abilities.

Orchestration patterns define this collaboration which turns isolated agents into intelligent, goal-driven systems capable of solving complex tasks together.

Here are 6 orchestration patterns that power the next generation of AI systems :

1.🔸Sequential Orchestration
Agents work one after another - each passing its result to the next.
Perfect for structured, step-by-step tasks like data pipelines or analysis chains.

2.🔸Concurrent Orchestration
Multiple agents work in parallel, handling different parts of a problem simultaneously.
This boosts speed, scalability, and overall task efficiency.

3.🔸Group Chat Orchestration
Agents communicate in a shared conversation, refining ideas together.
Useful for brainstorming, collaborative decision-making, or generating consensus.

4.🔸Maker–Checker Loops
One agent creates or proposes; another verifies, corrects, or approves.
Ideal for maintaining accuracy, compliance, and quality assurance in workflows.

5.🔸Handoff Orchestration
Agents transfer control dynamically based on context or expertise.
Best suited for adaptive workflows like support systems or multi-step automation.

6.🔸Magnet Orchestration
A central “manager” agent dynamically assigns tasks to the right specialists.
Enables fluid, context-aware collaboration across multiple intelligent agents.

AI Agents are evolving fast and orchestration is what makes them powerful.

Learning how these patterns can help you build smarter, more collaborative AI systems can be a huge advantage.

#AIAgent

AI Agent Orchestration Patterns

📚LLMs in the Enterprise are finally getting the playbook they deserve… LLMs in Enterprise by Ahmed Menshawy and Mahmoud Fahmy provides practical guides for building real AI systems that operate at scale.

Most teams talk about LLMs in theory. This book focuses on execution. It bridges foundational concepts with the hands-on design patterns that matter when you are integrating models into production environments.

Here are the insights that stood out👇

1.🔸Enterprise LLM integration is a data architecture problem
The book breaks down how to design pipelines, tune retrieval, and structure data so models operate with consistency and low latency in real workloads.

2.🔸Scaling LLMs requires pattern-level thinking
They go deep on architectural patterns that reduce complexity, improve efficiency, and streamline deployment. This includes RAG frameworks, fine-tuning strategies, segmentation techniques, and evaluation patterns that teams often overlook.

3.🔸Performance is not just about bigger models
The authors show how to optimize model behavior with advanced inferencing engines, contextual model customization, and monitoring systems that keep applications predictable.

4.🔸Real enterprise value comes from operational rigor
Security, fairness, transparency, and accountability are not afterthoughts. They are part of the design process, especially when LLMs touch business workflows and customer data.

5.🔸AI teams win by mastering both concepts and impact
The flow of the book reflects the real enterprise lifecycle: Concept → Customization → Impact
A clear, structured way to think about turning LLM capabilities into business outcomes.

If you are building production AI systems, leading an LLM program, or preparing for the next wave of enterprise adoption, you should definitely get a copy of this book.

Enterprise AI is evolving fast. Understanding these design patterns early puts at a career advantage which allows you to shape the next generation of intelligent applications.

#LLM

Check out this simple breakdown of the four core LLM architectures shaping most of today’s advanced systems.

Not all LLMs reason the same way. Building reliable AI systems, or choosing the right model for a workflow requires a clear mental model of the architectures that drive them.

Let’s review:

1.🔸Decoder Only (GPT, LLaMA, Grok)
Generates text by predicting one token at a time using previous context.

Best for generative workloads, conversational systems, and multi step reasoning.

2.🔸Encoder Only (BERT, RoBERTa)
Processes text with full bidirectional context to learn dense semantic representations.

Best for classification tasks, retrieval, and embedding generation.

3.🔸Encoder Decoder (T5, FLAN T5, BART)
Separates understanding from generation. The encoder builds meaning, the decoder transforms it.

Best for translation, summarization, and any structured text to text transformation.

4.🔸Mixture of Experts (Mixtral, Gemini 1.5)
Routes each input to a subset of specialized expert networks.

Best for scaling model capacity while controlling compute cost.

These architectures go beyond academic details to determine latency profiles, context handling, fine tuning strategies, and how models behave under real workloads. A clear grasp of them helps you design better pipelines, choose the right foundation models, and understand why two LLMs can produce very different outcomes on the same task.

Let me know your thoughts!

#LLM

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Andreas Welsch

Greg Coquillo

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The Battle

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