PE M&A Activity ↑12.4%│Industrial EBITDA Multiples 7.2×│B2B Buyer Trust Index ↓31%│SEO Click-Through Rate ↓44%│LMM Deal Volume ↑8.1%│Sales Hire Failure Rate 67%│Organic Growth Gap 2.3× plan│PE Dry Powder $2.59T│PRESS: Ed Marsh at PE Operating Partners Summit│DEAL: Midwest controls automation acquired at 7.8×│PE M&A Activity ↑12.4%│Industrial EBITDA Multiples 7.2×│B2B Buyer Trust Index ↓31%│SEO Click-Through Rate ↓44%│LMM Deal Volume ↑8.1%│Sales Hire Failure Rate 67%│Organic Growth Gap 2.3× plan│PE Dry Powder $2.59T│PRESS: Ed Marsh at PE Operating Partners Summit│DEAL: Midwest controls automation acquired at 7.8×│
David Anderson on Tacit Knowledge & AI and Industrial Marketing
David Anderson is an MIT degreed machine learning expert with deep insight into industrial sourcing and implications for AI and industrial marketing.
Ed Marsh
INDUSTRIAL REVENUE GROWTH
5 MIN READ
Will B2B Sales Reps Become Extinct? Machine Learning, AI, and Industrial Marketing and Sales
David Anderson is an MIT educated mechanical engineer who got frustrated with the amount of time he spent on administrative tasks related to specifying and sourcing components.
He realized that the intersection of tacit knowledge, accumulated over years of experience, and component research, a dry and repetitive task that adds little value, there was an opportunity to improve efficiency and outcomes.
So he set out to build it - diving deep into machine learning and creating the Engora.tech chat bot.
Engora is fascinating, and you might find it helpful. What's more important in our conversation is the implications for industrial sales and industrial marketing. We cover topics ranging from how marketing needs to adapt, the kind of content that will work for AI research, and whether you'll need to create your own AI sales rep!
Connect with David to learn about the future of technical sales and AI and industrial marketing from an insider's machine learning perspective.
In this conversation, Ed Marsh speaks with Dr. David Anderson, an expert in machine learning and engineering design, about the transformative potential of AI in the engineering sector.
They discuss the importance of tacit and explicit knowledge in engineering, the role of automation in streamlining processes, and the implications for sales and marketing in an increasingly automated world.
David shares insights from his journey in founding Engora, a company focused on leveraging AI to enhance engineering efficiency and knowledge transfer. They explore:
the evolving landscape of industrial supply chains
role of latent knowledge
transformative potential of large language models (LLMs) in engineering
challenges of creating comprehensive ontologies
importance of context in information retrieval
lessons learned from startup development.
The conversation also delves into the future of 3D printing, the significance of information security in AI solutions, and the changing expectations of buyers in the industrial sector. David emphasizes the need for companies to adapt their marketing strategies to leverage AI effectively and the importance of providing comprehensive content to meet buyer needs.
Takeaways
Machine learning can significantly enhance engineering design processes.
Tacit knowledge is often unstructured and hard to capture.
Automation can eliminate repetitive tasks in engineering.
Sales and marketing roles may evolve due to automation.
LinkedIn is a powerful tool for startups to connect with decision-makers.
The boundary between tacit and explicit knowledge is constantly shifting.
Generative AI can handle ambiguity in natural language but may lack precision.
The ideal users of Engora's technology are early-stage engineers.
Knowledge transfer is crucial for effective engineering practices.
Understanding the conflict between AI and engineering precision is essential. Latent knowledge in industrial supply chains is crucial.
LLMs can help contextualize information for engineers.
Prototyping is essential but can lead to wasted efforts.
3D printing requires a rethink of traditional engineering principles.
Information security is a major concern with AI solutions.
Sales reps must understand engineering trade-offs to add value.
Marketing must shift towards comprehensive content creation.
Websites need to serve both users and AI effectively.
Companies should prepare for AI's impact on their operations.
Tacit knowledge is vital for effective AI prompt writing.
Takeaway Quotes from David Anderson
"We can help automate the sourcing process."
"The goals of GenAI and engineering are orthogonal."
"We want a future where rote work is automated."
Outline
00:00 Introduction to Machine Learning in Engineering 02:47 The Role of LinkedIn in Startup Growth 06:00 Understanding Tacit and Explicit Knowledge 08:52 The Vision for Automation in Engineering 12:01 Sales and Marketing in the Age of Automation 15:00 The Need for Efficient Knowledge Transfer 17:57 David's Journey to Founding Engora 20:54 The Evolution of Engora's Technology 23:57 Applications of Engora in Engineering 30:04 Understanding Machine Learning and Generative AI 33:04 The Conflict Between AI and Engineering Precision 39:00 Navigating Latent Knowledge in Industrial Supply Chains 41:57 The Role of LLMs in Engineering Knowledge 45:02 Learning from Mistakes in Startup Development 48:11 The Future of 3D Printing and Digital Fabrication 51:00 Addressing Information Security in AI Solutions 53:06 Building Trust with Sales Reps 56:02 The Shift in Marketing Strategies for AI 01:00:05 The Importance of Comprehensive Content 01:03:01 The Role of Websites in the AI Era 01:05:54 Preparing for the Future of AI in Industry 01:10:03 Tacit Knowledge and AI's Limitations 01:12:08 Recommendations for Understanding AI in Industry 01:15:02 The Future of Industrial Marketing and Sales
Let's Start by Understanding the Fallacy of "Technical Sales"
If AI can replace typical industrial sales, then it seems everyone should work to build an atypical team. One place to look first is to the "technical sales" function which is rarely sales.
Most complex industrial purchases must clear five separate buying decisions.
Decisions 2 and 3 determine whether a purchase happens at all — and most reps don't know they exist.
Most big-ticket B2B industrial forecasts are fantasies because they ignore those decisions.
Buying teams typically include eight to twelve people.
Reps need business acumen — not just product knowledge — to engage decision-makers early.
Ed Marsh
Ed was going to be an architect because he loved the nexus of engineering and design. That was before was going to be an engineer; before he graduated from Johns Hopkins; before he was an Army Infantry Officer (Airborne Ranger); before he set B2B industrial sales records; before he was partners with a German capital equipment manufacturer; before he founded a distribution/rep company for industrial products in India; until he decided that managing a business and employees wasn’t what he enjoyed. Now that Ed’s got all of that out of his system he runs a consultancy that helps US manufacturing companies grow by applying process excellence to business development – completing the full circle back to an engineering & design combination. His practice is built on a unique methodology which combines powerful digital marketing methodologies (a HubSpot partner) with his extensive international biz dev experience.
STOP FORECASTING FANTASIES
Find out where your deals are really won.
Fifteen minutes with Ed Marsh. Sharp questions, honest answers, and a clear read on whether your team is selling to the right decision.