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Why ChatGPT Isn’t Ready to Be an SE

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Manage episode 447419563 series 3598876
Sisällön tarjoaa Vivun. Vivun tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.

ChatGPT may excel in conversation, but it’s not equipped to tackle the complex challenges that sales engineers face.

In this episode, CEO Matt Darrow speaks with Vivun’s Product & AI Leader, Russell Witham, about the criteria for AI that actually “gets” sales engineering.

Most existing models fail to appreciate the complexity and true value of SE work, resulting in outputs that fall short of what great sales engineers produce.

Tune in to discover a new, innovative approach to empowering sales engineers with AI that understands them.

In this episode, you’ll learn:

  1. The Essence of Sales Engineering: The core role of a sales engineer lies in crafting tailored solutions that address customer needs rather than simply answering product questions or filling out RFPs.
  2. The Limitations of Current AI Models: While AI and large language models are gaining traction, they often lack the nuanced understanding required for effective sales engineering. Successful integration of AI requires structured data and a top-down approach to fully leverage its potential in sales processes.
  3. The Four Core Benefits AI for PreSales: By focusing on the 60% of what SEs do, AI can free SEs from administrative burdens, collapse enablement times, improve agility to pursue new target markets, and increase operational efficiency.

Things to listen for:

(00:00) How companies are using AI in sales engineering
(04:42) The essence of a sales engineer's role
(08:48) Why Claude & ChatGPT isn’t good enough
(10:03) The importance of structured data for effective solutions in sales engineering
(11:53) The need for detailed knowledge representation in AI applications
(15:46) Why current AI approaches fall short for sales engineering

  continue reading

26 jaksoa

Artwork
iconJaa
 
Manage episode 447419563 series 3598876
Sisällön tarjoaa Vivun. Vivun tai sen podcast-alustan kumppani lataa ja toimittaa kaiken podcast-sisällön, mukaan lukien jaksot, grafiikat ja podcast-kuvaukset. Jos uskot jonkun käyttävän tekijänoikeudella suojattua teostasi ilman lupaasi, voit seurata tässä https://fi.player.fm/legal kuvattua prosessia.

ChatGPT may excel in conversation, but it’s not equipped to tackle the complex challenges that sales engineers face.

In this episode, CEO Matt Darrow speaks with Vivun’s Product & AI Leader, Russell Witham, about the criteria for AI that actually “gets” sales engineering.

Most existing models fail to appreciate the complexity and true value of SE work, resulting in outputs that fall short of what great sales engineers produce.

Tune in to discover a new, innovative approach to empowering sales engineers with AI that understands them.

In this episode, you’ll learn:

  1. The Essence of Sales Engineering: The core role of a sales engineer lies in crafting tailored solutions that address customer needs rather than simply answering product questions or filling out RFPs.
  2. The Limitations of Current AI Models: While AI and large language models are gaining traction, they often lack the nuanced understanding required for effective sales engineering. Successful integration of AI requires structured data and a top-down approach to fully leverage its potential in sales processes.
  3. The Four Core Benefits AI for PreSales: By focusing on the 60% of what SEs do, AI can free SEs from administrative burdens, collapse enablement times, improve agility to pursue new target markets, and increase operational efficiency.

Things to listen for:

(00:00) How companies are using AI in sales engineering
(04:42) The essence of a sales engineer's role
(08:48) Why Claude & ChatGPT isn’t good enough
(10:03) The importance of structured data for effective solutions in sales engineering
(11:53) The need for detailed knowledge representation in AI applications
(15:46) Why current AI approaches fall short for sales engineering

  continue reading

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