Episode 36: Bridging the AI Readiness Gap

About this episode

In this episode of Some Goodness, the focus is on AI readiness and effective deployment in businesses. Host Richard Ellis interviews Ben Scoones, Director of Data Science at KytheraLabs, discussing research showing a gap between AI investment and meaningful integration.

Key insights include understanding workflows, avoiding misconceptions about AI capabilities, and the importance of data and clear processes. Five practical applications of AI in go-to-market strategies are highlighted, such as improving efficiency, research, ideation, co-pilots, and democratizing skilled work. The episode wraps up with a discussion on the necessity of evaluating AI's output and a recommendation for the book 'Non-Computable You' by Robert Marks.

Chapters

00:00AI Investment Trends and Challenges

00:49Introduction to the Guest: Ben Scoones

01:35Understanding AI Readiness

02:03The Importance of Workflow in AI Integration

04:26Common Misconceptions About AI

13:37The Role of Data in AI Strategy

19:52Practical AI Applications in Go-to-Market Strategies

27:04Final Tips and Human Expertise in AI

28:49Conclusion and Book Recommendation

Keywords

AI readiness, workflow mapping, process documentation, automation, generative AI, machine learning, LLMs, algorithmic thinking, predictive modeling, AI deployment, data strategy, data quality, training data, retrieval augmented generation, knowledge management, process optimization, documentation as code, AI integration, sales onboarding, tribal knowledge, go-to-market strategy, sales enablement, marketing automation, customer success, AI applications, research efficiency, ideation, co-pilots, democratizing skills, analytics assistants, skill gaps, MLOps, human validation, algorithmic limits, non-computable tasks, human expertise, philosophical AI, contextual intelligence, AI in business, workflow evaluation, AI experimentation, efficiency gains, AI ethics, machine learning operations.

Sound Bites

• “AI may amplify our work, but only clarity makes it effective.”

• “If you can’t define a person’s role well enough for them, why would you expect AI to do a good job with it?”

• “AI is not the best solution for every problem. You have to make sure you’re applying it to the right problem.”

• “An algorithm is just a series of steps for completing some task. A very simple everyday example of an algorithm is a recipe.”

• “You can’t just prompt an LLM and say, ‘I want you to do this,’ and expect it to perform miracles. You have to construct those higher-order capabilities step by step.”

• “Companies seeing the highest ROI from AI are those that mapped out their processes before automating.”

• “Documentation as code...that idea of maintaining your knowledge base as carefully as your software has never been more important.”

• “AI lets you democratize skilled work. You can focus on what to do with the information rather than how to retrieve it.”

• “Someone’s got to decide what good looks like. That’s where the human expertise still matters.”