ChatGPT Integration with InsideSpin
As a validation of AI-augmented article writing, InsideSpin has integrated ChatGPT to help flesh out unfinished articles at the moment they are requested. If you have been a past InsideSpin user, you may have noticed not all articles are fully fleshed out. While every article has a summary, only about half are fleshed out. Decisions about what to finish has been based on user interest over the years. With this POC, ChatGPT will use the InsideSpin article summary as the basis of the prompt, and return an expanded article adding insight from its underlying model. The instances are being stored for later analysis to choose one that best represents the intent of InsideSpin which the author can work with to finalize. This is a trial of an AI-augmented approach. Email founder@insidespin.com to share your views on this or ask questions about the implementation.
Generated: 2025-06-30 22:08:33
AI for Product Teams
Over the last 30 years or so, the number of coders has grown dramatically to accommodate professional needs. Starting below a million in the US in the early 90’s it is estimated there are well over 30 million professional software engineers as we head into 2025. That count does not include the millions and millions of web development tool users managing their own needs, with little formal coding training, relying on tools such as WordPress, HubSpot, Spotify, GoDaddy, and AWS to generate the templated code that is needed.
The Rise of AI in Coding
For anyone who has used AI coding tools like CoPilot from GitHub, it is easy to see that AI tools thrive generating code. They are largely semantic language engines after all. Given most coding languages are meant to be semantically unambiguous for a computer to execute the code properly, the sophistication AI embodies to understand and generate ambiguous spoken languages like English is largely left unneeded.
Challenges of AI Tools
Code-generating tools still suffer from garbage-in/garbage-out risks (as do AI chat tools like ChatGPT). This is where AI-augmented skills for human operators (you and me) become critical, to get the value you want to realize, and possibly, to preserve the jobs.
Transforming Product Management
For Product managers, the essence of the Product role is the synthesis of streams of requirements (input) to create the output an Engineering team can use to economically build, and a business can take to market to generate revenue. The more unambiguous and consistent the output a Product team can produce, the more likely coders and sales teams will be able to meet the needs identified.
Benefits of AI for Product Teams
- Alignment: AI can help ensure that all team members are on the same page by providing consistent data and insights.
- Consistency: With AI tools, the output from product teams can be more uniform, reducing errors and miscommunication.
- Completeness of Analysis: AI can analyze vast amounts of data, providing comprehensive insights that might otherwise be overlooked.
The Risk of Homogenization
While there is a general risk of homogenization of thought and approach as we become dependent on AI (as there was with spreadsheets in Finance long ago), the benefit for Product is alignment, consistency, and completeness of analysis from the generated artifacts produced over time.
Future Roles and Skills in a Changing Landscape
Coders and Product managers are two of the areas most ripe to be transformed through comprehensive adoption of AI. Jobs will change, and we’ll explore how to migrate your talents to where AI drives them.
Adapting Skills for the AI Era
As AI technology evolves, it is crucial for Product teams to adapt their skill sets accordingly. Here are some key areas to focus on:
- Data Literacy: Understanding how to interpret and leverage data generated by AI tools will be essential for Product managers.
- Strategic Thinking: As routine tasks are automated, Product managers will need to focus on strategy and innovation.
- Collaboration: Working effectively with AI tools will require enhanced collaboration skills among teams, ensuring that human insights complement AI-generated outputs.
Conclusion
The integration of AI within product teams presents both challenges and opportunities. By understanding these dynamics, entrepreneurs can prepare themselves and their teams to embrace the future of technology in product management. As AI continues to influence the landscape, the ability to adapt and innovate will be critical for success.
Embracing AI is not just about using new tools; it’s about rethinking workflows and harnessing the power of human intelligence alongside machine capabilities.
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