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From now on, I will be writing about product inside a small B2B startup

Nice to see you again

Welcome to the first edition of the ProductFix newsletter in over a year. What the heck happened? In short, I got too busy to keep this up. Writing a newsletter is real work that takes time and energy. I do it because it helps me organize my thoughts and give back to the community at the same time.

Now I am back. My day (and night) job as VP of Product @ Fairwords will continue to take the majority of my cognitive energy. So I have decided that the best way to talk about product management is through leveraging the work I already do in my day job.

I can’t promise to write on a weekly basis. However, when I do write, I will share real-life activities, thinking, decision-making, and struggles that come with product leadership in my startup. With luck, this will be helpful to learn from, entertaining, or both.

If you keep with me here, I will share a lot of what we do, how we do it, and what we hope to accomplish. Be warned. A lot of it is messy.

Here are some highlights about Fairwords to get this new journey started right:

• Fairwords is a regtech B2B SaaS business, HQ in Denver but now fully remote

• We are a small and nimble product development team with a mix of FTE and contract employees spread out around the world

• Our core solutions include desktop software, a slack app, and a variety of customer-facing APIs to connect with our AWS cloud-based service

• We do a lot of integration work with various business messaging technologies like Teams, Slack, WhatsApp, Bloomberg IM, Email, SMS, Skype, etc.

• Various forms of AI are sprinkled throughout our solutions in both visible and invisible ways to help our users

Details Details Details

If there are questions or topics you want me to specifically cover then comment on this article or message me a message on Twitter.

Topics I plan to cover:

• Startup 0-1, when some people already think you are at 1.

• Organization design

• Health and unhealthy conflict with Engineering

• When to Invest in tools to help teams scale

• Build vs. Buy decisions

• Coping with the desire to go after the nice shiny new object

• Building things that take a long time in a world that needs results now

• Why Product-Led Growth didn’t work for us

• How I think about competition

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In the Next Newsletter

Everyone is talking about generative AI. At least, it seems they are but maybe OpenAI is automatically generating a lot of Tweets and articles with GPT-3. It is really hard to tell but in my next article, I will share a very practical way we are using generative AI today in my business.

If you know others that would benefit from these stories then please share my newsletter. It is the best way to show I am writing about the right things.

For now, I will leave you with the article I could have published… if I was lazy and just used ChatGPT to write it for you.

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ProductFix News via ChatGPT

Dear fellow product managers,

As you know, the field of product management is constantly evolving and adapting to new technologies and trends. One area that has seen significant growth and development in recent years is the use of artificial intelligence (AI) and machine learning (ML) in product development and management.

In this newsletter, we'll explore the various ways that AI and ML are being used in product management, as well as the potential benefits and challenges of implementing these technologies in your organization. We'll also discuss the skills and knowledge that product managers need to stay ahead of the curve in this rapidly-evolving field.

But first, let's take a step back and define what we mean by AI and ML. In simple terms, AI refers to the ability of a computer or machine to perform tasks that typically require human intelligence, such as recognizing patterns, learning from data, and making decisions. ML, on the other hand, is a subset of AI that involves the use of algorithms and statistical models to enable a computer or machine to learn from data and improve its performance on a specific task over time.

So, how are these technologies being used in product management? Here are a few examples:

• Automating routine tasks: AI and ML can be used to automate routine tasks in product management, such as data collection and analysis, market research, and customer feedback analysis. For example, Salesforce's Einstein AI platform allows product managers to automatically analyze customer feedback and identify common themes and trends. This can save time and resources and allow product managers to focus on more strategic and creative tasks.

• Enhancing decision-making: AI and ML can be used to process and analyze large amounts of data to inform product strategy and decision-making. For example, Amplify's AI-powered product management platform uses natural language processing and machine learning algorithms to help product managers understand customer sentiment and identify key insights and opportunities. This can help product managers make more informed decisions about product features, pricing, and marketing.

• Improving customer experience: AI and ML can be used to personalize and improve the customer experience. For example, eBay's AI-powered recommendation engine uses machine learning algorithms to generate personalized product suggestions for each individual customer. This can help customers discover new products and services that are tailored to their individual needs and interests, leading to a better overall experience.

While the potential benefits of AI and ML in product management are clear, there are also challenges and considerations to keep in mind. For example, implementing AI and ML technologies often requires significant investment in terms of time, resources, and expertise. Additionally, there are ethical and privacy concerns around the use of these technologies, and product managers need to be aware of and comply with relevant laws and regulations.

In terms of skills and knowledge, product managers who want to stay ahead of the curve in this field will need to be familiar with the latest trends and developments in AI and ML. This may involve attending conferences and workshops, such as the AI Product Management Summit, reading industry publications, such as the Product Management Today newsletter, and networking with other product managers and AI/ML experts. Additionally, product managers who are able to apply AI and ML techniques to their own work, such as using data analytics to inform product strategy and decision-making, will be well-positioned to take advantage of the opportunities presented by these technologies.

In conclusion, the use of AI and ML in product management is an exciting and rapidly-evolving field with many potential benefits and challenges. As product managers, it's important to stay up-to-date on the latest developments in this area and to be prepared to apply these technologies to your own work in order to stay ahead of the curve.

Best regards,

Sean Sullivan