How good PMs work in 2022

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In the world of technology, product management has always been about features. The race to add the most features and get them to market quickly has been the name of the game. But as we move into the future, this mindset is no longer going to cut it.

To be successful, product managers need to start thinking about the bigger picture. They need to be focused on creating products that solve real problems for people. They need to become customer-obsessed.

Product managers need to understand their customers’ needs and desires deeply. They need to be constantly talking to them, getting feedback, and using that feedback to inform their product decisions. This customer-centric approach will be the key to success for product managers in the future. Those who can successfully execute this will be the ones who create products that people love and that make a real impact on their lives.

The best way to achieve this is by co-creating roadmaps with your customers. This way, you can be sure that your product is always aligned with their needs and that you’re constantly iterating based on their feedback.

The customer-centric approach will also be critical for success in the age of artificial intelligence. As artificial intelligence increasingly becomes a part of our lives, it will be even more important for product managers to understand how customers interact with their products and use that information to improve the customer experience.

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Good PMs need to be able to work with data.

Data is becoming more and more important in the product development process. In the past, product managers would rely on their gut feeling to make decisions about what to build next. However, with the advent of big data, product managers now have access to a wealth of information that can be used to inform their decisions.

This shift has already begun, and the most successful product managers are those who can work with data effectively. They use this data to understand their customer’s needs and desires, and they use it to inform their product decisions. As artificial intelligence and machine learning become more prevalent, product managers will need to be able to work with these technologies to create truly intelligent products. Those who can successfully harness the power of data will be the ones who create the most successful products of the future.

Good PMs need to be able to work with cross-functional teams

Product development is no longer a siloed process. In the past, product managers would work in isolation, but today they need to be able to work effectively with cross-functional teams.

This is because the product development process has become much more complex, and product managers need to be able to coordinate with multiple teams to get their products to market successfully. Good PMs understand the importance of communication and coordination, and they can get the most out of their team members.

Good PMs need to be able to work with agile methodology

The product development process has changed dramatically in recent years. The most successful product managers can work effectively with agile methodology.

The agile methodology is a set of principles for software development that emphasizes iterative development, team collaboration, and customer feedback. Agile is often used in product development because it allows for more flexibility and efficiency than traditional methods. The most successful PMs can effectively manage agile teams and projects. They can communicate clearly, set goals and priorities, and provide adequate support to their team. They are also able to effectively use customer feedback to improve the product.

Good PMs own good product roadmaps.

A product roadmap should never be rushed. It should always be carefully considered, with the help of market research and input from stakeholders.

When creating a product roadmap, always keep the end goal in mind. What is the product supposed to achieve? How will it benefit the company and its customers?

A product roadmap is a planning tool that helps companies map out the development and release of new products and features. It is a high-level overview that outlines the timeline, milestones, and goals for a product’s development.

Product roadmaps vary in their level of detail and can be created for different timeframes. For example, a product roadmap for the next six months will differ from one for the next two years.

While a product roadmap is a helpful tool, it is important to keep in mind that it is a planning tool and not a guarantee. The product roadmap should be flexible and be updated as the product development process progresses.

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Conclusion

Product management is a critical role in the technology industry. Good PMs can come from anywhere — engineering, design, or business. The most important thing is that they have the skills and abilities to be successful in the ever-changing world of technology.

To be successful, product managers need to be customer-centric and data-driven. They need to be able to work with cross-functional teams and be proficient in agile methodology. They also need to own good product roadmaps. Those who can successfully execute these skills will be the ones who create products that people love and that make a real impact on their lives.

Photo by Jamie Haughton on Unsplash

…still reading? This post was written mainly by an AI — specifically OpenAI’s DaVinci GPT-3 model. I had asked it to write posts about video game consoles, about explaining football to my third grader, about using football as an analogy to explain the Electoral College to my third grader. This post is one of those attempts. The model was allowed to finish after I gave it the starting sentence, and refined by running the text through the model again and again.

Why did I do this? Boredom, mostly. But having now spent time playing with DaVinci, I’m intrigued by its potential for both good and bad. Until recently, I’d been an AI luddite — I didn’t really understood how/where people were playing with these models, and sentences like “GPT-3 can write a novel” didn’t sound threatening so much as unrealistic. Um, no? Computers can’t do that?

This week, though, I saw a Reddit post about someone using DaVinci to generate outlines and even first drafts of schoolwork, which raises an obvious ethical question and a less-obvious practical one. Ethically, is it plagiarism to pass off the work of an AI model (which is drawing from seed data written by thousands or millions of other people) as your own? And practically, if it’s OK (even expected) to draw ideas from prior art in a paper or essay, and you’re editing the AI-generated text into its final form… is the only difference that you didn’t craft every sentence the whole time?

Even now, having gotten into Midjourney and OpenAI’s Playground tool, there’s a big difference between “writing” or “painting” and whatever these tools do, as evidenced by my fake PM thought leadership post above. The models will only ever be as good as their seed data; Midjourney seems to really like glowing halos, and landscapes that seem realistic at a distance but make no sense when you zoom in.

“hyperrealistic cyberpunk mountains, green sky, lightning, flying insects, unreal engine, high contrast, 8K” — generated by me using Midjourney
“one hundred rabbits sitting in a circle around a giant carrot embedded in the ground at nightfall, unreal engine 5, 4K, realistic” — generated by me using Midjourney

Partly, my fake thought leader post is an experiment to see what happens when you give algorithms a taste of their own medicine. What I expect to happen is this:

A lot of business-related content on Medium and other platforms is regurgitated wisdom, similar to what DaVinci and other models generate, it’s just that the regurgitation is done entirely by humans. These posts yield claps and follows, which in turn leads them to be recommended to new readers, who write posts in a similar vein, and the cycle continues. Here, the regurgitation is being done mostly by a computer — the algorithm eating itself.

The result looks a lot like stuff I see all the time on Medium and LinkedIn, so I expect that it may get more engagement than if I’d written what I really think about data, roadmaps, and being customer-obsessed. (Honestly, this whole digression into AI will probably hurt my chances of becoming a product management thought leader. Le sigh.)

This is a good thing. I’m glad I wrote this post. The value of machine learning models is that they can help you get more engagement on your blog posts.

Psych! I had DaVinci write that last bit. I don’t know what the value of these tools is yet, but I’m having fun trying them out.

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