AI Product Manager: Exciting New Niche in Product Management
Let’s talk about what’s AI Product Management, its difference from traditional product management, how AI integrates into the current PM process flows, and what kind of companies you can end up working in.
For many AI enthusiasts, the past few weeks heralded a very exciting era for Artificial Intelligence. We have seen exciting use cases actually becoming a reality with the whole slew of AI products launching one after another.
We all knew it was coming .. but we didn’t know it will come this fast!
From convincing Generative AI tools like Runway, Midjourney, Stable Diffusion, reinventing the way we approach media creation, to AI-super powered search engines like Microsoft’s Prometheus and Opera AI, and even natural language processing tools (aka chatbots) like ChatGPT scoring in the 90th percentile of the bar exams, it’s not an exaggeration to say that we’re at the precipice of massive technological change - one so deeply integrated in the way we live, it might mark a significant start of a closely knit AI-assisted lifestyle. Forever.
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And while it’s exhilarating to be at the forefront of an unknown frontier, it comes without saying that it’s not all positive.
One other very interesting headline is the ‘Godfather of AI’ leaving Google to warn of its dangers. He said in an interview with New York Times:
“The idea that this stuff could actually get smarter than people — a few people believed that,” Hinton said in the interview. “But most people thought it was way off. And I thought it was way off. I thought it was 30 to 50 years or even longer away. Obviously, I no longer think that.”
— Geoffrey Hinton, dubbed as ‘Godfather of AI’ (2023)
AI integrations will virtually affect all human industries from finance, healthcare, to governance, and education. Again, AI is generally designed to make entire processes faster, cheaper, and more optimized — something that real people once were doing, held roles for, and are now starting to become obsolete.
Just recently, IBM paused the hiring of 7,800 jobs in anticipation of AI taking over a wide variety of jobs.
Regardless, experts say that artificial intelligence will take jobs but also create new ones.
’By 2025, the World Economic Forum predicts that 85 million jobs will be displaced by automation and technology, but it will also create 97 million new roles.’
So while there’s a valid criticism of how AI can fit into current economic models, there’s also a need to look into the overall impact and long-term growth that it will bring. In a previous article, we dug deep on whether it’s still worth pivoting into tech.
With all these said, it also underlines one important forecast: the rise of AI will bring a similar rise in AI products that will enter the market — each requiring an AI Product Manager to serve as its ship captain.
For the rest of this blog post, let’s talk about what’s AI Product Management, its difference from traditional product management, how AI integrates into the current PM process flows, and what kind of companies you can end up working in.
What is AI Product Management?
AI product management is a specialization in developing and managing products that leverage artificial intelligence (AI) technology, such as machine learning (ML), deep learning, or natural language processing (NLP). AI product managers oversee the entire product lifecycle, from ideation and research to development and launch, while collaborating with cross-functional teams of engineers, data scientists, designers, marketers, and business stakeholders.
This is not to be confused with AI-assisted Product Management. As AI tools become more powerful, we can predict the mass adoption of these tools even inside product teams.
But this is not AI Product Management. Just because a Product Manager uses AI does not make someone an AI Product Manager. They must primarily work on AI-powered products to be considered one.
AI Product Managers v. Traditional Product Managers
Unlike traditional product managers, AI Product Managers need to have a deeper understanding of the technical aspects of AI and ML, such as data collection, annotation, modeling, testing, deployment, and monitoring.
They also need to have a clear vision of how AI and ML can create value for the users and the business, and how to measure and communicate that value effectively. AI Product Managers may also face unique challenges and opportunities in their role, such as dealing with uncertainty, bias, ethics, and explainability of AI and ML systems to stakeholders.
AI Product Managers also need to approach their products differently. Being in a volatile and relatively lesser-known product space, there are some aspects of product development that make it different from the rest.
Here are some of the reasons why:
- AI products are more complex and uncertain. They require more experimentation, validation, and iteration to find the optimal solution for a given problem. They also depend on data quality, availability, and governance, which can introduce additional challenges and risks.
- AI products are more interdisciplinary and collaborative. They involve working with different teams and experts, such as data scientists, engineers, designers, domain experts, legal and ethical advisors, etc. They also require more stakeholder alignment and education to ensure a common understanding of the goals, benefits, and limitations of AI.
- AI products are more dynamic and adaptive. They can learn from data and user feedback, and improve or change over time. They also need to be monitored and maintained regularly to ensure their performance, reliability, and fairness.
How is AI changing Product Management?
As AI product management involves using AI and ML to enhance, improve, create, and shape products that solve real problems and meet customer needs, there are bound to be differences in how problems, processes, and solutions are approached.
However, it still falls under the umbrella of Product Management so there are hardcoded similarities as well.
Here are some of the ways AI does it differently:
- Market research: AI algorithms can analyze large amounts of data to identify trends, patterns, and opportunities in the market. This can help product managers understand customer behavior, preferences, and pain points, and design products that are aligned with market demand.
- Product development: ML algorithms can help product managers make better decisions about product features and functionality, based on customer feedback and usage data. AI can also automate routine tasks, such as testing and debugging, freeing up time for product managers to focus on more strategic activities.
- Customer feedback: AI-powered tools can help product managers gather, analyze, and act on customer feedback in real-time. This can help product managers improve product quality, usability, and satisfaction, and address customer issues before they escalate.
- Data analysis: AI algorithms can help product managers analyze data and make predictions about product performance, pricing, marketing, and sales. This can help product managers optimize their products and strategies, and measure their impact and ROI.
What kind of products will you be building as an AI Product Manager?
AI is transforming various industries and sectors, from healthcare to manufacturing, by enabling new capabilities and efficiencies. Because of this, we know that AI-centered products will flood the market in an attempt to capture a portion of the market share.
These are new and untried commercial spaces and precisely what makes AI product management very exciting.
Here are some of the newest use cases in AI that showcase its potential and diversity.
- AI and vaccine development: AI algorithms can help researchers design and optimize mRNA vaccines, which are faster and more adaptable than conventional vaccines. For example, Baidu's LinearFold and LinearDesign algorithms can predict the secondary structure of the RNA sequence of a virus and optimize the mRNA sequence design for candidate vaccines.
- AI and digital twins: Digital twins are virtual replicas of physical objects or systems that can be used to simulate, monitor, and optimize their performance. AI can enhance digital twins by enabling them to learn from data, adapt to changing conditions, and generate insights for decision making. For instance, digital twins can be used to improve energy efficiency, reduce maintenance costs, and increase safety in industrial settings.
- AI and document intelligence: Document intelligence is the ability to extract, analyze, and understand information from various types of documents, such as invoices, contracts, or resumes. AI can improve document intelligence by using natural language processing (NLP) and computer vision to automate data entry, validation, classification, and extraction. This can save time, reduce errors, and enhance customer experience.
- AI and supply chain management: Supply chains are complex networks of suppliers, manufacturers, distributors, retailers, and customers that need to be coordinated and optimized. AI can help supply chain management by providing demand forecasting, inventory optimization, logistics planning, quality control, and risk mitigation. For example, AI can help reduce waste, improve customer satisfaction, and increase resilience in the face of disruptions.
- AI and marketing: Marketing is the process of creating, delivering, and communicating value to customers. AI can help marketing by providing customer segmentation, personalization, recommendation, content generation, and campaign optimization. For example, AI can help marketers create more relevant and engaging content, increase conversions and retention rates, and measure the effectiveness of their strategies.
- AI and RegTech: RegTech is the use of technology to comply with regulatory requirements and standards. AI can help RegTech by providing data analysis, anomaly detection, fraud prevention, compliance monitoring, and reporting. For example, AI can help financial institutions detect money laundering activities, comply with anti-money laundering (AML) regulations, and reduce operational costs.
- AI and contact centers: Contact centers are places where customers interact with agents or representatives of a company or organization via phone, email, chat, or social media. AI can help contact centers by providing conversational agents, sentiment analysis, speech recognition, natural language understanding (NLU), and natural language generation (NLG). For example, AI can help contact centers improve customer service quality, reduce waiting times, and increase customer loyalty.
How do you become an AI Product Manager?
There are two main ways to become an AI Product Manager. First is to have extensive domain knowledge or experience. This route makes product management skills secondary, where you aim to position yourself as a product space expert and a primary source of market insight. Second is to be an existing product manager from a different space and shift to building AI products.
Whichever route you choose, building a product from scratch is an essential experience that will prove to be valuable as you build your career in AI. After all, there is no better way to prove that you can be an effective AI Product Manager besides building an AI product yourself.
And Co.Lab is the best way to get practical product management experience.
Ultimately, the AI Product Management space is an exciting field to be in. And we can expect it to get heated even further in the future. There has been a lot of game changers that made us reexamine the previous ways we do a wide array of things, and we can expect more.
To prove the point, did you know this blog post was 70% generated by AI?
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