🌁 Introduction
In this article, we will examine a few (AI) artificial intelligence product management themes: - First, we will examine why data analytics and predictive analytics are important skills for data-intensive initiatives. A discussion of AI’s impact on automated customer support, chatbot revolution 2.0, technical support, sales, and marketing will be followed; along with the future of the PM designation, as well as certifications and courses relating to AI-product management to help set PMs up for success in their current and future initiatives.
There is a lot to unpack when it comes to using (AI) in product management, so this post will serve as a starting point. I look forward to delving further into this topic in future posts.
✴️ Table-Of-Contents
(AI) Product Management 🤖 - Data Analysis and Predictive Analytics
Data analytics was the skill set product managers were most focused on perfecting in 2021, according to the State Of Product Management 2022 report published by Product Plan and released on ProductHunt.
This trend will likely continue in 2023, but with the difference being that product managers will need to refine their core skills around AI and ML, in order to better anticipate upcoming trends, market forces, initiatives, and projects, which will require core skills around training models to perform specific tasks as part of your product or service.
Additionally, this theme implies a need to gain a deeper understanding of how researchers, data scientists, and data engineers train models. Developing your SQL, Tableau, advanced Excel, and statistics skills will enable you to work more effectively with data scientists and data engineers on future data-intensive initiatives.
Data analytics and predictive analytics are increasingly used by product managers to inform their decisions. By analyzing data, product managers can better understand customer pain points, needs, and preferences; parse and spot trends in customer feedback across multiple channels, as well as detect security, design, and software bugs before they affect the entire customer base.
Together, these two skills will be among the most valuable for product managers in the coming years, aligning your skill set with data-intensive objectives.
🛂 Product Management - (AI) Customer Support and Chatbot 2.0
💡 Automated Customer Support
A strong relationship has always existed between product management (PM) and customer support (CS) teams. It is the responsibility of product management to identify customer needs and create products that meet those needs. Customer service helps customers with product issues and provides feedback to product management regarding problems that need to be addressed in upcoming releases by labeling or tagging feedback so that it can easily be identified.
With chatbots, online self-help tools, and other automated solutions, client service has become increasingly automated over the past decade. Businesses have been able to reduce costs and improve customer service (CS) as a result.
Chat-Bot Revolution 2.0
In a cross-functional team, customer support is among the departments that have been using artificial intelligence for some time – Around 2016, several companies released chatbots that were trained to provide customers with predetermined information, such as product information, pricing information, and directions to specific websites. The majority of chatbots simply answered predefined questions, even though some could perform non-circumspect tasks.
In terms of natural language processing (NLP), OpenAI’s newest language service represents a significant advance. We can now build a web-based chatbot that is truly conversational, with teams presumably working on MVPs and early beta products. In Customer Support, web-based chatbots are only one of the core use cases of AI and ML, but they represent a large theme with established product market fit and will be of substantial value to your customers.
Automated support is becoming more and more capable of providing accurate and efficient support to technical and customer service teams. AI-driven chatbots are capable of responding to customer inquiries, triaging, labeling, categorizing, and prioritizing them accurately and quickly. The value your customers, customer support, and product teams can derive from this is substantial.
Consider the possibilities of NLP chatbots beyond customer support. Imagine a world in which a chatbot could perform both customer service and sales, as well as marketing tasks at the same time. There is no sleep for this employee, they require only a constant amount of energy to function; 24 hours a day, 365 days a year, they do not become tired, and are entirely devoid of emotions.
Customer support, sales, technical support, and marketing will be forever changed once human-computer symbiosis is achieved and fully autonomous chat agents are developed.
🧑💼(AI) Sales and (AI) Marketing
Let us, for one moment, conceptualize a macrocosm where a chat agent up-sells or cross-sells during an open customer support conversation. Not only is this technologically feasible, but I presume it’s highly likely to be achieved by 2023, to some extent.
Chatbots can be used to increase sales through automated marketing campaigns based simply on a set of instructions, offer personalized recommendations to customers in real-time, and A/B testing Chatbots can also be used to streamline customer service and determine which support type is most optimal per user group or persona. Each of these use cases can increase sales by providing customers with more timely and accurate responses to their inquiries, among many other use cases and benefits to the business and end users.
Sales, marketing, and product managers can automate mundane, repetitive tasks such as lead generation, email follow-ups, and scheduling with AI and automation. Personalized customer interactions and smarter decisions will also be possible with AI chatbots. Furthermore, AI-driven automation will require fewer human sales representatives, allowing product managers to focus on mission-critical tasks like product development, innovation, customer acquisition, relationship building, and revenue growth.
In marketing, AI and automation can provide a more personalized experience along with targeted messaging. Tracking and analyzing customer data with AI-powered services can help product managers understand customer preferences and craft unique marketing campaigns based on a more granular picture of the targeted persona. Additionally, AI-driven automation will help marketers and product marketing create more effective campaigns and maximize (ROI) by eliminating, or greatly reducing the need for manual marketing and product marketing tasks, from product-page copy, to email content, content development, and more.
⚡️ AI Outreach & Lead Generation
It’s now feasible to give OpenAI’s GPT-3 language model, as well as some AI Writing tools, a task directive, such as creating a five-part email marketing campaign to introduce a new product to customers, with additional touch-points based on the customer’s response - With the right expertise in developing/training AI/ML applications, this functionality can be produced within a few release cycles. We are already able to do this with ChatGPT3, but soon we will see it more and more in CRMs and sales platforms.
In order to target the correct customer groups, we need accurate data sets of potential customers. (AI) can easily solve this use case as well. Through automating web crawling, parsing through email lists, and matching known contact information to a targeted individual for a campaign, AI-driven applications can be used for lead generation, as well as crawling social media using APIs and other available resources.
With AI-driven marketing, you can also identify potential leads and send them personalized messaging based on their preferences and needs. Furthermore, AI can be used to monitor customer behavior and determine when they are ready to make a purchase. This allows sales teams to close sales quickly and efficiently while simultaneously reporting and analyzing how the sale itself was achieved. Marketing and product teams can refine their messaging to better target customers with a better understanding of their behavior.
Will 🤖 (AI) Replace Product Managers? (Personal Opinion Call-Out)
A product manager’s role is mission-critical within a technical team and I personally do not believe it will be completely replaced by AI in the near future - However, PMs that fail to realize the full potential of AI will invariably be replaced, as they will not be able to effectively handle the complex challenges and constraints that all PMs will face in the future.
As the world changes rapidly, leaders will have to deal with an increasing number of complex and encompassing issues.
As more and more tools become available to solve core business and customer problems associated with AI, Machine Learning, Automation, and Big Data, it is unlikely that solving these problems will necessarily become more difficult. The only thing that will be required is to utilize new skill sets to the fullest extent possible.
While most PMs don’t have experience gathering data, labeling, or training models themselves, it’s imperative that we understand AI and machine learning so we can work effectively in post-industrial technical teams, when society pivots from an industrialized to a more service-based economy.
To validate and define acceptance criteria for AI initiatives within your organization, you will most likely need to conduct a significant amount of research. As a result, it may be of value to improve our ability to design and execute research, as well as experimentation design. In addition, Artificial Intelligence will have a dramatic impact on the product discovery process, as AI will soon be used as a tool to help elicit the requirements for initiatives, as large-scale language models become one of the primary web interfaces for querying or discovering new information and assisting with research, as opposed to traditional search engines.
In the past few years, low-code (AI), machine learning, and data science have been widely adopted, along with services such as Data Science as a service, AI as a service, and others that require fewer internal resources with expertise in the respective fields. Product Managers can now develop (AI)-based applications and incorporate complex data science into their business logic without having an internal counterpart trained in Data Science or Data Engineering.
Working knowledge and understanding of how these technologies can be integrated into their products’ business logic are all that’s needed. It is a remarkable change that will continue to grow and develop in 2023 and beyond.
Over the next few business cycles, Data Science and Data Analysis roles will not diminish, although they will change significantly, comparably to product management, in my opinion. For lack of a better explanation, humans will be required for quite some time to direct machines as the creativity layer due to the difficulty of modeling creativity. In the interest of posterity, job responsibilities will continue to change as new technologies and their ability to impact business success become more prevalent.
Our ability to remain competitive in the job market, technology-agnostic and antifragile will depend on our ability to acquire the necessary skills.
🌇 ☀️ The Future of (AI) Product Management
Time flies, as does the modern-day business cycle. In the past 12 months, we’ve seen a dramatic change in sentiment and focus within the tech industry. Last year, and for several years prior, decentralized-ledger-technology (DLT), such as blockchains and cryptocurrencies, was all the rage.
Within less than six months of the release of Stable Diffusion, Dall-E, and ChatGPT betas, the entire tech world shifted its focus from crypto to AI and machine learning. Generative AI hits a home run with this wave of products, models, technologies, and innovations, as it enlightens consumers about the usefulness and capabilities of AI for the first time. Within the last few months, we’ve seen one revolutionary technology trend follow another, with complete capitulation and subsequent insolvencies within crypto and blockchain.
During the next major business cycle and subsequent super-cycle, Product Managers will work increasingly with Data Science, and they will be learning increasingly hard skills associated with conducting research, analyzing and synthesizing data, and designing products and services that utilize AI and ML.
📚 AI Product Management Courses
The best way for a Product Manager to control personal outcomes and become more marketable, employable, and valuable to their organization is to obtain a certification or take an AI Product Management course for both current and future employers. As we reviewed earlier in this post, obtaining data analytics skills is highly valuable to product managers, too.
Advanced SQL, experimentation-design, financial modeling, statistics, Tableau, and Advanced Excel, are each skills that will set us apart from the crowd and show a potential and current employer our abilities and capacity to understand complex data-intensive challenges.
✴️ “Organizations in every industry are accelerating their use of artificial intelligence and machine learning to create innovative new products and systems. This requires professionals across a range of functions, not just strictly within the data science and data engineering teams, to understand when and how AI can be applied, to speak the language of data and analytics, and to be capable of working in cross-functional teams on machine learning projects”.
✴️ “You’ll learn how to evaluate the business value of an AI product. You’ll start by building familiarity and fluency with common AI concepts. You’ll then learn how to scope and build a data set, train a model, and evaluate its business impact. Finally, you’ll learn how to ensure a product is successful by focusing on scalability, potential biases, and compliance. Along the way, you’ll review case studies and examples to help you focus on how to define metrics to measure the business value for a proposed product”.
✴️ “PLA & The AI Product Institute have teamed up to bring you…the AI Product Manager Accelerator Program (AIPMA)”.
“AIPMA provides you with all the info and skills you need to understand AI and upskill for your next career opportunity. Whether you’re a product manager in marketing, logistics, production, customer management, user experience, or sales, the AIPMA program is your best foot forward in the competitive world of product management”.
“Along with the potential for significant salary increases, becoming an AI Product Manager will shine a spotlight on YOU in the job market”.
🌐🎖️Conclusion
It is becoming increasingly important for product managers to be able to generate actionable business decisions based on validated data. It is common for PMs to have a broad range of skills, capable of fulfilling a variety of roles in cross-functional teams. Generally speaking, the jack of all trades is the master of some.
The PM who uses AI to benefit the end user, and as a result, the business, will be a powerful force, like Lebron James or Kobe Bryant in their prime. As a result, I must develop the habit of defending against the unrelenting trend of AI, rather than fighting in a defensive manner, allowing the point guard to take uncontested shots.
The purpose of this post is to expand upon a few buckets around AI product management, including the need for PMs to gain predictive analytics and data analysis skills by learning SQL, Tableau, advanced Excel, and statistics, to be better prepared for upcoming data-intensive initiatives. Additionally, we discussed how AI and automation are changing customer support, the chatbot revolution 2.0, as well as AI sales, and marketing.
The discussion continued with whether AI will replace product management, a few key points related to the future of product management, and AI product management courses to help set PMs up for success in the future.
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