AI for Product Management: Advantages, Future & Tools

Introduction to AI in Product Management

Many industries have been implementing AI-powered tools into their work lately. However, the most popular branch of AI algorithms (according to an average bystander’s perspective) is still capable of writing code and creating applications without extensive knowledge of coding languages. It doesn’t change the fact that many industries benefit from AI implementation. This includes product management and those who make the product concept and strategy – product managers.

Product management is a popular business field in terms of AI adoption. Similarly, it is one of the most popular markets for AI tools. Product managers deal with many mundane and time-consuming tasks regularly, and AI can help reduce the time spent on these tasks to a minimum. That way, an average product manager would be capable of spending their time and resources on more meaningful and valuable tasks, boosting the effectiveness and productivity of the entire business.

The responsibilities of a product manager after the introduction of AI

Introducing AI and ML into the product management profession can bring plenty of benefits. Still, it also creates several additional responsibilities that the product managers themselves often have to work with. 

In the list below, you’ll find some of the most common examples of product manager tasks that either appear or expand their scope when AI is integrated into their job in some way.

• Heavier focus on each customer’s problems due to the automation of menial and monotone tasks, making it easier for product managers to solve more complex and meaningful problems of customers.
• Explaining how AI tools operate to the potential customer to build trust in AI.
• Working towards expanding the integration of AI-related features towards other fields of work within the same company, showcasing productivity benefits. 
• Expanding your knowledge about AI in general terms since knowing what specific question to ask an AI is at least half of the overall work.
• Seeking product usage growth opportunities – when the AI researches and the product manager makes informed decisions.

Of course, this list is far from complete, but it should be enough to understand that AI in product management is not a miracle solution for every single task. AI in product management is a tool that needs to be appropriately used to be practical and valuable. The opportunity of integrating AI into your business workflow is enticing, and it keeps growing at an impressive pace. However, it is not particularly easy to implement AI effectively.

Potential challenges of AI integration into Product Management

Here are some challenges that a product manager may encounter when implementing and using an AI-centric tool in their work.

• AI integration 

Integrating artificial intelligence in a product’s lifecycle is a complex topic that requires both experience in the AI field and a lot of time and budget. A proper AI implementation involves qualified prompt engineers, developers, and even product managers trained to use AI. Likewise, there is a possibility that the AI implementation would require drastic changes to the company’s existing IT infrastructure.

• Data protection

Another vital challenge of AI tools as a whole is their vulnerability in terms of cybersecurity. An AI tool’s protection level is the same as any other software in a company’s infrastructure, and AI often needs extensive permissions to perform its tasks. As such, keeping a close eye on an AI tool’s protection levels is highly recommended – including checking security protocols, implementing MFA, managing access control policies, and more.

• Algorithmic bias

Not all the algorithms are perfect by design, and not all the training data is consistently correct from an objective standpoint. Using these kinds of elements to work with your AI tool creates a phenomenon known as algorithmic bias – a chain of repeatable errors in a system that generates subjective, unfair, and biased outcomes. This kind of phenomenon is also possible in the field of AI-powered product management, possibly tarnishing the product’s reputation. It is highly recommended to check both your algorithms and data for inaccuracies to ensure this does not happen.

Artificial Intelligence in Product Management: Categories and Methodology

Even though the latest boom of AI-powered tools is relatively recent, there is already a massive number of different tools and services offering all kinds of features in the realm of product management. 

To make it easier to see how these solutions differ from one another, we have separated our examples into multiple categories based on what the specific solution is capable of. Our methodology, when it comes to choosing the best AI solution for product management, is listed below:

Customer ratings are used as a representative of what the overall clientele of the solution thinks about its capabilities and customer experience. It can vary quite a lot depending on plenty of factors, but the fact that there are only average collective marks here makes the reviews a lot more neutral. In our article, we have mostly relied on websites such as:

• Capterra – a review aggregator with more than two million verified reviews that uses its own system of thorough checks to ensure that every review is authentic and written by a human being.
• TrustRadius – a notable review platform that verifies user reviews for both authenticity and usefulness, offering a fair and comprehensive look at practically every solution out there.
• G2 – a well-known customer review platform that boasts more than 2.4 million reviews and a hundred thousand vendors to choose from. It has a dedicated review validation methodology, and there are also services that G2 provides to its users – such as marketing, tracking, and investing, among others.

Key features are a bit more difficult in our case since we are not dealing with the same category of software, but rather multiple categories at once. At the same time, it would be fair to mention that every competent solution in our case should have high performance when it comes to working with data and generating results. 

Other potential advantages include the ability to utilize AI algorithms to their fullest – not the bare-bones usage of providing an overview of a text, for example, but deep and thorough information about the customer’s necessities, product strategy recommendations, growth ideas, and more.

Pricing is a critical factor to consider – especially in AI-based products, since they are now becoming more widespread for smaller businesses and are gathering an audience of more prominent companies. The sheer hardware power is also necessary to perform AI-based calculations. 

One of the most popular LLM service providers, OpenAI (the creator of ChatGPT), had to stop selling the Plus version of their chatbot since their owned hardware was not keeping up with the demand, making AI responses slower for everyone. It would be easy to imagine how a smaller AI service provider encounters the same problem and raises the price of the service to recuperate these newfound expenses.

Mentioning other examples of AI-powered tools was also done deliberately to prove that the market is prosperous and highly competitive, offering various solutions for practically any use case. It is a surprising development, considering the market is only a few years old. However, representing multiple solutions for every single group was a good way of painting a more detailed picture of the current state of the market.

AI and Market Research

Market research is an integral part of product management. Building a product for a specific customer persona is difficult without properly understanding its needs. AI-powered tools are handy for these cases, offering many insights about customer behavior, preferences, opinions, and more. It should also be possible to use the power of natural language processing to analyze customer feedback from various sources, such as customer reviews, forums, and social media.

Other capabilities of AI-powered market research tools include competition monitoring, keeping up with the industry’s latest trends, etc. In our opinion, the most notable solution to review in this category is Mixpanel.

Mixpanel is an AI-powered tool that can offer several specific features to market researchers and product managers. It helps businesses a lot when it comes to data-driven decisions by tracking user behavior patterns of customers at all times. Mixpanel can help a lot when making important marketing or product development decisions. It can also identify patterns and provide insights into how customers interact with the product.

Customer reviews of Mixpanel:

• Capterra – 4.5/5 points from 129 customer reviews.
• TrustRadius – 8.4/10 points from 210 customer reviews.
• G2 – 4.6/5 points from 1,087 customer reviews.

Key features of Mixpanel:

• Capability to perform A/B testing to analyze how different product variations perform in the same circumstances. It is an excellent tool for improving data-driven decisions and boosting the efficiency of marketing campaigns.
• Easy presentation of real-time analytics for simple and effective monitoring of user behavior and activity. That way, businesses can stay flexible and responsible, reacting to various changes in the market as soon as possible.
• Event tracking can also be done with Mixpanel – practically every user action can be monitored or tracked if necessary, going as far as to track every button click or app installation.
• User retention can also be tracked with the help of an AI, providing quick and efficient insights into how often an average user returns to your product. This particular parameter is crucial when there is a need to evaluate user loyalty while also tracking down opportunities to improve retention.

Pricing of Mixpanel (at the time of writing):

• Mixpanel’s pricing model is not particularly difficult; it includes three different tiers:

• Starter – a completely free offering with a limit of 20,000,000 monthly events.
• Growth – $20 per user per month, including a limit of 100,000,000 events in that period.
• Enterprise – an offering starting from $833 per month, geared towards large-scale companies, and its event number limitation fluctuates depending on a customer's needs.

Other examples of AI-powered Market Research tools:

• IBM Watson is a platform built to extract insights from large unstructured data pools via the power of cognitive computing. It can analyze social media trends and general customer feedback by combining NLP and AI.
• Amplitude is a product manager-oriented tool with product analytics features as its strongest point. It can measure user conversion, user retention, user engagement, provide insights, and more.
• Google Trends is a useful tool that allows users to identify trends in search queries and perform general search trend analysis. Its most significant advantage in this field is that it’s completely free.
• Qualtrics is a small-scale solution explicitly built for surveying customer feedback data. Its two main features are data analysis and insight extraction.

AI and Sales Forecasting

An incorrect sales forecast can put the entire company in a highly unfavorable situation. Fortunately, AI can help by identifying deal-closing patterns, revenue trends, and other crucial sales metrics. It is a commodity now to train AI algorithms to see patterns in specific data sets – such as patterns in sales data that show trends, outliers, seasonal changes, and more. 

These patterns allow AI to offer accurate predictions about sales and other parameters, creating a suitable environment for informed decisions regarding marketing campaigns, pricing strategy, inventory, etc. As for the actual example, we would like to review Salesforce CRM Analytics.

Salesforce CRM Analytics uses a combination of ML and AI to offer various insights, sales forecasts, and other helpful information. It can identify trends and patterns in sales data, which helps to make informed decisions regularly.

Customer reviews of Salesforce CRM Analytics:

• Capterra – 4.3/5 points from 74 customer reviews.
• G2 – 4.2/5 points from 356 customer reviews.

Key features of Salesforce CRM Analytics:

• The ability to visualize the entire business on a single screen by consolidating data from multiple sources. The solution can generate insights, predictions, comprehensive visuals, and more.
• The solution is very user-friendly, making creating dashboards and entire AI models easy without coding or customer support help. Salesforce can offer custom-built dashboards, metrics, third-party apps, and customizable templates to create custom sales tools easily.
• Salesforce can analyze much information to forecast outcomes, offer recommendations, and identify growth opportunities for its users. Analytics can also be tailored to fit the company’s specific business goals, creating even more special and unusual recommendations only for your business.

Pricing of Salesforce CRM Analytics (at the time of writing):

• Salesforce CRM Analytics can offer three different pricing plans:

• Sales Cloud Einstein – €50 per month per user, offers advanced analytics, AI capabilities, and more.
• Einstein Predictions – €75 per month per user, expands upon the previous feature set by adding predictive insights and automated discovery.
• CRM Analytics Plus – €150 per month per user, the biggest offering of Salesforce in terms of sales forecasting, puts an entire AI-centric analytics platform into your hands.

Other examples of AI-powered Sales Forecasting tools:

• InsightSquared – a multifunctional sales analytics platform that can offer plenty of functions: insights into future trends, sales performance, better sales forecasting, and more.
• Zoho CRM – another AI-powered forecasting platform with a focus on sales data, it can identify potential risks, predict future sales trends, and even offer insights into sales performance along with recommendations about sales strategy optimizations.

AI and Product Development

Product development requires managing resources, organizing processes, analyzing performance metrics, and more. Artificial intelligence can help significantly in this scenario, automatically optimizing resource allocation and identifying potential problematic parts of the development plan. Most of these capabilities rely on the ability of AI algorithms to go through historical product development information to identify common bottlenecks, see potential weak spots, and offer optimization mechanics. 

There are plenty of different options on the AI solution market that can resolve this issue, and we would like to review Collato.

Collato is an AI-powered tool that aggregates product information, combining surveys, user testing results, customer support requests, and other sources of information into one centralized source of data. This approach makes it a lot easier to find opportunities for improvement and analyze the company's current state as a whole. Additionally, this system takes full advantage of user feedback, improving customer satisfaction rates and boosting the solution's overall performance.

Customer reviews of Collato:

• Product Hunt – 4.7/5 points from 77 customer reviews.
• OMR – 4.4/5 points from 12 customer reviews.
• G2 – 4.8/5 points from 5 customer reviews.

Key features of Collato:

• Capability to generate a large selection of documents automatically: user guides, PRDs, release notes, and more.
• Ability to connect with many commonly used tracking tools, such as Notion, Confluence, Google Drive, or Jira.
• AI-powered ability to find and summarize content in the existing data pool to offer quick and accurate answers at the earliest convenience.
• Collato can create summaries in multiple languages, promoting communication in diverse and multicultural teams.

Pricing of Collato (at the time of writing):

• Collato can offer a simple subscription-based licensing model with three distinct pricing plans.

• Free – unlimited number of integrations with up to 25 AI responses for a single user monthly.
• Pro – $6 per user per month, expands upon the free version’s offering by increasing the number of available AI responses to 250.
• Enterprise – the most expensive plan, with no public pricing available, completely removes all limitations regarding AI responses and adds various enterprise integrations.

Other examples of AI-powered Product Development tools:

• DataRobot – a sophisticated platform with AI capabilities capable of offering optimization and automation for multiple data analysis tasks, incl. model training, data cleaning, deployment, or feature engineering.
• Autodesk Fusion 360 – a product design and manufacturing solution that implements ML and AI to improve multiple product development aspects, such as manufacturing, generative design, simulation, and more.
• Sisense – a business intelligence platform powered by AI that specializes in performing analysis on large data pools to generate insights into various topics, like a product’s overall performance, market trends, and customer behavior.
• TensorFlow – an open-source framework from Google, which can be used to both create and deploy models with machine learning capabilities for predictive analytics, NLP, image recognition, and more.

AI and Personalization

Personalization is a significant part of why a customer wants to keep returning to the same product or service. It is also a challenging process when it scales up to thousands of users at the same time. Product managers often rely on target groups and other tactics to personalize products to a specific audience, but that process is not entirely foolproof.

Conversely, AI can greatly help this task, offering a complete analysis of user preferences and behavioral patterns to suggest only the best possible modifications or strategies. AI algorithms can analyze purchase history, browsing history, search queries, and other customer information to recommend promotions, content, and products relevant to the specific customer. Adobe Target is one of the best examples in this field, in our opinion.

Adobe Target is a personalization platform that utilizes the power of AI and ML to offer highly personalized experiences to the company’s customers. It can analyze customer behavior to provide various offers, messages, recommendations, etc.

Customer reviews of Adobe Target:

• Capterra – 4.3/5 points from 4 customer reviews.
• TrustRadius – 7.7/10 points from 160 customer reviews.
• G2 – 4.1/5 points from 62 customer reviews.

Key features of Adobe Target:

• A capable recommendation system with AI algorithms that can analyze purchase history and other customer data to provide personalized product recommendations.
• Both multivariate tests and A/B tests can be easily done by Adobe Target, including the capability to compare different iterations of websites or applications. 
• Customer experience can be personalized even more based on specific pre-set criteria, such as device type, user location, etc. That way, the content would be even more relevant and contextualized, making it far more personalized.

Pricing of Adobe Target (at the time of writing):

• Similar to most of the Adobe products, there is no official pricing information about Adobe Target that could be found on its website.

Other examples of AI-powered Personalization tools:

• Acquia Lift – makes delivering personalized experiences much easier. It is an AI-based platform that utilizes ML mechanisms to offer customized content and recommendations based on customer behavior.
• Dynamic Yield – a personalization platform that analyzes data with ML algorithms and offers an overview of average customer behavior and some recommendations on how to improve the current situation.

AI and Customer Support

In just a few years, AI-powered customer chatbots have become far more helpful and capable than ever before. This allowed product managers to free up time for more meaningful tasks. AI chatbots can also aggregate data and gather insights into what issues need to be resolved faster, what needs to change to improve the overall customer experience, and so on. An excellent example of such a solution would be Zeda.io.

Zeda.io is a great strategy and product discovery platform specializing in working with customer feedback-oriented tasks. Not only can it create product strategies, but it can also identify pressing customer problems and suggest various steps to mitigate or resolve the issues in question.

Zeda.io offers a centralized overview of customer feedback while also segmenting customers themselves to analyze potential product growth opportunities. It can generate insights with the help of artificial intelligence based on the feedback received from customer reviews, and can even manage the overall product development process.

Customer reviews of Zeda.io:

• Capterra – 4.0/5 points from 7 customer reviews.
• G2 – 4.5/5 points from 27 customer reviews.

Key features of Zeda.io:

• Capability to integrate with various software that is often used in the context of business environments, such as Amplitude, Teams, HubSpot, Slack, Zendesk, etc.
• Easily organizable feedback with the ability to create custom filters, making it easier to segregate customer reviews into categories for further processing.
• Artificial intelligence as the means of gaining valuable insights into the overall state of the product, with customer attributes, product intelligence, and more.
• Zeda.io can easily transfer feedback from other systems using a basic CSV file.

Pricing of Zeda.io (at the time of writing):

• There are only three versions of Zeda.io pricing available:

• The free version includes 2 weeks of unrestricted access to all features, which later has to transform into one of the other two subscription tiers.
• Pro – $99 per user per year, includes a primary feature set with little to no restrictions in terms of regular functionality.
• Custom – an enterprise-oriented offering with no public pricing that supports SAML, SSO, on-premise deployment, and more.

Other examples of AI-powered Customer Support tools:

• Intercom – a conversational platform that utilizes AI capabilities to automate plenty of customer interactions, be it targeted messaging or chatbots. 
• Zendesk – a customer service platform with cloud-based customer interaction support capabilities. It uses a ticketing system to track and resolve customer queries while offering a live chat, social media integration, a knowledge base, automation, and more.
• Helpshift – a platform with customer service as its primary target, it can offer ML capabilities for customer interaction automation, chatbots, and sentiment analysis capabilities for higher customer support efficiency.
• Freshdesk – offers a tandem of NLP tools and AI-powered chatbots to automate the customer interaction experience with easier inquiry routing.

Lazy AI for Product Management

We have shown several examples of solutions that can be used to support product management activities. One game-changing product that we also want to mention here is Lazy AI.

Lazy AI is a platform that offers no-code application creation for various goals and purposes. It expands upon the capability of LLMs to generate code by natural language processing. Lazy allows you to create production software just by words.  

Lazy templates provide users with a library of pre-configured workflows for common developer tasks. They encapsulate best practices, allowing users to jumpstart their application development journey without the need for writing code from scratch but adding functionality with the natural language instead.

Some of the more noticeable advantages of Lazy AI are:

• A template-driven approach that makes application creation more accessible than ever before.
• Extensive usage of NLP capabilities to create application components based on customer words.
• A very low skill level requirement for app creation (absolutely no need to be a developer).

Lazy AI can offer multiple different categories of templates to choose from. It can be finance, dev tools, marketing, integrations, and even bots in various applications like Discord. It creates a fascinating perspective on app development, greatly simplifying the entire app creation process. 

Conclusion

Product management is a challenging task. Product managers often have to juggle multiple tasks and factors to manage their products properly – a significant part of this job can be described as repetitive and time-consuming. As such, the introduction of AI-powered features into this field carries much potential in it.

AI software for product management already has several variations and specialization fields – capable of helping with various tasks from product development to forecasting and analysis. These tools can be a lifesaver for an average product manager, saving time and money while freeing up the resources for more valuable and meaningful tasks.  

Our article represents a comprehensive overview of what AI in product management looks like at the beginning of 2024. At the same time, there is an essential factor that is worth remembering. This field constantly evolves, and any product manager must stay on top of new developments and technological advancements. The age-old saying “knowledge is power” works exceptionally well here – a properly educated product manager would be ready for a sudden new change in the industry when that inevitably happens.