The field of product management has dramatically changed and transformed with artificial intelligence that is transforming the product development process, product launching process, and optimal use of the product. Contemporary product managers have an overwhelming amount of tasks due to user research and roadmap development, as well as interacting with stakeholders and data analysis. AI Tools Product manager Product managers AI tools have now become game changers in that they can automate repetitive work, and even more importantly, give intelligent data that would have taken hours to compile.
These tools assist PMs to be faster and more data-driven, as well as concentrate on strategic thinking and creativity. Now, whether you are working on the initial product of a startup or maintaining platforms of the enterprise level, the capabilities of AI to boost your productivity and efforts in decision-making could be highly efficient. This is a complete reference manual on the best 15 tools of artificial intelligence that a product manager should look at in 2026.
What is AI in Product Management?
AI in product management can be defined as the use of machine learning, natural language analysis, and predictive analytics to provide efficiency to product development processes. These technologies assist product managers in analysing user response, forecasting market trends, automating documentation and deriving actionable insights in complicated datasets.
The tools based on AI capabilities are able to analyse the reviews of thousands of customers within a few seconds, prioritise features by the frequency of their use, and even create a product requirements document. Presenting the product manager with time to make strategic decisions, create innovative solutions, and enhance their relationships with their team and stakeholders, AI enables them to deal with time-consuming analytic work.
Benefits of Using AI Tools for Product Managers
- Saving Time: AI streamlines automated manual processes such as data collection, generation of reports and summary of meetings, freeing up product managers with 10-15 hours per week to do their strategic work.
- Data-Driven Insights: Sophisticated analytics tools subject a large volume of user data to massively analyse its trends, needs and provide insights that humans may overlook.
- Greater Prioritisation: AI models compare feature requests, support, and business objectives, recommending the best priorities in the roadmap and resource allocation to allocate for developed features.
- Better Communication: Natural language AI can be of use in drafting a good product brief, user story, and stakeholder update consistently and professionally.
- Predictive Analytics: Machine learning algorithms predict the behaviour of products/organisations in the market, as well as possible risk, before it affects your schedule.
- Enhanced User Research: AI solutions will process customer interviews, surveys, and behavioural data to derive quality themes and recommendations to act on them within short durations.
Suggested Read: Best-selling Products in India
What are the Different Types of AI Tools?
- Analytics & Insights Tools: These websites involve machine learning to review metrics of products, user behaviour, and market data to give practical recommendations on how to enhance.
- Documentation & Writing Assistants: Artificial intelligence-driven applications assist in the rapid and coherent creation of PRDs, user stories, release notes and any other documentation.
- User Research Tools: The user research solutions that will analyse the feedback, sentiment analysis, and interview transcriptions to reveal the user needs and discomfort areas.
- Roadmap Planning Tools: AI-enhanced services characteristics enable roadmap prioritisation, resource allocation, and visualisation of product strategy based on analytical scores.
- Communication Tools: AI assistants to summarise meetings, write emails, and make presentations to automate the communication process with stakeholders and collaborate with the team.
- Design & Prototyping Tools: Smart sites that create mockups of designs and variations of designs, which can be optimised based on the best practices and the data they have to work with.
Top 15 AI Tools for Product Managers (2026 Recommended List)
| Name | Type | Key Features | Free/Paid | Best For |
| ProductPlan | Roadmap Planning | Strategic roadmap visualisation | Paid | Content generation, brainstorming, and documentation |
| Aha! | Product Management Suite | Strategy mapping, AI insights, workflow automation | Paid | Enterprise product strategy |
| Productboard | Feature Prioritization | Customer feedback analysis, AI scoring | Paid | Customer-driven prioritization |
| Pendo | Product Analytics | In-product surveys, AI analysis, and session replays | Paid | Usage analytics and guidance |
| Amplitude | Behavioral Analytics | Predictive analytics, cohort analysis, ML insights | Free/Paid | Analysis, writing, research, and coding support |
| Mixpanel | Event Analytics | Real-time tracking, AI predictions, funnel analysis | Free/Paid | Event-based product analytics |
| ChatGPT | Writing Assistant | Deep behavioural analysis | Free/Paid | Documentation and ideation |
| Claude | AI Assistant | Smart writing, summaries, and knowledge management | Free/Paid | Complex problem-solving |
| Notion AI | Documentation | Mind mapping, brainstorming, and AI suggestions | Paid | Collaborative documentation |
| Miro AI | Visual Collaboration | In-product surveys, AI analysis,and session replays | Free/Paid | Visual planning and workshops |
| Dovetail | User Research | Interview analysis, insight extraction, tagging | Paid | Qualitative research analysis |
| UserTesting | User Research | AI-powered insights, video analysis, sentiment detection | Paid | Remote user testing |
| Fireflies.ai | Meeting Assistant | Transcription, summaries, action item extraction | Free/Paid | Meeting documentation |
| Monterey AI | Feedback Analysis | Multi-source feedback aggregation, AI insights | Paid | Omnichannel feedback analysis |
| Sprig | User Insights | In-app analytics, user segmentation, and AI recommendations | Paid | Continuous user feedback |
Top 15 AI Tools for Product Managers (2026)
1. ProductPlan
ProductPlan is an influential road mapping tool that assists product managers in visualising the strategy and equips the teams using attractive, adaptable road maps. The prioritisation functions of the AI-powered features consider a variety of variables, such as the business value, effort, and strategic alignment and offer the best sequence in terms of optimal features. ProductPlan is easy to use, allowing all company members to understand your product vision, and it has powerful integrations with Jira, Azure DevOps, and more, with its drag-and-drop simplicity and ability to share product vision widely. The platform is superb in scenario planning where PMs will be able to develop several roadmap versions and compare these versions with each other before making a commitment towards a strategy.
Key Features:
- Prioritization scoring based on AI.
- Visual roadmaps which can be customized.
- Collaboration capabilities in real time.
- Interconnection with development.
Pros:
- The drag and drop interface is intuitive.
- Superior stakeholder presentations.
- Competent integration ecosystem.
Cons:
- Premium pricing structure
- Learning curve initially
- Limited free options
Pricing:
- No frills: $49/editor/month (payable every year)
- Professional: 99/editor/month (charged on an annual basis).
- Business: Full featured cost plus customization.
Website: productplan.com
2. Aha!
Aha! is a multi-purpose product development application that integrates strategy, road mapping, and idea management into a single, powerful application. The product managers are assisted by the AI capabilities to bridge strategy to execution by analysing dependencies, resource constraints and business goals. Aha is good at developing the strategy frameworks, which make the vision flow down to specific features, so that no team member does not know how his or her work is beneficial to the goals of the company. The notebook button of the platform is a knowledge base on product history, competition, and research results.
Key Features:
- Strategic goal alignment
- AI-driven capacity planning
- Full Idea Management.
- State of the art reporting dashboards.
Pros:
- Complete product suite
- Customization with great capabilities.
- Excellent customer support
Cons:
- Higher price point
- Complex initial setup
- Overwhelming feature set
Pricing:
- Aha! Roadmaps: $59/user/month(annually billed)
- Aha! Ideas: $ 39/user/month (payable after annual subscriptions)
- Aha! Create: $9/user/month yearly billed.
- Custom-priced enterprise strategies offered.
Website: aha.io
3. Productboard
Productboard combines all sources of customer feedback and processes them with AI to discover patterns and rank features according to the needs of the user. The platform ranks and grading feature requests It is automatically graded and ranked on customizable structures to assist PMs in objective prioritization decisions. The portal aspect of Productboard enables clients to post and vote on ideas giving it a transparent and interactive quality. The feedback sentiment and frequency of the AI-powered insights engine are analyzed to generate the most impactful opportunities so that product teams can generate what is of most importance to users and business growth.
Key Features:
- Summation of customer feedback.
- The priority frameworks of AI.
- Public feedback portals
- Product roadmap sharing
Pros:
- Very good feedback management.
- Intuitive user interface
- Well-developed integration skills.
Cons:
- Expensive for small teams
- There should be an improvement in reporting.
- Portal limited customization.
Pricing:
- Necessities: $20/ maker/ month (charged yearly)
- Pro: $65/maker/month (bountied on an annual basis).
- Scale: 130/ maker/month (pay once a year)
- Enterprise: Custom prices are available.
Website: productboard.com
4. Pendo
Pendo is an ecosystem of product analytics, in-app guidance, and user feedback to assist product managers with the goal of bettering user experiences. The AI-based analytics automatically recognize the unusual patterns, retention, and adoption opportunities without being set up manually. The guides feature of Pendo enables PMs to implement in-app walkthroughs and announcements without engineering assistance, speeding up the process of adoption of the features. NPS and polling allow you to have first-hand user sensation data, whereas retroactive analytics allow you to analyse user behaviour even on events that you were not initially tracking, and it is invaluable in data-driven decision making.
Key Features:
- In-app user guidance
- Tracking of behavioral analytics.
- AI-powered feature insights
- No-code survey deployment
Pros:
- All-encompassing analytics platform.
- Easy guide creation
- Retroactive data analysis
Cons:
- Steep learning curve
- Higher pricing tiers
- Setup requires effort
Pricing:
- Free: Small features The small team features are limited.
- Entry: Custom pricing (begins at an annual average of around $7,000)
- Expansion: Usage-based custom pricing.
- Portfolio: Personalized enterprise pricing.
Website: pendo.io
5. Amplitude
Amplitude is a product analytics platform featuring advanced machine learning and artificial intelligence to assist product managers in understanding user behaviour at scale. The predictive analytics on the platform provide the ability to predict churn, to define power users and suggest individual personalization strategies depending on the behavioral cohort. The patented method used by Amplitude helps people see the way users go up and down the ladder instead of the number of page views, which will show how people actually experience your product. The Compass feature relies on AI to automatically bring to the fore important changes in metrics and user behavior so that PMs can never overlook any important trends that have the potential to change the success of a product and ability to retain users.
Key Features:
- Anticipatory behavioral analytics.
- Cohort analysis tools
- Discovery of insights in an automated fashion.
- Inter-system user tracking.
Pros:
- Efficient behavioral monitoring.
- A good retention analysis.
- Strong data visualization
Cons:
- Complex for beginners
- Expensive at scale
- Needs implementation effort.
Pricing:
- Sample: Comp Free: Pro to 10 million events/month.
- America: Lowest price is $49/month (volume-based)
- Growth: Use of custom pricing on high volume pricing requests.
- Business: Full featured cost plus customization.
Website: amplitude.com
6. Mixpanel
Mixpanel is an AI-based real-time product analytics tool where product managers can optimise user experiences and grow their product with real-time insights. It is also strong in funnel analysis meaning it displays the precise location of the user abandonment of specific workflows, including where they could be enhanced. The AI of Mixpanel prevents real-time events and predicts probable converters and churners, allowing engagement strategies. The signal detection of the platform automatically invites PMs to the drastic changes in metrics or odd trends. Mixpanel is built with flexible tracking events and having the ability to segment a vast amount of data, allowing teams to answer complicated product questions and iterate on them in front of data.
Key Features:
- Real-time event analytics
- AI churn prediction
- Advanced funnel analysis
- Automated metric alerts
Pros:
- Quick process of implementation.
- Intuitive dashboard design
- Good appearance of funnels.
Cons:
- Can be pricey
- Limited free tier
- There is an increased complexity in reporting.
Pricing:
- Free: 20 million and less events/month.
- Growth: The amount begins with $25/month (events scaling)
- Enterprise: Personal pricing of large-volume requirements.
Website: mixpanel.com
7. ChatGPT
ChatGPT by OpenAI has emerged as the invaluable writing and brainstorming tool of product managers all around the globe. This chat robot can write product requirement documents, write user stories, write competitive analysis, and assist in writing presentations within a few seconds. ChatGPT is used by product managers to summarise long research reports in a few seconds, brainstorm on feature ideas, write release notes, and even write SQL statements to analyse the data. The ability to reason in context and follow discussion threads is why it is ideal for making documents perfect through refinement. Regardless of whether you have a communication with stakeholders or need to prepare technical documentation, ChatGPT is also much faster at writing.
Key Features:
- Syntactic generation Natural language generation
- Document drafting
- Brainstorming and ideation
- Previously, there was no multi-language support.
Pros:
- Extremely versatile tool
- Fast content generation
- Raising capabilities on a continuous basis.
Cons:
- Infrequent historical errors.
- Requires prompt expertise
- Connection on the internet requisite.
Pricing:
- Free: GPT-3.5among conditions
- of Free Base.
- Wave octuplet: $30/month inquisite GPT-4 and precedence.
- Team: USD 25/user/month (not less than 2 users)
Website: chat.openai.com
8. Claude
Claude is a sophisticated AI assistant that is developed by Anthropic and is capable of detailed analysis, research, and deep thinking to solve analytical problems among product managers. In contrast to less complex chatbots, Claude is capable of handling extended texts, gives transcripts of user research a meaningful analysis and presents recommendations that are considered within strategic contexts. Product managers like Claude because when it comes to complex product decisions, he understands the situation in all possible ways and offers objective views. The AI will be able to check the PRDs to make sure they are clear and complete, assist in competitive landscape analysis, generate technical documentation, and even basic coding jobs. The answers that Claude gives are well thought and detailed, which makes it perfect to use in strategic planning and complex analysis.
Key Features:
- Advanced document analysis
- Strategic thinking support
- The coding and technical assistance.
- Prolonged context of conversation.
Pros:
- Insightful, sensitive reactions.
- Increased document processing.
- Well-developed analytical skills.
Cons:
- Subscription needed the best.
- Rate limits apply
- Newer than alternatives
Pricing:
- Free: The use is limited with Claude Sonnet.
- Pro: Increase the higher limits and priority to $20/month.
- Team: 25 users/month (only 5 users 12 months)
- Enterprise: Individual price quotation.
Website: claude.ai
9. Notion AI
Notion AI can be easily added to the widespread workspace platform, so product managers can write, edit, and organize documentation without exiting their workflow. The AI is capable of creating initial versions of PRDs, meeting notes and project briefs without adding extra information as simple prompts are given, and refuses to keep editing it until it becomes perfect. Notion AI is efficient in summarising long documents, uniting action items in the meeting notes, and translating the information across languages. To product teams who are already operating using Notion as their knowledge base, the AI features can facilitate productivity powering ahead and streamlining mundane product documentation by automating all the routine documentation tasks and assisting them make the underlying product documentation consistently formatted and toned.
Key Features:
- Integrated workspace AI
- Selecting tools of document summarisation.
- Action item extraction
- Organisation of the knowledge base.
Pros:
- Integration of seamless Notion.
- Fast document generation
- Working group characteristics.
Cons:
- Needs Notion subscriptions.
- Poor independent capability.
- Additional AI cost
Pricing:
- Standard AI: $10/creed an individual/month (superior to the fundamental Notion plan)
- Base Notion pricing: Free, Plus ($8/user/month) and Business (15/user/month).
- Enterprise: Custom pricing
Website: notion.so
10. Miro AI
Miro AI is an intelligent brainstorming and planning tool offered to product teams that converts the popular collaborative whiteboard solution to an intelligent assistant. The AI capabilities allow product managers to rapidly create mind maps, to sticky note organization into themes, and to create structured structures out of freeform thoughts. Miro AI can also propose relations between the ideas in case of workshops and planning, and create some new ideas within the existing ones, as well as assist in remote collaboration. Initially, the infinite canvas of the platform is ideal in product discovery workshops, user journey mapping, and sprint planning sessions requiring a visual structure with the assistance of AI.
Key Features:
- AI-powered mind mapping
- Smart Sticky Note Autoclustering.
- Assistance in template generation.
- Live Team Work Tools.
Pros:
- Visionary teamwork excellence.
- Intuitive interface design
- Good facilitative characteristics.
Cons:
- AI has features that need to be subscribed to.
- Possible to get bogged down at the start.
- Board control of performance performance.
Pricing:
- Free: Small team basic features.
- Entry level: 8/member/month (Billed annually)
- Business: $16/member/month (annually)
- Enterprise: Customized pricing and superior-level security.
Website: miro.com
11. Dovetail
Dovetail is an effective user research library, which uses AI to apply qualitative data (interviews, surveys, and usability tests) at scale. The platform will use machine transcribing to transcribe research, detect important themes and findings, thus labeling meaningful moments within it without human effort. Product managers are able to search through hundreds of interview transcripts quickly to uncover solutions to certain user pain points or requests. The AI-driven insight extraction with Dovetail saves millions of hours of time spent on manual analyses, including its highlighting and tagging features that make it simple to compile evidence-based cases when making decisions about a product and share the results with the relevant stakeholders in an effective way.
Key Features:
- There is an automatic interview transcription.
- AI-powered insight tagging
- Cross project pattern analysis.
- Cooperation and collaboration tools.
Pros:
- Outstanding methodology in research.
- Governmental powerful searching.
- Time-saving auto-tagging
Cons:
- Premium pricing model
- Learning curve exists
- Heavy for small teams
Pricing:
- Free: Basic projects and functionality.
- Professional: Prices begin at $29/user/month (paying in one year).
- High-end: Discount pricing of bigger teams.
- Enterprise: Customized pricing and superior-level security.
Website: dovetail.com
12. UserTesting
UserTesting offers on-demand testing of actual users that test your product and AI analyses their behavior, facial expressions, and verbal feedback. Based on the video recordings, the AI features of the platform recognize the sentiment automatically, identify the points of confusion or delight, and flag important usability concerns. Prototypes can easily be validated, new features can be tested, and feedback can be obtained without having to recruit participants or perform manual analysing of the same. The AI-driven insights dashboard by UserTesting will reveal the important themes in numerous sessions, and it is simple to see the patterns and decide on what to improve that will make the biggest difference in user experience.
Key Features:
- On-demand user testing
- AI sentiment analysis
- The extraction of video highlights.
- Generation of insights with artificial accuracy.
Pros:
- Quick recruitment of participants.
- Gathering of quality feedback.
- AI-accelerated analysis
Cons:
- Premium pricing structure
- Participants can vary
- Requires planning tests
Pricing:
- Basic: A basic individual subscription costs about 199/month (depending on volume of Internet needed).
- Premier: Personalized pricing depending on the volume of testing.
- Ultimate: Custom business pricing.
- Flexible credit-driven system on offer.
Website: usertesting.com
13. Fireflies.ai
Fireflies.ai is an artificial intelligence meeting assistant that takes notes during meetings which then contains detailed transcripts with timestamps. In product management where product managers have to coordinate with different stakeholders and meet with people and colleagues, Fireflies does not require taking complex notes manually. The AI recognises speakers, extracts action items, and even creates summaries, as well as crust-up emails. Product managers are able to search all the transcripts of meetings to locate certain discussions of features or decisions made in weeks prior. Integration with such tools as slack, notion, and CRMs is the way to make sure that meeting insights will be shared and accessible to the whole team.
Key Features:
- Transcription of meetings is automatic.
- AI generated meeting summary.
- Action item extraction
- Archive of conversation that can be searched.
Pros:
- Hands-free note-taking
- Good design: Good search features.
- Effective integration ecosystem.
Cons:
- Privacy concerns possible
- Seldom mistakes in transcription.
- Requires meeting access
Pricing:
- Basic transcription (800 minutes/month) Some free.
- Pro: 10/user/month (a limitless amount of transcription)
- Business: 19USD/user/month (advanced)
- Enterprise: Custom quotations on offer.
Website: fireflies.ai
14. Monterey AI
Monterey AI takes the feedback of customers on various platforms such as support tickets, customer reviews, social media, and surveys and analyses them to derive practical insights about the products using AI. The platform will automatically divide their feedback into themes, monitor the sentiment trends in the long run, and discover new issues before they turn into huge problems. Product managers can gain some insight quickly into what they are requesting the most, bugs that are creating the most frustrations, and how the sentiment is evolving following the release. The flexibility of the feedback processing and capacity of Monterey AI to understand feedback in various languages and through various channels makes it highly valuable when it comes to the management of products that are global.
Key Features:
- This is multi-source feedback aggregation.
- AI theme extraction
- Sentiment trend analysis
- Cross channel analytics dashboard.
Pros:
- Extensive feedback coverage.
- Automatic categorization accuracy.
- Actionable information development.
Cons:
- Enterprise pricing focus
- Setup requires integration
- Complexity for small teams
Pricing:
- Promotion: Introductory rate on the basis of feedback numbers.
- Business: Dynamic professional pricing.
- Enterprise: Full feature custom pricing.
- Contact sales of particular quotes.
Website: monterey.ai
15. Sprig
Sprig is a combination of in-product surveys, session replays, and AI powered analysis, which assists product managers to learn how they are used and how they feel in real-time. The platform can be used by PMs to launch targeted surveys instead of specific actions by users, gather feedback at the key stages of the user journey, and replay sessions to understand precisely how people use features. Sprig AI is used to analyze the answers of the open-ended survey to recognize themes and trend patterns. Such qualitative and quantitative data will assist product managers to make sound decisions with a lot of certainty on what to build, fix or optimize based on average user requirements.
Key Features:
- Provision of in-product surveys.
- Response analysis using AI.
- Session replay integration
- User behavior tracking
Pros:
- The gathering of contextual feedback.
- Visual session analysis
- Quick survey deployment
Cons:
- Pricing for mid-market
- It took a lot of implementation effort.
- Survey fatigue possible
Pricing:
- Free: Survey and features: Limited.
- Base servers: At 175/month starting.
- Pro: Higher volume prices are custom.
- Enterprise: Pricing with high functionality.
Website: sprig.com
How to Choose the Best AI Tool for Product Managers
- Find Your most critical points of pain: Buy the tools that help you solve the most time, or problematic tasks such as user research analysis or roadmap planning or stakeholder communication.
- Take into account Team Size and Budget: Pricing models are different, some AI Tools designed to assist Product Managers can grow in scale depending on the number of users or usage which may place a substantial use on the economic outlay of an expanding group.
- Check integration features: Make sure that the tool integrates within your current technology stack with the help of Jira, Slack, analytics tools, and design tools that would not create any data silos.
- Test the Learning Curve: Select tools that have easily usable interfaces and well-documented programs since complex solutions do not always get adopted regardless of their capabilities.
- Test Before You Buy: Use free trial to ensure that the tool is indeed fixing your problems and compatible with your workflow and only then buy.
- Consider Data Protection: Study policy on privacy and security awareness ideally when working with sensitive customer data or product sensitive information that belongs to the company.
Best Practices for Using AI as a Product Manager
- Check AI-Produced Orders: Before making crucial product-related decisions, verify AI suggestions with your professional expertise, as well as other sources of information.
- Keep Human Judgment: Apply AI as a great assistant but keep the ultimate decision-making power, in particular with the strategic decisions related to product direction.
- Be Open with Stakeholders: Be categorical when representing AI-generated analysis or recommendations to show what it was generated by AI instruments and what was generated by your interpretation.
- Keep Prompts Constantly: Spend time understanding the best way to form effective prompts and questions in order to achieve the best and useful results on the use of AI tools.
- Protect Customer Privacy: Be sure that all AI-based tools working with user data are in accordance with GDPR, CCPA, and other privacy laws in the markets of your country.
- Combine several tools: Use various AI Tools to Product Managers to solve particular tasks instead of attempting to use a single tool to deal with all issues poorly.
Conclusion
The future of AI Tools in Product Manager 2026 presents more possibilities of helping to work smarter, faster, and more often than ever before. These fifteen tools are the best examples of the AI-driven product management, starting with the analytics tools forecasting user behavior and finishing with the writing assistant capable of writing a documentation in just a few seconds. The trick is not trying all of them out but choosing the solutions that suit your challenges in a particular way and just be easy to fit in your work process.
A product manager who has adopted these AI technologies but still uses human judgment and creativity will guide his or her team to develop a more superior product that will appeal to users the most as AI keeps evolving. Begin with trying out a couple of tools that will fit your largest pain points, and add more and more to your AI tool arsenal as you learn which ones best fit your unique product management style, and organizational requirements.
FAQs
Does AI eliminate product managers?
No, AI tools enhance the ability of product managers but do not substitute it. AI performs data analysis and automation whereas PMs think, be creative, and make decisions based on human judgment that machines are not capable of doing.
What are the average prices of Product Manager AI Tools?
Prices range between free plans and enterprise plans that range in the hundreds per month. All the tools are priced on a tiered basis starting in the range of 20-50/month per individual only with an additional scaling based on teams and functionalities.
Is an AI product management tool useful to small startups?
Absolutely. Numerous tools have a high free tier or low-starter plans which are ideal for startups. Even simple AI tools are able to contribute to the productivity of lean product teams with multitasks to a considerable degree.
When is it possible to realize ROI on AI tools?
Overwhelmingly most product managers save time in the first month of implementation. Full ROI is normally realized in 6-3 months with teams becoming fluent using the tools and incorporating them into working processes.
Am I required to have technical skills in the use of AI product management tools?
The majority of contemporary AI solutions are developed to be used by non-technical people and include user-intuitive interfaces and no-code solutions. Nevertheless, certain analytics platforms might be associated with the necessity to be data literate to make the most of them.
