Navigating the Frontier: Generative AI's Promise and Peril for Product and Product Marketing Managers

Product managers and product marketing managers have historically taken advantage of the opportunities of AI, but generative AI presents unknowns and risks that must be managed carefully to stay competitive.

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Navigating the Frontier: Generative AI's Promise and Peril for Product and Product Marketing Managers

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Generative AI's Promise and Peril for Product and Product Marketing Managers

Executive Summary

AI's impact on product development and marketing is transformative, with generative AI aiding in content creation and machine learning providing data insights and automation. With business drivers enhancing revenue, trimming costs, spurring innovation, and diminishing risks, product marketing managers (PMMs) have an excellent opportunity to steer their organizations into a new era.

AI Introduction

Product Development With AI, From Basic Logic Tasks to Modern Integrated and Specialized Applications

AI enhancements in PMM software have become paramount in today's tech-driven landscape. They offer unparalleled benefits such as expedited decision making, invaluable predictive analytics, deeper customer and competitive insights, and finely tuned product roadmaps with optimized pricing.

However, the journey has hurdles. Challenges include ensuring high-quality data and navigating integration complexities. Furthermore, there are pressing issues, notably ethical concerns and the potential for over-reliance on automation. For ambitious professionals like you, integrating AI means staying ahead of the competition and making more data-driven decisions that maximize product success and minimize risks.

The integration of AI across software markets has become increasingly prominent, with specific sectors taking the lead in its early adoption. It's worth noting that the distinction between AI-enhanced and traditional data analytics can sometimes be blurry. However, the trend is clear: More and more software vendors are incorporating AI capabilities into their products and using AI for predictive insights, automation, and enhanced user experiences.

Generative AI and Machine Learning AI Have Significant Impacts on Product Marketing Managers, Albeit in Diverse Ways

Generative AI: This type of AI is particularly effective at creating content such as text, images, videos, or audio. For PMMs, generative AI can be used for:

  • Content Creation: PMMs can use generative AI to quickly create blog posts, product descriptions, ad copy, social media posts, or user testimonials. This can significantly reduce the time and effort spent on content creation, allowing PMMs to focus on strategy and execution.
  • Personalization: Generative AI can personalize marketing messages, tailor content to specific audience segments, and create more engaging and relevant campaigns.
  • A/B Testing: Generative AI can help PMMs quickly create multiple variations of marketing messages for A/B testing to identify the most effective messages.

Machine Learning: This type of AI focuses on learning from data to make predictions or decisions. PMMs can use machine learning for:

  • Data Analysis: Machine learning can analyze vast customer data to identify trends, preferences, and patterns. PMMs can then use this information to understand their target audience better and tailor their marketing strategies.
  • Customer Segmentation: Machine learning can help PMMs segment their audience more effectively, grouping customers based on behavior, interests, and demographics.
  • Marketing Automation: Machine learning can automate marketing tasks such as lead scoring, customer journey mapping, or even email campaign management, allowing PMMs to focus on more strategic initiatives.
  • Predictive Analytics: Machine learning can predict future trends or customer behavior, enabling PMMs to anticipate market changes and adapt their strategies accordingly.

“Machine learning has enabled the development of AI systems that can perform certain human activities often better than humans.”

Source: World Economic Forum

What makes AI different - traditional program inputs data and program into a computer, which outputs output. Machine learning inputs data and output and outputs program.

Source: The Era of Generative AI C-Suite Presentation, Info-Tech Research Group

In summary, generative AI helps PMMs create more relevant and engaging content, while machine learning provides better insights and automation capabilities. By combining the strengths of both types of AI, PMMs can create more effective and efficient marketing strategies tailored to their specific audience needs.

B2B Software PMMs Have Been Early Adopters of AI

Historically, business-to-business (B2B) product managers in the software industry have been early adopters of AI, using it to refine their offerings and streamline operations. By leveraging AI-driven analytics tools, they've been able to make data-informed decisions. Machine learning has been instrumental in analyzing vast data sets for product feedback, with companies such as Zendesk integrating it for enhanced customer support solutions. A study by Accenture found that B2B companies using AI in product management improved their customer engagement rates by up to 40%.

PMMs have used AI to enhance user experience, anticipate market shifts, automate routine tasks, and drive more personalized B2B engagements. Artificial intelligence offers an array of tools and methods that can be harnessed by product managers to refine the product planning, building, and launching process.

AI's use cases over history provide a snapshot of its evolution from basic logic tasks to the modern world's deeply integrated and specialized applications. As AI advances, its applications are expected to penetrate various sectors further, bringing unparalleled benefits and novel challenges.

Top AI Use Cases in PMM

Market Research and Competitive Analysis: Machine learning models can analyze vast amounts of data from various sources to identify market trends, competitive landscapes, price optimization, and emerging customer needs.

Product Roadmap Prioritization: Predictive analytics can forecast which features or improvements will significantly impact user satisfaction and business metrics.

Price Optimization: AI aids product managers in price optimization by analyzing vast data, predicting demand, enabling dynamic pricing, and assessing competitor prices to ensure profitable and competitive pricing strategies.

Customer Feedback and Sentiment Analysis:Natural language processing (NLP) can analyze customer feedback from reviews, forums, and social media to derive insights about product strengths and weaknesses.

Customer Support Analysis: PMMs can use AI to analyze support tickets and chatbot interactions to identify common product issues or areas of confusion.

Predictive Analytics: AI can analyze data from past product developments to identify patterns, trends, and potential risks, enabling teams to make more informed decisions about their roadmaps.

Resource Allocation: Machine learning algorithms can optimize the allocation of resources for each task in the product roadmap based on their historical data, complexity, and importance. This can help maintain the balance of resources, reduce costs, and avoid bottlenecks.

Prioritization: AI can help product managers prioritize features based on expected impact, difficulty, cost, or strategic importance. This can be done through an AI model trained on historical data and user feedback.

Timeline Predictions: AI can predict project timelines based on historical performance, team availability, complexity of tasks, and other factors. This can provide more accurate delivery dates for tasks or the overall project.

Risk Identification: AI can help identify potential risks in the product roadmap, such as bottlenecks, resource shortages, or potential delays. This can help the team address these issues before they become significant problems.

Personalized Dashboards: AI can generate personalized dashboards for different stakeholders, showing them the most relevant information. For instance, a developer might see detailed task information, while an executive might see high-level progress metrics.

Automated Updates: AI can automate routine updates in the product roadmap, saving time for the product team and ensuring that all stakeholders have the latest information.

Sentiment Analysis: AI can analyze user feedback and determine user sentiment toward certain features or products. This can help inform decisions about which features to prioritize or how to improve existing features.

User Experience and Interface Design: AI can analyze user interactions with prototypes or existing products to recommend design optimizations.

In conclusion, using AI in PMM software management can streamline the process, increase efficiency, reduce costs, and lead to more successful product design, development, launch, and management. However, it's important to note that AI should assist the product team, not replace human judgment and experience.

AI Makes PMMs More Agile and Effective to Drive Innovation and Customer Satisfaction

The growing infusion of AI in product management enables product managers to be more agile and effective, driving significant enhancements in product innovation and user satisfaction. The benefits and opportunities AI offer to product managers include the following:

  • Predictive Analysis: Forecast user behavior or sales trends based on historical data. A study by McKinsey showed that “companies leveraging predictive analytics had a 126% profit improvement over those that did not.”
  • Optimized Product Roadmaps: AI algorithms analyze market trends to suggest product updates. “70% of leading tech companies are integrating AI for product roadmap optimization” (TechCrunch).
  • Efficient Resource Allocation: Use AI to allocate team resources based on project needs and skill sets. “AI-optimized projects experience a 30% reduction in resource waste” (Harvard Business Review).
  • Faster Decision Making: Using AI tools to quickly analyze vast amounts of data for market fit. “AI-driven organizations make critical decisions 6x faster than competitors” (Deloitte).

Addressing legal and ethical challenges is paramount in the realm of B2B software. Here are key challenges and considerations for PMMs, along with strategies to address them:




Data Privacy and Security

Handling and storing client data securely is critical, especially in cloud-based solutions.

Ensure compliance with data protection regulations like GDPR or CCPA. Implement robust encryption and cybersecurity measures. Educate clients and staff about security best practices.

Intellectual Property

Protect proprietary software features, algorithms, or designs from imitation or theft.

Use patents, trademarks, copyrights, and trade secrets effectively. Also, draft strong licensing agreements and non-disclosure agreements for partnerships.

Compliance With Local Laws

Different jurisdictions have various legal requirements for the software.

Engage legal experts familiar with local regulations in target markets. Regularly review and adapt the software to meet these requirements.


Being forthright about software capabilities that might have inherent biases, especially if AI or machine learning elements are involved.

Offer clear documentation and reporting about how algorithms function and how decisions are made. Foster a culture of openness.

Fairness in Pricing

Ensuring that pricing structures are consistent and do not exploit specific clients.

Create transparent pricing models. Regularly review and benchmark pricing against market standards.

Ethical Use

Ensuring the software isn't used for malicious purposes or in a way that harms individuals or groups.

Clearly define acceptable use policies. Build features that prevent misuse (e.g., audit trails, usage flags).


The United States Patent and Trademark Office (USPTO) and European Patent Office (EPO) have not granted patent rights to inventions discovered solely by AI without human involvement.

If an AI is used to generate a brand name, if the name meets the requirements for trademark registration, it could be trademarked just like any other name.


Patents protect inventions and offer inventors exclusive rights to their innovations for a limited time, typically 20 years from the filing date. In return, the inventor must disclose details of the invention to the public. However, it needs to be clarified whether AI-generated inventions can be patented.

AI can be involved in two main ways: AI itself, or methods involving AI, is the subject of a patent, or AI assists in the discovery or optimization of patentable inventions. For example, if an AI system invents new software, who can patent that software? The person who created the AI system? The person who programmed the system? The person who owns the data that the system was trained on?

Source: Wisconsin Law Journal

Our analysis shows that while there is a plethora of potential use cases for PMM consideration, B2B software PMMs should give attention to three critical use cases first:

  • Competitive Intelligence (CI) Software: Harness AI to provide real-time insights on market trends and competitor strategies, ensuring ongoing knowledge of what's needed for competitive differentiation and protecting intellectual property (IP) while measuring competitive parity and gaps.
  • Leveraging AI for Product Roadmaps With Product Lifecycle Management (PLM) Software: Understand what defines product leadership, understand and predict customer needs, refine product features, and align development with what drives product-market fit and competitive differentiation.
  • Dynamic Price Optimization Using AI-Enabled Configure, Price, Quote (CPQ) Software: Automate pricing strategies based on numerous market and internal factors, maximizing revenue potential.

AI Use Case #1 – Competitive Intelligence (CI) Software

Modern CI software solutions harness artificial intelligence to gather, analyze, and interpret data related to competitors. This software offers insights into the competitive landscape, predicting product, pricing, and distribution trends by analyzing competitor actions, including IP infringement, and assisting businesses in making informed strategic decisions based on real-time and forecasted data.

Standard Features: Competitive monitoring, competitor profiling, market share analysis, price intelligence, competitive benchmarking, data aggregation and integration into other tools, customizable dashboards, real-time alerts, collaboration and sharing capabilities.

Source: Build Competitive Intelligence to Improve Sales Wins, SoftwareReviews

Challenges of Infringement in IP: Infringement of copyright, trademarks, and product naming is a growing concern in the digital era, putting businesses and creative minds at risk. This infringement dilutes brand value and can result in substantial financial losses. Companies often wade through the murky waters of online content, trying to determine unauthorized use or misrepresentation of their intellectual property. Furthermore, the international nature of the internet complicates jurisdictional issues, making legal recourse challenging. The rapid pace of digital innovation also means businesses must be vigilant, ensuring their products, content, and trademarks are only being used or replicated with proper authorization.

Case Study – LegalZoom: Filing Trademark Violations

Source: Kompyte

LegalZoom and Kompyte CI Software: Considering these challenges, LegalZoom, a leader in online legal solutions, has found value in Kompyte's CI software. The platform offers automated, real-time surveillance of the digital landscape, enabling LegalZoom to monitor any unauthorized use or imitation of its services, product names, and trademarks. By leveraging Kompyte's capabilities, LegalZoom can proactively identify potential infringements, gaining a head start in addressing them. This safeguards LegalZoom’s intellectual property rights and ensures that imitation services do not mislead customers. Using Kompyte, LegalZoom affirms its commitment to innovation, ensuring its offerings remain genuine, authentic, and protected in a fast-evolving digital marketplace.

Other Vendors Reviewed: Crayon, Klue, and Contify

Lessons Learned

Organizations have encountered several lessons when using CI software to set up real-time alerts for new market entrants and, in this case, for trademark violations. These insights help refine the process and ensure optimal use of the CI tool:

  • Avoiding Alert Fatigue: Setting up too many alerts or overly broad criteria can flood users with notifications, leading to alert fatigue. Over time, this might cause users to ignore or overlook critical alerts. It's essential to fine-tune the parameters and ensure specific and actionable alerts.
  • Cross-Checking Alerts: While real-time alerts can be immensely valuable, it's essential to cross-check the information. Automated alerts sometimes result in false positives, especially in densely populated markets.
  • Regularly Reviewing and Refining Criteria: As the market evolves, the criteria for a new competitor or a potential trademark violation might change. Periodic reviews ensure that the alerts remain relevant.
  • Balancing Proactivity With Patience: While it's essential to be proactive when a new competitor emerges or a potential trademark violation is detected, organizations should also ensure they gather enough information before taking any drastic steps.

Top Impacts on the Business

Detecting IP violations early can lead to significant cost savings and protect brand equity for organizations. By monitoring the impact, businesses can understand the financial implications of trademark violations and the effectiveness of their monitoring and enforcement strategies.

  • Legal Fees: Addressing a trademark violation early can reduce the duration and complexity of potential legal proceedings, leading to lower attorney and court fees.
    • Avoidance of Settlement Costs: In some cases, companies might opt for a financial settlement over prolonged litigation. Detecting violations early can reduce or eliminate such costs.
  • Costs of Enforcement: Monitor the expenses related to enforcing trademark rights, including legal fees, rebranding costs, and settlement fees.
  • Brand Equity Protection: Protecting IP means protecting the brand's reputation and customer trust, which can have long-term financial implications.
  • Rebranding Costs: Early detection can prevent substantial investments in marketing and branding that might otherwise be lost if a company had to rebrand due to an unresolved infringement.
    • Customer Confusion Instances: Track cases where customers confuse the company's brand with another due to trademark infringements. Higher instances can indicate significant brand dilution.

AI Use Case #2 – Product Lifecycle Management (PLM) Software

PLM digital tools and platforms are designed to assist teams in visualizing, planning, and managing products’ strategic development and release over time while leveraging AI to provide enhanced insights, forecasting, prioritization, and recommendations based on data analysis. PLM software assists in managing the lifecycle of the product, part, or component, including conception, design, prototype, build, engineering change, and ultimately, end-of-life.

Standard Features: Collaboration, analytics and reporting, inventory and field parts management, workflow management, manufacturing scheduling, quality management, material requirements planning, design management, product data management, asset management, production and change management.

Source: Product Life Cycle Management Software, SoftwareReviews

Challenges of Managing Multiple Product Roadmaps Through the Lifecycle: Managing multiple product roadmaps through their lifecycles without PLM software presents many challenges. Organizations often need help maintaining data consistency across diverse products, ensuring real-time team collaboration, and avoiding versioning conflicts. The lack of a centralized system can lead to misalignment in strategic goals, difficulty tracking and prioritizing resources efficiently, and increased risk of errors due to manual data entry and updates. Moreover, forecasting challenges arise without a consolidated view of all product roadmaps, making it harder to predict market trends, allocate resources effectively, and ensure timely product deliveries.

Source: Sunset or EOL a Software Product in a Cloud Environment, SoftwareReviews

Case Study – Vision Critical: Incorporating Customer Feedback Beyond Traditional Methods

Source: Aha!

Vision Critical and Aha! Software: Vision Critical’s product management team, overseeing distinct product components, evolved its approach to customer feedback. Beyond traditional methods of analysis, AI-driven analysis tools within Aha! interpret the vast influx of feedback, detect patterns, and predict emerging trends. “Customers can now centrally submit, view, and track the popularity and status of their ideas, with AI-enhanced recommendations promoting engagement and ensuring we capture the most desired features. Voting, commenting capabilities, and real-time sentiment analysis, paired with Slack integration, enable our team to identify and respond to trending requests swiftly. As ideas transition to features, Aha! uses AI to match customer profiles with feature developments, ensuring personalized collaboration. This AI-integrated system has streamlined our idea management and supercharged our customer-centric approach, harnessing machine learning to proactively involve users in feature development and prioritization.”

Other Vendors Reviewed: ProductBoard, Propad, Producthunt, and Airfocus

Lessons Learned

While the foundational structures provided by platforms like Aha! and others are essential, integrating AI can further optimize processes, leading to more customer-centric product development and informed decision making. The primary lessons emphasize the importance of centralized and transparent feedback systems, quick response mechanisms, effective backlog management, and precise team alignment. Based on the information provided by the case study, here are the top lessons learned:

  • Centralized Customer Feedback Is Crucial: Before implementing PLM software, the company used surveys to gather customer feedback. This often led to duplicated ideas and a lack of visibility into which ideas were popular among the users. A centralized system, such as the one offered by Aha!, allows customers to see what ideas are trending and enables businesses to streamline the feedback process.
  • Transparency and Engagement Enhance Customer Relations: The previous method used by Vision Critical needed more transparency in how decisions were made. Businesses can boost customer engagement and trust by providing an open platform where customers can submit ideas, view the status of their requests, and see which ideas are popular.
  • Quick Response to Customer Feedback Is Essential: Using a Slack integration to notify the product management team about new ideas allowed them to respond quickly to customer feedback. Immediate responses can improve customer satisfaction and show the company values its users' opinions.

Top Impacts on the Business

The top impact is the importance of centralized and transparent feedback systems, quick response mechanisms, effective backlog management, and team alignment.

  • Reduction in Manual Labor: Automated categorization, sentiment analysis, and predictive analytics can decrease the amount of manual work required, leading to labor cost savings.
  • Fewer Post-Launch Corrections: By identifying and rectifying potential product issues during the development phase (with the help of AI insights), companies can save money that might otherwise be spent on post-launch patches, fixes, or damage control.
  • Optimized Marketing Efforts: By better understanding customer sentiments and preferences, businesses can tailor their marketing efforts more effectively, yielding better results with the same or reduced marketing budgets.
  • Reduced Resource Wastage: By working on high-impact features (as suggested by AI), businesses can allocate resources more efficiently, ensuring that time and money are spent on tasks that offer the best ROI.

AI Use Case #3 – Dynamic Price Optimization Using Configure, Price, Quote (CPQ) Software

CPQ software helps companies automate their data, configure products and pricing, and generate quote or proposal documents. CPQ software can improve accuracy and errors, increase consistency across customers, empower employees, and shorten quote response times. CPQ solutions consider customizations, discounts, and add-ons to quote accurate prices quickly.

This use case uses AI techniques to dynamically adjust prices based on various internal and external factors to achieve specific business objectives, such as maximizing revenue, profit, or market share.

  • Dynamic: The prices are not static and can be changed in real time or near real time based on the latest available data.
  • Price Optimization: This is the process of setting prices to meet specific business objectives, which could be to maximize profits, sales, or other metrics.
  • With AI: Advanced machine learning algorithms, data analytics, and other AI tools are employed to predict, analyze, and recommend the optimal pricing strategy.

Standard Features: Product and service configuration, quote development and administration, guided selling, application integration, reporting and analytics, workflow and approvals, catalog management, discounting management, mobile, renewal management, rules engine, visual configuration, multi-currency, omnichannel, aftermarket agreements and asset tracking, contract negotiation and authoring, order and subscription management, self-service selling, CX journey modeling, agnostic to revenue, cost of serve.

Source: Configure, Price, Quote (CPQ) Software, SoftwareReviews

Challenges of Managing Dynamic Price Movement to “Optimize” Against Competitors and Market Changes: Product managers today are navigating a complex landscape of challenges. With rising supplier costs and inflation, they're witnessing shrinking margins, which strain customer budgets. Compounding this, upper management has more pressure to rely on on-the-fly decisions due to time constraints, skill shortages, and process inadequacies. Concurrently, these managers grapple with balancing the divergent pricing priorities of the product, sales, and finance departments. Often, efforts to raise product prices falter, primarily because the preliminary research lacks a thorough understanding of the competition, buyer hurdles, inherent value, and critical price points. Such missteps in pricing aren't just met with potential customer backlash but also have long-term repercussions, with the true impact of these decisions often not manifesting until over a year later.

Source: Optimize Software Pricing in a Volatile Competitive Market, SoftwareReviews

Case Study – Dell Technologies: Improving Price Inefficiencies to Boost Profitability

Source: Vendavo

Dell Technologies and Vendavo Software: Dell Technologies, a $60-billion tech giant, partnered with Vendavo to overhaul its pricing strategy, targeting inefficiencies and enhancing profitability. Moving away from introductory gross margin pricing, Dell aimed to optimize prices for better win rates. Gaining C-suite buy-in was crucial to ensure widespread adoption across their extensive product range. Vendavo's solutions enabled swift and efficient quoting, with 90% of quotes dispatched within four hours. Moreover, 75% of these quotes, as noted by Executive Director of Global Pricing Transformation Arunkumar Narayanan, now autonomously use AI-driven pricing recommendations, eliminating manual approval and boosting operational efficiency.

Other Vendors Reviewed: Prisync, Pricefix, and Pros

Lessons Learned

The benefits of dynamic price optimization with AI include responding quickly to market changes, personalizing prices for segments or individual customers, and ensuring that businesses remain competitive and profitable in rapidly changing environments. This strategy is commonly observed in e-commerce, airlines, hospitality, and ride-sharing services.

Implementing dynamic price optimization with AI can lead to significant cost savings and increased business revenues. To ensure the successful implementation and measure its efficacy, businesses should consider direct metrics (related to pricing and revenue) and indirect metrics (related to broader business objectives and customer behaviors). From the Dell Technologies and Vendavo case study, several key lessons can be distilled:

  • Reduced Manual Intervention: Automating pricing decisions with AI means less time is spent by staff in making these decisions, leading to labor cost savings.
  • Evolve Traditional Processes: Dell moved beyond rudimentary gross margin pricing calculations. This shift illustrates the importance of revisiting and evolving outdated processes in changing market dynamics.
  • Speed and Efficiency Matter: With Vendavo's solution, a significant portion of Dell's quotes were dispatched promptly, showcasing the value of efficiency in customer interactions and potential sales conversions.
  • Leverage Technology for Autonomy: Using AI-driven pricing recommendations, most quotes didn't require manual oversight. It's a testament to the potential of technology in removing bottlenecks and improving decision making.
  • Improved Marketing and Promotion Efficiency: By better understanding price elasticity and customer behaviors, businesses can design more effective promotions and marketing campaigns, reducing marketing waste.

Top Impacts on the Business

  • Technology Integration: Adopting CPQ tools and solutions would have involved setup, training, and maintenance costs.
  • Change Management: Ensuring the organization, especially at the C-suite level, is aligned and informed about new processes can also have associated costs related to time, training, and potential disruptions.
  • Quote Turnaround Time: The case study highlighted that 90% of the quotes went out within four hours, indicating a focus on response speed as a critical performance metric.
  • Autonomous Pricing Decisions: Another metric given was that 75% of quotes didn’t require pricing approval, showing an increased reliance on AI-driven recommendations and reduced manual interventions.
  • Regular Reporting: The introduction of a new reporting engine indicates that various data-driven metrics were used to measure and report on the progress and success of the new pricing strategies.
  • Although the specific metrics from this CPQ engine aren't listed in the provided case study, they likely encompass sales conversions, profitability, and operational efficiency factors.


PMMs have various tools and strategies for different aspects of product management.

  • Regarding market research and competitive analysis, some tools can provide insights into competitors' online activities, including keyword and backlink strategies. Conducting a SWOT (strengths, weaknesses, opportunities, and threats) analysis can help PMMs understand the competitive landscape and identify opportunities based on their product's strengths and weaknesses.
  • For product roadmap prioritization, various software solutions are available to assist in organizing and prioritizing features. Using a scoring model, PMMs can evaluate features based on potential impact and feasibility.
  • In price optimization, AI-powered pricing software solutions offer dynamic pricing, price optimization, and competitor price analysis. A value-based pricing strategy can help align pricing with the product's perceived value to customers.
  • Customer feedback and sentiment analysis are crucial in understanding product strengths and weaknesses. Some tools can monitor brand mentions and collect customer feedback. Implementing a voice of the customer (VoC) program can help systematically gather, analyze, and act upon customer feedback.

To fully understand the impact of AI in these areas on business and cost savings, companies should conduct a before-and-after analysis, comparing the efficiency, cost structure, and business outcomes before and after successfully integrating AI. This will provide a clearer picture of the tangible benefits that AI brings.

In all these use cases, the essential thing for PMMs is to understand their target audience's specific needs and their product's unique value proposition. This will enable them to make data-driven decisions and strategies that resonate with their customers and stand out in the competitive landscape.


The role of generative AI has dramatically evolved over the past five years. Today, AI can generate predictive insights from vast data, facilitating market research, competitive analysis, and price optimization. Additionally, AI has revolutionized customer feedback analysis through natural language processing (NLP) capabilities, which can extract sentiment and insights from unstructured text. AI-driven automation also significantly reduces the time and effort required for data collection, analysis, and reporting. All these advancements have led to more data-driven decision making and a deeper understanding of the market landscape, enabling PMMs to better align their product offerings with the needs and preferences of their target audience.

The transformative potential of big data and analytics is evident in B2B software, particularly for PMMs. However, many B2B software businesses have yet to fully embrace an insight-driven approach, often due to deep-rooted cultural and behavioral challenges. Yet, with the business drivers for generative AI— namely, enhancing revenue, trimming costs, spurring innovation, and diminishing risks — PMMs have a golden opportunity to steer their organizations into a new era.

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