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The Rise and Fall of Enterprise AI: How to Get Value Out of It Again

The rise and fall of enterprise AI is not due to broken technology, but rather strategic misalignment, poorly framed problems, and a lack of rigor in execution.
The success of AI projects depends on solving what truly matters for someone at the right time, with a structured process and a focus on using data to drive decisions.
AI governance is essential, as AI is already shaping various sectors such as hiring, healthcare, finance, and education. However, most companies lack a framework for AI governance and only a small percentage of universities teach it.
Consumer AI and enterprise AI differ significantly, and the virality of tools like ChatGPT has created misleading expectations in business environments. The future of AI is not just technological, but also relies on human involvement and augmentation rather than automation.

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Breaking Into Product Management: A Guide for Career Changers

Product management requires constant demonstration of value to the development team and deep understanding of product direction and positioning.
Key responsibilities of a product manager include defining product objectives, creating roadmaps, gathering requirements, and serving as a liaison between various departments.
Writing detailed requirements and managing communication among teams are crucial aspects of the product manager's role.
To enter the field of product management, gaining experience in related roles like project management or customer success management can be beneficial.
Internships provide valuable learning opportunities and a flexible path to becoming a product manager.
Engaging in competitions, building side projects, and networking with industry professionals can further enhance your product management skills and opportunities.
Documenting your work, sharing insights, and actively participating in product management communities are recommended strategies for career growth in this field.
Product management is about problem-solving, collaboration, and value addition, skills that can be honed through practice and networking.
Each individual's journey into product management is unique, and perseverance is key to success in this dynamic and challenging role.
By taking proactive steps to learn, network, and demonstrate your abilities, you can pave the way for a fulfilling career in product management.

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Medium

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Technical Product Manager: How Much Tech Do You Really Need?

A Technical Product Manager (TPM) bridges the gap between technical and non-technical teams, combining product management with tech understanding.
TPMs evaluate technical feasibility, define requirements, facilitate communication, support development, and stay updated on tech trends.
While not required to code, TPMs need a broad understanding of technical concepts and know about development processes and technical depth.
Knowing enough to ask the right questions and leveraging technical skills for credibility are essential for TPMs.
A TPM should focus on context over code, understanding their core role as the voice of customers.
Having technical knowledge as a TPM aids in decision-making and problem-solving, as exemplified by real-world scenarios.
While not needing to be the most technical person, having enough knowledge to assist or contribute to development is beneficial for a TPM.
Continuous learning through coding classes, technical courses, and hands-on experience is vital for a TPM to improve their technical proficiency.
A TPM's goal is to enable communication between technical and non-technical teams, balancing tech understanding with business needs.
The level of technical expertise required for a TPM varies depending on product complexity and organizational expectations.

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AI and the Art of the Product Operating Model Transformation

Generative AI has the potential to accelerate transformation but can create disruption in business strategy and chaos.
AI in product development increases output velocity and emphasizes organizational gaps in processes.
Leaders must leverage AI while maintaining their center through the product operating model.
A product operating model focuses on continuous value delivery through enduring products over projects.
Established companies often use a project-based model, prioritizing output over outcomes.
Adopting a product operating model is crucial for companies navigating digital transformation.
The first principles of a product operating model provide stability amidst AI disruption.
Prioritizing a strong product foundation is key for success in the AI-accelerated future.
A sound product operating model is essential for balancing AI's impact on operations and maintaining strategic focus.
Excellence in product operations enables true transformation through AI capabilities.

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Medium

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How to Design for Data-Heavy Products

Designing for data-heavy products can be a balancing act, requiring user-friendly interfaces that simplify the experience while delivering essential data.
Progressive disclosure is a strategy that organizes and reveals information in manageable layers, making the interface approachable and efficient.
To design intuitive data-heavy products, consider visual hierarchy and prioritize important information to guide users' attention.
By leveraging these strategies, designers can create actionable and effective data-heavy products that balance complexity and clarity.

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Medium

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Product Management — Startup vs a Large Company

In a startup, product management involves ad-hoc development, wearing multiple hats, and close collaboration with engineers for granular task prioritization.
In a large company, there are defined processes, less micromanagement of sprints, and a focus on high-level priorities but at a slower pace.
Startups offer uncertainty, involvement in various aspects of the business, and opportunities to interact closely with top leadership.
Large companies provide stability, less risk-taking, and a more structured hierarchy with limited exposure to top leadership.
Startups allow for quick feature implementation, immediate impact assessment, and fast iterations based on real user feedback.
Large companies involve longer lead times for changes, extensive approval processes, and focus on incremental improvements impacting a larger user base.
Startups demand intense work hours, frequent pivots, and high-risk experimentation, leading to high rewards and rapid learning opportunities.
Large companies offer more predictable work hours, stable routines, and a focus on refining existing strategies rather than radical changes.
In startups, failure is expected, learning through trial and error is encouraged, and scope of work is broader, encompassing various business aspects.
In large companies, risk is minimized, failure is less tolerated, work is more defined and focused, with an emphasis on refining established processes.

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Medium

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Product Analytics 101

Product Analytics is the process of understanding how users interact with a digital product and using insights to improve user experience, retain customers, and boost revenue.
Generative AI tools like ChatGPT or image generators can benefit from Product Analytics by utilizing user interaction data for continuous improvement.
To start with Product Analytics, it is important to define objectives, identify key performance indicators (KPIs), set up tracking tools, map user journeys, segment users, analyze trends over time, and implement insights based on findings.
Product Analytics provides valuable insights into user behavior, enabling data-driven decisions that improve experiences and drive growth.

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Medium

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MECE for product managers: the forgotten principle that helps AI to understand your context

MECE (Mutually Exclusive, Collectively Exhaustive) is a powerful principle underutilized in product management, offering clarity and precision in communication.
Barbara Minto developed the MECE principle to improve communication, focusing on finding out what one truly thinks.
MECE comprises mutually exclusive categories that don't overlap and collectively exhaustive categories that cover the entire scope.
Issues in product documentation include overlapping concepts, hidden gaps, and inconsistent abstraction levels.
Applying MECE principles can transform product documentation by eliminating overlap, confusion, and gaps in requirements.
MECE principles help in organizing data into clear categories, reducing implementation time, lowering defect rates, and improving team alignment.
Implementing MECE principles can lead to significant benefits such as reduced development cycle time and improved team understanding.
MECE not only improves documentation but also serves as a thinking tool, clarifying product managers' understanding before communication.
Creating MECE templates and practicing MECE thinking can help in consistently applying the principle in product management.
Structured documentation systems like Paelladoc enforce MECE principles to ensure clarity, completeness, and consistency in product development.

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Medium

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Can AI Be Ethical? Lessons from Spotify, IBM, and More

Spotify initially favored popular genres with its AI algorithms, creating inequality for niche artists and limited user choice.
IBM faced criticism for its face recognition algorithms performing worse for people with darker skin color.
Google's Perspective API incorrectly classified texts in African American dialect as toxic, leading to criticism.
Accenture implemented measures to minimize bias in recruitment algorithms and strengthen its reputation in ethical AI.

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Medium

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APIs, Explained Like You’re a Product Manager (Not an Engineer)

An API acts as a intermediary between the frontend and backend, just like a waiter taking your order and communicating with the kitchen.
APIs are used for retrieving information or performing actions within a system.
APIs serve as the glue connecting different components of a product.
Understanding APIs leads to better collaboration with developers and less surprises.

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Medium

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Discovery and Delivery — Principles & Best Practices

Discovery and delivery are important aspects for IT professionals looking for better ways of working.
For discovery, a set of principles and techniques is more important than the specific approach to be taken.
Recommended discovery techniques include interviews, prototypes, and iterations for optimal results.
Delivery principles involve considering other roles, such as data analysts, to ensure goal achievement.

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The End of Enterprise Theatre

Organizations worldwide invest billions in digital transformations, with over 70% failing to achieve desired outcomes.
Conventional project management techniques are ineffective for complex adaptive systems like modern organizations.
Continuous change and evolution are essential for organizations to thrive, not periodic 'transformations.'
Traditional metrics often track activity rather than outcomes, leading to false perceptions of success.
Frameworks like SAFe and LESS are treated as solutions instead of tools, resulting in superficial transformations.
Leading with technology in digital transformations often results in digitized versions of old processes.
Project management assumptions of predictability and control often lead to wasted budgets and missed opportunities.
Focusing on adaptability and enabling value streams rather than transformations may be more effective.
Current approaches to enterprise change can lead to organizational cynicism, innovation suppression, and talent loss.
A shift in thinking about organizations and value creation is necessary for real change to occur.

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Medium

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Deconstructing the Product Manager

Product Managers play a crucial role in managing the product lifecycle and taking end-to-end ownership of products.
They work with cross-functional teams like engineering, design, sales, and marketing to develop features that meet customer needs.
PMs engage in customer research to understand their needs, create hypotheses, and develop product strategies.
They prioritize features based on several factors, document them in a Product Requirement Document, and oversee implementation.
After launch, PMs monitor success metrics, gather user feedback, and address any issues to ensure product success.
Empathy, critical thinking, influence, market understanding, data analysis, and collaboration are key skills for PMs.
Formal training is not mandatory to become a Product Manager; core skills like empathy and critical thinking are essential.
PMs need to understand market dynamics, predict trends, and effectively communicate with stakeholders to drive product success.
Successful PMs blend analytical and emotional intelligence, lead from the front, and focus on skills rather than past experience.
Product Management is a mindset that requires a balance of business acumen, creativity, and strategic thinking.

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Medium

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User Generated Content (UGC) — How to scale for Omnichannel retail

UGC is not just about star ratings and text reviews.
The modern retail landscape includes multiple content formats.
A robust UGC strategy ensures that each content type enhances different stages of the customer journey.
Scaling UGC in an omni-channel environment requires a strategic blend of AI-driven moderation, seamless integration across platforms, and a data-driven approach to measuring impact.

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Logrocket

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The future of user experiences: Products without UI

Product teams often prioritize UI polish over efficient user experiences, leading to feature bloat, UI-dependent friction, and missed innovation.
Successful products like Uber and Nest prioritize automation and convenience, showing that great UX is more about functionality than a beautiful UI.
Companies are embracing API-first, automation-driven products like Stripe and Zapier to reduce user interfaces and streamline interactions.
Voice-driven interfaces such as Alexa and Google Assistant exemplify a shift towards seamless, natural interactions without traditional UI elements.
Building products that provide value without a visible UI is crucial for future product management success.
Transitioning to UI-free design involves leveraging AI, prioritizing API development, and reducing cognitive load for users.
Challenges with UI-free products include user trust, adoption resistance, and accessibility concerns that need to be addressed for inclusivity.
Rethinking UX involves focusing on seamless experiences, automation, effortless utility, predictive design, and true functionality over visual appeal.
The future of product innovation lies in integrating seamlessly into users' lives, providing value effortlessly, invisibly, and intelligently.

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