Product Management News, Latest Updates and Recent Announcements on Techminis

A naukri.com initiative

New

Home

Product Management News

Medium

294

Image Credit: Medium

APIs for Product Managers — The Basics You Need to Know

APIs are like a store that provides ready-to-use tools for developers, saving time and effort in development.
APIs work by one system making a request, the API performing the necessary work, and then returning the data.
An example is a delivery app sending a request to a restaurant's system via an API to confirm food availability.
APIs can experience failures, emphasizing the importance of backups and reliability for connected systems.

Read Full Article

17 Likes

Medium

327

Image Credit: Medium

3 Questions of a Software Product Pipeline

The software product pipeline consists of three stages: WANTs, SHOULDs, and CANs.
In the WANTs stage, ideas and feature requests are collected from various sources.
In the SHOULDs stage, ideas are prioritized based on company goals and feasibility.
In the CANs stage, engineering and design teams determine the technical feasibility of the selected ideas.

Read Full Article

19 Likes

Medium

173

Image Credit: Medium

Food for Agile Thought #486: Reshaping Teamwork, Product’s Code-first Future, Unintentional…

Zvi Mowshowitz critiques Sam Altman's stance on AGI.
Aakash Gupta and Tal Raviv demonstrate building an AI-powered product management copilot.
McKinsey explores organizational restructuring for unlocking gen AI value.
Matheus Lima argues that actual psychological safety thrives on respectful conflict.

Read Full Article

10 Likes

Medium

101

Image Credit: Medium

AI is Moving Fast, and Innovation Doesn’t Happen in 30-Minute Calendar Blocks

AI is moving faster than your org chart.
Most companies are still debating how to structure and strategize for AI, instead of creating the space to actually use it.
Product and engineering teams need time for discovery and experimentation with new AI capabilities.
AI strategy starts by creating the space and time for team members to think and innovate.

Read Full Article

5 Likes

Medium

336

Image Credit: Medium

Can AI Therapy make mental healthcare more accessible?

Therapy is currently inaccessible to many due to long waiting lists, high costs, and limited availability.
Generative AI, such as ChatGPT, is being used in mental health care to provide support to those who can't access traditional therapy.
AI systems can offer guidance and support to individuals, such as Wysa's mental health AI, Woebot for cognitive behavioral therapy, and Reflectly for journaling.
AI tools can assist human therapists by automating tasks, providing insights, and specializing in conditions, while therapists offer empathy and human connection.

Read Full Article

20 Likes

Medium

292

Image Credit: Medium

A year into product management — At least AI is not yet taking over

Stepping into the role of product management comes with weightier responsibilities and broader impact.
The main duties of a product manager revolve around bridging business goals, user needs, and technical execution.
Prioritization, breaking down bigger projects, defining success metrics, understanding user needs, and staying competitive are key aspects of the role.
While AI is becoming more prevalent, it still has a long way to go in carrying out the nuanced decision-making tasks required in product management, but can serve as a useful assistant for specific tasks.

Read Full Article

17 Likes

Medium

393

Image Credit: Medium

How we revamped the ASOS QA engineering competency

Faced with restructuring and a new product operating model, ASOS revamped their QA engineering competency to align with the changing landscape.
The legacy competency was based on three fundamental quality principles: test first, test automation first, and continuous quality.
The revamp focused on incorporating the Product Operating Model (POM) principles of 'product' and outcomes into the competency framework.
ASOS introduced changes in language, KPI identification, product risk management, and role distinctions to better reflect the POM.
The QA Learning Programme (QALP) was aligned with the competency framework to support skills development and growth.
New role levels were introduced in the QA engineering competency to reflect mastery of skills and ability to apply them effectively.
The revamped competency matrix aims to empower QA engineers for growth, align with the POM, and remain adaptable for the future.
ASOS emphasizes continuous improvement, striving to ensure that the QA engineering competency evolves in parallel with changing needs and standards.

Read Full Article

23 Likes

Medium

Image Credit: Medium

Determining market readiness for software products (english)

Revenue is generated when a product is considered market-ready in the defined target group.
Market readiness is a metric that describes how usable and valuable a product is for the target group.
Requirements represent solutions to problems in business processes the product supports.
Identifying market clusters and understanding supported processes are key in determining market readiness.
Standardization plays a crucial role in achieving high market readiness.
A 7-phase model of product maturity was developed for assessing market readiness.
Investments in market readiness are needed early in the product lifecycle.
Market readiness serves as an early indicator of software performance.
Product lifecycle phases correspond to different levels of market readiness.
Tracking key metrics like Net Promoter Score and quality index is vital for assessing market maturity.

Read Full Article

5 Likes

Medium

Product Management: Problem → Idea → Solution Cycle

Product management is about identifying a problem and developing a solution that effectively addresses it.
The product development journey follows a problem → idea → solution cycle, involving specific methodologies and teamwork.
Product Managers focus on turning ideas into reality by creating well-defined solutions for specific user groups.
Product management simplifies the process of creating products and does not require complex terminology.

Read Full Article

2 Likes

Medium

155

Image Credit: Medium

What does a PM Do?

A product manager (PM) decides what should be built, why, and when.
The PM works with engineers to build the right features, but does not write code.
The PM sets priorities, analyzes data, and communicates with various stakeholders.
The PM iterates and tries to improve if the built product is not successful or discovers something new.

Read Full Article

9 Likes

Medium

Image Credit: Medium

Blending Agile and Traditional Approaches for Success

A financial services company successfully modernized its core banking system by blending Agile development sprints with Waterfall compliance phases.
Creating 'dual-purpose' artifacts and using the right tools ensured smooth execution in the hybrid approach.
Aligning teams and using KPIs and dashboards helped track progress in the Agile-Waterfall model.
Other industries, such as construction and marketing, have also found success by combining Agile and Waterfall methodologies in different areas of their projects.

Read Full Article

2 Likes

Medium

Image Credit: Medium

How to Launch a New Product: A Strategic Approach

Validate the market opportunity before launching a new product by assessing market demand and profitability.
Craft a structured go-to-market (GTM) strategy by analyzing competitors, defining product vision and goals, planning operational and technical execution, determining optimal pricing strategy, and selecting appropriate sales channels.
Adapt the GTM strategy based on market conditions, such as saturated markets, emerging markets, and oligopoly-dominated markets.
Launching a product is just the beginning, and the focus should be on adapting post-launch based on customer feedback, retention metrics, and competitive shifts.

Read Full Article

Medium

110

Image Credit: Medium

MLOps Explained for Non-Engineers: What Every AI PM Should Know

MLOps is the discipline of managing the lifecycle of machine learning models in production — from development to deployment, monitoring, and continuous improvement.
MLOps ensures that AI solutions are scalable, reliable, and maintainable, delivering consistent value in the real world.
Key components of MLOps include automated model monitoring and retraining, data validation pipelines, continuous integration and deployment (CI/CD) pipelines, model explainability and governance, and defined workflows and collaboration tools.
As an AI Product Manager, understanding the impact of MLOps is crucial for making strategic decisions and integrating them into AI strategy to ensure AI projects go beyond experimentation and deliver real business value.

Read Full Article

6 Likes

Scrum-Master-Toolbox

Marina Lazovic: Leadership Skills Make the Difference for Product Owners

Great Product Owners demonstrate strong empathy and lead by example, creating a supportive environment for the team.
Some Product Owners fail to understand their team's composition, leading to unrealistic expectations.
Regular sync meetings can help Product Owners understand team dynamics and foster more realistic planning and expectations.
Marina Lazovic is a Scrum Master and Kanban Trainer passionate about optimizing processes and fostering collaboration.

Read Full Article

3 Likes

Medium

345

Image Credit: Medium

Why .99 Still Wins Even with AI Setting the Prices

.99 pricing has been popular for over a century due to its psychological impact on consumer behavior.
The practice originated as a way to prevent theft in the early 1900s.
Odd prices like $39 can feel more calculated and offer a perception of greater value than round numbers.
Algorithms and AI models have learned from consumer behavior and still utilize .99 pricing for better conversion rates.

Read Full Article

20 Likes

For uninterrupted reading, download the app

Product Management News

Discover more