How to Go from Idea to MVP as a First-Time Founder

There’s this moment every first-time founder hits, usually late at night, with too many tabs open and a half-written Notion doc: What exactly am I supposed to do next?
You’ve got an idea that makes sense in your head. Maybe even a few sketches or a name. But moving from vague concept to something real, like a Minimum Viable Product (MVP), can feel like stepping into fog.

Here’s what that journey looks like when you're building your first MVP from scratch.
/1. Stop obsessing over the product

The biggest trap new founders fall into is building too soon. Everyone I spoke to said they started with the wrong thing: features, branding, even UI mockups, before they even understood the actual problem.

Instead of starting with the product, most experienced founders now start with something much simpler: conversations. Specifically, talking to the people they’re trying to build for. That doesn’t mean launching a survey to your friends. It means talking to real potential users—asking how they currently solve the problem, what frustrates them, and what they’ve already tried.

/2. What’s the smallest version of your idea that’s still useful?

Once you’ve grounded your idea in a real, painful problem, the next step is figuring out the minimum you need to build to test it.

This is your MVP, not a buggy prototype, but the simplest version of your product that delivers value.

In India, one solo founder building a peer-to-peer tutoring app skipped development altogether. Instead, she launched with a landing page, a Google Form for bookings, and WhatsApp to manually match students and tutors. It wasn’t scalable. It wasn’t sleek. But it worked. And within two weeks, she knew exactly what her users cared about and what they didn’t.

/3. Tools don’t matter as much as learning fast

A lot of first-time founders feel stuck because they think they need a developer, or a big budget, or a “technical cofounder.” But what they often need is momentum, and that usually starts with no-code or low-effort tools.

/4. Launch early. Like, earlier than you think.

Here’s the truth: most first MVPs don’t flop because the product is bad; they flop because they never launch.

Founders overthink. They polish. They wait until it’s perfect. But every founder I spoke to who made progress launched early, often within a few weeks of shaping the idea. The launch wasn’t always public. Sometimes it was a quiet rollout to 10 users or even five. The point was to get feedback fast.

/5. Use feedback to decide what not to build next

Most MVPs aren’t magical. They break. They confuse users. That’s the point.

The difference between founders who make it past this phase and those who don’t? The ones who iterate based on feedback instead of defending the product they spent weeks building.

After the first launch, smart founders treat feedback like data; not criticism. You’re not trying to please everyone. You’re trying to find patterns: where users drop off, what features they’re asking for, and what you assumed that turned out to be wrong.

What success looks like at the MVP stage

It’s easy to get distracted by what success looks like on tech Twitter. But none of the founders I spoke to measured MVP success by virality, downloads, or press. Their benchmarks were simpler and way more practical.

For some, it was 10 people using the product more than once. For others, it was someone willing to pay. One founder’s success metric? “When someone I didn’t know messaged me asking if they could invite a friend.”

That’s traction. Not in the flashy, venture-backed sense, but in the real, messy, you’re onto something kind of way.

The original content of the note was published on Techloy.com. To read the full note visit here

The no-marketing-trap: Why even product-led growth companies need to invest in marketing early

Product Lead Growth (PLG) is usually considered the most powerful go-to-market model for SaaS businesses. Driven by the strength of the product and word-of-mouth recommendations, customers come inbound and sign up for your product by either self-serve on the website or after a light sales touch from the “Inbound” team. So far so good. But what do you do once you have a successful PLG model?

From my experience, it is obvious that even with the strongest PLG momentum you need to invest early in building a marketing engine, long before you even think about moving to Outbound sales. Here’s why:

Hitting the adoption curve
To understand why it’s important to build a high-performing marketing function early, it helps to take a look at who adopts your product and when — the so-called Adoption Curve.

The first users will be Innovators. Trying new things is important to them. They are the driving force behind your initial growth. They are risk-takers, inspired to try new things by technology communities, by what they read in blogs and social media, etc. The more momentum you create in this early phase, the better. Following on are the Early Adopters.

The third group is the Early Majority. While it is not quite so critical to them, they still do have an interest in staying at the forefront of innovation but are concerned about the risk of failure. They are typically unwilling to do a lot of research, so relevant information needs to be easily found. The final two groups are Late Majority and Laggards, but they are less relevant for you now.

Each of these groups needs to be spoken to through marketing. However, each audience requires different channels and ways to connect. If you fail to adapt your marketing as you progress through the adoption curve, or you don’t do any marketing in the early phases, your PLG will slow down prematurely.

Think about website offerings
Your website is your shop window and you only have a few seconds to catch a visitor’s interest (estimated to be 10-20 seconds). It’s vital, therefore, to invest the time to craft clear compelling messaging that will capture their attention and there are a few things to consider as you think about how that website will support the next growth stage.

Bringing more traffic is the obvious next step, but making sure you are attracting the right traffic is key.

Content fuel
“Content is the fuel your new marketing engine runs on” says Liz Smyth, VP of Marketing at Slack. “First and foremost capture and tell your best customer stories.”

They are not just the proof points that your prospective new customers need to hear, but also critical in helping the market understand new product offerings, their use cases and the value that can be derived. Also, take the time to create a few key pieces of thought leadership content.

Optimise the engine

Once the engine is running, it will be time to find ways to optimise it, often by looking at conversion rates and finding ways to improve them. Look at your website analytics to understand where your website visitors are engaging or dropping off. At this stage paid lead generation, webinars and content for specific personas and buying stages become increasingly important.

This content, often called ‘full-funnel’, is designed to meet the buyers where they are on the attract-engage-convert journey.

My advice to teams is to start by creating a few content assets for each stage, then build out your library and optimize it over time.

Laying a strong foundation
If you have a strong PLG business, delaying building a strong marketing function or, as we’ve seen in extreme cases, almost leapfrog marketing and jump directly to building an outbound sales motion, is a mistake.

If you don’t invest in a marketing team, you are likely to face significant difficulties building an Outbound motion, and eventually even experience premature slow-downs of your PLG. Instead, companies that use the initial time of strong PLG to build out a proper marketing function experience a longer period of healthy PLG and Inbound growth and are eventually more successful in their efforts to go Outbound.

The original content of the note was published on Eu-startups.com. To read the full note visit here

The Hidden Strategies Behind High-Growth Startups

In the fast-moving world of entrepreneurship, high-growth startups often seem like overnight success stories. A company launches a product, gains traction, raises millions, and becomes a household name — all within a few short years. But behind the scenes, these rapid growth stories are seldom the result of chance. They are driven by deliberate decisions, calculated risks, and strategic foresight. Founders of high-growth startups employ methods and systems that set them apart from the thousands of other businesses that never move beyond survival. Understanding the hidden strategies behind their success can reveal the real engines of scale and innovation in today’s startup ecosystem.

Product-Market Fit Obsession

One of the earliest and most critical strategies employed by successful startups is the obsessive pursuit of product-market fit. High-growth founders understand that no amount of funding or marketing can make up for a product that doesn’t solve a real, painful problem. This understanding leads them to focus on user feedback loops, MVP iterations, and agile development cycles long before large-scale marketing efforts.

Rather than launching a product and waiting to see if it sticks, these startups engage in continuous validation. They listen, adjust, and relaunch—sometimes dozens of times. The goal is to land a version of the product that users can’t imagine living without.

Building a Strategic Knowledge Base

Many high-growth founders prioritize building a deep strategic foundation before scaling their operations. Contrary to the popular image of entrepreneurs “learning on the go,” several of them actively pursue structured knowledge to sharpen their decision-making. This is particularly relevant when dealing with market volatility, investor relations, or pivoting a product in response to customer feedback.

Programs like an accelerated MBA program offer entrepreneurs condensed yet intensive exposure to key business disciplines—strategy, finance, leadership, and operations—without taking them out of the game for extended periods. For many founders, such programs serve as a valuable bridge between raw entrepreneurial drive and the disciplined thinking required to scale sustainably. It’s not just about theory; it’s about equipping oneself with frameworks that help assess risk, read markets more intelligently, and lead teams with clarity.

Leveraging Data as a Compass, Not Just a Mirror

In the modern startup landscape, data is everywhere. However, what separates high-growth startups from the rest is how they use it. Instead of treating data as a report card of what has already happened, they treat it as a compass for where to go next.

These companies set up analytics infrastructure from day one. They don’t just track vanity metrics like downloads or traffic—they measure user engagement, cohort retention, and conversion pathways. This data is integrated into product meetings, marketing strategies, and even customer support. It becomes part of the company’s operational rhythm.

More importantly, they invest in the capability to interpret this data. Rather than drowning in dashboards, high-growth startups assign teams or individuals to surface actionable insights. This leads to quicker course corrections, better user experiences, and smarter decisions around resource allocation.

Operational excellence becomes a competitive advantage. While other startups struggle with dropped handoffs and knowledge silos, these companies deliver consistent value without burning out their teams.

Mental Resilience and Long-Term Vision

Finally, behind every high-growth startup is a founder—or team of founders—with extraordinary mental resilience. Building a company from scratch is emotionally taxing, and scaling it adds new layers of pressure. The best founders cultivate habits and mindsets that keep them grounded through turbulence.

They avoid chasing hype cycles and focus instead on long-term vision. They understand that real growth isn’t just vertical—it’s holistic. They invest in team culture, personal well-being, and continuous learning. This gives them the capacity to make tough decisions without losing sight of the big picture.

All in all, the spectacular rise of high-growth startups is never just luck or timing—it’s strategy executed with discipline. From the strategic use of education and obsessive product-market fit to precision hiring, data-led decision-making, scalable systems, and community cultivation, each layer plays a crucial role in building momentum that lasts.

The original content of the note was published on Worldbusinessoutlook.com. To read the full note visit here

Two-thirds of organisations at significant risk of software outage within next year

As businesses accelerate AI development, two-thirds of organisations face a significant risk of software outages in the coming year due to prioritising speed over quality.

According to a new report by testing and quality engineering firm Tricentis, nearly half (45%) of teams are focused on increasing delivery speed, while only 13% are prioritising software quality.

As a result, 66% of global organisations are at high risk of experiencing a software outage within the next 12 months. Alarmingly, over 60% admit to deploying code without fully testing it.

The report, which surveyed more than 2,700 DevOps and quality assurance leaders and software developers worldwide, included input from CIOs, CTOs, and VPs of engineering across sectors such as public services, energy and utilities, manufacturing, and financial services.

This puts companies at risk of security breaches, compliance failures, and increasing technical debt, maintenance costs, and customer churn, the report states.

McDonald’s, Sainsbury’s, and Tesco say IT outages were “unrelated”

An overwhelming 90% of CIOs, CTOs, and delivery teams are confident in AI’s ability to autonomously make software release decisions, and almost 100% see value in autonomous testing for quality assurance.

“Recent software outages caused by unchecked code changes underscore how vital high-quality software is to the broader organisational ecosystem,” said Kevin Thompson, CEO of Tricentis.

“As AI evolves, tech leaders must define what quality means for their organisation, finding the right balance between speed, quality, and cost through comprehensive testing strategies to drive better business outcomes.”

Andrew Power, head of UKI at Tricentis, added that the growing risk of software outages in the UK, now higher than the global average, is increasing the urgency for engineering teams to improve their development processes.

He noted that agentic AI offers a significant opportunity to boost productivity and software quality. By adopting autonomous testing and AI-led delivery tools, organisations can meet tight deadlines without compromising reliability.

The original content of the note was published on Techinformed.com. To read the full note visit here

Product-Led Growth: How Consultants And In-House Users Fuel Adoption

Many B2B software companies—Notion, Airtable and Zapier, to name a few—have used product-led growth (PLG) as an alternative or supplement to traditional sales channels.

Product-led growth, as described by McKinsey, is when a company gives "the product itself a critical role in acquiring, growing and retaining customers." A ProductLed survey found that the majority of B2B SaaS companies are already using this strategy, and 91% of those companies plan to invest more in PLG initiatives this year.

The most successful PLG B2B tools share two traits:

1.Ease Of Adoption: Minimal friction to onboard, such as no IT approval needed.
2.High Output Leverage: Produce significantly better results compared to legacy systems.

This mix creates a flywheel: Users experience value quickly and often share the product via templates, automations or recommendations—driving organic spread.

However, one thing that's generally not well understood about PLG for B2B software sales is how to achieve a self-sustaining adoption loop involving service providers and in-house teams at companies that would use the product.

How Service Providers Drive Early Adoption
Service providers are often the first to adopt new tools because their work depends on staying current. They test, validate and scale solutions across client accounts. Here are a few reasons why they can serve as the first-movers on the adoption of new B2B products:

1.Early Adoption And Experimentation: Freelancers and agencies adopt tools to increase efficiency.

2.Content Creation And Thought Leadership: Once confident in the solution, providers create content—tutorials, LinkedIn posts, YouTube guides—sharing their success and use cases.

3.Client Implementation And Validation: Validated tools are introduced to clients via consulting engagements, workflow builds and system replacements. Each implementation brings the product into a new business, often with long-term use and expansion.

4.Becoming Product Champions: Successful providers often brand themselves around the product, build services around it and earn referrals—transforming the product into a key part of their business model.

How In-House Teams Sustain And Expand Adoption
While service providers introduce products, in-house teams drive continued use and expansion. Here's how:

1.Exposure And Internal Adoption: In-house employees exposed to tools via freelancers often become proficient users. Teams working with Webflow agencies, for instance, might keep using the platform even after the agency’s role ended.

2.Employee Mobility and Cross-Company Seeding: Employees regularly switch jobs. If they found success with a tool, they often bring it to their next company—seeding growth across organizations. At Clay, we often see users reintroduce the product within 60 to 90 days of starting a new role.

3.The Side-Gig Acceleration Effect: Many full-time professionals also freelance. They adopt tools at work, then use them with personal clients—spreading the product across even more companies and audiences.

How To Leverage The Service Provider-In-House Loop
Growth teams at B2B software companies should treat service providers and in-house users as distinct but interconnected audiences. Here’s how to optimize their PLG strategy for both:

1.Prioritize service provider acquisition. Providers introduce tools to new companies. Focus acquisition here with:

-Targeted campaigns
-Partner directories
-Exclusive incentives (discounts, early access)

2.Turn providers into champions. Support providers with certification programs, white-label or client-facing features and affiliate or revenue-share programs. They’ll grow the tool if it helps them grow their business.

3.Streamline provider-to-client handoff. Make it easy for providers to transfer ownership or collaborate with clients through transferable workspaces, co-editing features and role-based access and dashboards.

4.Deepen in-house adoption. Once in-house users are active, help them expand internally with pre-built templates, cross-functional use cases and lightweight onboarding.

Conclusion: Best Practices For Sustainable PLG Growth
The service provider-to-in-house loop isn’t a side effect; it’s a proven growth mechanism. To unlock it fully:

-Track your loop. Use analytics or CRM to identify how users first heard about your product.
-Segment your messaging. Service providers want scale; in-house users want collaboration and simplicity.
-Invest in your champions. Build community, offer education and recognize the people growing your product for you.

This growth loop doesn’t require massive ad budgets or big sales teams. It just requires a product that works, a strategy that supports both segments and a willingness to let users lead.

The original content of the note was published on Forbes.com. To read the full note visit here

What software developers need to know about cybersecurity

In 2024, cyber criminals didn’t just knock on the front door—they walked right in. High-profile breaches hit widely used apps from tech giants and consumer platforms alike, including Snowflake, Ticketmaster, AT&T, 23andMe, Trello, and Life360. Meanwhile, a massive, coordinated attack targeting Dropbox, LinkedIn, and X (formerly Twitter) compromised a staggering 26 billion records.

These aren’t isolated incidents—they’re a wake-up call. If reducing software vulnerabilities isn’t already at the top of your development priority list, it should be. The first step? Empower your developers with secure coding best practices. It’s not just about writing code that works—it’s about writing code that holds up under fire.

Start with the known
Before developers can defend against sophisticated zero-day attacks, they need to master the fundamentals—starting with known vulnerabilities. These trusted industry resources provide essential frameworks and up-to-date guidance to help teams code more securely from day one:

OWASP Top 10: The Open Worldwide Application Security Project (OWASP) curates regularly updated Top 10 lists that highlight the most critical security risks across web, mobile, generative AI, API, and smart contract applications. These are must-know threats for every developer.

MITRE: MITRE offers an arsenal of tools to help development teams stay ahead of evolving threats. The MITRE ATT&CK framework details adversary tactics and techniques while CWE (Common Weakness Enumeration) catalogs common coding flaws with serious security implications. MITRE also maintains the CVE Program, an authoritative source for publicly disclosed cybersecurity vulnerabilities.

NIST NVD: The National Institute of Standards and Technology (NIST) maintains the National Vulnerability Database (NVD), a repository of security checklist references, vulnerability metrics, software flaws, and impacted product data.
Training your developers to engage with these resources isn’t just the best practice, it’s your first line of defense.

Standardize on secure coding techniques
Training developers to write secure code shouldn’t be looked at as a one-time assignment. It requires a cultural shift. Start by making secure coding techniques are the standard practice across your team. Two of the most critical (yet frequently overlooked) practices are input validation and input sanitization.

Get access control right
Authentication and authorization aren’t just security check boxes—they define who can access what and how. This includes access to code bases, development tools, libraries, APIs, and other assets.

Don’t forget your APIs
APIs may be less visible, but they form the connective tissue of modern applications. The top security risks? Broken authentication, broken authorization, and lax access controls. Make sure security is baked into API design from the start, not bolted on later.

Assume sensitive data will be under attack
Sensitive data consists of more than personally identifiable information (PII) and payment information. It also includes everything from two-factor authentication (2FA) codes and session cookies to internal system identifiers. If exposed, this data becomes a direct line to the internal workings of an application and opens the door to attackers.

Log and monitor applications
Application logging and monitoring are essential for detecting threats, ensuring compliance, and responding promptly to security incidents and policy violations. Logging is more than a check-the-box activity—for developers, logging can be a critical line of defense.

Integrate security in every phase
You don’t have to compromise security for speed. When effective security practices are baked in across the development process—from planning and architecture to coding, deployment, and maintenance—vulnerabilities can be identified early to ensure a smooth release.

Build on secure foundations
While secure code is important, it’s only part of the equation. The entire SDLC has its own attack surface to manage and defend. Every API, cloud server, container, and microservice adds complexity and provides opportunities for attackers.

In fact, one-third of the most significant application breaches of 2024 resulted from attacks on cloud infrastructure while the rest were traced back to compromised APIs and weak access controls.

Manage third-party risk
So, you’ve implemented best practices across your development environment, but what about your supply chain vendors? Applications are only as secure as their weakest links. Software ecosystems today are interconnected and complex. Third-party libraries, frameworks, cloud services, and open-source components all represent prime entry points for attackers.

A software bill of materials (SBOM) can help you understand what’s under the hood, providing a detailed inventory of application components and libraries to identify potential vulnerabilities.

Commit to continuous monitoring
Application security is a moving target. Tools, threats, dependencies, and even the structure of your teams evolve. Your security posture should evolve with them.

The original content of the note was published on Infoworld.com. To read the full note visit here

Product Management in 2025: Is It Time to Embrace AI?

Artificial Intelligence (AI) slowly crept into our daily lives and businesses in a way that seemed so remote, like a scene from a high-budget Sci-Fi movie. As the year wraps up, it’s time to reflect and face the reality that AI is here to stay.

The big question isn’t if AI exists or whether it will replace humans because both are already relatively true. But it’s time to ask if AI is truly the future of Product Management as we know it or if there’s a way to marry the two without compromising quality and output.

This review analyses the effect of AI in product management so far and why it’s time to welcome its support instead of avoiding it.

AI and Product Management
Today, when you open social media, smartphones, and search engines, you get prompts to use AI. The first answers on Google these days are AI-generated, which shows how much technology now relies on this thing that used to be a phenomenon.

By extension, every business that exists on the internet or has any form of data on the web now has to worry about AI’s impact on its algorithms and data collection.

If you care about evolving your business in 2025, you must find a way to fuse AI into your Product Management.

Here are four ways AI has helped product managers in the last few years.

Information Gathering With AI
Quick, seamless, and accurate information gathering is the stuff of every product manager’s dream, and AI has made that a reality.

Pause for a moment and think of every time a related ad popped up after you’ve discussed or even so much as thought of a product over the phone. Amazon is the biggest example of a brand that leveraged AI for better product promotion, positioning, and increased sales.

The Machine Learning tools study user patterns to give them what they want based on their browsing history and purchase behaviour. As a product manager, consider translating that to sales for your brand.

Virtual Assistants for Better Customer Services
We’ve all seen an ad that makes us want to buy a product we don’t need but get saved when the customer service is slow or terrible.

As Product Managers, you know the struggle of catering to several customers simultaneously, but there’s only so much one person can do.

Here come the Virtual Assistants and chatbots to the rescue! They’re everywhere now, from banking apps to cable TV subscriptions, telecommunications, and every business that uses an online customer support system.

Accurate Data Analysis and Decision Making
Let’s bring it back to the administrative part of Product Management. AI makes studying market trends for predictive analysis faster and more accurate than without the technology.

When interpreting data and using it for relevant decision-making, you can rely on AI to give you real-time answers for optimal results.

Increased Efficiency and Output
This might be my favourite part of using AI in Product Management, and it’s about to be yours too! Sometimes, we droll on when work isn’t challenging, but you can’t avoid mundanity in product management or any business, for that matter.

The job’s “boring” aspects are the wheels that keep the whole mechanism spinning.

So here’s a solution with AI. You automate your basic duties so that the technology leaves time for you to handle more complex and demanding aspects of your job!

The Verdict
AI is the future of product management if you want your business to be at the fore of its peers when discussing success stories in 2025. However, the desire for success is one thing, while working towards achievement is another.

Thankfully, AI isn’t an abstract idea anymore, and you can take steps to improve your knowledge in the field and bolster your business.

Identify your aims and objectives for 2025.
Highlight the business aspects you need to improve.
Note how AI can help with numbers one and two.
The ultimate takeaway is that we shouldn’t fear AI coming to take over product management but instead harness its powers to evolve the business. Shore up your skills where necessary and prepare for the future. I look forward to seeing how Product Managers interact with AI in the coming year.

The original content of the note was published on Techpoint.africa. To read the full note visit here

Agile Trends 2025: The Next Wave of Agile Transformation

The significance of company resilience saw a dramatic shift in 2022 due to the impact of Covid-19. Enterprises were compelled to streamline production operations and processes. Looking at the looming uncertainties, organizations are increasingly turning to agile project management processes and ways to maintain competitiveness and adaptability.

Agile practices target optimizing intricate production processes, empowering project managers to deliver projects in a functional state. This approach facilitates rapid improvements throughout the project. Agile testing prompts project teams to detect issues and deploy solutions throughout the growth cycle, with a strong emphasis on meeting customer needs.

Agile Trends for 2025

Some of the latest Agile trends for 2025 are listed below:

Agile Design Thinking
Design thinking emphasizes creating customer-centric products that align with users' end needs and requirements. This recent Agile trend ensures that products under development empathize with consumers, thereby enhancing customer value and satisfaction.

Dominance of Scaled Agile and Scrum
Scaled Agile is a framework adopted by organizations aiming to facilitate Agile development practices across their teams and departments. It comprises workflow patterns primarily implemented at the enterprise level. This approach is instrumental in monitoring progress and achieving enhanced efficiency, collaboration, and resilience on a larger scale, presenting significant advantages.

The Scrum creation method promotes efficient organization of cross-functional team activities, aiming to produce functional code at each iteration’s end or sprint. Software QA companies are increasingly aiming to expand their Scrum operations to drive greater value and foster improved collaboration.

Agile AI and Machine Learning Integration
Despite implementing agile approaches, project teams like product developers and testers still undertake substantial theoretical work. Machine learning algorithms and AI play a crucial role in analyzing growth data and projects, offering real-time insights and rapid advanced analytics. For instance, they enable precise forecasts regarding the completion timelines of project phases. This becomes especially pertinent as projects approach the release phase, attracting significant attention from executives monitoring schedules closely.

Cloud Agility
In pursuit of a competitive edge, agile teams are leveraging cloud-based technology to explore innovative approaches for forecasting, faster development, testing, and accelerated releases.

Organizations employing Agile cloud-based methodologies experience substantial competitive advantages, fostering increased efficiency, heightened agility, faster market responsiveness, reduced costs, and improved customer service, among other benefits.

Accelerated Feedback Cycles
While assumptions aid in planning, the dynamic nature of work conditions, evolving demands, updated quality standards, and other factors often steer outcomes away from initial projections. Recent findings reveal a significant shift in agile management trends towards prioritizing immediate input for improvements rather than solely focusing on anticipated outcomes.

Business Value-Centric User Stories
Business value-centric user stories in Agile is a practice of prioritizing and crafting user stories to emphasize the value they bring to the business or end-users. These stories focus on the benefits, outcomes, or impacts they will deliver, aligning closely with the organization's goals and objectives.

Agile for Non-IT Teams
While Agile methodology has traditionally been linked to Software and IT through Agile development, it's now increasingly embraced by departments like sales, marketing, finance, and human resources across non-IT verticals. In the foreseeable future, many non-IT teams are expected to adopt agile practices to enhance their workflow, productivity, and value delivery to stakeholders.

Frameworks like Scrum, Lean, and Kanban within Agile can facilitate aligning the goals and objectives of non-IT teams. This alignment enables collaborative work environments, allowing these teams to iterate and respond more efficiently to continually evolving requirements.

Conclusion
In today's expanding digital business, customer needs evolve rapidly, making it impractical to delay adapting to trends for extended periods. This demands significantly faster and smoother turnarounds, promoting many companies to enhance their tools, processes, and organizational culture.

The original content of the note was published on Simplilearn.com. To read the full note visit here

Do Lean Startup Methods Work for Deep Tech?

Over the last decade, a niche slice of the tech sector has delivered some of its most impressive breakthroughs. Deep-tech innovation — the practice of harnessing the most recent advancements in scientific understanding to create technologies that were previously inconceivable — has delivered groundbreaking companies like SpaceX and products such as mRNA vaccines.

Deep tech’s share of venture capital has doubled over the last decade, growing from approximately 10% to 20%. Deep tech-focused investment funds outperform traditional venture capital, delivering an average internal rate of return of 26% compared to 21%.

But deep-tech startups come with their own unique set of business challenges. Their products often involve prolonged R&D periods and substantial upfront costs, making it hard to iterate quickly and maintain cost efficiencies. The stringent regulatory landscapes and the technical complexities of deep tech necessitate a more sophisticated approach.

The lean startup methodology emphasizes quick iteration cycles, allowing startups to rapidly test and refine their products based on customer feedback. However, for deep-tech ventures — which often face prolonged R&D periods, high upfront costs, and complex technologies — this approach can sometimes be challenging to implement.

Deep-tech ventures face different kinds of risk. Their technologies do not yet exist and must navigate a labyrinth of technological uncertainty that goes beyond the scope of market feedback loops. De-risking a technology is fundamentally different from de-risking a market.

To reduce technological uncertainty, deep-tech founders can:

Deep-tech startups face greater complexity and resource demands than their low-tech counterparts. These tailored approaches mitigate the inherent uncertainty and pave the way for groundbreaking innovations that can transform industries and society.

The original content of the note was published on Hbr.org. To read the full note visit here

How Seed Funding Helps Startups Achieve Product-Market Fit

Throughout a start-up's journey from inception to maturity, several rounds of funding are raised, typically progressing from pre-seed to seed, Series A, B, C, and eventually an Initial Public Offering (IPO) if all goes well. Each funding round is significant, but early-stage funding— particularly seed funding—is especially critical. At the seed stage, start-ups often have little to no traction, and the capital provided helps them bridge the gap between a promising idea and a viable business. This is where seed funding plays a pivotal role in helping startups achieve what is known as Product-Market Fit (PMF), an important milestone on the path to long-term success.

Early-stage investors more often than not provide more than just capital. Many bring industry expertise, strategic guidance, and valuable networks, which are crucial for navigating the challenges of achieving PMF.

At its core, seed funding provides start-ups with the financial runway to develop a Minimum Viable Product (MVP) and test it in the market. The MVP is an early version of the product, designed to gather feedback from initial users with minimal development.

In addition to building the MVP, seed funding allows startups to conduct market research, acquire early customers, and refine their go-to-market strategy. Without this initial influx of capital, many start-ups would lack the resources to gather customer insights, make necessary product adjustments, and ultimately validate whether their product fits the needs of the market.

WHY PRODUCT-MARKET FIT MATTERS

PMF is the holy grail for any startup, representing the moment when a company finally finds a product that satisfies the needs of a specific market. PMF is when a start-up's value proposition finally aligns perfectly with the market demand. PMF is often described as the first step toward scalability.

Given the significance of PMF, many venture capitalists now use it as a gating mechanism for funding rounds, especially Series A. A company with a well-established PMF has a higher chance of securing further investments, but reaching that point often requires early-stage capital. This is where seed funding comes in.

The original content of the note was published on Entrepreneur.com. To read the full note visit here