SME

Series “Blind Spots in Entrepreneurship”: Why startups don’t fail because of a lack of ideas — but because of what founders don’t see

Through coaching startups, KisStartup has observed a recurring reality: most startups do not fail because their ideas are weak, their technology is poor, or their founders lack effort. They fail because of familiar blind spots in founders’ thinking and decision-making.

We call these “founder blind spots.”

Why “blind spots”?

A blind spot is not something founders don’t know.
More dangerously, it is often something founders believe they already understand well enough—so they stop questioning, measuring, or validating it.

In everyday life, a blind spot is an area our eyes cannot see, yet the brain automatically fills in the gap, making us believe we see the whole picture. Entrepreneurship works the same way. When founders are:

  • too close to the product,
  • too emotionally attached to the original idea, or
  • too busy with daily operations,

they can develop misplaced confidence and make decisions based on intuition, past experience, or personal beliefs—rather than real data and market feedback.

Blind spots rarely kill a startup instantly. Instead, they slowly push it off course, draining time, money, and energy until there is no room left to recover.

The most common founder blind spots

This KisStartup series focuses on the blind spots we repeatedly observe in Vietnamese and global startups, especially in early stages and during the transition from “idea” to “scalable model.”

Market and customer blind spots
Many founders believe they understand customers simply because they themselves are users. But “understanding” is not the same as measuring. Lack of systematic customer discovery, confusion between user–buyer–payer, or relying on polite feedback often prevents startups from ever reaching product–market fit.

Financial and cash-flow blind spots
Startups rarely “die suddenly” from running out of cash. More often, founders fail to notice deteriorating cash flow: rising burn rate, shrinking runway, and fixed costs growing faster than sustainable revenue. Avoiding numbers does not remove risk—it only delays and amplifies it.

People and organizational blind spots
“We’re a small, flexible team” often hides deeper issues: unclear roles, blurred accountability, and early hiring mistakes. Founders frequently underestimate the real cost of internal conflict and organizational distraction.

Product and technology blind spots
Some technical founders fall into over-engineering—building too much, too complex, for too long. Others launch too early with products that fail to solve core pain points. Both stem from the absence of clear learning criteria for an MVP.

Failure and pivot blind spots
Perhaps the most dangerous blind spot is psychological: reluctance to admit flawed assumptions, lack of a learning system from failure, and tying personal ego too tightly to the product. Many startups don’t lack pivot opportunities—they lack the courage and structure to pivot at the right time.

Who is this series for?

The “Blind Spots in Entrepreneurship” series is not meant to criticize or lecture founders. It is written for:

  • founders building startups or innovation-driven SMEs,
  • those who feel “something isn’t right” but can’t yet name it,
  • investors, partners, and support organizations seeking deeper insight into why startups fail—and how to reduce that risk.

Each article will explore one blind spot in depth, with analysis, real cases, and practical approaches KisStartup uses in mentoring, training, and ecosystem building.

An open question for you:
If you had to choose the most dangerous blind spot for your startup right now, which one would it be?

© Copyright KisStartup. Any reproduction, quotation, or reuse must clearly credit KisStartup.

Author: 
Nguyễn Đặng Tuấn Minh

Digital Transformation – A Catalyst for Self-Learning and Strategic Innovation of Business Owners

Digital Transformation (DT) is no longer just about applying technology; it is increasingly seen as a journey of growth that enhances the self-learning ability of business owners. For small and medium-sized enterprises (SMEs), the capacity for continuous and independent learning is a key factor to adapt to a volatile market and to drive strategic decision-making (McKinsey, 2023). Drawing on experience from projects implemented by KisStartup in Vietnam, this article highlights how DT – especially through Artificial Intelligence (AI) – empowers entrepreneurs to strengthen their self-learning skills, which in turn directly reshapes their strategic choices.

 

Self-directed learning has long been considered an important ability for organizations to adapt (Knowles, 1975). In the digital age, the integration of AI tools turns learning activities from passive intake into an active process of exploration and strategic reflection. Instead of asking “What is this concept?”, business owners gradually learn to search by themselves, experiment, and test strategic options through digital platforms.

Recent studies confirm that AI not only automates processes but also creates personalized learning environments for managers and employees. Platforms such as LinkedIn Learning, Coursera for Business, and Udemy Business have applied AI algorithms to analyze learning progress, detect knowledge gaps, and suggest suitable skill paths (MISA, 2023). This shortens the time to acquire new capabilities and at the same time promotes the adjustment of business strategies.

AI and Business Effectiveness

Adoption rate and learning effectiveness
Global surveys show that the rate of AI adoption in business management has increased rapidly. According to IBM, Forbes, and McKinsey, the percentage of businesses applying AI rose from 33% in 2022 to 72% in 2024 (SkillsBridge, 2023). Another study of 7,500 companies showed that 35% had integrated AI into their processes, while 42% were experimenting (IBM, 2023).

The effect on learning is very clear. AI-based training systems can detect skill gaps, forecast progress, and adjust the curriculum accordingly (AMIS, 2024). Thanks to this, business owners develop a habit of guided self-learning, which is both repetitive and evidence-based.

Performance and strategic innovation
AI-based automation brings significant improvements in performance:

  • Businesses report a 20–30% increase in labor productivity thanks to data analysis and decision support (McKinsey Global Institute, 2023).

  • In customer service, AI helps increase productivity 1.71 times while reducing staff from 600 to 350 people (Nhân Dân, 2024).

  • The application of AI in work management saves an average of 5.4% of weekly working time (~2.2 hours per employee) (Louis, 2024).

These numbers show that AI not only optimizes processes but also creates conditions for employees to focus more on strategic activities, giving business owners more space for critical thinking and strategic innovation.

Cost optimization and human resource development
AI also helps reduce operating costs by up to 25% (Gartner, 2023). This saving allows SMEs to reinvest in training and innovation. When AI is integrated into human resource management and development, entrepreneurs themselves become active learners, ready to test different pricing scenarios, market strategies, and partnership models.

KisStartup’s Approach: Stronger Businesses through Smarter Entrepreneurs

KisStartup’s projects show that digital transformation is not about “doing things for businesses,” but about empowering them to do it themselves. Businesses are encouraged to directly use AI tools, analyze results, reflect, and adjust their own strategies.

In digital transformation accelerator programs in the Northern mountainous region of Vietnam, small homestay owners applied AI tools to design their own marketing campaigns. Export-oriented SMEs used data analysis to adjust product prices and find new markets. Although at first they still made mistakes—such as not providing non-sensitive data to the tools—it was precisely these experiences that helped them understand that openness and transparency are conditions for AI to maximize effectiveness.

We emphasize that digital transformation is a process of nurturing lifelong learning capacity for business owners. The goal is not only short-term productivity, but also forming the habit of self-learning, experimenting, and continuously adapting—qualities that are essential for strategic innovation in an uncertain environment.

Theoretical and Practical Implications

  • Resource-Based View (RBV): AI-supported self-learning helps businesses reconfigure resources into competitive advantages.

  • Dynamic Capabilities Framework (DCF): Continuous self-learning strengthens the ability to “sense, seize, and transform” which is necessary for strategic flexibility (Teece, 2018).

  • Scaling implications: When businesses build internal learning capacity with AI, scaling becomes more efficient, reducing marginal costs and improving operational performance.

International data matches KisStartup’s observations: SMEs applying AI not only improve productivity but also shift their strategic mindset from reactive to proactive. Therefore, digital transformation is not only a technological change but also a transformation of awareness and organization.

Conclusion

Digital transformation, especially with AI, should be understood as a catalyst for the self-learning capacity and strategic innovation of entrepreneurs. Evidence shows that AI increases productivity, reduces costs, and creates personalized learning environments. The greatest value lies in business owners being able to self-learn, self-reflect, and shape their own strategies.

KisStartup’s approach emphasizes this factor: equipping businesses with the ability to explore and ask questions, so that digital transformation becomes the path toward adaptability and long-term competitiveness. In the context of globalization, successful businesses are not necessarily those with the most advanced technology, but those whose leaders know how to learn and continuously adapt.

 

References

Author: 
KisStartup