Most startups do not fail because they cannot build. They fail because they validate too slowly, refine too late, and spend too much time developing products, pitches, and messages without enough honest feedback from the right people. This masterclass gives founders a practical method for building structured feedback loops into startup development, across product, positioning, pitch decks, and go-to-market. Participants will learn the critical difference between two questions that are often confused: “Is this well-made?” and “Would I actually want this?” The first requires expert feedback. The second requires user feedback. When founders mix the two, they often end up with polished ideas that nobody wants or promising ideas that are badly framed. Using a live AI-assisted example, the session shows how startups can structure both expert panels and user personas to generate faster, more honest, and more actionable feedback between real-world customer and investor interactions. The goal is not more feedback for its own sake, but better decisions, faster iteration, and fewer wasted cycles.
Learning objectives:
By the end of the session, participants will be able to:
1. Distinguish when they need expert feedback versus user feedback
2. Build AI-supported expert panels grounded in real domain perspectives
3. Create target-user personas based on mindset, context, and likely objections
4. Apply a structured feedback loop to their own pitch, product, or go-to-market challenge
5. Use feedback more systematically to improve validation speed and decision quality
Securing the right capital at the right time is one of the most consequential decisions an early-stage founder will make, yet the fundraising landscape can feel opaque and overwhelming. This masterclass cuts through the noise by mapping the full spectrum of funding options available to startup founders, from bootstrapping and angel networks to seed funds, venture capital, strategic investors, and non-dilutive sources such as grants and public programs. Drawing on her experience as a venture investor at Fusion Fund, Lu Zhang will offer a practitioner's perspective on how different funding structures shape a startup's trajectory, culture, and decision-making. Founders will learn how to assess which path best fits their stage, business model, and long-term goals, and how to approach investors strategically and with confidence. Through real-world examples and interactive discussion, this session equips participants with a clear framework to demystify the fundraising process and make more informed, intentional financing decisions.
Learning objectives:
By the end of this session, participants will be able to:
1. Distinguish between the major funding options available to early-stage startups, including bootstrapping, angel investment, seed and venture capital, corporate venture, and grants, and understand the trade-offs of each.
2. Match funding strategy to startup stage and sector, identifying which type of capital is most appropriate given their business model, traction, and growth ambitions.
3. Understand the investor perspective, including what VCs and angels evaluate when making investment decisions and how to position a startup compellingly.
4. Recognize key deal terms and dilution dynamics, so founders can approach term sheets with a baseline understanding of equity, valuation, and founder control.
5. Build a fundraising roadmap, outlining when to raise, how much to raise, and how to sequence outreach for maximum effectiveness.
In this Masterclass, Tomas will share insights from the in-depth analysis of over 50 startup brands conducted over the past months within his international branding studio GoBIGNAME. Drawing from 15 years of branding expertise and experience with 350+ brand launches and brand evolutions, his team has distilled essential fundamentals that every startup in this age needs to know. Participants will learn key principles and lessons backed by real-world examples of successful startups and scale-ups that not only thrive in their markets but also attract global attention.
Learning objectives:
By the end of this session, participants will be able to:
1. Understand the difference between product-market fit and real-world adoption dynamics. Analyze the stakeholder and decision-making structures surrounding their solution. Evaluate common scaling barriers, including trust, distribution, and institutional friction. Apply a structured approach to identifying appropriate partners and channels for scale. Create an initial adoption strategy tailored to their specific ecosystem. Identify the costliest mistakes startups make in branding and marketing and avoid them.
2. Break down the key drivers behind brand growth with practical frameworks and examples.
3. Define and select brand codes that resonate with your target audience and differentiate your business.
4. Analyze which branding elements directly impact market share and prioritize them effectively.
AI startups can move from prototype to global reach quickly, but trust is often harder to build than the technology itself. In AI, scale can amplify both value and harm. Founders today must navigate safety, ethics, bias, privacy, accountability, and user confidence while also growing quickly and attracting investment. This interactive masterclass helps founders turn trust, safety, and ethics into a strategic advantage. Through real-world examples, founder scenarios, and guided discussion, participants will explore how product, data, and growth decisions can create risk or build confidence. Participants will learn practical Safety by Design approaches to reduce foreseeable harm, strengthen investor readiness, and support sustainable scale. The session concludes with a clear action plan for building AI products that are trusted, investable, and growth-ready.
Learning objectives:
By the end of this session, participants will be able to:
1. Identify common trust, safety, and ethics risks in AI startups.
2. Analyse how growth and design decisions affect trust and reputation.
3. Apply practical Safety by Design strategies before scaling.
4. Evaluate stakeholder expectations for trustworthy AI ventures.
5. Develop an action plan to embed trust and ethics into growth strategy.
Most startups building ‘AI for good’ are driven by purpose. What separates the products that work from the ones that cause harm, often invisibly, is governance.
This practical session helps participants operationalise Responsible AI. It examines three case studies from humanitarian and social impact programming, showing what goes wrong when communities are excluded from AI design and oversight, with practical lessons for getting it right. You’ll learn to avoid pitfalls like The Visibility Trap, and build a record of decisions that enhances transparency and investor confidence.
You’ll leave with three tools: a governance audit benchmarked against humanitarian standards, a framework for building your AI governance team, and a risk matrix calibrated to your actual cost of error.
Takeaways are grounded in Suzy Madigan’s work in Responsible AI, including the co-authored SAFE AI Framework supported by the UK Government, research on inclusive AI across 12 Global Majority countries, and frontline experience in 18 crisis responses.
Learning objectives:
By the end of this session, participants will be able to:
1. Analyse hidden risks of AI products for affected communities and plan how to mitigate them
2. Evaluate whether their current governance practices are robust and inclusive, and identify how to plug any gaps
3. Outline a community engagement approach that goes beyond consultation to meaningful participation
4. Apply a risk-proportionate accountability framework through the AI lifecycle
5. Explain why inclusive AI governance is a product performance requirement, a risk mitigation strategy, and the foundation of authentic AI for Good
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