Startup accelerator programme

Most AI startups build exceptional technology but struggle to translate it into market traction. This session bridges that gap, turning product capability into a clear, repeatable go-to-market strategy that reaches the right buyers, partners, and ecosystems. Drawing on 20+ years of international B2B technology marketing experience across EMEA, this session gives Innovation Factory founders a practical framework for positioning their AI solution, identifying the right route to market, and building partnerships that accelerate growth, without losing focus on their core mission. We will explore how to align marketing, sales, and technical messaging so all three speak the same language to the market. We will examine real-world examples of AI startups that successfully scaled through partner channels, and work through the key decisions every founder must make before going to market. Founders will leave with a clear structure they can apply immediately: a working framework built for the realities of scaling AI in global markets, not just theory. This session is practical, interactive, and designed specifically for founders who are ready to grow.

Learning objectives:

By the end of this session, founders will be able to:

1. Analyse their current go-to-market position and identify the biggest gaps between product capability and market readiness.

2. Design a partner-led growth strategy aligned to their target customer segment and geographic market.

3. Evaluate which distribution channels (direct, channel partner, or platform) best fit their stage and solution type.

4. Apply a messaging alignment framework to ensure their marketing, sales, and technical communications work together consistently.

5. Create a prioritised action plan for their first 90 days of structured market entry.

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

Many AI startups build solutions that are technically sound and socially valuable, yet struggle to achieve real-world adoption. This session examines why. Drawing on cross-sector experience in scaling technology through enterprise, government, and public–private ecosystems, the workshop introduces a practical framework for understanding “adoption dynamics” beyond product-market fit. Participants will explore why the end user is often not the decision-maker, how trust and distribution function as hidden infrastructure, and why partnerships frequently determine success more than product features. Rather than focusing on specific industries, the session distills universal patterns that apply equally to healthcare, agriculture, climate systems, and enterprise software. Through case-based discussion and interactive exercises, founders will learn how to reframe their solutions as part of broader systems involving institutions, incentives, and stakeholders. The goal is to help participants move from building products to designing pathways for adoption at scale.

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.

2. Analyze the stakeholder and decision-making structures surrounding their solution.

3. Evaluate common scaling barriers, including trust, distribution, and institutional friction.

4. Apply a structured approach to identifying appropriate partners and channels for scale.

5. Create an initial adoption strategy tailored to their specific ecosystem.

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.

You ship a product. It seems to work. But does it work for everyone it’s meant to serve? And did you avoid serious but invisible harms? This masterclass supports AI startup founders in embedding safety and accountable governance into their processes from day one. The result is AI that communities trust and funders can invest in with confidence. Drawing on responsible AI research across twelve Global South countries and frontline experience in humanitarian and crisis settings, this session is for founders seeking hands-on ways to contextualise and technically assure products designed for social good. Founders will explore how AI products designed without communities result in ethical and performance failures, and how to avoid this mistake. They will examine how to mitigate a range of technical and governance risks including data security, model bias, and system accountability, and how to avoid The Visibility Trap: the dangerous assumption that backend systems inherently carry lower risk. The session introduces practical approaches for assessing governance gaps across the full AI lifecycle from problem identification to decommissioning. By the end, attendees will be able to explain why responsible AI is not only a values statement, but a clear product decision determining whether their solution works.

Learning objectives:

By the end of this session, participants will be able to:

1. Critically analyze and evaluate key equity-related challenges in innovation design, with particular reference to digital services and systems.

2. Assess and critique structural barriers to social equity and examine their implications for innovation processes and outcomes.

3. Describe and illustrate how equity-oriented design principles can be applied in practice to support more equitable innovation processes.