Startup accelerator programme

In the race to deploy AI, the path from a successful prototype to a scalable product often stalls in regulated sectors. This masterclass provides a battle-tested operating model for scaling AI with integrity.

Drawing on the rapid growth of a leading RegTech scale-up, the session presents a framework built on a clear mission, defined ways of working, and a North Star strategy. Attendees will explore two core strategic principles: establishing foundations for advanced AI and developing high-priority prototypes.

The session emphasizes a unified delivery model in which Data Science acts as a catalyst to inspire Product innovation and empower Engineering execution. It examines how high-performing, autonomous teams can be fostered through Agile ceremonies and rigorous processes. It also analyses the "Human Architecture" of trust, using Capability Champions to bridge Data Science and Data Governance. Participants will gain practical insights into formalizing Model Review Boards and Model Handbooks, ensuring compliance without sacrificing research velocity. This is a hands-on guide for leaders seeking to build collaborative teams that deliver measurable business impact while serving the broader mission of responsible, humanitarian-aligned AI.

Learning Objectives

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

• Analyze the structural requirements for moving AI from R&D prototypes to enterprise-grade products within highly regulated environments.
• Formulate a strategic framework based on "Core Foundations" and "High-Priority Prototypes" to balance long-term scalability with immediate innovation.
• Design an integrated organizational model that leverages Data Science to inspire Product and empower Engineering delivery.
• Construct specific governance instruments, such as a Model Review Board and Model Handbook, to standardize compliance and transparence.
•  Implement Agile/Scrum ceremonies (Standups, Retrospectives, Demos) and tooling tailored for Data Science workflows to improve team autonomy and collaboration.

Fundraising is not only about technology or impact. It is also about understanding how investors think, assess risk, and make decisions under uncertainty. This masterclass explores the psychology behind venture capital decision-making and how founders can translate complex AI solutions into clear, credible, and compelling narratives without oversimplifying or overselling.

The session focuses on how investors evaluate teams, execution risk, trust, governance awareness, and scalability, particularly in regulated and impact-driven environments such as healthcare, climate, and public-sector AI. Founders will examine common pitch pitfalls, learn how key signals are interpreted during fundraising conversations, and understand how a pitch can be structured to align ambition with realism.

The masterclass is highly practical and interactive, combining real-world examples with frameworks that can be applied immediately in preparation for fundraising, partnerships, and scale within the AI for Good ecosystem.

Learning Objectives

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

• Understand how venture capital investors evaluate AI startups beyond technology and traction.
• Analyze common cognitive biases and decision drivers influencing investor behavior.
• Evaluate why technically strong pitches often fail to convert.
• Apply practical frameworks to structure clearer, more persuasive pitch narratives.
• Refine their own pitch approach to better align impact, scalability, and investor expectations.

Many AI for Good startups can build impressive demos, but turning those demos into production‑grade services that enterprises and governments will trust is a different challenge. Finalists must navigate reliability, security, data governance, and cost constraints while moving fast and staying mission‑driven. This hands‑on masterclass is designed specifically for AI for Good Innovation Factory finalists who want their solutions to be “trust‑ready” for institutional partners. Through concrete architecture patterns and real‑world examples, the session will show how to evolve from notebooks and ad‑hoc pipelines to pragmatic, lightweight production setups that can pass enterprise and public‑sector scrutiny without requiring “big tech” budgets. Participants will work with a simple blueprint and checklist to map their current state, identify critical gaps (in infrastructure, data, and risk controls), and define next steps they can execute in the coming 3-6 months. The focus is on practical, vendor‑neutral guidance with no sales content, tailored to early‑stage teams aiming to scale responsibly and sustainably.

Learning Objectives

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

• Identify the key technical and governance criteria that enterprises and governments use to assess whether an AI solution is trustworthy and production‑ready.
• Differentiate between prototype‑level and production‑grade AI architectures in terms of reliability, security, monitoring, and data management. 
• Design a lightweight, cost‑conscious infrastructure blueprint that takes their current “notebook‑first” setup one step closer to a production‑ready environment.
• Prioritize the top 3–5 technical and process improvements needed in their own startup to meet enterprise and public‑sector expectations within the next 3–6 months.
• Formulate a concise, non‑technical explanation of their AI solution’s trustworthiness (reliability, safety, and governance) that resonates with enterprise and government stakeholders. 

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:

• 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.

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:

• 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.