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- DaysHoursMinSec
As AI rapidly becomes embedded in business processes and decision-making, organisations must adopt a governance, risk, and compliance (GRC) lens to manage both its promise and its exposure. This session explores AI security beyond technical controls, focusing on accountability, policy frameworks, and enterprise risk alignment. We will examine how AI introduces new categories of risk including data privacy concerns, model bias, regulatory uncertainty, and third-party dependencies while amplifying existing cybersecurity and operational risks. Participants will gain insight into emerging regulatory expectations and how to interpret evolving standards such as AI governance frameworks, data protection obligations, and sector-specific compliance requirements. At the same time, AI presents significant opportunities to strengthen organisational resilience. From improving risk analytics and enhancing compliance monitoring to enabling more proactive threat detection, AI can become a strategic enabler when governed effectively. The talk will highlight best practices for integrating AI into existing GRC models, establishing clear accountability, and ensuring ethical and transparent use.
Learning objectives:
By the end of this session, participants will be able to:
1. Describe key AI-related security and compliance risks within a GRC context.
2. Explain how emerging regulations and governance frameworks shape AI risk management practices.
3. Apply GRC principles to assess and manage AI-related risks in organisational settings.
4. Analyse the impact of AI on existing risk, control, and compliance frameworks.
5. Evaluate strategies for balancing AI innovation with governance, trust, and regulatory requirements.
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.
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 this session, participants will be able to:
1. Understand how venture capital investors evaluate AI startups beyond technology and traction.
2. Analyze common cognitive biases and decision drivers influencing investor behavior.
3. Evaluate why technically strong pitches often fail to convert.
4. Apply practical frameworks to structure clearer, more persuasive pitch narratives.
5. Refine their own pitch approach to better align impact, scalability, and investor expectations.
Every startup journey has a “next owner” of the vision, whether that is the public market, a strategic acquirer, or a new governance model. Capturing more than two decades of digital transformation leadership around the globe, this session aims to help founders connect their long-term exit scenarios with the daily decisions they make about technology, talent, and partnerships. Rather than treating exits as a strict financial milestone, participants will examine how to preserve mission and AI ethics as their ventures scale and transition.
Through real-world scenarios and guided reflection, founders will map how their desired exit outcomes connect to their funding choices and impact goals, and articulate a guided canvas exercise. Participants will outline who needs to win at exit (founders, early employees, investors, communities) and what success looks like beyond valuation alone. They will walk away with a simple, shareable one-page view of their venture’s possible exit paths and key trade-offs.
Learning objectives:
By the end of this session, participants will be able to:
1. Explain how exit choices influence product, data, and partnership strategy for AI-driven startups.
2. Compare multiple exit scenarios for their own venture and identify key trade-offs between financial, social, and governance outcomes.
3. Critique common misalignments between investor expectations and mission-driven exits, especially in AI for Good contexts.
4. Produce a one-page exit map outlining preferred scenarios, key stakeholders, and next-step actions.
