Excited about adding AI-augmented research into the mix but unsure how best to integrate it?

Using the Ontario sports betting market as a case study, iGaming experts, Andrew Faria and Tyler Gilchrist, delved into the transformative potential of synthetic data and generative AI in the market research industry. They explored how researchers can use these advanced technologies to imagine future scenarios and provide insights into consumer behaviours and trends.

We believe, human-led AI-augmented research is crucial for meaningful insights: AI provides a base of knowledge, while humans contribute individual experiences, cultural relevance, and context-specific sense-making. This combined approach enhances the value of projects and offers deeper, more reliable insights.

Key takeaways:

• AI tools, such as synthetic personas, can provide a head start at speed and scale, particularly for hard-to-reach individuals
• At present, human input is essential to make sense of tacit knowledge, latent needs, and unconscious thinking
• At RESEARCH STRATEGY GROUP, we use sense-making tools, such as change maps, to explore drivers of change and uncertainties, enhanced by AI-augmented brainstorming and horizon scanning
• Exercises like mapping change help provide insight into the types of questions to ask synthetic personas that push and challenge AI models

A full recording of the session is included below.

Case Study: Sports Betting in Ontario

Two years since legalization and the Ontario iGaming market continues to grow. With a diverse array of legal online sports books, the province offers a robust, regulated market for both consumers and operators. Focusing on the Ontario sports betting market, Andrew and Tyler used generative AI to create synthetic audiences, representing various types of bettors, to explore potential future scenarios and market dynamics.

Key findings from this study include:

Market Differentiation: In a highly competitive market with many platforms to choose, bettors expressed a desire for educational resources, transparency in odds and statistics, personalized betting experiences, and localized community engagement. These elements could help shape future points of differentiation for vendors in the market.

Responsible Gambling: The increasing integration of sports betting content into broadcasts has enhanced viewer engagement but also raised concerns about normalizing gambling among younger audiences. There is a call for public health education and robust age verification measures to ensure responsible gambling practices.

Integrity in Sports Betting: Despite a potential erosion of trust as cases emerge, bettors are not likely to abandon the market but will increase their due diligence when researching a bet. The study found that ongoing smaller game-fixing scandals could erode trust more significantly than a single large-scale incident. There is also a push for measures like removing ’Under’ player propositions to maintain betting integrity.

Enhancing generative AI with Human Insights

While generative AI provides a solid foundation, RESEARCH STRATEGY GROUP emphasizes the importance of human input in refining and contextualizing data. Real-world human qualities, such as biases and personal experience, are crucial for deep, meaningful insights that AI alone is currently unable to provide. By integrating human expertise, particularly in strategic foresight and context-specific sense-making, Tyler believes the value of synthetic data can be significantly enhanced.

The role of human researchers and participants is to complement and enhance the capabilities of AI and synthetic data. By providing context specific sense-making, tacit knowledge, strategic insights, and ethical considerations, humans ensure that AI applications are not only effective but also grounded in a deep understanding of the real world. This collaborative approach uses the strengths of both AI and human intelligence, leading to richer, more actionable insights and better strategic outcomes.

In summary, generative AI offers immense potential in processing and analyzing vast amounts of data. Yet it cannot replicate the nuanced understanding and contextual insights that humans bring to the table.

Four key areas where human input is crucial:

1. Context Specific Sense-making

Context specific sense-making is the process of understanding complex, evolving challenges and environments in a way that is relevant and meaningful to a group of people to enable more informed and effective strategic decisions. This is something that AI cannot currently do. This process is critical for developing a nuanced understanding of an industry or market. Researchers can connect disparate dots, identify underlying patterns, and provide meaningful interpretations that go beyond the surface-level findings generative AI might present.

For example, in the Ontario sports betting industry, human researchers can analyze the cultural, regulatory, and technological factors that influence betting behaviours. They can identify subtle shifts in consumer attitudes and predict how these might evolve in response to new developments. This depth of understanding is essential for creating strategies that are both effective and adaptable.

2. Tacit Knowledge and Latent Needs

Human research participants contribute tacit knowledge – the unspoken, often unconscious insights gained through personal experiences. These insights – elicited directly from real people – are invaluable because they reflect the real-world complexities and subtleties that generative AI might overlook. Tacit knowledge includes the biases, misconceptions, and emotional responses that shape human decision-making.

In the context of responsible gambling, for instance, human participants can provide insights into the personal and social factors that influence gambling behaviours. They can reveal the implicit beliefs and attitudes that drive their actions, offering a richer, more detailed picture of the factors at play. This level of understanding is crucial for developing interventions and strategies that resonate with real people.

3. Strategic Inputs from Experts

Strategic foresight and planning require inputs from experts who can apply their deep knowledge and experience to the analysis. These experts bring a wealth of institutional and category knowledge about how organizations and industries operate, which is vital for making informed strategic decisions. They can offer perspectives on the broader implications of trends and changes, ensuring that strategic plans are grounded in a realistic understanding of the environment and pass the ‘gut-check’.

4. Enhancing Generative AI Outputs

Tyler and Andrew emphasized, while AI can generate valuable data and initial insights, human researchers are essential for interpreting and refining these outputs. Researchers can challenge AI-generated findings, identify gaps or inaccuracies, and provide the contextual understanding needed to make the data actionable. This iterative process of human-AI collaboration leads to more robust, reliable, and insightful outcomes.

For instance, AI might identify a trend in sports betting behaviours, but human researchers can delve deeper to understand ‘the Why’ behind the trend. They can explore the motivations, desires, and fears that drive behaviour, providing a more comprehensive understanding that can inform strategic decisions.

4. Addressing Ethical and Moral Considerations

Humans need to ensure that AI applications align with social norms and morality, as understood by the researchers. This may mean, fine-tuning models to reflect reality. For example, we know there are bettors who use unlicensed sports betting platforms, which was not expressed by off-the-shelf generative AI models.

Mapping Change and Strategic Foresight

One of the sense-making tools we use is the change map, a strategic foresight tool that illuminates driving forces of change over time to help businesses navigate uncertain futures. A change map tracks periods of relative stability, separated by disruptions that reshape the landscape. These events could be technological advancements, regulatory changes, or shifts in consumer behaviour. Below this, we plot the forces that create the conditions for these eras and their transitions. For example, the growing popularity of generative AI will undoubtedly feature prominently in future change maps due to its transformative impact on various industries.

As the timeline progresses towards the future, two critical elements are highlighted:

• Predetermined elements are certain to occur, such as demographic shifts. These are predictable and form a stable foundation for strategic planning.
• Critical uncertainties, on the other hand, are variables that can significantly impact the future but are currently unpredictable. These uncertainties are crucial for scenario planning, as they represent potential game-changers that could alter the course of an industry. By acknowledging and preparing for these uncertainties, businesses can develop more resilient strategies.

By feeding critical uncertainties into AI models, Andrew and Tyler explored how different futures of sports betting in Ontario might unfold and how a range of factors might interact. This process not only enhanced the robustness of AI-generated predictions but also ensured that strategic decisions were grounded in a comprehensive understanding of potential future dynamics.

Integrating AI-augmented findings with strategic foresight allows the RESEARCH STRATEGY GROUP team to simulate various future scenarios. Combining context specific sense-making exercises – like creating a Change Map – with synthetic data and generative AI enables organizations to visualize and assess multiple future scenarios, making it possible to plan for a range of outcomes. This proactive approach helps businesses stay ahead of the curve, anticipate challenges, and seize opportunities as they arise.

Looking Ahead: A Partnership Between AI and Humans

By combining the strengths of AI with human expertise, the sports betting industry can navigate its future with greater confidence and creativity. AI can rapidly process and analyze large datasets, offering a base of knowledge, while humans bring in-depth understanding, context, and nuanced insights. This synergy can drive innovative solutions and informed strategic decisions in the Ontario sports betting industry and beyond.

For more details on the tools and methods discussed, and to explore how synthetic data and AI-augmented research can benefit your projects, email Andrew Faria at andrewfaria@rsginc.net and Tyler Gilchrist at tylergilchrist@rsginc.net.