Citizens, Market Research, and Artificial Intelligence

Context – AI sees wider adoption, reducing concerns globally 

Artificial Intelligence (AI) has emerged as a transformative force in the modern world, influencing various aspects of our everyday lives. As AI continues to evolve, it brings with it a sense of urgency for societies to adapt while balancing hope and apprehension. On one hand, AI holds the promise of revolutionizing industries, improving efficiency, and solving complex problems. On the other hand, it raises concerns about ethics, privacy, and employment, making it a topic of global significance that demands our attention. We do see a trend towards positivity among global citizens➡️ significant jumps in usage (from 33% to 48%) and feeling excited rather than concerned about the technology (excitement outweighing concerns 57% to 43%, compared to a 50/50 split last year). 

Data Source: Google Ipsos Multi-country AI survey

The Citizen and AI –opposite poles of optimism and apprehension: 

When we look at the emerging relationship between citizens and AI, it is interesting to look at Indians versus their Global Counterparts. Indians, like others, are increasingly interacting with AI in their daily lives, from virtual assistants to personalized recommendations on digital platforms.

What sets Indian citizens apart is their unique blend of optimism and caution. There is a palpable sense of hope regarding the potential of AI to drive economic growth and enhance quality of life (63% of Urban Indians are “excited” about AI enabled products and services, versus 53% global average). However, concerns about data privacy, job displacement, and the ethical use of AI are prevalent. The Indians’ worries stem from…

• Around 40% are worried about companies and firms not protecting citizens’ data privacy, while 54% are worried about the impact of AI on their own job security.

On the other hand, there is a sense of optimism about AI being effective allies in managing the elusive “work-life balance”, with around 46% believing that AI will help them do their jobs faster and better!

Data Source: Ipsos AI Monitor

The Market Research Fraternity and AI

Within the market research industry, there are varying degrees of awareness and confidence regarding AI. Traditionally, AI and Machine Learning (ML) were seen as complex, technical domains requiring specialized expertise. These were often viewed as tools for data scientists and IT professionals rather than something accessible to the average market researcher. 

However, the advent of Generative AI (GenAI) has been a great equalizer. Tools like ChatGPT and DALL-E have democratized access to AI capabilities, allowing researchers with little technical background to leverage AI for tasks like content creation, data summarising and insight generation. This has rapidly scaled up awareness, usage, and excitement about AI within the market research community. Unfortunately, this widespread adoption has also led to some misuse, with concerns about the authenticity and quality of AI-generated content. Nevertheless, GenAI has created a broad base of AI literacy within the industry, setting the stage for the next phase of AI evolution in market research. The next stage of AI in market research is likely to unfold in the following manner:

1. Specialization within GenAI:

We can expect to see more targeted applications of generative AI, such as Agentic AI and specialized bots designed for specific research tasks. These tools will go beyond simple content generation, potentially assisting with survey design, respondent engagement, preliminary data analysis and Persona Agents.

2. Evolution of Analytical AI: 
As researchers become more comfortable with AI, we'll likely see a shift from basic Machine Learning (ML) to more advanced Deep Learning (DL) techniques. This progression will enable more sophisticated analysis of large complex structured and unstructured datasets, including sources like social media, customer reviews, and video content.

3. Synthetic Data:

The use of AI to generate synthetic datasets that mimic real-world data will become more prevalent. This approach can help researchers overcome data scarcity issues and privacy concerns, allowing for more robust analysis without compromising individual data privacy. By being able to generate large datasets from smaller seed datasets, we can look at prioritizing quality over quantity when thinking about designing programs which depend large samples of consumer responses, for example.

Concluding thoughts:
As the dynamic landscape of Generative AI (GenAI) evolves, embracing AI technology is essential while remaining aware of its current constraints. 
In the realm of Market Research, developing capabilities and expertise is crucial, as AI is poised to evolve into several specialised expert domains. ‘Human’ Specialisation within domains of AI will be an integral part of focused expertise. This will involve robust Research & Development (R&D) efforts and partnerships with academic think-tanks to drive innovation and understanding.