Mr. Rajesh Kari, VP and Business Leader, Infovision Social—the Research, Digital Experience, Social Media Analytics arm of InfoVision.
Generative AI has exploded into the mainstream in recent months. The introduction of tools like ChatGPT and Google’s Bard has really set the ball rolling on AI. And it is picking up momentum with every passing day!
Generative AI algorithms have opened up a whole new opportunity to create content and redefine the research landscape. With its ability to generate new text, analyze complex data and create entirely new forms of content, generative AI is evolving into an increasingly valuable tool for integrated research.
Generative AI can play a crucial role in creating homogenous sets of data by going through larger sets of information from different sources. It can create meaningful clusters from diverse data sets. It can also help in simulating certain specific conditions within data sets.
With a career spanning over 17 years, I have been working with clients across verticals, including the pharma space, to deliver advanced research and insights. Generative AI could be a game changer in the research and insights world. I have been exploring the use of generative AI around brand, industry and customer insights. Even in this exploratory stage, generative AI has shown tremendous potential to revolutionize research. Here’s how companies can take advantage of generative AI.
Create synthetic data and protect privacy.
Research, on most occasions, leads to actionable insights. But it also often runs into the risk of revealing sensitive consumer-level information. This can further lead to legal issues, especially within the healthcare industry vertical.
Pharma research companies have to go to great lengths to ensure that they comply with the regulations in place with respect to personally identifiable information of the target audience. Generative AI could help address this challenge. Companies can leverage generative AI to create synthetic data sets for analysis purposes. The synthetic nature of these data sets ensures that there is no infringement of patient privacy.
Extrapolate clusters for rare audience groups.
When it comes to analyzing niche audience segments, the sample size of the cluster created may make the research findings unreliable. But, the rarity of selection criterion itself can make it hard to create a larger audience cluster. A good example here would be people suffering from rare medical conditions.
Given the fact that these conditions are rare, the patient population can be hard to find. This can further make it hard to understand the patient journeys, their unmet needs, the burden of the illness, etc. Companies can use generative AI to help extrapolate patient groups based on the medical conditions for deep dive analysis that reveals actionable insights.
Simulate conditions to understand market behaviors.
A lot of market research these days involves a predictive angle. Business leaders want to stay prepared for different scenarios and their impact on the business. There are a lot of what-ifs that they want to add to the research methodology to understand how the market behaviors would change.
Generative AI can help simulate conditions within a certain audience cluster. For example, leaders can ask the AI to create a different price point for a product and reveal how that would change the purchasing behaviors of customers. Similarly, they could ask the AI to add a certain discount offer or change product packaging to uncover customer response to these changes.
This becomes extremely crucial for business leaders who want to make data-backed strategic decisions. They can pre-empt the market reaction to their change in strategies and prepare for it proactively.
Visualize data to reveal patterns.
When it comes to integrated research, one needs to combine structured data sets with unstructured information. These data harmonization efforts take time when attempted through traditional methodologies.
Generative AI can help here as well. It can help in harmonizing diverse data sets and slicing and dicing this information to reveal patterns and nuances that could have been missed earlier. The technology can analyze larger data volumes faster, identify patterns and create visual dashboards to make it easy to comprehend for executive leaders.
Create research surveys.
A lot of research depends on the firsthand feedback shared by users or consumers. However, to draw meaningful insights from this feedback, one needs to ask the right questions. But how to identify these right questions?
Here, leaders can ask generative AI to recommend survey questions based on the prompts and the survey objective. It can step into the role of a survey designer and create a questionnaire that would reveal answers to complex business problems. Generative AI—when trained to understand the scope of research, the type of data that needs to be samples and the market condition—could help drive primary research efforts.
Make sure you understand the risks of AI.
While there are a lot of positives with the use of generative AI, the technology is not completely risk-free. Here are a few red flags one needs to consider when using AI for research.
• AI would not give correct information/insights if the AI models are trained on inaccurate data.
• AI can be impacted by the personal biases of the people training the models in the first place.
• The complexity of AI algorithms can make it hard to determine the decision pathways and assign accountability.
• AI can often disregard human experiences and judgments that are equally important for pharma decisions.
• Loosely trained AI models can create data security threats.
What is the future of generative AI?
It is true that generative AI is becoming an increasingly valuable tool for integrated research. It can help address latent problems like human errors, time taken to analyze data and more in research. And this is just the beginning!
We are standing at a precipice of change right now. I think the use of generative AI will lead to the evolution of more advanced research methodologies and custom research solutions. It could unlock more unexpected research insights to drive strategic decision making across different organizational levels.
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