Roadblocks to adopting generative AI

Numerous substantial barriers prevent the use of generative AI in market research for organizations, preventing its mainstream adoption and deployment. Understanding consumer behavior, spotting trends, and making wise business decisions all depend heavily on market research.

There are difficulties in bringing gen AI into this process, though. To fully utilize generative AI in market research and give organizations the ability to get deeper insights and confidently make data-driven decisions, these obstacles must be removed. Below mentioned are some of the roadblocks to generative AI:

Insufficient data quality and quantity

Quality of data

Insufficient data can be a major barrier to the adoption of generative AI, especially when it comes to market research in enterprises.

According to a report by O’Reilly, ‘insufficient data quality’ was listed as one of the main obstacles in machine learning projects by 56% of surveyed AI practitioners in 2019. 

Let’s explore this in detail:

• Data reliability: Accurate and trustworthy data are crucial for market research. Data quality in generative AI relates to the precision, thoroughness, and applicability of the data used to train models.

• Biased data: Data that is erroneous or biased can provide biased results or faulty predictions when it is used to train generative AI models. For instance, the derived insights might not apply to a larger audience if the training data mostly represents a certain demographic or geographic region.

• Noise and inconsistencies: Information for market research is frequently gathered from a variety of sources, including questionnaires, interviews, social media, and transactional data. There may be noise, mistakes, or discrepancies in this data. It can impact the caliber of the training data and, as a result, the dependability of the generative AI models if not appropriately cleaned or filtered.

• A lack of labeled data: For training, several generative AI techniques, such as supervised learning, call for labeled data. Labeling market research data, however, may be costly and time-consuming. The construction and functionality of generative AI models can be hampered by the lack of labeled data availability.

Quantity of data

Deep learning models used in generative AI frequently need a lot of data to learn well. It can be difficult to gather a significant amount of pertinent data for market research for the following reasons:

• Sample size: Market research studies may have a limited sample size because of a variety of restrictions, including financial limits, time restraints, or access to target demographics. Small sample numbers can lead to inadequate data for generative AI model training, which can result in poor generalization and incorrect results.

• Privacy and confidentiality issues: Sensitive information, such as customer or confidential corporate data, is frequently used in market research. The collection and use of data may be constrained by privacy laws and ethical issues, which may reduce the amount of data accessible for training generative AI models.

• Long data gathering cycles: Completing thorough market research data can take some time. The process of gathering data may take weeks or months, delaying the training of generative AI models or the ability to make quick commercial choices.

ALSO READ: Can generative AI promote a human-centered approach?

Ethical considerations and the risk of potential misapplication

Data security and privacy

To train efficiently, generative AI models frequently need a lot of data. Market research is gathering and examining consumer data, including private data. There is a chance of data leaks, privacy violations, or unauthorized usage of sensitive information if the situation is not managed effectively. To maintain compliance with rules and safeguard customer data, businesses must employ effective data privacy and security procedures.

The number of deepfake videos discovered online has almost doubled since the previous year, according to a 2019 study published in the journal Science, underscoring the growing worry.

Bias and discrimination

Generative AI algorithms might unintentionally incorporate biases contained in the training data when learning from the data they are trained on. Biased data in market research might result in biased predictions or recommendations, maintaining prejudice or bolstering already-existing inequities.

Researchers from the Universities of Maryland and North Carolina showed that commercial facial analysis systems have gender and skin-type biases, with darker-skinned females experiencing higher mistake rates. 

Transparency and explainability

Because generative AI models frequently function as ‘black boxes’, it might be difficult to comprehend how they come to their outputs or choices. Particularly in market research, where stakeholders must comprehend the reasoning behind recommendations or forecasts, this lack of transparency can erode trust. It is critical to create AI methods that are transparent to users and stakeholders on the constraints, biases, and potential flaws of AI models.

Impact on society and unforeseen consequences

The use of generative AI in market research may have unanticipated effects on people and society. For instance, the over-personalization of recommendations may result in filter bubbles that limit the perspectives of different people and foster echo chambers.

Limitations in robustness and generalization

The inherent restrictions in robustness and generalization are significant barriers to the use of generative AI in businesses. These constraints have a substantial impact on the efficacy and dependability of AI-generated insights in market research.

• The term ‘robustness’ describes an AI model’s capacity to function reliably and accurately under a wide range of conditions and inputs. However, resilience is typically a problem for generative AI models, especially when they encounter data that is outside of their training distribution. This means that generative AI models may provide inaccurate or unreliable results if they are trained on a certain dataset and then applied to data that is drastically different. This might result in erroneous market research predictions or insights, which can be harmful to company decision-making.

• Another significant component that is impacted by generative AI’s limits is generalization. The ability of an AI model to apply previously learned information to the novel, unexplored material is known as generalization. Generic AI models may struggle to generalize adequately to novel contexts, even though they can be trained on enormous datasets to understand patterns and provide realistic outputs. The applicability and dependability of AI-generated insights to real-world settings may be constrained in market research as a result.

The application of generative AI in market research may be hampered by these shortcomings in robustness and generalization. Businesses rely on precise and trustworthy market insights to help them make decisions, and if AI models can’t reliably and accurately apply what they’ve learned to fresh data, the insights they produce could not inspire the level of confidence and trust that`s needed.

Requirements for computational power and infrastructure

The demanding requirements for processing power and infrastructure, particularly when it comes to doing market research, pose a substantial barrier to the adoption of generative AI in enterprises. This necessitates the use of significant computational resources to analyze enormous amounts of data, produce precise insights, and create trustworthy predictive models.

Businesses frequently rely on generative AI techniques in the field of market research to obtain a competitive edge by comprehending consumer behavior, spotting patterns, and making data-driven decisions. However, significant computational capacity and infrastructure are required due to the complexity and size of market research data.

Large dataset processing and analysis, intricate statistical calculations, and the development of complicated AI models all result in high computational demands. For these jobs to be completed effectively, high-performance computing tools, such as potent CPUs or GPUs, are frequently required. The infrastructure also needs to have enough storage to handle the large amount of data created during market research procedures.

A Figure Eight (now Appen) poll found that 37% of AI practitioners cited ‘lack of necessary hardware’ as a major barrier to the adoption of AI.

Issues regarding interpretability and explainability

The prevalence of challenges relating to interpretability and explainability is one of the major barriers to the adoption of generative AI in organizations, particularly in the context of market research.

Businesses frequently have trouble comprehending and justifying the conclusions produced by generative AI models when using them for market research. It might be challenging to understand how generative AI models, such as deep learning-based neural networks, arrive at their outputs or forecasts because of their complexity and non-linear nature.

• The term ‘interpretability’ describes the capacity to understand and articulate the justifications underlying the decision-making process of an AI model. Businesses must fully comprehend the generative AI model’s reasoning behind any recommendations or predictions given in market research. However, many generative AI models are black boxes, making it difficult to draw any real conclusions or justifications from them.

• On the other hand, explainability entails offering concise and understandable justifications for the outputs of the AI model. Businesses must explain and explain to stakeholders, such as clients, regulators, or internal teams, the decisions made by AI systems. Lack of explainability can breed mistrust, doubt, and resistance to generative AI adoption in market research procedures.

Making important business decisions based on the insights gained through data analysis is a common part of market research. Businesses may find it difficult to comprehend the underlying causes and variables that contribute to the outputs of the AI model without interpretability and explainability. The use of generative AI in market research may be hampered by this lack of transparency and reduced confidence in the outcomes.

According to a Deloitte poll, ‘lack of transparency and interpretability of AI models’ was cited as one of the top worries among executives when it came to the adoption of AI by 42% of respondents.

Addressing the disparity in skills and training needs

The implementation of generative AI within organizations is significantly hampered by the discrepancy in skills and training requirements, especially when it comes to doing market research. To obtain information and decide on their products or services, firms frequently turn to market research. However, employing generative AI systems for market research efficiently necessitates a team with the required expertise.

Methodologies used in market research typically include surveys, focus groups, and data analysis. Businesses now have the chance to use cutting-edge methods like natural language processing, picture identification, and predictive modeling to obtain a deeper understanding of consumer behavior, preferences, and market trends thanks to the development of generative AI.

Organizations require staff members with the ability to navigate and comprehend the results produced by these technologies to realize the potential of generative AI in market research fully. This entails comprehending the algorithms utilized, analyzing the data provided, and using critical thinking to glean insights that can be put into practice.

Wrapping up 

The adoption of generative AI in market research for businesses faces considerable hurdles that must be overcome for it to be widely adopted.

Protecting client information requires using massive datasets while maintaining data privacy and security. Understanding the reasoning behind generative AI models’ generated insights requires improving the interpretability and explainability of those models.

Additionally, it is crucial to establish confidence among stakeholders by addressing worries about bias, fairness, and possible abuse of created content. Businesses can successfully use generative AI in market research, get deeper insights, and make wise decisions that contribute to success in a fast-changing business environment by proactively tackling these barriers.

The entire potential of generative AI for market research in organizations will be unlocked through embracing responsible practices and encouraging collaboration between researchers, industry leaders, and regulatory bodies.

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