July 25, 2023

Generative AI in Data and Analytics: Benefits, Challenges, and Use Cases

Generative AI in Data and Analytics: Benefits, Challenges, and Use Cases

We came across a recent report by Gartner, Inc., titled "Generative AI Surveys" that provides an overview of the current state of Generative AI adoption, its benefits, challenges, and potential use cases.

This survey is particularly useful for data and analytics leaders who are using or building generative AI models to learn what their counterparts and colleagues in other teams care about when it comes to adopting generative AI. 

Let’s delve into these insights.

Adoption Barriers

The Gartner report is based on surveys conducted with 833 leaders across 7 business functions, 21 industries and 3 continents. These leaders, despite acknowledging the potential of Generative AI, also highlight several barriers to its adoption.

The main barriers data and analytics leaders face in adopting generative AI are the lack of real world source data for training or fine-tuning, inherited bias in the output, and inaccurate results from noisy data. These concerns are particularly relevant to, and can be resolved, during data collection, curation, and data labeling, where the volume, diversity, and accuracy of data are paramount. 

Data and analytics leaders will also need to take caution about the concerns of their colleagues in other teams. Top concerns from folks in the IT, InfoSec, Engineering, Sales, and Marketing teams are data security, vulnerabilities and inaccuracy of generated results, lack of corporate governance policies, and integration with existing tools. Collaboration with other teams to address these barriers are essential to ensure the models built are adopted and used to their full potential.

Benefits and Use Cases

Despite the challenges, the benefits of Generative AI are immense. 

Leaders across IT, InfoSec, and software engineering believe Generative AI can improve work productivity, agility to changing situations, and top & bottom-line financial performance. 

The report identifies several use cases for Generative AI, including AI-generated content creation for marketing and sales, personalized advertising, collaborative coding with AI, AI-assisted code generation, root cause analysis using AI, predictive analytics, threat detection and prevention, and AI-driven demand forecasting.

We recently posted about how Generative AI could change the game for the certain industries. Check it out here.

Generative AI for Data Labeling

Generative AI, with its ability to generate synthetic data, which can augment and complement the existing datasets to improve the performance of machine learning models. This could address the challenges of data scarcity and imbalance, which are common in machine learning projects. Furthermore, Generative AI could automate the data labeling process, reducing the time and resources required and improving the quality of labeled data.

However, the adoption of Generative AI for supplementing real-world data also raises several challenges. The potential for inaccurate or biased results is a significant concern, as it could affect the performance of machine learning models. Therefore, businesses need to establish robust governance policies and mechanisms to ensure the accuracy and fairness of AI-generated labels. 

Examples of such mechanisms include, but are not limited to:

  • Data collection, generation, and curation by a globally distributed workforce to ensure data diversity and reduce biases before model training and fine-tuning 
  • Accurate high-volume data labeling to reduce noise and overfitting to certain cases
  • Model output ranking and validation to ensure accurate results 
“Businesses need to establish robust governance policies and mechanisms to ensure the accuracy and fairness of AI-generated labels”


The Gartner report provides valuable insights into the adoption of Generative AI across various industries and business functions. It's clear that this technology could play a pivotal role in improving business productivity and outcomes. However, businesses must also address the challenges and barriers associated with the use of Generative AI to fully harness its benefits.

Check out our generative AI use cases.

Bryce Wilson
Data Engineer at Black.ai

Consistent support

If there's one thing that makes SUPA stand out, it's their commitment to providing consistent support throughout the data labeling process. The team actively and efficiently engaged with us to ensure any ambiguity in the dataset was cleared up.

Jonas Olausson
Data Engineer at Black AI
The best interface for self-service labeling.

Everything from uploading data to seeing it labeled in real time was really cool. This is just way simpler to use compared to Amazon Sagemaker and LabelBox. I was also very impressed with how the platform delivered exactly what we needed in terms of label quality.

Sravan Bhagavatula
Director of Computer Vision at Greyscale AI
Launch a revised batch within hours

I was also able to view the labels as they were being generated, which gave me quick feedback about the label quality, rather than waiting for the whole batch. This replaced my standard manual QA process using external tools like Voxel's Fiftyone, as the labels were clear and easy to parse through in real-time.

Sparsh Shankar
Associate ML Engineer at Sprinklr
Really quick

The annotators were really quick. I would upload and 5 minutes later - 10 images done. I checked 5 minutes later - 100 images done.

Puneet Garg
Head of Data Science at Carousell
Good quality judgments

The team at [SUPA] has been very professional & easy to work with since we started our collaboration in 2019. They've provided us with good quality judgments to train, tune, and validate our Search & Recommendations models.

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