Interview with Rohan Agrawal - CEO of Cogito

Shauli Zacks Shauli Zacks

SafetyDetectives had an insightful interview with Rohan Agrawal, CEO of Cogito, delve deep into the rapidly evolving world of AI training data. Cogito is a global leader in this niche, offering human-in-the-loop workforce solutions in Computer Vision, Natural Language Processing, and more. With a mission to create 360° value for AI and Business Initiatives, the company serves a diverse clientele including Fortune 100 giants like Medtronic, Siemens, and Amazon. Agrawal candidly discusses the major challenges facing the industry, such as quality and bias in training data, as well as the promising trends in AI that are revolutionizing customer engagement. Read on to gain a thorough understanding of how Cogito is shaping the future of AI and technology applications.

Can you introduce yourself and talk about your role at Cogito?

I come from a biomedical engineering background and was working with Pfizer as a Researcher. Back then, in the absence of a reliable and accurate data sourcing technology, we faced significant challenges to create workable models. The whole process was devoid of structured & labelled data, making our work cumbersome. It was then I realized the need for curated data to train and build our AI models, which could process humongous datasets efficiently. I could see the huge untapped potential and the role AI could play, and started Cogito.

What are Cogito’s main services?

Cogito is an AI training data company and is a global leader in its domain, offering human-in-the-loop workforce solutions comprising Computer Vision, Natural Language Processing, Content Moderation, data, and document processing. Cogito’s mission is to embrace the power of human ingenuity and technology to create 360° value for AI and Business Initiatives. The company’s vision is to support the development of game-changing AI and Technology Applications by providing cutting-edge workforce solutions to solve everyday business needs. We serve numerous Fortune 100 companies; our clients include Medtronic, Siemens, Amazon, Vimeo, Insitro, Unilever, and many more…

What are the biggest challenges facing the AI training data industry today?

While AI has become the poster boy of the tech industry, I strongly believe that the success or failure of AI initiatives has more to do with people than with technology.

The AI training data industry faces several significant challenges. The following are a few key challenges that I see our industry facing:

  • Quality and Bias in Training Data: The quality and fairness of training data are of paramount concern. Biases present in data can lead to biased AI models, which can have real-world ethical and social consequences.
  • Data Privacy and Security: Collecting, storing, and managing large datasets while adhering to data privacy regulations (such as GDPR and CCPA) can be challenging. Ensuring data security and protecting personal and sensitive information is crucial.
  • Data Labeling and Annotation Costs: Labeling and annotating data for AI training can be expensive and time-consuming, especially for tasks that require human involvement. Finding cost-effective and efficient labeling partners is a constant challenge for most AI model developers.

What trends in Artificial Intelligence (AI) and machine learning do you find most promising for the future of customer engagement?

As AI-powered customer service become the fastest and most efficient means for organizations to provide tailored, forward-looking interactions that help boost customer involvement and loyalty.

Following are some promising trends which I can foresee.

  • Personalization at Scale: AI has the uncanny ability to deliver highly personalized customer experiences by analyzing data and tailoring recommendations, content, and interactions to individual preferences.
  • Conversational AI: Chatbots and virtual assistants, powered by natural language processing (NLP), provide instant, human-like interactions, offering 24/7 support and enhancing customer engagement.
  • Generative AI: Generative AI, including language models like GPT-3 & visual models like Stable diffusion & DALL-E can generate human-like text and images, enabling businesses to create personalized content, responses, and product descriptions, thereby enhancing customer interactions.
  • Predictive Analytics: Machine learning algorithms predict customer behavior, helping businesses anticipate needs and engage customers with relevant offers and solutions.
  • Sentiment Analysis: AI-driven sentiment analysis tools assess customer sentiment from various sources, allowing companies to gauge satisfaction levels and address issues promptly, improving overall customer engagement.

What are some key performance indicators (KPIs) that companies should focus on when measuring the impact of AI-driven solutions?

Measuring the effectiveness of AI systems and projects within organizations involves assessing them through a set of critical metrics. While some of these key performance indicators (KPIs) are primarily quantitative, others are of qualitative nature. As per me some of the important ones are listed here:

  • ROI and Cost Efficiency: One must evaluate the financial impact and cost-effectiveness of their AI solutions, ensuring it delivers value and reduce operational expenses.
  • Customer Satisfaction and Engagement: As an ongoing process, companies should measure changes in customer satisfaction, retention, churn, and other engagement matrices to assess the overall impact of AI on the customer experience.
  • Operational Efficiency and Productivity: Businesses need to continuously monitor improvements in efficiency, error reduction, and productivity achieved through AI-driven automation and insights.
  • Data Quality and Accuracy: One of the most important things that companies need to monitor the quality of input data, output of AI-generated predictions and its accuracy vastly relies on data cleanliness which eventually drives the success of any AI model.

As AI technologies become more integrated into daily operations, how do you think the role of human employees in customer service will evolve?

The role of human employees in customer service is set to evolve significantly. AI-driven tools, such as chatbots and virtual assistants, are increasingly automating routine and repetitive tasks, allowing human employees to focus on more complex and value-added aspects of customer interactions and ultimately enhancing the overall customer experience.

In near future it will be AI that is likely to take on the routine workload, allowing employees to provide personalized, and high-value interactions. Human employees will transition from handling routine inquiries to becoming customer experience specialists. They will play a crucial role in handling complex issues that require empathy, creativity, and nuanced problem-solving areas where AI currently falls short!

About the Author

About the Author

Shauli Zacks is a tech enthusiast who has reviewed and compared hundreds of programs in multiple niches, including cybersecurity, office and productivity tools, and parental control apps. He enjoys researching and understanding what features are important to the people using these tools.