Back to Blogs

Harnessing Generative AI for Better Business Decisions Insights from Industry Leaders

Last fall, our CEO, Shashank Garg, had the privilege of moderating a thought-provoking panel discussion at the CDAO Fall event in Boston. The topic of the discussion was “Exploring the Potential and Challenges of Gen AI,” and it featured distinguished leaders from various sectors, lending their expertise and insights to the conversation about how Gen AI benefits their businesses. The panelists included:

  • Larry Allen, Vice President and General Manager of Data Enablement at Comcast, overseeing their ad sales data monetization business.
  • Colin Coleman, Chief Data and Analytics Officer at Inspire Brands, the second-largest restaurant company in the United States.
  • Anu Sundaram, Vice President of Business Analytics at Rue Gilt Groupe, a portfolio of 3 different brands focused on luxury goods.
  • Kamal Distell, Data & Technology Leader at Toyota Motor Corporation, focusing on integrating data and technology to drive business decisions.

The Visionary Potential of Generative AI in Business

Shashank discussed the evolving techniques of Generative AI, highlighting the importance of AI foundation models, also known as LLMs, which are trained on broad, unlabeled data for versatile applications. He expressed excitement about the availability of these models to revolutionize tasks like classification, simulations, forecasting, and summarization. To start the discussion, Shashank invited the panelists to imagine a world without restrictions, asking them to envision the grandest potential of Generative AI within their industries. Here are the top insights from the panelists:

  • Personalized Consumer Experiences: One of Gen AI’s greatest business use cases lies in creating highly individualized consumer experiences. By understanding and predicting consumer preferences, behaviors, and patterns, businesses can deliver personalized advertising, messaging, and dynamic pricing. This level of precision ensures that every interaction is tailored to meet the unique needs and desires of each consumer, enhancing satisfaction and loyalty.
  • Rapid Productization of Solutions: Gen AI’s computational power and rich data can be harnessed to quickly develop and deploy personalized productized solutions. This enables businesses to respond to consumer demands promptly, creating a continuous feedback loop that adapts and evolves with consumer preferences. The ability to produce these solutions swiftly is a significant competitive advantage that Gen AI provides for businesses.
  • Enhanced Sales Capabilities: For sales teams, Gen AI offers deep personalized insights into clients’ needs. Sales professionals can access data that helps them craft successful media campaigns, generate proposals efficiently, and ensure both advertisers and customers are satisfied. This enhances the human element in sales with precise, data-driven predictions.
  • Efficient Analytics Operations: From an analytics perspective, Gen AI can automate mundane tasks like documentation, allowing teams to focus on storytelling and delivering actionable insights. This not only increases efficiency but also improves the quality of the insights generated.

Addressing Challenges and Concerns with Gen AI

As with any new technology, Generative AI presents numerous challenges and concerns. The panel discussed three critical issues that must be addressed to ensure the value and success of Gen AI initiatives in business:

  • Change Management: One of the biggest challenges is managing the change that Gen AI brings. Ensuring that teams have the necessary skills to handle this technology is crucial. The shift from structured data analysis to dealing with unstructured data and new types of queries requires extensive training and adaptation.
  • Fraud Prevention: Fraud is a major concern in the media and advertising industry. The automation of digital environments and content creation through Gen AI can make it difficult to distinguish between human and machine-generated content. This could lead to an increase in low-quality or fraudulent content, siphoning off advertising dollars and impacting high-quality journalism and media production.
  • Data Security and Privacy: The speed at which data is consumed and utilized by Gen AI poses significant risks. Vendors seeking to access data to train their models raise substantial security and privacy concerns. Robust policies and agreements are necessary to protect data and ensure compliance with regulations.

Balancing Potential with Prudence

Shashank emphasized the importance of balancing the potential benefits of Generative AI for business with careful consideration and strategic planning, offering four specific recommendations:

  1. Safe Experimentation: Creating safe spaces for experimentation is crucial. This involves setting up cross-functional teams that include legal, governance, customer service, and marketing to ensure a holistic approach to testing and understanding the impact of Gen AI applications.
  2. Responsible AI Frameworks: Developing responsible AI frameworks and policies is essential. These frameworks should address compliance, privacy, cybersecurity, and data governance, ensuring that all stakeholders are comfortable with the technology’s use and its implications.
  3. Showcasing Value: Demonstrating the return on investment (ROI) from Gen AI initiatives in business is vital. Small-scale, high-impact projects can showcase the technology’s potential and build momentum for broader adoption.
  4. Cross-functional Collaboration: Setting up cross-functional teams to drive Gen AI initiatives ensures diverse perspectives and comprehensive oversight, helping to mitigate risks and maximize benefits.

Generative AI for Business: Key Takeaways

The discussion underscored the transformative potential of Generative AI across various industries, highlighting several key insights, summarized below:

  1. Hyper-Personalization and Enhanced Consumer Experiences: Gen AI enables unprecedented opportunities for creating personalized interactions, predictive sales insights, and improved operational efficiency.
  2. Navigating Challenges: Effective implementation of Gen AI in business requires careful consideration of change management, fraud prevention, and data governance.
  3. Safe Experimentation and Robust Frameworks: Fostering safe experimentation, establishing robust frameworks, and encouraging cross-functional collaboration are essential to harnessing Gen AI’s power to drive innovation and achieve significant business outcomes.
  4. Strategic Planning: Integrating Gen AI into business processes is a complex journey, but with strategic planning and thoughtful execution, the rewards can be substantial.
  5. Ethical Considerations: As we explore the possibilities of Generative AI, it is crucial to remain vigilant about ethical considerations and to prioritize the responsible use of this powerful technology.

Let’s embrace the future of Gen AI with optimism and caution. To learn more about how Infocepts can help incorporate Generative AI into your Data & Analytics strategy, visit our Generative AI page.

Shanthi Srinivasan


Head of Marketing

Shanthi Srinivasan is an accomplished data professional with over 20 years of experience delivering transformative data and AI solutions to global enterprises. As Head of Marketing at Infocepts, she drives go-to-market strategies and brand awareness. She recently received the Women in Tech Leadership Award for her contributions to the field.

Read Full Bio
Recent Blogs