Generative AI stands as a transformative force, redefining the contours of consumer-business interactions. Its sophisticated algorithms, combined with deep learning capabilities, empower businesses to autonomously create compelling content, ranging from captivating visuals to persuasive copy. Concurrently, social media platforms have evolved into dynamic ecosystems, serving as epicentres of digital engagement where brands and consumers converge, fostering trust and community.

Empowering content creation

By continuously analysing user preferences and interests, Generative AI facilitates the seamless generation of tailor-made images, videos, and graphics. This, in turn, enables effective automation of creative processes, ensuring visually captivating content at scale. Consequently, operational efficiencies are achieved, saving users invaluable time and effort. This optimises the creative workflow, guaranteeing the production of engaging, high-quality content.

For instance, OnePlus, the global smartphone brand, recently unveiled its AI Music Studio that empowers its users to compose music using gen-AI-powered tools. The platform facilitates lyric creation and integration with AI-generated beats, offering diverse musical genres like rap, hip-hop, and EDM. With a user-friendly visual interface, it enables music video creation and supports downloading and sharing on various social media platforms.

Furthermore, Generative AI transcends traditional automation by actively learning from user engagement patterns. This adaptive approach enables the delivery of hyper-personalised content to users, elevating user satisfaction and bolstering retention rates. 

In the marketing realm, Generative AI's impact is transformative. By generating customised texts, images, and ad campaigns aligned with specific geographic and linguistic nuances, it not only amplifies productivity and creativity but also substantially reduces costs associated with content development, localisation, and multi-brand management. For instance, Cadbury recently partnered with, a company specialising in producing realistic, "studio quality" advertisements using AI tools and synthetic narrators. The #NotJustACadburyAd initiative empowered local store owners in India to create personalised ads for their businesses at no cost. Utilising Artificial Intelligence (AI) and Machine Learning (ML), the campaign allowed users to feature Shah Rukh Khan's face and voice seamlessly in their custom ads, offering a creative solution to support businesses affected by the pandemic. 

Baskin Robbins India, the global ice cream brand, recently introduced its  #ReimaginedinAi campaign that leveraged AI-generated images for the launch of new flavours. The images, crafted using Midjourney by AI artist Tapan Aslot, garnered attention across social media platforms. The images presented a visually appealing representation of the new flavours, including Unicorn Sundae, Mermaid Sundae, and Caramel Milk Cake.

Lastly, Indian biscuit brand Britannia leveraged AI in its latest campaign, creating a magical biscuit-themed world using Midjourney and Adobe Firefly. This AI-powered approach delivered significant creative gains while resulting in significant cost savings.

Enhancing customer care 

With Generative AI, businesses have a potent opportunity to boost productivity, enhance personalised support, and foster growth. Large Language Models (LLMs) are taking customer service automation to new heights. Trained on vast data, LLMs can handle complex tasks, providing swift, human-like responses. According to a 2022 survey, 95% of global customer service leaders expect AI bots to play a crucial role in customer interactions within the next three years, reflecting the industry's rapid adoption of advanced AI solutions.

For instance, MakeMyTrip, the online travel company, is introducing a voice chat service driven by artificial intelligence (AI) leveraging generative AI. The service will be initially available in English and Hindi, accessible on MakeMyTrip's mobile app and website. The voice assistant aims to streamline flight and holiday bookings through simple visual cues and voice commands in native Indian languages.

Two AI-generated news anchors, Lisa and Sana, have been introduced to Indian news networks in recent months. Lisa, with her monotone voice and blinking quirks, reads news headlines for Odisha TV. Sana, her counterpart, works for Aaj Tak, part of the India Today network. 

As per Octopus Energy, a renewable energy group, emails composed by an AI application in its customer service platform resulted in 18% higher customer happiness scores compared to email responses generated by humans alone.

The importance of social listening and analytics

Social listening plays a pivotal role in Generative AI by providing valuable insights into user preferences, behaviours, and trends. By analysing social media conversations and feedback, Generative AI models can better understand the language, tone, and context that resonate with users. These insights enable the AI algorithms to generate content that aligns with the audience's interests, creating more relevant and engaging outputs. Social listening helps in fine-tuning the algorithms, ensuring that the generated content is not only accurate but also culturally and contextually appropriate. 

Myntra, the Indian fashion e-commerce platform, has deployed MyFashionGPT, a ChatGPT-powered feature in its shopping app, guiding users in simple conversational language about their shopping requirements. The tool saves users from carrying out multiple searches for products they were looking for. For instance, if users need to shop for a wedding function, they could give a prompt to MyFashionGPT on their requirements, and the tool would suggest outfit choices.

Similarly, Spotify has smoothly incorporated ChatGPT into its applications, offering listeners personalised DJs. This creative method customises the listening journey, crafting a unique musical experience for every user. This integration enhances interactions, making them more engaging and intimately individual, enhancing the overall user experience.

Addressing privacy concerns and navigating algorithmic transparency

As we delve deeper into the fusion of Generative AI and social media, it becomes imperative to address the ethical considerations surrounding this synergy. Issues like data privacy, algorithmic biases, and the responsible use of AI-generated content are paramount. Striking a balance between innovation and ethical practices is crucial to building consumer trust. Companies need to invest in robust ethical frameworks, ensuring that their AI applications align with societal values and respect user privacy.

Generative AI faces significant challenges concerning the extraction and use of human-generated content. Often, content creators never intended their work to be utilised for training such models, leaving them powerless in this process. This issue is particularly alarming because these models can replicate individual styles, essentially taking over human-created content, and rendering the original creators obsolete. What makes this situation worse is the lack of compensation or legal recourse for these content producers, underscoring a major flaw in current regulations governing data extraction and usage for model training.

Manish Gangwar, Associate Professor, Marketing, says, “Generative Artificial Intelligence (AI), as showcased by ground-breaking technologies like ChatGPT and Midjourney represents a new paradigm for consumer engagement. Working alongside social media platforms, Generative AI transforms mere basic communication tools into vibrant ecosystems. It is not just about cutting-edge technology; it is about crafting authentic connections, personalisation, and nurturing trust.”

Key strategies for business leaders:

To successfully use Generative AI, businesses must carefully consider both the use cases and implementation. Firstly, it is essential to identify relevant use cases. Businesses should carefully consider where Generative AI can add the most value, and focus on those areas. Secondly, it is important to implement Generative AI solutions following industry best practices. This will help to ensure that the solutions are efficient, effective, and scalable. 

Effectively measuring the success of Generative AI and social media integration initiatives is key to refining strategies. Metrics such as engagement rate, sentiment analysis, and conversion rates provide valuable feedback. Implementing robust analytics tools and methodologies allows buss to quantify the impact of AI-generated content on consumer behaviour. By analysing these metrics, businesses can make data-driven decisions, fine-tuning their approaches and maximising the ROI of their Generative AI investments.

We recommend the following:

  1. Develop a Comprehensive Strategy:

    Creating a comprehensive strategy for Generative AI involves a multifaceted approach. Businesses should initiate by conducting a thorough analysis of their existing AI initiatives, and understanding their strengths and limitations. Integration with Generative AI should be seamless, ensuring synergy rather than redundancy. Collaborating with external partners and businesses is invaluable. These collaborations can provide access to diverse perspectives, technologies, and resources, enabling businesses to navigate the rapidly evolving landscape effectively. This strategy should be adaptable, allowing for iterative improvements as technology advances and market demands evolve.

  2. Educate and Train Your Workforce:

    A well-informed workforce is the cornerstone of successful Generative AI integration. Regular and targeted training sessions are essential to educate employees about Generative AI technologies, their capabilities, and limitations. Awareness of potential risks and ethical considerations is crucial. Workshops, seminars, and continuous learning modules can keep the workforce updated on advancements, ensuring that they remain adept at leveraging Generative AI effectively. Additionally, fostering a culture of curiosity and innovation within the workforce encourages the exploration of new possibilities with Generative AI, leading to creative solutions and novel applications.

  3. Foster Cross-Disciplinary Collaboration:

    Generative AI's potential is vast and often best realised through collaboration. Engaging with diverse experts from various disciplines such as data science, psychology, design, and business strategy can uncover innovative use cases. External specialists, especially those from academia and research institutions, bring fresh perspectives and cutting-edge knowledge. Collaborative brainstorming sessions and hackathons can ignite creativity, leading to the development of novel Generative AI solutions. Cross-disciplinary collaboration not only enhances the quality of AI applications but also fosters a culture of innovation within the organisation.

  4. Curate Proprietary Data:

    Data is the lifeblood of AI, and curating proprietary datasets is pivotal. Businesses should focus on collecting unique and high-quality data sets relevant to their specific industry or domain. These datasets serve as the foundation for training Generative AI models tailored to the organisation's needs. The data curation process involves cleaning, structuring, and annotating data to ensure its accuracy and relevance. By leveraging proprietary data, businesses can develop distinctive Generative AI applications that provide unique value propositions to their customers. Data security and compliance with relevant regulations should be prioritised during the data curation process to safeguard sensitive information

  5. Build Trust and Transparency for Adoption:

    Building trust in Generative AI applications is essential for user acceptance and long-term success. Addressing challenges related to bias, misinformation, transparency, and accountability is paramount. Implementing explainable AI techniques allows users to understand how AI-generated decisions are made, enhancing transparency. Rigorous testing and validation processes can identify and mitigate biases in the AI models, ensuring fairness. Regular communication with users about how their data is used and the purpose of AI applications builds transparency and trust. Additionally, being accountable for the impact of AI applications and addressing any unintended consequences promptly demonstrates a commitment to ethical AI practices. Upholding these principles creates a foundation of trust, fostering positive relationships with users and stakeholders.


For businesses embarking on this AI journey, the key lies in a balanced approach. It's not just about adopting the trend but about meticulous planning and execution. Identifying relevant use cases and adhering to industry best practices are foundational steps.

Most importantly, building trust through addressing biases, ensuring transparency, and upholding accountability principles is paramount.


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Manish Gangwar