I read that the time required to produce a live-action film was anywhere between 2-6 weeks. So when “Chai and Secrets”, produced by Paco Torres, shown at the Pune International Film Festival, created in 240 minutes, stitched with a bunch of AI tools, one becomes aware of the narrative shift

Living with, embracing, learning and using AI will disrupt and reveal new avenues. Large Language Models, when in the hands of good actors, create masterpieces of experience. I lean on these to share learning and points of view about AI in storytelling, branding and marketing.

Deep learning models are continuously evolving to transform content generation and marketing, enabling brands to adapt to customer and market changes faster than ever before. 

Led by transformer-based models for text generation like GPT (from OpenAI), T5 by Google, Generative Adversarial networks (StyleGAN, CycleGAN) and Diffusion Models (Dall-E) for image generation, coupled with Audio, Music and Video generation (JukeBox, Synthesia) make life for a content creator more exciting. 

An interesting campaign that I’ve been following for a few months now is by Air India. Ever since it was taken over by the Tata Group, which has been using AI for their branding, content and marketing campaigns. One look at their YouTube playlist and there’s something for everyone. It appears AI has well and truly embraced AI! My sense, watching its content, tells me that a team of highly creative people have worked with AI to produce prolificly, frequently, and fairly personalized content while maintaining the brand voice of Air India that reflects an emotionally engaging, globally aspirational and premium service. A quick glance at the old v/s new tagline from “Your Palace in the sky” to “Fly the new feeling” says it all.

For a content marketeer, there is a plethora of AI playgrounds that can be tuned with prompt engineering combined with rapid iteration to ease the challenge of creating highly tailored marketing content. Building and managing volumes of tailored variants has become seamless and measurable thanks to hyper-automated workflows in marketing tech stacks running on top of data intelligence platforms.

AI-enabled workflows improve efficiency, improve feedback and continue to keep the human in the loop while producing better results and models with less additional effort.

Let me go deeper into how deep learning is changing brand storytelling:

1. Personalized content creation

   • Marketing campaign models are augmented user interaction data (clicks, dwell time, purchase history) to update and improve content preferences and create tailored recommendation while remaining aligned with broader campaign goals.

   • Generative AI systems trained on brand campaigns produce variations tailored to different customer segments and automation creates thousands of personalized ad variants.

2. Improved visual storytelling

   • Text-to-image models like DALL-E and Midjourney enable brands to rapidly generate visuals that match specific brand guidelines and emotional tones.

   • Computer vision algorithms analyze successful campaigns to identify visual elements that drive engagement, helping brands optimize their imagery.

3. Voice and tone consistency

   • Large language models continuously fine-tuned on brand materials learn to mimic specific brand voices, enabling consistent messaging across thousands of content pieces and across multiple campaigns.

   • Sentiment analysis tools scan all brand communications to ensure emotional consistency and flag potential misalignments.

4. Real-time adaptation

   • A/B testing platforms powered by reinforcement learning automatically optimize content based on real-time performance metrics and content lifecycle management for retiring and/or refreshing content that improves AI recommendations

   • Natural language processing monitors social conversation about campaigns and recommends narrative adjustments based on audience reception.

5. Emotional intelligence

   • Facial recognition in digital ads tracks emotional responses to specific storytelling elements, helping brands understand what resonates.

   • Multimodal AI systems like the ones mentioned in the film creation earlier,  analyze text, images, and audio together to measure emotional coherence across brand touchpoints.

6. Multilingual and multicultural reach

   • Neural translation models preserve brand voice and cultural nuances when adapting content to new markets.

   • AI tools scan for cultural sensitivities and recommend adjustments to make stories relevant across different cultural contexts.

Tying it all in with an AI-led content strategy offers clarity on how best to leverage deep learning and neural networks to transform the content creation, delivery and optimization processes. Company strategy can leverage AI across 

• Planning for content intelligence by using AI to conduct audience analysis, gather competitive insights and trend forecasts

• Enhance the creation process using AI with human collaboration for emotion & creativity while easily integrating multimedia

• Optimize distribution channels with AI models that predict campaign performance, adapt to specific market segments and enable quick A/B testing

• Measure and continuously improve branding strategies with natural language processing to correlate content elements with brand performance and content refreshes.

Investing in an AI led content strategy is a bold move that will change the ways businesses and products evolve. Areas to improve on include –

• Biases – most models are trained on a “western” view of the world and are biased with their training data. Case in point being the story of an Indian musician who designed Sur, an AI-powered stem splitter. No existing music production tool had been trained on Indian Classical Music until then.

• Guardrails that safeguard interest of the business and its customers. Smart data intelligence platforms allow for deep customization of content generation with guardrails, so results are reviewed before delivery.

• Using models that can respond to different languages and cultural data sets. Global open source models are a great place to start, coupled with the Government of India’s AI Kosha data sets can help personalized marketing & content platforms reach wide and deep across the sub-continent and the Global South, democratizing AI for everyone.

These are a few noteworthy ones. LLMs are improving fast. Machines are on track to be a lot smarter, says the godfather of AI, Geoffrey Hinton, than he’d previously thought. “Few-shot” learning models means that it takes very little for LLMs to quickly “learn” a new thing it wasn’t previously trained on. With this premise, content marketing, branding and storytelling are poised to experience a dramatic shift. 

I wear an optimist’s hat in saying that disruptions will create new avenues for humans. Learning how to embrace what’s upon us can only allow our brains to uncover new paths and, who knows, give new challenges for neural networks to surpass.

P.S. As an Indian, one will spot several flaws in the movie. They are gone in 60 minutes.