TEACHING NOTE:
1. Case Synopsis (Summary)
This case explores the journey of a small boutique owner who experiences declining website traffic as traditional SEO-driven search results weaken due to the rising influence of AI-powered search tools like ChatGPT, Gemini, and Perplexity. The case showcases the boutique’s challenge: despite strong keyword rankings, organic traffic and conversions drop dramatically as users shift from keyword-based search behaviors to conversational, intent-rich AI queries.
The owner adopts AI Engine Optimization (AIEO) strategies to align with emerging user behavior patterns and AI retrieval mechanisms. The case integrates Diffusion of Innovation Theory to explain adoption challenges and Conversational Search Theory to explain why user search behavior is evolving.
2. Learning Objectives
By the end of this case discussion, students should be able to:
Understanding the Shift
Differentiate between SEO and AIEO, and articulate why traditional SEO is becoming less effective.
Understand how AI-driven search engines retrieve and deliver content using vector embeddings rather than keyword matching.
Frameworks & Theory
Apply Diffusion of Innovation Theory to analyze the adoption of AIEO by a small business.
Use Conversational Search Theory to explain emerging changes in user search behavior.
Strategic Application
Develop actionable AIEO strategies for small businesses.
Recommend a roadmap for transitioning from SEO to AIEO in resource-constrained environments.
Target Audience and Positioning
- Courses: E-commerce, Digital Marketing, Retailing, AI in Marketing, Technology Strategy.
Sita, a small-time entrepreneur from Mangaluru, runs a small boutique in the heart of the city. She sources her products from local weavers and designs long kurtas and dresses, which are then stitched by the tailors who work for her. She named her boutique “Manara,” which means illumination or inspiration in the Arabic language. She had started her boutique in 2019. In the first few months, the footfalls were as low as 200 per month, but with word of mouth, the popularity of her designs increased. Gradually, the footfall increased to about 1200 a month. Sita, being ambitious, felt that she had to increase the reach, and hence she decided to create a website “Manara.com”. With a shoestring budget, she began working towards it in 2021. She hired a freelance website developer and employed several interns from local B-schools in Mangalore. The students assisted with the photoshoot and the creation of the catalog, while the developer worked on building the website. She made sure that the website was optimized for search engines like Google and Bing.
Sita and her team worked on the conventional steps required for SEO, like identifying primary commercial keywords like “ethnic party wear for women”, “party anarkalis”. They ensured their product hierarchy was structured, and they mapped keywords to each category and party type. On-page SEO initiatives were taken, for example, they ensured every category and product page had conversion-focused meta descriptions like “chikankari anarkalis”, and the product descriptions were optimized for search. They used a minimum of 4 images per product. They also ensured that page load time was fast, aiming for < 5 seconds. Eventually, she started creating lookbooks and style guides. One of the most important things the team ensured was to have an absolutely clear “Call to Action” to improve conversion. The website was ready for launch by 2022. In the first year, the monthly average traffic was around 2500, with about 250 monthly orders. Sita still got the majority of her revenue from offline stores. In 2023, she further optimized the online store to hit website traffic of 5000 per month and monthly orders of 400. Sita then began planning to open two more branches in Mangalore and Bangalore, focusing on these initiatives. She reduced her attention to the website and anticipated Business as Usual in the coming years.. In the year 2024, there was not much of a change, but in 2025, she noticed that footfalls on her website reduced to 2500, which eventually led to a decline in her orders. Sita panicked and contacted her brother-in-law, who worked at Google Seattle, to figure out the problem. Her Brother-in-law told her the next wave in Digital transformation, i.e., AI, has changed the way things work. If we apply the Diffusion of Innovation theory, Sita can be categorized as an early adopter. She quickly recognized that traditional SEO elements like signals, keywords, backlinks, and page ranking are no longer sufficient in a landscape increasingly influenced by conversational AI systems. Current data indicates that while AI adoption is growing rapidly among small businesses in India—with surveys showing that around 70–80% are already using or experimenting with AI tools—the maturity of adoption varies widely. A significant portion employs basic AI functions such as chatbots, automation, or simple personalization, but only an estimated 20–40% have progressed toward more advanced, strategic AI Experience Optimization (AIEO) capabilities that integrate semantic search, recommendation engines, and end-to-end customer experience enhancement. Overall, AIEO adoption among Indian small businesses is still emerging but accelerating as affordability improves, awareness increases, and digital competitiveness becomes a priority.
He mentioned that, unlike in the past, where the majority of the product discovery happened through search engines, now most of the search happens through AI engines. Increased AI adoption is leading to “zero-click searches”. According to a report by Martech (2026), Google searches reduced by almost 20%; however, this decline is smaller in Europe, at about 2% to 3%. He further explained to her how AI engine optimization can be done, AIEO, which reads as Artificial Intelligence Experience Optimization, refers to the practice of designing, refining, and optimizing digital experiences specifically for AI-driven systems and interactions. The main focus of SEO is human users and search algorithms, whereas AIEO focuses on human engagement and AI comprehension.
Her brother-in-law mentioned that search terms are evolving from traditional phrases like “Pink party Anarkali kurtas” to more conversational inquiries such as, “What do you think would suit me for a Punjabi wedding where the Mehndi function theme is pink?” or “I am fairly stout and short; what kind of ethnic wear would look good on me?” Essentially, searches are becoming more dialogue-oriented. Consequently, your website should be equipped to provide answers to these questions in order to participate in AI-driven responses. This change highlights the need for your catalog pages to be more clearly defined, and your style guides should serve as comprehensive response resources for various potential queries. The conversational search theory explains the evolving user behaviour towards natural, multi-turn, intent rich queries, requiring content to be structured for semantic retrieval rather than keyword matching. A theoretical framework for conversational search was provided by Filip Radlinski and Nick Craswell in 2017, according to which the traditional information retrieval can be reframed by multi-turn, dialogue-based interaction between a user and a search system using natural language rather than a single isolated query. He also mentioned that the content has to be structured in such a way that it improves vectorized representations. He further explained to her that AI models convert text into vectors (a list of numbers), which makes content easier for AI models to understand, index, and use accurately. In short, vectorization converts inputs like sentences, a product description, an image, a user query into a list of numbers (a vector) that captures the meaning or features or inputs. He gave examples of content that can lead to poor vectorization and is good for vectorization.
Example 1 – Product Description
Confused Statement (bad for vectorization)
“Our outfits are kind of like traditional but also modern, suitable for many occasions, and people usually love the colors we use because they’re vibrant and stuff, making them fit any festival or celebration you might think of.”
Why this produces poor vectors:
Too vague (“kind of,” “many occasions,” “stuff”)
No clear subject
No specific attributes
Mixes too many ideas in one chunk
Lacks semantic structure
Clear Statement (good for vectorization)
“This embroidered Anarkali dress features hand-stitched mirror work, soft georgette fabric, and a floor-length silhouette. It is designed for festive occasions such as Diwali, weddings, and cultural celebrations.”
Why this produces strong vectors:
Specific product type → “Anarkali dress”
Clear attributes → “embroidered,” “mirror work,” “georgette,” “floor-length”
Clear use cases → “Diwali, weddings, cultural celebrations”
One idea per sentence → Clean chunking
AI can recognize style, fabric, purpose, and audience
He further gave her the metrics required for AIEO, what they measure, and why it matters.
| AIEO Metric | What It Measures | Why It Matters |
| AI Answer Share | Usage of your content in AI answers | Visibility in AI ecosystems |
| AI Citations | Mentions or links in AI output | Builds brand authority |
| Embedding Quality | Semantic alignment | AI can match your content to queries |
| Structured Data Clarity | Machine-friendly formatting | Better AI extraction |
| Factual Consistency | Reliability across sources | Reduces AI hallucinations about you |
| Entity Authority | Recognized expertise | AI promotes credible sources |
| AI Referral Traffic | Visits driven by AI answers | Replaces declining organic traffic |
Source:ChatGPT
Sita got a gist of what her brother-in-law explained to her. She started working towards building a stronger website with improved, structured content that could eventually lead to better AI engine discoverability. She worked on stylebooks, FAQs, and product catalogues. She avoided the earlier tactic of keyword fluffing, ensured there was consistent terminology, structured the content into logical chunks using short paragraphs, and started focusing on clear, simple, direct language.
Questions
- How is AI reshaping product discoverability compared to traditional search mechanisms?
- What are the major measures Sita has to take to convert her website to improve AI discoverability?
- Explain conversational search theory on the basis of the example given in the case.
- List and explain a few AIEO metrics
- Can you recommend a few measures that can improve “Manara’s” Product Visibility through SEO?
- What specific catalogue improvements are necessary before vectorization can work effectively?
References
