AI in Marketing - Devishi Sabharwal & Janvee Yadav.pdf
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Oct 09, 2025
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About This Presentation
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Slide Content
AI in Marketing
Personalization, Chatbots & Automation Driving Smarter Engagement
By Devishi Sabharwal &
Janvee Yadav
Introduction — AI in Marketing: an Indian
moment
Artificial intelligence (AI) is moving fast from pilot projects to mission-critical systems in marketing. In India — where
hundreds of millions of people have gone online in a few short years, a mix of global platforms and nimble domestic
startups compete for attention, and businesses must scale personalization across extreme diversity — AI is not just
an efficiency play. It’s becoming the central mechanism by which brands find customers, predict needs, automate
service, and create content at scale. Why India matters right now: India’s digital population is massive and still growing (hundreds of millions of active
internet users across urban and rural markets). That scale creates enormous volumes of behavioral data, which in
turn makes AI personalization and predictive systems both possible and highly valuable. At the same time, Indian
businesses span large legacy industries (banks, manufacturing, retail) and a booming services/tech sector — so the opportunity for AI-driven marketing is broad and uneven. (iamai.in)
Which Indian industries will be affected (and how)
AI in marketing will ripple across virtually every sector — but the shape of impact will differ by industry:
●Retail & E-commerce — hyper-personalized product recommendations, dynamic pricing, micro-segmented
promotions, visual search, and automated customer journeys. These applications directly lift conversion and
AOV. (BCG Media Publications)
●Financial services (banking, NBFCs, fintech) — AI improves churn prediction, next-best-offer engines,
fraud-informed targeting, and conversational agents that handle KYC and simple service flows. (BCG Media
Publications)
●Telecom & D2C consumer services — churn reduction, loyalty personalization and targeted upsell using
usage and device data. (BCG Media Publications)
●Media, entertainment & gaming — AI powers content recommendations, programmatic ad targeting, and
automated creative A/B generation. (BCG Media Publications)
Personalization, Chatbots & Automation Driving Smarter Engagement
By Devishi Sabharwal &
Janvee Yadav
Introduction — AI in Marketing: an Indian
moment
Artificial intelligence (AI) is moving fast from pilot projects to mission-critical systems in marketing. In India — where
hundreds of millions of people have gone online in a few short years, a mix of global platforms and nimble domestic
startups compete for attention, and businesses must scale personalization across extreme diversity — AI is not just
an efficiency play. It’s becoming the central mechanism by which brands find customers, predict needs, automate
service, and create content at scale. Why India matters right now: India’s digital population is massive and still growing (hundreds of millions of active
internet users across urban and rural markets). That scale creates enormous volumes of behavioral data, which in
turn makes AI personalization and predictive systems both possible and highly valuable. At the same time, Indian
businesses span large legacy industries (banks, manufacturing, retail) and a booming services/tech sector — so the opportunity for AI-driven marketing is broad and uneven. (iamai.in)
Which Indian industries will be affected (and how)
AI in marketing will ripple across virtually every sector — but the shape of impact will differ by industry:
●Retail & E-commerce — hyper-personalized product recommendations, dynamic pricing, micro-segmented
promotions, visual search, and automated customer journeys. These applications directly lift conversion and
AOV. (BCG Media Publications)
●Financial services (banking, NBFCs, fintech) — AI improves churn prediction, next-best-offer engines,
fraud-informed targeting, and conversational agents that handle KYC and simple service flows. (BCG Media
Publications)
●Telecom & D2C consumer services — churn reduction, loyalty personalization and targeted upsell using
usage and device data. (BCG Media Publications)
●Media, entertainment & gaming — AI powers content recommendations, programmatic ad targeting, and
automated creative A/B generation. (BCG Media Publications)
●Travel, hospitality & mobility — dynamic offers, predictive cancellations/upsell, and chatbots for bookings
and support. (BCG Media Publications)
●Education & EdTech — personalized course suggestions, automated outreach to reduce dropouts, and
scaled content generation.
●Healthcare & Pharma — patient outreach, appointment reminders, targeted awareness campaigns while
balancing regulatory/privacy needs.
●Manufacturing & B2B — account-based predictive marketing, lead scoring, and sales enablement with
AI-generated briefs and case studies. Note: reports also flag broad job impacts in manufacturing, retail and
education as AI adoption accelerates — meaning marketing teams will both automate tasks and redirect human work to higher-value strategy and creative roles. (The Economic Times)
How India’s population & digital behaviour are evolving
(implications for marketers)
●Scale + rural growth: Active internet users have surged into the high hundreds of millions, with rural
adoption increasingly important — meaning marketing must work across low-bandwidth contexts, regional
languages, and different device capabilities. Personalization needs to be multilingual and context aware. (iamai.in)
●Young, mobile-first audience: A large share of users are young and open to new interfaces (chat, voice,
short video). This makes chatbots, conversational commerce, and short-form creative especially effective.
(DataReportal – Global Digital Insights)
●Higher engagement, lower per-user spend: India’s engagement metrics are world-class, but monetization
patterns differ from mature markets — so marketers must optimize for volume, lifetime value, and low friction
conversions (e.g., UPI checkout, wallet flows). (Financial Times)
Tools available today (practical toolkit for Indian marketers)
A broad ecosystem already exists — a mix of global platforms and strong Indian players. Typical tool categories and
examples you can adopt now:
●Customer Data Platforms (CDPs) & Analytics: Clevertap, MoEngage, Adobe Analytics, Google Analytics
+ server-side integrations for unified profiles. (BCG Media Publications)
●Chatbots & Conversational AI: Yellow.ai, Haptik, native bots from Zendesk/Intercom, and bespoke
solutions using OpenAI/LLMs for complex queries.
●Personalization & Recommendation Engines: In-house ML models, recommendation SaaS, and
platforms integrated with e-commerce stacks.
●Content creation & creative automation: Generative AI tools (ChatGPT / OpenAI, Jasper, Copy.ai), image
generators and auto-video tools plus marketing workflow automations. (HubSpot Blog)
●Programmatic advertising & predictive analytics: DSPs with AI bidding, and predictive lead-scoring
models built on CRM + behavioral data.
and support. (BCG Media Publications)
●Education & EdTech — personalized course suggestions, automated outreach to reduce dropouts, and
scaled content generation.
●Healthcare & Pharma — patient outreach, appointment reminders, targeted awareness campaigns while
balancing regulatory/privacy needs.
●Manufacturing & B2B — account-based predictive marketing, lead scoring, and sales enablement with
AI-generated briefs and case studies. Note: reports also flag broad job impacts in manufacturing, retail and
education as AI adoption accelerates — meaning marketing teams will both automate tasks and redirect human work to higher-value strategy and creative roles. (The Economic Times)
How India’s population & digital behaviour are evolving
(implications for marketers)
●Scale + rural growth: Active internet users have surged into the high hundreds of millions, with rural
adoption increasingly important — meaning marketing must work across low-bandwidth contexts, regional
languages, and different device capabilities. Personalization needs to be multilingual and context aware. (iamai.in)
●Young, mobile-first audience: A large share of users are young and open to new interfaces (chat, voice,
short video). This makes chatbots, conversational commerce, and short-form creative especially effective.
(DataReportal – Global Digital Insights)
●Higher engagement, lower per-user spend: India’s engagement metrics are world-class, but monetization
patterns differ from mature markets — so marketers must optimize for volume, lifetime value, and low friction
conversions (e.g., UPI checkout, wallet flows). (Financial Times)
Tools available today (practical toolkit for Indian marketers)
A broad ecosystem already exists — a mix of global platforms and strong Indian players. Typical tool categories and
examples you can adopt now:
●Customer Data Platforms (CDPs) & Analytics: Clevertap, MoEngage, Adobe Analytics, Google Analytics
+ server-side integrations for unified profiles. (BCG Media Publications)
●Chatbots & Conversational AI: Yellow.ai, Haptik, native bots from Zendesk/Intercom, and bespoke
solutions using OpenAI/LLMs for complex queries.
●Personalization & Recommendation Engines: In-house ML models, recommendation SaaS, and
platforms integrated with e-commerce stacks.
●Content creation & creative automation: Generative AI tools (ChatGPT / OpenAI, Jasper, Copy.ai), image
generators and auto-video tools plus marketing workflow automations. (HubSpot Blog)
●Programmatic advertising & predictive analytics: DSPs with AI bidding, and predictive lead-scoring
models built on CRM + behavioral data.
●Martech suites with AI features: Salesforce, HubSpot, Netcore/Customer-engagement platforms that
embed AI features (audience segmentation, subject-line optimization). (HubSpot Blog)
The near-term future — realistic expectations & strategic moves
●From experiments to scaling: Surveys show marketers are rapidly moving from pilots to scaling AI across
campaigns — expect adoption to accelerate in the next 12–24 months for organizations that pair AI tools
with clean data and governance. (Marketing AI Institute)
●Human + AI workflows: The highest ROI will come from workflows where AI handles repetitive
personalization, variant generation, and 24/7 conversational loads — while humans steer strategy, creative
direction, and ethical oversight.
●Ethics, privacy & regulation: As models touch personal data and influence decisions, Indian firms will
need clear privacy practices and transparent AI policies to maintain trust (and to prepare for eventual
regulation).
●Competitive bifurcation: Early adopters who invest in data foundations, talent and MLOps will pull ahead;
laggards risk commoditization and weaker customer relationships.
AI-Driven Personalization in Marketing
Personalization has become a cornerstone of modern marketing. With the explosion of customer data across
websites, apps, and digital touchpoints, generic campaigns are no longer sufficient. Today’s consumers expect
brands to know their preferences, anticipate their needs, and deliver tailored experiences across every channel.
Artificial intelligence (AI) enables this at scale by analyzing vast datasets, detecting patterns in real time, and
dynamically delivering the right message to the right customer.
Personalization across marketing channels
SEO (Search Engine Optimization)
AI-driven SEO allows marketers to understand and respond to search intent more effectively:
●Analyzing search queries to distinguish informational, navigational, and transactional intent.
●Automatically generating optimized landing pages for different customer personas.
●Tailoring content clusters and long-tail keyword strategies for regional or linguistic segments in India.
Example: An Indian edtech company like BYJU’S could serve parent-focused content (long-term learning benefits)
while simultaneously generating student-focused pages (exam preparation strategies) using AI-driven keyword and
intent analysis.
SEM (Search Engine Marketing / Paid Ads)
AI optimizes paid campaigns through:
●Predictive modeling to identify which ad copy and visuals resonate with specific micro-segments.
embed AI features (audience segmentation, subject-line optimization). (HubSpot Blog)
The near-term future — realistic expectations & strategic moves
●From experiments to scaling: Surveys show marketers are rapidly moving from pilots to scaling AI across
campaigns — expect adoption to accelerate in the next 12–24 months for organizations that pair AI tools
with clean data and governance. (Marketing AI Institute)
●Human + AI workflows: The highest ROI will come from workflows where AI handles repetitive
personalization, variant generation, and 24/7 conversational loads — while humans steer strategy, creative
direction, and ethical oversight.
●Ethics, privacy & regulation: As models touch personal data and influence decisions, Indian firms will
need clear privacy practices and transparent AI policies to maintain trust (and to prepare for eventual
regulation).
●Competitive bifurcation: Early adopters who invest in data foundations, talent and MLOps will pull ahead;
laggards risk commoditization and weaker customer relationships.
AI-Driven Personalization in Marketing
Personalization has become a cornerstone of modern marketing. With the explosion of customer data across
websites, apps, and digital touchpoints, generic campaigns are no longer sufficient. Today’s consumers expect
brands to know their preferences, anticipate their needs, and deliver tailored experiences across every channel.
Artificial intelligence (AI) enables this at scale by analyzing vast datasets, detecting patterns in real time, and
dynamically delivering the right message to the right customer.
Personalization across marketing channels
SEO (Search Engine Optimization)
AI-driven SEO allows marketers to understand and respond to search intent more effectively:
●Analyzing search queries to distinguish informational, navigational, and transactional intent.
●Automatically generating optimized landing pages for different customer personas.
●Tailoring content clusters and long-tail keyword strategies for regional or linguistic segments in India.
Example: An Indian edtech company like BYJU’S could serve parent-focused content (long-term learning benefits)
while simultaneously generating student-focused pages (exam preparation strategies) using AI-driven keyword and
intent analysis.
SEM (Search Engine Marketing / Paid Ads)
AI optimizes paid campaigns through:
●Predictive modeling to identify which ad copy and visuals resonate with specific micro-segments.
●Real-time bid adjustments based on predicted lifetime value rather than immediate clicks.
●Automated generation of adaptive creatives tailored to context, device, and location.
Example: Flipkart adjusts ad spend dynamically during sales events, using AI to target high-intent users with sharper
discounts while serving brand-reinforcement ads to new customers.
Affiliate Marketing
AI enhances affiliate programs by:
●Identifying high-quality affiliates based on audience engagement, not just raw traffic.
●Personalizing commission structures and incentives for affiliates with different performance patterns.
●Dynamically generating banners and promotional creatives aligned with the affiliate’s audience.
Example: A travel platform like MakeMyTrip can deliver tailored affiliate banners — showing honeymoon packages to
one segment and student travel deals to another — powered by AI-driven audience segmentation.
Content Marketing
AI transforms content delivery by:
●Recommending articles, videos, or case studies depending on the user’s stage in the journey.
●Generating multiple content variants (headlines, product descriptions, social media posts) to suit different
audiences.
●Predicting the “next best content” on websites, ensuring users are served material that keeps them
engaged.
Example: Zomato personalizes app experiences by recommending restaurants, blog articles, and offers based on
prior food choices, time of day, and even weather conditions.
Omnichannel personalization
A critical but often overlooked area is cross-channel consistency. AI ensures personalization is not limited to one
platform but extends across:
●Email campaigns with individualized subject lines and recommendations.
●Mobile push notifications customized by behavior, location, and preferences.
●In-app experiences that adapt dynamically as the user navigates.
●Offline integration where in-store staff or call centers receive AI-powered insights into customer history.
This omnichannel integration is particularly important in India, where customers may begin their journey online but
complete transactions offline (e.g., in retail, insurance, or automobile sectors).
Industry-wise applications in India
●Automated generation of adaptive creatives tailored to context, device, and location.
Example: Flipkart adjusts ad spend dynamically during sales events, using AI to target high-intent users with sharper
discounts while serving brand-reinforcement ads to new customers.
Affiliate Marketing
AI enhances affiliate programs by:
●Identifying high-quality affiliates based on audience engagement, not just raw traffic.
●Personalizing commission structures and incentives for affiliates with different performance patterns.
●Dynamically generating banners and promotional creatives aligned with the affiliate’s audience.
Example: A travel platform like MakeMyTrip can deliver tailored affiliate banners — showing honeymoon packages to
one segment and student travel deals to another — powered by AI-driven audience segmentation.
Content Marketing
AI transforms content delivery by:
●Recommending articles, videos, or case studies depending on the user’s stage in the journey.
●Generating multiple content variants (headlines, product descriptions, social media posts) to suit different
audiences.
●Predicting the “next best content” on websites, ensuring users are served material that keeps them
engaged.
Example: Zomato personalizes app experiences by recommending restaurants, blog articles, and offers based on
prior food choices, time of day, and even weather conditions.
Omnichannel personalization
A critical but often overlooked area is cross-channel consistency. AI ensures personalization is not limited to one
platform but extends across:
●Email campaigns with individualized subject lines and recommendations.
●Mobile push notifications customized by behavior, location, and preferences.
●In-app experiences that adapt dynamically as the user navigates.
●Offline integration where in-store staff or call centers receive AI-powered insights into customer history.
This omnichannel integration is particularly important in India, where customers may begin their journey online but
complete transactions offline (e.g., in retail, insurance, or automobile sectors).
Industry-wise applications in India
●Retail & E-commerce: Myntra’s AI-powered fashion recommendations and Amazon India’s personalized
homepage experiences.
●Banking & Financial Services: HDFC Bank offers AI-based personalized credit card and loan
recommendations; Paytm personalizes offers and cashback promotions.
●Telecom: Jio and Airtel tailor recharge packs and OTT bundles for specific usage cohorts.
●Entertainment & OTT: Netflix India and Disney+ Hotstar recommend regional language content and live
sports highlights based on user profiles.
●Education/EdTech: BYJU’S and Unacademy personalize course recommendations and test-prep pathways
using predictive performance data.
●Healthcare: Apollo and Practo are experimenting with AI-driven health reminders, tailored wellness tips, and
appointment scheduling.
Key enablers for personalization
1.Data Management: Customer Data Platforms (CDPs) like MoEngage, CleverTap, and global platforms like
Salesforce help unify profiles across touchpoints.
2.Natural Language Processing (NLP): Enables personalization in regional Indian languages, addressing
linguistic diversity.
3.Recommendation Engines: Algorithms that generate tailored product suggestions in real time.
4.Predictive Analytics: Forecasts customer needs before they are explicitly expressed.
5.Generative AI: Automates creation of text, visuals, and video at scale while still maintaining personalization.
Ethical considerations and limitations
While AI-powered personalization drives higher engagement and conversions, it raises challenges:
●Privacy and data protection: In India, with the Digital Personal Data Protection Act (2023), businesses
must ensure customer data is handled transparently.
●Algorithmic bias: Over-personalization may exclude certain groups or reinforce stereotypes.
●Trust erosion: Customers may feel “spied on” if personalization is too intrusive.
Marketers must balance personalization with transparency, offering clear opt-ins and respecting user consent.
The future of personalization in India
●Hyper-local targeting: AI will increasingly deliver micro-segmented campaigns based on pin-code level
data, local festivals, and cultural patterns.
homepage experiences.
●Banking & Financial Services: HDFC Bank offers AI-based personalized credit card and loan
recommendations; Paytm personalizes offers and cashback promotions.
●Telecom: Jio and Airtel tailor recharge packs and OTT bundles for specific usage cohorts.
●Entertainment & OTT: Netflix India and Disney+ Hotstar recommend regional language content and live
sports highlights based on user profiles.
●Education/EdTech: BYJU’S and Unacademy personalize course recommendations and test-prep pathways
using predictive performance data.
●Healthcare: Apollo and Practo are experimenting with AI-driven health reminders, tailored wellness tips, and
appointment scheduling.
Key enablers for personalization
1.Data Management: Customer Data Platforms (CDPs) like MoEngage, CleverTap, and global platforms like
Salesforce help unify profiles across touchpoints.
2.Natural Language Processing (NLP): Enables personalization in regional Indian languages, addressing
linguistic diversity.
3.Recommendation Engines: Algorithms that generate tailored product suggestions in real time.
4.Predictive Analytics: Forecasts customer needs before they are explicitly expressed.
5.Generative AI: Automates creation of text, visuals, and video at scale while still maintaining personalization.
Ethical considerations and limitations
While AI-powered personalization drives higher engagement and conversions, it raises challenges:
●Privacy and data protection: In India, with the Digital Personal Data Protection Act (2023), businesses
must ensure customer data is handled transparently.
●Algorithmic bias: Over-personalization may exclude certain groups or reinforce stereotypes.
●Trust erosion: Customers may feel “spied on” if personalization is too intrusive.
Marketers must balance personalization with transparency, offering clear opt-ins and respecting user consent.
The future of personalization in India
●Hyper-local targeting: AI will increasingly deliver micro-segmented campaigns based on pin-code level
data, local festivals, and cultural patterns.
●Voice and vernacular personalization: With the rise of voice search and regional-language internet users,
AI-driven personalization in Hindi, Tamil, Bengali, etc., will be crucial.
●Predictive personalization: Anticipating customer needs before expression — such as offering rainwear
before the monsoon or recommending EMI offers before festival sales.
●Integrated customer journeys: Seamless movement between online and offline personalization, where a
customer receives a consistent, AI-tailored experience at every brand touchpoint.
Here’s a section on Chatbots and Customer Service Automation, with recent news, industry examples
(India-focused), strengths & challenges. You can drop this directly into your whitepaper.
Chatbots & Customer Service Automation
As customer expectations rise, brands across sectors are using chatbots and customer service automation to
improve efficiency, reduce costs, and provide 24/7 support. In India — with its linguistic diversity, large user base, and
varied infrastructure — chatbots are becoming a critical tool for scaling customer service while maintaining
responsiveness.
What does “chatbot & automation” cover
●Text chatbots (website chats, in-app, WhatsApp, Messenger, etc.) that answer FAQs, guide users through
issue resolution, or escalate to human agents if needed.
●Voice bots / IVR automation which can understand spoken queries, route calls, handle simple requests
(balance enquiry, outage report, booking status), and escalate complex issues.
●AI-agents / conversational AI that combine multiple channels (text/voice), context retention, personalized
responses, and capability to execute tasks (e.g., order cancellations, rescheduling, status tracking).
●Hybrid models where bots handle initial interactions / triage, handing over to humans for complex cases.
●Automation behind the scenes: auto-categorization of tickets, auto-tagging, routing, sentiment detection,
summarization of customer feedback or chats, proactive outreach.
Recent News & Initiatives in India
1.Haptik “AI for All”
Haptik (part of Reliance Jio Platforms) has launched “AI for All” to bring enterprise-grade AI agents to small
and medium businesses. Their Interakt platform (WhatsApp-first CRM, marketing & support) is now
enhanced with WhatsApp and Voice AI Agents. This aims to democratize advanced chatbot + automation tools, making them accessible even to smaller businesses. (The Times of India)
2.Kerala State Electricity Board (KSEB)
KSEB plans to deploy a voice bot to handle customer service complaints (power outages, transformer
issues). The bot will register, categorize, redirect complaints, verify customer details, distinguish
emergencies vs non-emergencies, and escalate when required. It will support Malayalam among other languages. (The Times of India)
AI-driven personalization in Hindi, Tamil, Bengali, etc., will be crucial.
●Predictive personalization: Anticipating customer needs before expression — such as offering rainwear
before the monsoon or recommending EMI offers before festival sales.
●Integrated customer journeys: Seamless movement between online and offline personalization, where a
customer receives a consistent, AI-tailored experience at every brand touchpoint.
Here’s a section on Chatbots and Customer Service Automation, with recent news, industry examples
(India-focused), strengths & challenges. You can drop this directly into your whitepaper.
Chatbots & Customer Service Automation
As customer expectations rise, brands across sectors are using chatbots and customer service automation to
improve efficiency, reduce costs, and provide 24/7 support. In India — with its linguistic diversity, large user base, and
varied infrastructure — chatbots are becoming a critical tool for scaling customer service while maintaining
responsiveness.
What does “chatbot & automation” cover
●Text chatbots (website chats, in-app, WhatsApp, Messenger, etc.) that answer FAQs, guide users through
issue resolution, or escalate to human agents if needed.
●Voice bots / IVR automation which can understand spoken queries, route calls, handle simple requests
(balance enquiry, outage report, booking status), and escalate complex issues.
●AI-agents / conversational AI that combine multiple channels (text/voice), context retention, personalized
responses, and capability to execute tasks (e.g., order cancellations, rescheduling, status tracking).
●Hybrid models where bots handle initial interactions / triage, handing over to humans for complex cases.
●Automation behind the scenes: auto-categorization of tickets, auto-tagging, routing, sentiment detection,
summarization of customer feedback or chats, proactive outreach.
Recent News & Initiatives in India
1.Haptik “AI for All”
Haptik (part of Reliance Jio Platforms) has launched “AI for All” to bring enterprise-grade AI agents to small
and medium businesses. Their Interakt platform (WhatsApp-first CRM, marketing & support) is now
enhanced with WhatsApp and Voice AI Agents. This aims to democratize advanced chatbot + automation tools, making them accessible even to smaller businesses. (The Times of India)
2.Kerala State Electricity Board (KSEB)
KSEB plans to deploy a voice bot to handle customer service complaints (power outages, transformer
issues). The bot will register, categorize, redirect complaints, verify customer details, distinguish
emergencies vs non-emergencies, and escalate when required. It will support Malayalam among other languages. (The Times of India)
3.SUMAN SAKHI (Madhya Pradesh health sector)
A chatbot developed under the National Health Mission (MP government) called SUMAN SAKHI is being
launched to help women with maternal health, reproductive health information, and awareness of
government health schemes. It operates 24/7 in Hindi and via WhatsApp, addressing both rural and urban women. (Indiatimes)
4.Centre (Government of India) — Public Grievances Portal (CPGRAMS)
The government is set to introduce a ChatGPT-like chatbot for public grievance redressal. It will help users
file complaints or appeals, assist them in navigating the portal, and use AI to categorize grievances (including by village level) and filter spam. (The Indian Express)
5.Dukaan
A well-known startup in the Indian e-commerce space replaced ~90% of its customer support staff with an
AI chatbot. According to the CEO: first response time dropped from ~1 minute 44 seconds to “instant”; resolution time dropped from over 2 hours to ~3 minutes; costs reduced by ~85%. (CNBC TV18)
6.Jeep India
Jeep India launched a “Jeep Expert” chatbot (built using ChatGPT-3.5) embedded in its mobile app, offering
24 × 7 brand-related, product, maintenance and service answers so customers don’t have to dig through manuals. (The Hindu)
Industry Examples & Use Cases
Industry Use Case / Example Key Benefits
E-commerce /
D2C
Dukaan replacing support staff with AI bot:
instant responses, massive cost
reduction, faster resolution times.
(CNBC TV18)
Cost savings; scalability; consistent
responses; 24/7 support.
Automobiles /
Automoti
ve
Jeep India’s “Jeep Expert” offering product
& service info via ChatGPT-based
chatbot. (The Hindu)
Improves customer satisfaction;
reduces friction in finding
information; lowers load on
service centers.
Utilities /
Public
Services
KSEB’s planned multilingual voice bot for
complaint registration, escalation,
emergency handling. (The Times of
India)
Reduces wait times; handles high
volume; frees up human
agents for complex/more
emotional tasks.
Government
& Health
SUMAN SAKHI chatbot for women’s health;
CPGRAMS grievance chatbot.
(Indiatimes)
Greater reach; improved access;
helps address public needs
more efficiently; improves
transparency.
SMB sector Haptik’s AI for All enabling small and
medium businesses to deploy AI agents
on WhatsApp/voice. (The Times of
India)
Lowers barrier to entry; improves
customer care even for small
brands; competitive edge.
A chatbot developed under the National Health Mission (MP government) called SUMAN SAKHI is being
launched to help women with maternal health, reproductive health information, and awareness of
government health schemes. It operates 24/7 in Hindi and via WhatsApp, addressing both rural and urban women. (Indiatimes)
4.Centre (Government of India) — Public Grievances Portal (CPGRAMS)
The government is set to introduce a ChatGPT-like chatbot for public grievance redressal. It will help users
file complaints or appeals, assist them in navigating the portal, and use AI to categorize grievances (including by village level) and filter spam. (The Indian Express)
5.Dukaan
A well-known startup in the Indian e-commerce space replaced ~90% of its customer support staff with an
AI chatbot. According to the CEO: first response time dropped from ~1 minute 44 seconds to “instant”; resolution time dropped from over 2 hours to ~3 minutes; costs reduced by ~85%. (CNBC TV18)
6.Jeep India
Jeep India launched a “Jeep Expert” chatbot (built using ChatGPT-3.5) embedded in its mobile app, offering
24 × 7 brand-related, product, maintenance and service answers so customers don’t have to dig through manuals. (The Hindu)
Industry Examples & Use Cases
Industry Use Case / Example Key Benefits
E-commerce /
D2C
Dukaan replacing support staff with AI bot:
instant responses, massive cost
reduction, faster resolution times.
(CNBC TV18)
Cost savings; scalability; consistent
responses; 24/7 support.
Automobiles /
Automoti
ve
Jeep India’s “Jeep Expert” offering product
& service info via ChatGPT-based
chatbot. (The Hindu)
Improves customer satisfaction;
reduces friction in finding
information; lowers load on
service centers.
Utilities /
Public
Services
KSEB’s planned multilingual voice bot for
complaint registration, escalation,
emergency handling. (The Times of
India)
Reduces wait times; handles high
volume; frees up human
agents for complex/more
emotional tasks.
Government
& Health
SUMAN SAKHI chatbot for women’s health;
CPGRAMS grievance chatbot.
(Indiatimes)
Greater reach; improved access;
helps address public needs
more efficiently; improves
transparency.
SMB sector Haptik’s AI for All enabling small and
medium businesses to deploy AI agents
on WhatsApp/voice. (The Times of
India)
Lowers barrier to entry; improves
customer care even for small
brands; competitive edge.
Benefits & Strengths
●24/7 Availability: No need to restrict based on office hours; especially important in India where customers
are distributed across time zones and may prefer off-hours interaction.
●Cost Efficiency: Automation reduces staffing costs, training, and operational overhead. Also reduces
resolution time, which improves customer satisfaction.
●Scalability: Bots can handle large volumes of simple queries; human agents can focus on complex issues.
●Multilingual & Localisation: Chatbots that support regional languages and local context (dialects,
colloquialisms) help in markets that are not English-dominant.
●Data Insights: Conversations generate data on what customers ask — gives feedback loop to product
teams, marketing, UX.
Challenges & Limitations
●Complex queries / emotional issues: Bots may struggle with empathy, nuance, or problems that deviate
from predefined categories.
●Language & dialect barriers: India has many languages and dialects; NLP models must be adapted
carefully, or risk mis-understanding.
●Trust & acceptance: Some customers prefer speaking to human agents; bots may frustrate if their scope is
limited.
●Over-automation risk: Replacing too much human customer support can lead to negative perception or
reduced quality. As seen in the Dukaan case, the decision to replace 90% support staff drew criticism.
(www.ndtv.com)
●Integration & maintenance costs: To be effective, bots need clean data, constant retraining, integrations
with backend systems (CRM, order management etc.), monitoring, fallback pathways to human agents.
Recent Innovations & What’s Next
●Voice bots & multilingual support are rising in importance. For example, KSEB’s bot supports Malayalam;
SUMAN SAKHI in Hindi. (The Times of India)
●Accessible AI agents for SMBs with cost-effective pricing (Haptik’s Interakt). (The Times of India)
●Government-led chatbots in public welfare and grievance redressal, expanding to handle local
languages, large scale usage, real-world complaint categorization. (The Indian Express)
●Conversational agents based on powerful LLMs (e.g. ChatGPT style) being embedded into brand apps
for customer redressal and support. Jeep India’s “Jeep Expert” is one example. (The Hindu)
●24/7 Availability: No need to restrict based on office hours; especially important in India where customers
are distributed across time zones and may prefer off-hours interaction.
●Cost Efficiency: Automation reduces staffing costs, training, and operational overhead. Also reduces
resolution time, which improves customer satisfaction.
●Scalability: Bots can handle large volumes of simple queries; human agents can focus on complex issues.
●Multilingual & Localisation: Chatbots that support regional languages and local context (dialects,
colloquialisms) help in markets that are not English-dominant.
●Data Insights: Conversations generate data on what customers ask — gives feedback loop to product
teams, marketing, UX.
Challenges & Limitations
●Complex queries / emotional issues: Bots may struggle with empathy, nuance, or problems that deviate
from predefined categories.
●Language & dialect barriers: India has many languages and dialects; NLP models must be adapted
carefully, or risk mis-understanding.
●Trust & acceptance: Some customers prefer speaking to human agents; bots may frustrate if their scope is
limited.
●Over-automation risk: Replacing too much human customer support can lead to negative perception or
reduced quality. As seen in the Dukaan case, the decision to replace 90% support staff drew criticism.
(www.ndtv.com)
●Integration & maintenance costs: To be effective, bots need clean data, constant retraining, integrations
with backend systems (CRM, order management etc.), monitoring, fallback pathways to human agents.
Recent Innovations & What’s Next
●Voice bots & multilingual support are rising in importance. For example, KSEB’s bot supports Malayalam;
SUMAN SAKHI in Hindi. (The Times of India)
●Accessible AI agents for SMBs with cost-effective pricing (Haptik’s Interakt). (The Times of India)
●Government-led chatbots in public welfare and grievance redressal, expanding to handle local
languages, large scale usage, real-world complaint categorization. (The Indian Express)
●Conversational agents based on powerful LLMs (e.g. ChatGPT style) being embedded into brand apps
for customer redressal and support. Jeep India’s “Jeep Expert” is one example. (The Hindu)
Implications & My Insights
●Brands that deploy chatbots without a human fallback or clear understanding of limitations risk damaging
reputation. For example, customers will quickly lose patience if the bot fails on simple tasks or cannot
escalate.
●Bots must be trained continuously, with feedback loops from real conversations. Data from chat logs, user
sentiment, failure cases need to be used to improve performance and expand scope.
●Regional language and voice capability will be the differentiator. Brands that make their bots work well in
multiple Indian languages, with local colloquial speech, will win in smaller towns and rural areas.
●Transparency is key: Let users know when they are chatting with a bot, options to reach human agents,
what the bot can or cannot do. This builds trust.
●Regulatory / data privacy concerns: collecting transcripts, storing user info, voice recordings need to comply
with emerging laws. Brands should have policies, disclosures.
●Brands that deploy chatbots without a human fallback or clear understanding of limitations risk damaging
reputation. For example, customers will quickly lose patience if the bot fails on simple tasks or cannot
escalate.
●Bots must be trained continuously, with feedback loops from real conversations. Data from chat logs, user
sentiment, failure cases need to be used to improve performance and expand scope.
●Regional language and voice capability will be the differentiator. Brands that make their bots work well in
multiple Indian languages, with local colloquial speech, will win in smaller towns and rural areas.
●Transparency is key: Let users know when they are chatting with a bot, options to reach human agents,
what the bot can or cannot do. This builds trust.
●Regulatory / data privacy concerns: collecting transcripts, storing user info, voice recordings need to comply
with emerging laws. Brands should have policies, disclosures.
●
Here’s a detailed section on Use of AI in Content Creation & Curation—with industry/brand examples in India,
recent statistics, future directions, and additional insights you might find useful.
AI in Content Creation & Curation
Content creation and curation are two sides of the same coin. Creation is about generating new content (text, image,
video), while curation is about selecting, organizing, personalizing, and presenting content in a meaningful way for
audiences. AI is enhancing both massively.
Recent Statistics & Indian Landscape
●In a survey by Adobe (“Digital Trends 2024 Asia Pacific & Japan”) 82% of Indian brands see benefits in
using AI for content creation. (BMI)
●The same report shows that 70% of senior Indian executives feel well-prepared to set up guidelines /
governance for generative AI, and 58% are actively investing in such frameworks. (BMI)
●Among content marketers globally, many believe AI improves efficiency and content quality. For instance, in
one set of statistics:
○~81% of marketers using AI report a positive impact on their roles. (Forbes)
○~85% report that AI improved content quality. (Ahrefs)
●Indian SMBs: In a Salesforce report, 78% of Indian SMBs using AI reported revenue growth. Their top AI
use cases included content generation, marketing optimization, among others. (The Economic Times)
●On video content: Brands like Volkswagen India, Nerolac, and Tata Power have already released brand
campaigns / films generated using AI. (Indian Business Times)
Here’s a detailed section on Use of AI in Content Creation & Curation—with industry/brand examples in India,
recent statistics, future directions, and additional insights you might find useful.
AI in Content Creation & Curation
Content creation and curation are two sides of the same coin. Creation is about generating new content (text, image,
video), while curation is about selecting, organizing, personalizing, and presenting content in a meaningful way for
audiences. AI is enhancing both massively.
Recent Statistics & Indian Landscape
●In a survey by Adobe (“Digital Trends 2024 Asia Pacific & Japan”) 82% of Indian brands see benefits in
using AI for content creation. (BMI)
●The same report shows that 70% of senior Indian executives feel well-prepared to set up guidelines /
governance for generative AI, and 58% are actively investing in such frameworks. (BMI)
●Among content marketers globally, many believe AI improves efficiency and content quality. For instance, in
one set of statistics:
○~81% of marketers using AI report a positive impact on their roles. (Forbes)
○~85% report that AI improved content quality. (Ahrefs)
●Indian SMBs: In a Salesforce report, 78% of Indian SMBs using AI reported revenue growth. Their top AI
use cases included content generation, marketing optimization, among others. (The Economic Times)
●On video content: Brands like Volkswagen India, Nerolac, and Tata Power have already released brand
campaigns / films generated using AI. (Indian Business Times)
These numbers imply that AI adoption in content creation/curation is well underway in India; it’s not speculative —
many firms are rethinking their workflows and watching returns.
How Brands & Industries in India are Using AI for Creation &
Curation
Here are some real-world examples:
Brand / Industry What They Are Doing
Volkswagen India,
Nerolac, Tata Power
(Automotive / Paint /
Utilities)
Released festive / brand films entirely generated by AI — visuals,
storytelling, creative rendering. These campaigns reduced
production time and cost, while generating buzz. (Indian Business
Times)
Appy Pie Launched PixelYatra, the first Hindi AI-design tool to let users
create social creatives, posters, banners using Hindi prompts. This
helps democratize content creation in India’s regional / vernacular
language markets. (The Times of India)
Indian SMBs more
broadly
Many are using AI to generate content (blogs, social media posts),
optimize content for SEO, repurpose existing content across
channels, or adapt content for specific audiences. (As per
Salesforce report) (The Economic Times)
Brands with governance
& strategy
From the Adobe report: Indian brands are not just using tools but
also building roadmaps, ethics/governance frameworks, investing
in training to use AI, and embedding AI content workflows into
organizational strategy. (ETBrandEquity.com)
How Content Curation Works (and AI’s Role)
AI in content curation helps in:
●Personalized content feeds — showing content tailored to each user, based on past behavior,
preferences, device, time of day.
●Trending topic detection / editorial ideation — AI tools can monitor what’s trending (search terms, social
media), suggest topics or headlines.
●Repurposing content — taking existing content and adapting it (for example: converting long-form blog
articles into short social posts, infographics, video snippets).
many firms are rethinking their workflows and watching returns.
How Brands & Industries in India are Using AI for Creation &
Curation
Here are some real-world examples:
Brand / Industry What They Are Doing
Volkswagen India,
Nerolac, Tata Power
(Automotive / Paint /
Utilities)
Released festive / brand films entirely generated by AI — visuals,
storytelling, creative rendering. These campaigns reduced
production time and cost, while generating buzz. (Indian Business
Times)
Appy Pie Launched PixelYatra, the first Hindi AI-design tool to let users
create social creatives, posters, banners using Hindi prompts. This
helps democratize content creation in India’s regional / vernacular
language markets. (The Times of India)
Indian SMBs more
broadly
Many are using AI to generate content (blogs, social media posts),
optimize content for SEO, repurpose existing content across
channels, or adapt content for specific audiences. (As per
Salesforce report) (The Economic Times)
Brands with governance
& strategy
From the Adobe report: Indian brands are not just using tools but
also building roadmaps, ethics/governance frameworks, investing
in training to use AI, and embedding AI content workflows into
organizational strategy. (ETBrandEquity.com)
How Content Curation Works (and AI’s Role)
AI in content curation helps in:
●Personalized content feeds — showing content tailored to each user, based on past behavior,
preferences, device, time of day.
●Trending topic detection / editorial ideation — AI tools can monitor what’s trending (search terms, social
media), suggest topics or headlines.
●Repurposing content — taking existing content and adapting it (for example: converting long-form blog
articles into short social posts, infographics, video snippets).
●Summarization and recommendation — for example, summarizing articles or videos, or recommending
what content to consume next (on websites, apps).
●Filtering & moderation — selecting good quality content, removing or demoting spam / low-quality content,
ensuring consistency in style and messaging.
Future Trends & How It Can Evolve
Looking ahead, here are ways content creation & curation via AI are likely to evolve, especially in India’s context:
1.Multimodal Content: Content that combines text, image, video, audio, maybe AR/VR. AI will help generate
across modalities. For example, converting written content into video, or producing interactive audio
narratives.
2.Vernacular & regional content expansion: Tools like PixelYatra are early signs; more tools will support
many Indian languages, dialects, regional idioms. This will be essential as internet penetration deepens in
non-metro, rural areas.
3.Real-time / live content adaptation: Imagine content (blogs, social media, app content) that updates itself
based on latest trends, sentiment, or real-time data. Headlines and visuals adapting in real time for news,
social trends, festivals, etc.
4.User-generated content integration + AI curation: AI curation will sift through user-generated content
(reviews, photos, social posts) and promote or integrate the best into official channels. Brands might use AI
to pick “fan content” and rewrite or repackage it.
5.Personalized content experiences: Not just “which content you see next” but formats (video vs text vs
audio), length, style, tone — all tailored to user preference.
6.Ethics, Copyright, Authenticity: As more content is generated by AI, questions about originality, bias,
plagiarism, copyright, and sourcing will grow. Brands and platforms will need to ensure attribution, avoid
over-reliance on training data that violates IP, and maintain transparency.
7.AI Co-creation with Humans: AI as assistant, not replacement. Content creators using AI tools for drafts,
ideation, first cuts; human editors refining tone, voice, brand personality.
8.Tools with better feedback loops: AI systems will increasingly incorporate engagement metrics (likes,
dwell time, shares) to refine what content to create / promote / demote.
Additional Insights / Considerations
●Content saturation & differentiability: As more brands use AI, many will produce similar content.
Differentiation via brand voice, high artistic quality, authenticity will matter more.
●Resource balance: Even if AI automates much, brands will need skilled people (editors, storytellers, creative
directors) to supervise output.
●Tool selection & training: Better ROI when tools are chosen to fit content needs (e.g. good at long-form vs
video vs visuals), and when teams are trained to use them well.
what content to consume next (on websites, apps).
●Filtering & moderation — selecting good quality content, removing or demoting spam / low-quality content,
ensuring consistency in style and messaging.
Future Trends & How It Can Evolve
Looking ahead, here are ways content creation & curation via AI are likely to evolve, especially in India’s context:
1.Multimodal Content: Content that combines text, image, video, audio, maybe AR/VR. AI will help generate
across modalities. For example, converting written content into video, or producing interactive audio
narratives.
2.Vernacular & regional content expansion: Tools like PixelYatra are early signs; more tools will support
many Indian languages, dialects, regional idioms. This will be essential as internet penetration deepens in
non-metro, rural areas.
3.Real-time / live content adaptation: Imagine content (blogs, social media, app content) that updates itself
based on latest trends, sentiment, or real-time data. Headlines and visuals adapting in real time for news,
social trends, festivals, etc.
4.User-generated content integration + AI curation: AI curation will sift through user-generated content
(reviews, photos, social posts) and promote or integrate the best into official channels. Brands might use AI
to pick “fan content” and rewrite or repackage it.
5.Personalized content experiences: Not just “which content you see next” but formats (video vs text vs
audio), length, style, tone — all tailored to user preference.
6.Ethics, Copyright, Authenticity: As more content is generated by AI, questions about originality, bias,
plagiarism, copyright, and sourcing will grow. Brands and platforms will need to ensure attribution, avoid
over-reliance on training data that violates IP, and maintain transparency.
7.AI Co-creation with Humans: AI as assistant, not replacement. Content creators using AI tools for drafts,
ideation, first cuts; human editors refining tone, voice, brand personality.
8.Tools with better feedback loops: AI systems will increasingly incorporate engagement metrics (likes,
dwell time, shares) to refine what content to create / promote / demote.
Additional Insights / Considerations
●Content saturation & differentiability: As more brands use AI, many will produce similar content.
Differentiation via brand voice, high artistic quality, authenticity will matter more.
●Resource balance: Even if AI automates much, brands will need skilled people (editors, storytellers, creative
directors) to supervise output.
●Tool selection & training: Better ROI when tools are chosen to fit content needs (e.g. good at long-form vs
video vs visuals), and when teams are trained to use them well.
●Infrastructure & data: Good content creation & curation need clean, organized content archives, metadata,
historical performance data, style/brand guidelines.
●Measurement: Brands will need more sophisticated metrics to track not just content output but content
effectiveness (engagement, SEO rank, conversion, retention).
Ethical Considerations of AI in Marketing
As AI becomes more deeply embedded in marketing, the ethical stakes rise. Misuse or unintentional harm can
damage consumer trust, lead to legal liabilities, and hurt brand reputation. Marketers need to understand what these
risks are, where they are most acute, and how to guard against them.
Key Ethical Risks & Issues
1.Privacy & Data Security
○Personal data collection, including sensitive data (financial, health, location, biometric), is central to
personalized AI marketing. If mishandled, it exposes individuals to identity theft, discrimination, or
unauthorized profiling.
○Cross-border flow of data and use of third-party tools (where governance or oversight is weaker)
increases risk.
○Shadow AI: employees using AI tools outside official oversight or without clarity about data usage,
which creates blind spots.
2.Transparency & Consent
○Users often do not know how their data is being used—whether for targeting, training AI models, for
predictive profiling.
historical performance data, style/brand guidelines.
●Measurement: Brands will need more sophisticated metrics to track not just content output but content
effectiveness (engagement, SEO rank, conversion, retention).
Ethical Considerations of AI in Marketing
As AI becomes more deeply embedded in marketing, the ethical stakes rise. Misuse or unintentional harm can
damage consumer trust, lead to legal liabilities, and hurt brand reputation. Marketers need to understand what these
risks are, where they are most acute, and how to guard against them.
Key Ethical Risks & Issues
1.Privacy & Data Security
○Personal data collection, including sensitive data (financial, health, location, biometric), is central to
personalized AI marketing. If mishandled, it exposes individuals to identity theft, discrimination, or
unauthorized profiling.
○Cross-border flow of data and use of third-party tools (where governance or oversight is weaker)
increases risk.
○Shadow AI: employees using AI tools outside official oversight or without clarity about data usage,
which creates blind spots.
2.Transparency & Consent
○Users often do not know how their data is being used—whether for targeting, training AI models, for
predictive profiling.
○Many tools do not clearly disclose how user data is used to train generative AI or whether their
voice, image, or behavior might be replicated/used in synthetic content.
3.Bias, Discrimination & Fairness
○Training data may underrepresent certain groups (e.g. non-English speakers, rural populations,
minority demographics), leading to biased recommendations or content.
○AI-driven ad targeting may inadvertently exclude or misrepresent protected or marginalized classes
(e.g., by gender, caste, age, region).
4.Misleading / Deceptive Content (Deepfakes etc.)
○Using AI to generate content that misrepresents reality, e.g. fake testimonials, fabricated
endorsements, or deepfake voices/images.
○Misleading advertising using AI to impersonate celebrities or public figures.
5.Intellectual Property & Copyright
○AI models trained on copyrighted works without permission.
○Generated content that closely mimics or copies existing works, raising questions of ownership,
attribution, and originality.
6.Autonomy & Manipulation
○Highly personalized or predictive marketing can feel manipulative: pushing individuals toward
decisions before they are ready, exploiting biases, or nudging behavior in ways they may not fully
understand.
7.Accountability & Liability
○Who is responsible when an AI-driven campaign goes wrong? The marketer, the AI vendor, the
data provider?
○Legal/regulatory frameworks in many jurisdictions (including India) are still catching up, so risk of
exposure to regulatory action or fines.
Industries Most Affected in India
Some sectors are particularly exposed to ethical risks due to the nature of the data they handle, the sensitivity of their
offerings, or the vulnerability of their customers:
Industry Why At Higher Risk
Finance / Banking /
Insurance
Deals with sensitive financial data, KYC, identity; exposure to fraud, phishing,
deepfake scams.
voice, image, or behavior might be replicated/used in synthetic content.
3.Bias, Discrimination & Fairness
○Training data may underrepresent certain groups (e.g. non-English speakers, rural populations,
minority demographics), leading to biased recommendations or content.
○AI-driven ad targeting may inadvertently exclude or misrepresent protected or marginalized classes
(e.g., by gender, caste, age, region).
4.Misleading / Deceptive Content (Deepfakes etc.)
○Using AI to generate content that misrepresents reality, e.g. fake testimonials, fabricated
endorsements, or deepfake voices/images.
○Misleading advertising using AI to impersonate celebrities or public figures.
5.Intellectual Property & Copyright
○AI models trained on copyrighted works without permission.
○Generated content that closely mimics or copies existing works, raising questions of ownership,
attribution, and originality.
6.Autonomy & Manipulation
○Highly personalized or predictive marketing can feel manipulative: pushing individuals toward
decisions before they are ready, exploiting biases, or nudging behavior in ways they may not fully
understand.
7.Accountability & Liability
○Who is responsible when an AI-driven campaign goes wrong? The marketer, the AI vendor, the
data provider?
○Legal/regulatory frameworks in many jurisdictions (including India) are still catching up, so risk of
exposure to regulatory action or fines.
Industries Most Affected in India
Some sectors are particularly exposed to ethical risks due to the nature of the data they handle, the sensitivity of their
offerings, or the vulnerability of their customers:
Industry Why At Higher Risk
Finance / Banking /
Insurance
Deals with sensitive financial data, KYC, identity; exposure to fraud, phishing,
deepfake scams.
Healthcare / Pharma Sensitive personal health data, patient records; privacy breaches have serious
consequences; misuse of content could mislead about health.
Telecom / OTT / Media Large user bases, content moderation issues; deepfake/media manipulation risks.
E-Commerce / Retail Fake product reviews, counterfeit endorsements; misleading ads; targeting vulnerable
populations with high-risk deals.
EdTech Youth engagement, exam/prep, influence; possibility of overpromising results;
potential misuse of personal performance data.
Government / Public
Services
Public trust is key; misuse of AI for citizen data, profiling, or deepfakes in public
communications can be especially damaging.
Recent Incidents & Scams in India & Globally
●Deepfake Scams / Misleading Advertising
Tennis legend Rafael Nadal had to publicly warn that his image and voice were being used in AI-generated
videos promoting fake investment advice. (The Times of India)
There are reports of deepfake videos impersonating government officials, e.g. manipulated versions of
Union Finance Minister Nirmala Sitharaman endorsing fake investment schemes. (The Times of India)
●Fake Deal / E-commerce Scams
In Prime Day periods and other big sale events, large numbers of fake websites, scam texts, fake
offers/timely promotions are used to lure shoppers. (India Today)
●Data Breaches & Costs
The IBM report (2025) shows that the average cost of data breach in India reached INR 220 million (~22
crore) and that many organizations lack proper AI governance, access controls, or oversight. (IBM India
News Room)
Shadow AI (unauthorized/unmonitored use of AI tools) was a notable cost driver. (CRN - India)
●Voice Cloning / Impersonation Scams
In survey data, 40% of respondents in India believed their voice was cloned and used to trick someone they
know. (India Today)
●Malicious Chatbot / Bot-based Leaks
The Star Health incident: hacker used Telegram chatbots to leak sensitive data belonging to millions of
customers. (Reuters)
●Fraudulent Companies Using “AI” Branding
Some scams use AI in their name (or domain), fake websites, claim large numbers of satisfied customers,
etc., to appear legitimate. For example, the “Harvest AI Technology Private Ltd” case in Jaipur where people were duped into paying for an SUV scheme, with fake website, false claims, etc. (The Times of India)
Impacts from a Digital Marketing Perspective
From the standpoint of marketers and brands, ethical lapses related to AI can lead to multiple adverse outcomes:
consequences; misuse of content could mislead about health.
Telecom / OTT / Media Large user bases, content moderation issues; deepfake/media manipulation risks.
E-Commerce / Retail Fake product reviews, counterfeit endorsements; misleading ads; targeting vulnerable
populations with high-risk deals.
EdTech Youth engagement, exam/prep, influence; possibility of overpromising results;
potential misuse of personal performance data.
Government / Public
Services
Public trust is key; misuse of AI for citizen data, profiling, or deepfakes in public
communications can be especially damaging.
Recent Incidents & Scams in India & Globally
●Deepfake Scams / Misleading Advertising
Tennis legend Rafael Nadal had to publicly warn that his image and voice were being used in AI-generated
videos promoting fake investment advice. (The Times of India)
There are reports of deepfake videos impersonating government officials, e.g. manipulated versions of
Union Finance Minister Nirmala Sitharaman endorsing fake investment schemes. (The Times of India)
●Fake Deal / E-commerce Scams
In Prime Day periods and other big sale events, large numbers of fake websites, scam texts, fake
offers/timely promotions are used to lure shoppers. (India Today)
●Data Breaches & Costs
The IBM report (2025) shows that the average cost of data breach in India reached INR 220 million (~22
crore) and that many organizations lack proper AI governance, access controls, or oversight. (IBM India
News Room)
Shadow AI (unauthorized/unmonitored use of AI tools) was a notable cost driver. (CRN - India)
●Voice Cloning / Impersonation Scams
In survey data, 40% of respondents in India believed their voice was cloned and used to trick someone they
know. (India Today)
●Malicious Chatbot / Bot-based Leaks
The Star Health incident: hacker used Telegram chatbots to leak sensitive data belonging to millions of
customers. (Reuters)
●Fraudulent Companies Using “AI” Branding
Some scams use AI in their name (or domain), fake websites, claim large numbers of satisfied customers,
etc., to appear legitimate. For example, the “Harvest AI Technology Private Ltd” case in Jaipur where people were duped into paying for an SUV scheme, with fake website, false claims, etc. (The Times of India)
Impacts from a Digital Marketing Perspective
From the standpoint of marketers and brands, ethical lapses related to AI can lead to multiple adverse outcomes:
●Loss of consumer trust, which is hard to rebuild.
●Reputational damage spreading rapidly via social media.
●Regulatory and legal penalties (as governments implement stricter data protection laws, such as India’s
DPDP Act, etc.).
●Advertising platforms (Google, Meta) may penalize or delist brands involved in misleading deepfake content
or dishonest targeting.
●Reduced effectiveness: customers who feel manipulated or deceived are less likely to convert or stay loyal.
What Brands Can Do — Ethical Best Practices &
Recommendations
Here are ways brands can build ethical AI marketing practices:
1.Data Governance & Privacy by Design
○Collect only what is necessary, anonymize/pseudonymize sensitive data where possible.
○Secure user consent explicitly. Include opt-ins and opt-outs, especially when using personal data
for profiling, training models, or generating content.
○Ensure data storage and processing comply with applicable laws (e.g. India’s Digital Personal Data
Protection Act, or DPDP Act).
2.Transparency & Disclosure
○Be clear when content is AI-generated (deepfakes, synthetic voices, etc.).
○Disclose if user data is used for training or model improvement.
○Provide clear privacy policies / notices, accessible to all readers (not just legalistic).
3.Bias Mitigation & Fairness Audits
○Use diverse datasets, including regional／linguistic／demographic variety.
○Test for biases in outputs (does one region/language or gender get under-served?).
○Include human oversight — allow humans to review or override AI decisions, especially for
sensitive uses (credit offers, health, etc.).
4.Robust Security & Access Controls
○Control access to sensitive data; limit internal tools’ exposure.
●Reputational damage spreading rapidly via social media.
●Regulatory and legal penalties (as governments implement stricter data protection laws, such as India’s
DPDP Act, etc.).
●Advertising platforms (Google, Meta) may penalize or delist brands involved in misleading deepfake content
or dishonest targeting.
●Reduced effectiveness: customers who feel manipulated or deceived are less likely to convert or stay loyal.
What Brands Can Do — Ethical Best Practices &
Recommendations
Here are ways brands can build ethical AI marketing practices:
1.Data Governance & Privacy by Design
○Collect only what is necessary, anonymize/pseudonymize sensitive data where possible.
○Secure user consent explicitly. Include opt-ins and opt-outs, especially when using personal data
for profiling, training models, or generating content.
○Ensure data storage and processing comply with applicable laws (e.g. India’s Digital Personal Data
Protection Act, or DPDP Act).
2.Transparency & Disclosure
○Be clear when content is AI-generated (deepfakes, synthetic voices, etc.).
○Disclose if user data is used for training or model improvement.
○Provide clear privacy policies / notices, accessible to all readers (not just legalistic).
3.Bias Mitigation & Fairness Audits
○Use diverse datasets, including regional／linguistic／demographic variety.
○Test for biases in outputs (does one region/language or gender get under-served?).
○Include human oversight — allow humans to review or override AI decisions, especially for
sensitive uses (credit offers, health, etc.).
4.Robust Security & Access Controls
○Control access to sensitive data; limit internal tools’ exposure.
○Monitor for “shadow AI” usage (unauthorized tools use) and set policy for usage of AI tools.
○Incident response plans for data breaches, including communication to users.
5.Ethical Use of Deepfakes / Synthetic Media
○Avoid using deepfake-like content in misleading ways (e.g. fake endorsements).
○If using synthetic voices/images for marketing, ensure permissions and likeness rights.
6.Regulatory Compliance & Staying Updated
○Keep abreast of data protection law, advertising standards, consumer protection laws.
○Engage with industry bodies for guidelines.
7.User Education & Feedback Loops
○Educate users/customers about how their data may be used.
○Provide means for customers to see, challenge, or remove profiling, or opt out.
○Use feedback from customers to discover unintended harms.
8.Ethics Committees / Internal Oversight
○Establish internal review boards or ethics committees to vet new AI-driven campaigns.
○Conduct ethical risk assessments before launch.
How Ethical Risk May Evolve in the Future
●Increased regulation: More laws like DPDP in India, plus more global pressure for transparency (e.g. laws in
EU, US). Brands will need to be proactive.
●Standards / certification: Possible emergence of “AI Ethics badges” or audit certifications for marketing
campaigns.
●Rise of detection tools: Tools to detect deepfake content, synthetic voices, manipulated images will get
better. Brands may need to pre-emptively ensure authenticity so they aren’t flagged.
●Demand for explainability: Consumers, regulators, and platforms will want to know why an AI made a certain
decision — e.g., “why was this user shown this ad?” — especially in sensitive areas (finance, employment,
health).
●Growing scrutiny of AI's carbon footprint / environmental impact. Producing large models, storing data,
generating content has energy costs. Ethical marketing may extend to sustainability.
○Incident response plans for data breaches, including communication to users.
5.Ethical Use of Deepfakes / Synthetic Media
○Avoid using deepfake-like content in misleading ways (e.g. fake endorsements).
○If using synthetic voices/images for marketing, ensure permissions and likeness rights.
6.Regulatory Compliance & Staying Updated
○Keep abreast of data protection law, advertising standards, consumer protection laws.
○Engage with industry bodies for guidelines.
7.User Education & Feedback Loops
○Educate users/customers about how their data may be used.
○Provide means for customers to see, challenge, or remove profiling, or opt out.
○Use feedback from customers to discover unintended harms.
8.Ethics Committees / Internal Oversight
○Establish internal review boards or ethics committees to vet new AI-driven campaigns.
○Conduct ethical risk assessments before launch.
How Ethical Risk May Evolve in the Future
●Increased regulation: More laws like DPDP in India, plus more global pressure for transparency (e.g. laws in
EU, US). Brands will need to be proactive.
●Standards / certification: Possible emergence of “AI Ethics badges” or audit certifications for marketing
campaigns.
●Rise of detection tools: Tools to detect deepfake content, synthetic voices, manipulated images will get
better. Brands may need to pre-emptively ensure authenticity so they aren’t flagged.
●Demand for explainability: Consumers, regulators, and platforms will want to know why an AI made a certain
decision — e.g., “why was this user shown this ad?” — especially in sensitive areas (finance, employment,
health).
●Growing scrutiny of AI's carbon footprint / environmental impact. Producing large models, storing data,
generating content has energy costs. Ethical marketing may extend to sustainability.
●More public awareness: As incidents accumulate (voice cloning, deepfakes, misuse of identities),
consumers will demand more control, more transparency, and will favor brands with good ethical credentials.
1. Awareness Stage — Reaching & Attracting Prospects
Goal: Make people aware of your brand or product.
How AI helps:
●AI‑Driven Audience Segmentation: AI analyzes massive datasets (social, search behavior, demographics)
to identify target groups most likely to engage.
○Example: Netflix uses AI to identify viewing preferences for targeted trailers and promotions.
●Programmatic Advertising: AI uses real‑time bidding and contextual targeting to place ads for maximum
impact.
○Example: In India, Flipkart uses AI to decide ad placements during Big Billion Days sales.
●Content Recommendation Engines: AI predicts which type of content will resonate with audiences.
○Example: Amazon recommending products via “Inspired by your browsing history.”
●SEO & SEM Optimization: AI tools like Clearscope, Surfer SEO, and ChatGPT‑powered keyword analysis
ensure content ranks higher and reaches relevant audiences.
Impact on Engagement: Higher click‑through rates (CTR) and reach through targeted ads, relevant content, and
personalized recommendations.
2. Consideration Stage — Educating & Engaging Prospects
Goal: Help prospects evaluate your product/service.
How AI helps:
●Personalized Email Campaigns: AI tools segment and personalize emails for higher engagement.
○Example: Myntra uses AI to send personalized style recommendations.
●Chatbots & Virtual Assistants: AI‑powered chatbots (e.g., Intercom, Freshdesk, or WhatsApp bots)
provide instant answers and product suggestions.
○Example: HDFC Bank uses AI chatbots to answer banking queries, improving response time.
●Predictive Analytics: AI predicts which content or offers are likely to convert based on behavior patterns.
○Example: HubSpot AI tools predict which leads are most likely to convert.
●Dynamic Content Curation: AI creates or curates content tailored to users’ journey stage.
○Example: Curated product feeds in e‑commerce platforms.
Impact on Engagement: Longer site visits, higher interaction with content, and greater product/service exploration.
consumers will demand more control, more transparency, and will favor brands with good ethical credentials.
1. Awareness Stage — Reaching & Attracting Prospects
Goal: Make people aware of your brand or product.
How AI helps:
●AI‑Driven Audience Segmentation: AI analyzes massive datasets (social, search behavior, demographics)
to identify target groups most likely to engage.
○Example: Netflix uses AI to identify viewing preferences for targeted trailers and promotions.
●Programmatic Advertising: AI uses real‑time bidding and contextual targeting to place ads for maximum
impact.
○Example: In India, Flipkart uses AI to decide ad placements during Big Billion Days sales.
●Content Recommendation Engines: AI predicts which type of content will resonate with audiences.
○Example: Amazon recommending products via “Inspired by your browsing history.”
●SEO & SEM Optimization: AI tools like Clearscope, Surfer SEO, and ChatGPT‑powered keyword analysis
ensure content ranks higher and reaches relevant audiences.
Impact on Engagement: Higher click‑through rates (CTR) and reach through targeted ads, relevant content, and
personalized recommendations.
2. Consideration Stage — Educating & Engaging Prospects
Goal: Help prospects evaluate your product/service.
How AI helps:
●Personalized Email Campaigns: AI tools segment and personalize emails for higher engagement.
○Example: Myntra uses AI to send personalized style recommendations.
●Chatbots & Virtual Assistants: AI‑powered chatbots (e.g., Intercom, Freshdesk, or WhatsApp bots)
provide instant answers and product suggestions.
○Example: HDFC Bank uses AI chatbots to answer banking queries, improving response time.
●Predictive Analytics: AI predicts which content or offers are likely to convert based on behavior patterns.
○Example: HubSpot AI tools predict which leads are most likely to convert.
●Dynamic Content Curation: AI creates or curates content tailored to users’ journey stage.
○Example: Curated product feeds in e‑commerce platforms.
Impact on Engagement: Longer site visits, higher interaction with content, and greater product/service exploration.
3. Conversion Stage — Encouraging Action
Goal: Turn prospects into customers.
How AI helps:
●Personalized Product Recommendations: AI predicts the right product at the right time.
○Example: Amazon increases conversions by showing “Frequently Bought Together” or “Customers
Also Bought” suggestions.
●Dynamic Pricing Optimization: AI algorithms adjust prices in real time based on demand, competition, and
user behavior.
○Example: Swiggy and Zomato use dynamic pricing for discounts and surge pricing.
●Conversion Rate Optimization (CRO): AI tools like Google Optimize or Sentient Ascend run multivariate
testing to optimize landing pages and calls‑to‑action (CTAs).
●Retargeting Campaigns: AI identifies users who showed interest but didn’t convert, and serves them
personalized ads.
○Example: Flipkart’s retargeting ads during festive sales.
Impact on Conversions: Increased checkout rates and average order value.
4. Retention Stage — Keeping Customers Coming Back
Goal: Build loyalty and encourage repeat purchases.
How AI helps:
●Personalized Post‑Purchase Engagement: AI sends tailored emails, offers, or product recommendations
after a purchase.
○Example: Nykaa sends personalized skincare tips based on past purchases.
●Customer Feedback Analysis: AI analyzes reviews and surveys to detect issues or opportunities.
○Example: Swiggy uses AI sentiment analysis to improve food delivery experience.
●Churn Prediction: AI predicts when customers are likely to disengage and triggers retention campaigns.
○Example: Netflix uses viewing habits to suggest content and keep subscribers engaged.
Impact on Engagement: Increased customer loyalty and lifetime value (CLV).
5. Advocacy Stage — Turning Customers into Brand Advocates
Goal: Encourage satisfied customers to promote your brand.
How AI helps:
Goal: Turn prospects into customers.
How AI helps:
●Personalized Product Recommendations: AI predicts the right product at the right time.
○Example: Amazon increases conversions by showing “Frequently Bought Together” or “Customers
Also Bought” suggestions.
●Dynamic Pricing Optimization: AI algorithms adjust prices in real time based on demand, competition, and
user behavior.
○Example: Swiggy and Zomato use dynamic pricing for discounts and surge pricing.
●Conversion Rate Optimization (CRO): AI tools like Google Optimize or Sentient Ascend run multivariate
testing to optimize landing pages and calls‑to‑action (CTAs).
●Retargeting Campaigns: AI identifies users who showed interest but didn’t convert, and serves them
personalized ads.
○Example: Flipkart’s retargeting ads during festive sales.
Impact on Conversions: Increased checkout rates and average order value.
4. Retention Stage — Keeping Customers Coming Back
Goal: Build loyalty and encourage repeat purchases.
How AI helps:
●Personalized Post‑Purchase Engagement: AI sends tailored emails, offers, or product recommendations
after a purchase.
○Example: Nykaa sends personalized skincare tips based on past purchases.
●Customer Feedback Analysis: AI analyzes reviews and surveys to detect issues or opportunities.
○Example: Swiggy uses AI sentiment analysis to improve food delivery experience.
●Churn Prediction: AI predicts when customers are likely to disengage and triggers retention campaigns.
○Example: Netflix uses viewing habits to suggest content and keep subscribers engaged.
Impact on Engagement: Increased customer loyalty and lifetime value (CLV).
5. Advocacy Stage — Turning Customers into Brand Advocates
Goal: Encourage satisfied customers to promote your brand.
How AI helps:
●Social Listening: AI monitors brand mentions, reviews, and competitor activity to identify opportunities for
engagement.
○Example: Zomato uses AI‑powered tools to track feedback and engage with customers in real time.
●Automated Review & Testimonial Requests: AI triggers personalized review requests post‑purchase.
○Example: Amazon and Flipkart use AI to automatically prompt reviews after delivery.
●Content Generation for Advocacy: AI tools generate shareable content like summaries, personalized
discount offers, and brand stories.
○Example: Canva’s AI tools help brands create social media posts to highlight customer success
stories.
Impact on Conversions: Organic growth through word‑of‑mouth and social proof.
Summary Chart — AI Impact Across the Marketing Funnel
Funnel Stage AI Application Example Impact
Awareness Programmatic ads, SEO optimization Flipkart, Netflix Higher CTR, reach
Consideration Predictive content, chatbots Myntra, HDFC
Bank
More engagement, lead
generation
Conversion Personalized recommendations, CRO Amazon, Swiggy Increased conversion rates
Retention Churn prediction, personalized
follow‑ups
Nykaa, Netflix Higher loyalty, CLV
Advocacy Social listening, automated reviews Zomato, Amazon Organic growth, trust
1. AI in SEO — Challenges & Limitations
a. Content Quality & Search Intent Understanding
●Challenge: AI tools can generate content quickly but often lack deep human insight into user intent, subtle
nuances, and originality. Search engines reward content that solves a specific problem — AI-generated
content can miss that unless carefully guided.
●Limitation: Over-reliance on AI-generated content can lead to generic, low-value content that doesn’t rank
well. Google’s algorithms increasingly penalize purely auto-generated content if it lacks originality or
relevance.
engagement.
○Example: Zomato uses AI‑powered tools to track feedback and engage with customers in real time.
●Automated Review & Testimonial Requests: AI triggers personalized review requests post‑purchase.
○Example: Amazon and Flipkart use AI to automatically prompt reviews after delivery.
●Content Generation for Advocacy: AI tools generate shareable content like summaries, personalized
discount offers, and brand stories.
○Example: Canva’s AI tools help brands create social media posts to highlight customer success
stories.
Impact on Conversions: Organic growth through word‑of‑mouth and social proof.
Summary Chart — AI Impact Across the Marketing Funnel
Funnel Stage AI Application Example Impact
Awareness Programmatic ads, SEO optimization Flipkart, Netflix Higher CTR, reach
Consideration Predictive content, chatbots Myntra, HDFC
Bank
More engagement, lead
generation
Conversion Personalized recommendations, CRO Amazon, Swiggy Increased conversion rates
Retention Churn prediction, personalized
follow‑ups
Nykaa, Netflix Higher loyalty, CLV
Advocacy Social listening, automated reviews Zomato, Amazon Organic growth, trust
1. AI in SEO — Challenges & Limitations
a. Content Quality & Search Intent Understanding
●Challenge: AI tools can generate content quickly but often lack deep human insight into user intent, subtle
nuances, and originality. Search engines reward content that solves a specific problem — AI-generated
content can miss that unless carefully guided.
●Limitation: Over-reliance on AI-generated content can lead to generic, low-value content that doesn’t rank
well. Google’s algorithms increasingly penalize purely auto-generated content if it lacks originality or
relevance.
b. Algorithm Updates
●Challenge: Search engines, especially Google, frequently update ranking algorithms (e.g., Google’s Helpful
Content Update, Core Web Vitals, MUM). AI tools may not immediately adapt to these changes.
●Limitation: SEO strategies that rely on AI need continuous tweaking. AI tools are not always real-time
updated for algorithm changes.
c. Keyword Context and Semantic Search
●Challenge: AI can find keywords based on volume and difficulty, but understanding semantic relationships
and evolving language trends is still difficult without human analysis.
●Limitation: Overusing keywords suggested by AI without context can lead to unnatural content and even
penalties.
d. Link Building
●Challenge: AI can help identify opportunities but cannot perform authentic relationship-building, which is
essential for quality backlinks.
●Limitation: Automated link-building approaches may violate search engine guidelines.
e. Over-dependence & Creativity Gap
●Challenge: SEO requires creative problem-solving (e.g., designing strategies for niche markets), which AI
tools cannot fully replicate.
●Limitation: Heavy reliance on AI can make SEO campaigns formulaic and less adaptable.
2. AI in SEM — Challenges & Limitations
a. Lack of Transparency in AI-driven Bidding
●Challenge: Platforms like Google Ads use AI bidding strategies (e.g., Target CPA, Maximize Conversions),
but the inner workings of these algorithms are opaque.
●Limitation: Marketers have limited control over exact bid logic and must trust the AI, which can lead to
unexpected costs.
b. Budget Efficiency Issues
●Challenge: AI optimizes campaigns based on past data, but it can misinterpret short-term trends or new
product launches.
●Limitation: SEM budgets can be wasted if AI prioritizes high-cost conversions without strategic human
oversight.
c. Data Quality & Attribution Problems
●Challenge: AI relies on accurate tracking data (via Google Analytics, Conversion APIs, etc.). Issues like
cookie restrictions, data privacy laws (GDPR, CCPA), and incomplete tracking make AI predictions less
reliable.
●Challenge: Search engines, especially Google, frequently update ranking algorithms (e.g., Google’s Helpful
Content Update, Core Web Vitals, MUM). AI tools may not immediately adapt to these changes.
●Limitation: SEO strategies that rely on AI need continuous tweaking. AI tools are not always real-time
updated for algorithm changes.
c. Keyword Context and Semantic Search
●Challenge: AI can find keywords based on volume and difficulty, but understanding semantic relationships
and evolving language trends is still difficult without human analysis.
●Limitation: Overusing keywords suggested by AI without context can lead to unnatural content and even
penalties.
d. Link Building
●Challenge: AI can help identify opportunities but cannot perform authentic relationship-building, which is
essential for quality backlinks.
●Limitation: Automated link-building approaches may violate search engine guidelines.
e. Over-dependence & Creativity Gap
●Challenge: SEO requires creative problem-solving (e.g., designing strategies for niche markets), which AI
tools cannot fully replicate.
●Limitation: Heavy reliance on AI can make SEO campaigns formulaic and less adaptable.
2. AI in SEM — Challenges & Limitations
a. Lack of Transparency in AI-driven Bidding
●Challenge: Platforms like Google Ads use AI bidding strategies (e.g., Target CPA, Maximize Conversions),
but the inner workings of these algorithms are opaque.
●Limitation: Marketers have limited control over exact bid logic and must trust the AI, which can lead to
unexpected costs.
b. Budget Efficiency Issues
●Challenge: AI optimizes campaigns based on past data, but it can misinterpret short-term trends or new
product launches.
●Limitation: SEM budgets can be wasted if AI prioritizes high-cost conversions without strategic human
oversight.
c. Data Quality & Attribution Problems
●Challenge: AI relies on accurate tracking data (via Google Analytics, Conversion APIs, etc.). Issues like
cookie restrictions, data privacy laws (GDPR, CCPA), and incomplete tracking make AI predictions less
reliable.
●Limitation: Poor data quality can result in wrong targeting and ineffective bidding.
d. Ad Copy & Creative Optimization
●Challenge: AI can generate multiple ad variations quickly, but it lacks cultural sensitivity and nuanced brand
voice understanding.
●Limitation: AI may generate ads that are technically good but not emotionally resonant with the target
audience.
e. Competition & Auction Dynamics
●Challenge: AI bidding works in real-time auctions, where competitors also use AI. This can lead to
unpredictable results and rapid bid inflation.
●Limitation: AI-driven campaigns can escalate costs without proportionate ROI if competitors’ AI systems
outperform yours.
3. Overarching Challenges Across SEO & SEM
●Ethical Concerns: AI misuse (e.g., spamming keywords, creating fake reviews).
●Bias & Data Limitations: AI learns from historical data — if the data is biased, results will be skewed.
●Dependence on Data: AI can’t function effectively without high-quality, sufficient data.
●Skill Gap: Marketers need technical skills to integrate and interpret AI outputs effectively.
●Regulatory Risks: Increasing regulation on AI-generated content and data privacy impacts both SEO and
SEM.
d. Ad Copy & Creative Optimization
●Challenge: AI can generate multiple ad variations quickly, but it lacks cultural sensitivity and nuanced brand
voice understanding.
●Limitation: AI may generate ads that are technically good but not emotionally resonant with the target
audience.
e. Competition & Auction Dynamics
●Challenge: AI bidding works in real-time auctions, where competitors also use AI. This can lead to
unpredictable results and rapid bid inflation.
●Limitation: AI-driven campaigns can escalate costs without proportionate ROI if competitors’ AI systems
outperform yours.
3. Overarching Challenges Across SEO & SEM
●Ethical Concerns: AI misuse (e.g., spamming keywords, creating fake reviews).
●Bias & Data Limitations: AI learns from historical data — if the data is biased, results will be skewed.
●Dependence on Data: AI can’t function effectively without high-quality, sufficient data.
●Skill Gap: Marketers need technical skills to integrate and interpret AI outputs effectively.
●Regulatory Risks: Increasing regulation on AI-generated content and data privacy impacts both SEO and
SEM.
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