
Enabling the last mile with AI generated insights
In a country like India, enabling effective
last-mile customer experiences
is crucial. When a customer walks into a retail outlet, they must have a positive interaction, one that ultimately leads to conversion. The question is, how can AI help drive better last-mile conversions?
To explore this, ETBrandEquity.com, in association with Sharpsell.ai, has released an episode of 'Enabling
Phygital Experiences
: Empowering the Last Mile with AI-Generated Insights', featuring industry leaders from the marketing world.
This episode delves into how AI can transform last-mile delivery by equipping
sales representatives
with real-time insights, simulating
customer interactions
, and improving pitch precision. Featuring voices from
Harman India
, Signify, and Sharpsell.ai, it highlights AI's role as a value multiplier when applied thoughtfully across sales, marketing, and customer support.
The episode features Akhil Sethi, head of digital marketing, Harman India, Anmol Reen Jaloota, head of the digital enabling function, Signify Greater India, and Arun Subramaniam, co-founder, Sharpsell.ai.
Jaloota observed, 'At the ground level, sales representatives need to be able to sell better. They need to understand the unique selling points of a product or project and how to effectively pitch it to the customer. AI helps answer the fundamental question: What do I sell to this customer?
By analysing past purchases, financial background, affluence, and even geographic preferences, AI helps identify customer needs, whether someone would want a fan or not, for instance.'
He added that AI is already playing a significant role across various functions at Signify, saying, 'We've been using AI in lighting design. Imagine large offices or open spaces where someone is selling lighting solutions but doesn't know the best placement. We've introduced an agentic AI lighting designer that suggests optimal lighting layouts.'
AI is not just a tool, it is a powerful mechanism that helps sales representatives better understand their customers, gain deeper insights into products, and ultimately sell more effectively. That is the real value AI brings to the table.
Elaborating further, Sethi said, 'What we've observed is that AI is advancing at a very rapid pace across every department. In customer support and marketing, we use AI to track conversations happening both in the social media space and offline, using various tools at our disposal.
We conduct sentiment analysis to understand how different products are performing, looking at ratings, reviews, and customer feedback. On the marketing front, AI not only helps us target specific cohorts more effectively from an engagement perspective but also plays a crucial role in driving conversions and sell-through.'
One of the biggest challenges in last-mile engagement is that every conversation is unique, no two are ever the same. Unlike marketing, where you often have access to rich data or assumptions about the customer, sales often operates with far less information.
Counterintuitive as it may seem, a salesperson typically knows less about a customer walking in than a marketer does, even at an aggregate level. Equipping sales reps with relevant data the moment a customer enters is easier said than done.
Subramaniam noted, 'Need analysis is what most customers ask for, yet it is often underused. Why? Because sellers on the field typically lack the confidence to ask personal questions like "How many people are in your family?" or "What's your salary bracket?" Budget-related queries are easier, but deeper profiling becomes uncomfortable. So, how can we support sellers with AI without demanding too much input from them?
We see AI helping in two ways. First, by giving sellers a tool, like a ChatGPT-style assistant, they can use it to get quick answers. While possible, it may not be well received during live customer interactions. Second, and more effectively, AI can simulate real-world sales scenarios. We use AI to conduct role plays, sending virtual customers with different personalities, needs, and behaviours to train the seller. Some may be polite, others more difficult. This helps salespeople prepare and adapt, improving performance without direct pressure during a live pitch.'
This episode explored how AI can enhance last-mile delivery, highlighting both practical use cases and potential pitfalls. When applied in the right context, AI serves as a powerful value multiplier, capable of simulating diverse customer interactions and helping sales representatives better prepare, personalise, and perform.

Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles


Time of India
2 hours ago
- Time of India
Empowering young minds: How 4 friends are teaching AI in low-income communities
Pune: "Why are firefighters always men? Why is a black, old, fat woman never the first image when we ask for a person?" These were some of the sharp questions posed by 11- to 14-year-old children learning about artificial intelligence (AI), its reasoning, and its biases. As part of Pune-based THE Labs, a not-for-profit organisation founded by four friends, these children from low-income communities are not just learning how AI works but also how to challenge and reshape its inherent prejudices, how to train it, how to leverage it, and how to evaluate it. Since June 2024, its first cohort of 20 students explored AI through image classification and identification, learning how machines perceive the world. Now, they are gearing up to train large language models, equipping themselves with skills to shape AI's future. A new batch of 63 students has joined. THE Labs is a non-profit after-school programme blending technology, humanities and entrepreneurship. It was founded by tech entrepreneurs Mayura Dolas and Mandar Kulkarni, AI engineer Kedar Marathe, and interdisciplinary artist Ruchita Bhujbal, who saw a gap — engineers lacked exposure to real-world issues, and educators had little understanding of technology. "We first considered building a school, but the impact would have been limited. Besides, there were logistical hurdles," said Dolas, who is also a filmmaker. Kulkarni's acceptance into The Circle's incubation programme two years ago provided 18 months of mentorship and resources to refine their vision. In June 2024, THE Labs launched a pilot at a low-income English-medium school in Khadakwasla, training 20 students from standards VI-VIII (12 girls, 8 boys). With no dedicated space, they conducted 1.5-hour morning sessions at the school. Students first learned about classifier AI — how AI identifies objects — and image generation AI, which creates visuals based on prompts. Through hands-on practice, students discovered how AI's training data impacts accuracy and how biases emerge when datasets lack diversity. They experimented with prompts, analysed AI-generated images, and studied errors. "We asked them to write prompts and replicate an image, and they did it perfectly. That is prompt engineering in action," Dolas said. A key takeaway was AI bias. Students compared outputs from two AI models, identifying gaps — such as the underrepresentation of marginalised identities. "For example, children realised that a black, fat, older woman was rarely generated by AI. They saw firsthand how biases shape digital realities," Dolas added. Parents and students are a happy lot too. Mohan Prasad, a construction worker, said he is not sure what his daughter is learning, but she is excited about AI and often discusses its importance at home. Sarvesh, a standard VIII student, is thrilled that he trained an AI model to identify Hindu deities and noticed biases in AI searches — when prompted with "person", results mostly showed thin white men. "I love AI and want to learn more," he said. His father, Sohan Kolhe, has seen a surge in his son's interest in studies. Anandkumar Raut, who works in the private sector, said his once-shy daughter, a standard VI student, now speaks confidently, does presentations, and is more outspoken since joining the programme.


Time of India
3 hours ago
- Time of India
AI explained: Your simple guide to chatbots, AGI, Agentic AI and what's next
Note: AI-generated image The tech world is changing fast, and it's all thanks to Artificial Intelligence (AI). We're seeing amazing breakthroughs, from chatbots that can chat like a human to phones that are getting incredibly smart. This shift is making us ask bigger questions. It's no longer just about "what can AI do right now?" but more about "what will AI become, and how will it affect our lives?" First, we got used to helpful chatbots. Then, the idea of a "super smart" AI, called Artificial General Intelligence (AGI), started taking over headlines. Companies like Google , Microsoft , and OpenAI are all working hard to make AGI a reality. But even before AGI gets here, the tech world is buzzing about Agentic AI . With all these new terms and fast changes, it's easy for most of us who aren't deep in the tech world to feel a bit lost. If you're wondering what all this means for you, you're in the right place. In this simple guide, we'll answer your most important questions about the world of AI, helping you understand what's happening now and get ready for what's next. What is AI and how it works? In the simplest terms, AI is about making machines – whether it's smartphones or laptops – smart. It's a field of computer science that creates systems capable of performing tasks that usually require human intelligence. Think of it as teaching computers to "think" or "learn" in a way that mimics how humans do. This task can include understanding human language, recognising patterns and even learning from experience. Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Skype Phone Alternative Undo It uses its training -- just like humans -- in achieving its goal which is to solve problems and make decisions. That brings us to our next query: "How is a machine trained to do tasks like humans?" While AI might seem like magic, it works on a few core principles. Just like humans get their information from observing, reading, listening and other sources, AI systems utilise vast amounts of data, including text, images, sounds, numbers and more. What are large language models (LLMs) and how are they trained? As mentioned above, AI systems need to learn and for that, they utilise Large Language Models, or LLMs. They are highly advanced AI programmes specifically designed to understand, generate and interact with human language. Think of them as incredibly knowledgeable digital brains that specialise in certain fields. LLMs are trained on enormous amounts of text data – billions and even trillions of words from books, articles, websites, conversations and more. This vast exposure allows them to learn the nuances of human language like grammar, context, facts and even different writing styles. For example, an LLM is like a teacher that has a vast amount of knowledge and understands complex questions as well as can reason through them to provide relevant answers. The teacher provides the core knowledge and framework. Chatbots then utilise this "teacher" (the LLM) to interact with users. The chatbot is the "student" or "interface" that applies the teacher's lessons. This means AI is really good at specific tasks, like playing chess or giving directions, but it can't do other things beyond its programmed scope. How is AI helpful for people? AI is getting deeply integrated into our daily lives, making things easier, faster and smarter. For example, it can be used in powering voice assistants that can answer questions in seconds, or in healthcare where doctors can ask AI to analyse medical images (like X-rays for early disease detection) in seconds and help patients in a more effective manner, or help in drug discovery. It aims to make people efficient by allowing them to delegate some work to AI and helping them in focusing on major problems. What is Agentic AI? At its core, Agentic AI focuses on creating AI agents – intelligent software programmes that can gather information, process it for reasoning, execute the ideas by taking decisions and even learn and adapt by evaluating their outcomes. For example, a chatbot is a script: "If a customer asks X, reply Y." A Generative AI (LLM) is like a brilliant essay writer: "Give it a topic, and it'll write an essay." Agentic AI is like a project manager: "My goal is to plan and execute a marketing campaign." It can then break down the goal, generate ideas, write emails, schedule meetings, analyse data and adjust its plan – all with minimal human oversight – Just like JARVIS in Iron Man and Avengers movies. What is AGI? AGI is a hypothetical form of AI that possesses the ability to understand, learn and apply knowledge across a wide range of intellectual tasks at a level comparable to, or surpassing, that of a human being. Think of AGI as a brilliant human polymath – someone who can master any subject, solve any problem and adapt to any challenge across various fields. While AI agents are created to take up specific tasks in which they learn and execute, AGI will be like a ' Super AI Agent ' that virtually has all the information there is in this world and can solve problems on any subject. Will AI take away our jobs and what people can do? There is a straightforward answer by various tech CEOs and executives across the industry: Yes. AI will take away repetitive, predictable tasks and extensive data processing, such as data entry, routine customer service, assembly line operations, basic accounting and certain analytical roles. While this means some existing positions may be displaced, AI will more broadly transform roles, augmenting human capabilities and shifting the focus towards tasks requiring creativity, critical thinking, emotional intelligence and strategic oversight. For example, AI/Machine Learning Engineers, Data Scientists , Prompt Engineers and more. The last such revolution came with the internet and computers which did eat some jobs but created so many more roles for people. They can skill themselves by enrolling in new AI-centric courses to learn more about the booming technology to be better placed in the future. AI Masterclass for Students. Upskill Young Ones Today!– Join Now


Time of India
3 hours ago
- Time of India
Banks back status quo on co-lending
New Delhi: Banks have reached out to the Reserve Bank of India (RBI) suggesting continuing with the current co-lending model with non-banking finance companies (NBFCs). The RBI has sought suggestions on draft guidelines on Co-lending Arrangements Directions, 2025 issued in April, said officials aware of the developments. Under the proposed draft guidelines, the RBI has suggested restricting the co-lending model to where both a bank and NBFC jointly originate and disburse loans. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Leading a New Era of Innovation with AI: An Interview With Dr Marcus Weller, Founder and CEO - TechBullion TechBullion Undo Under the current co-lending model (CLM-2), NBFCs originate loans from their books and later assign part of the loan to the bank. This helps NBFCs maintain liquidity and speed up the process. The RBI now wants to shift to CLM-1, where both bank and NBFC disburse loan jointly from the outset. "We want both models to exist and the decision should be left to individual banks and other regulated entities," said a bank executive on suggestions made last month through the Indian Banks' Association . Live Events An email sent to the RBI did not elicit a response until press time. "Each single loan under the arrangement shall be shared among the funding regulated entities right from the time of first disbursement. This shall be done on the basis of a non-discretionary ex ante Inter Creditor Agreement with joint nature of rights," the RBI noted in its draft guidelines. Lenders have reasoned that at present around 80% of co-lending is done through CLM-2, which gives the banks a choice to pick loan portfolios they want to fund. According to a report by CareEdge Ratings, each loan under the arrangement will be shared among the funding REs right from the first disbursement. "Transactions currently being carried out under the CLM-2 model are expected to shift to the direct assignment (DA) model," it said.