Latest news with #Sumaiya
Martechvibe
19 hours ago
- Business
- Martechvibe
7 Hard Truths About Building AI Products That Last
For years, product innovation has been fueled by excitement: a new framework, a breakthrough model, a rising trend. But in 2025, as AI weaves itself into every interface, the question that separates good AI products from enduring ones isn't 'What can we build?'—it's 'What should we build?' According to the 2025 State of Martech Report by Scott Brinker, product management is now one of the most pivotal roles in the Martech ecosystem tasked with balancing rapid innovation, data complexity, and the promise (and pitfalls) of AI. As boundaries between product, marketing, and customer experience blur, the need for clarity, focus, and intentional design has never been greater. Because behind every buzzword, be it LLM, Web3, or genAI , is a simple truth: if it doesn't solve a real customer problem, it's noise. To ground this exploration, we turn to insights from Sumaiya Noor, Product, AI & Technology Leader, who has built B2B, B2C, and B2B2C SaaS products across emerging tech domains like AI and Web3. Drawing from her product, engineering, and customer experience background, Sumaiya offers a refreshingly pragmatic lens, one focused not on hype cycles, but on human problems. Strategy Can't Be Built in Silos In high-velocity environments, roadmaps shift, features morph, and priorities blur. So how do you keep AI product and marketing aligned ? The answer lies in dissolving the silos entirely. 'We don't build and then inform,' says Sumaiya. 'We build together.' Cross-functional planning, with inputs from sales, marketing, CX, and engineering, not only improves go-to-market timing, it ensures that every feature is designed with customer communication in mind. And yet, even with cross-functional harmony, another trap remains: building for the tech instead of the problem. That's where disciplined product thinking becomes essential. The Case for Problem-First Product Thinking There's a temptation to fall in love with an idea or worse, a technology. But as Sumaiya puts it: 'Even the best idea is irrelevant if no one will pay for it.' The most impactful products today aren't those that chase AI for the sake of AI. They start with deep listening. They define the problem before prescribing the tech. And only then do they decide whether that shiny new model is the right tool. But what happens when customer needs evolve faster than the solutions built to serve them? When Customer Pain Points Evolve Faster Than You Build Technology is changing rapidly but so are customer expectations. The feature they needed last month might feel redundant next week. This is where continuous product discovery becomes non-negotiable. Beta feedback, prototype testing, and agile pivots must be baked into the build process. 'It's not about being right from the start,' says Sumaiya. 'It's about being flexible enough to shift fast, based on what your users actually tell you.' Flexibility is key. Not just in building, but in knowing when to stop building. Because holding on to outdated features can be just as risky as launching the wrong ones. Sunsetting Isn't Failure, It's Focus Great teams know when to quit. That feature your team launched with pride may no longer serve its purpose—maybe a competitor has done it better, or your users have outgrown it. The hardest part? Internal buy-in. 'You're not just retiring code,' Sumaiya notes. 'You're sunsetting people's effort, pride, and belief.' But with clear metrics and shared goals, this becomes a strategic move, not an emotional one. And as AI becomes embedded in more features, another layer of complexity emerges: unpredictability. Especially when the tech behaves in ways even its creators can't fully control. You Can't Eliminate AI Hallucinations But You Can Contain Them As large language models make their way into every workflow, a difficult truth remains: hallucinations are part of the system. 'If someone in the product world says that hallucination can completely be eliminated or can be mitigated, I think they don't understand the technological side of LLMs or AI, artificial intelligence or agents that much,' says Sumaiya. Rather than over-promise, product teams must scope narrowly, train models on proprietary data, and design safeguards to guide behaviour. 'You can't fully control LLMs but you can control how, where, and why you deploy them.' But responsible deployment isn't enough. You also need to know if your AI is actually adding value, which brings us to the challenge of building effective feedback loops. Feedback Loops in AI Products Are Twice As Hard Feedback is already tough in traditional product development. In AI, it's even more layered. 'You need two types of feedback loops. One for validating the feature itself, the service itself, whatever you are trying to provide to the customer. In terms of solving their problem or pain point. If it's an AI integrated or AI-based product, the additional feedback loop is also required to validate what type of value addition this AI integration is adding to your overall solution,' says Sumaiya. You're not only asking whether a feature works, you're asking whether AI is meaningfully improving the experience. This means comparing pre- and post-AI metrics, collecting real-time usage data, and isolating AI's impact on usability and satisfaction. Even with feedback in place, product teams still face a difficult judgment call: which technologies are worth betting on, and which ones are just noise? How to Tell If a Technology Will Stick or Fizzle When everyone's chasing the next 'platform shift,' how do you know what's real? Sumaiya's take: measure cost (financial, environmental, ethical), problem-fit, and long-term sustainability. Her critique of blockchain coin mining, versus her long-term belief in AI, isn't about trendiness. It's about impact. 'Tech that creates more problems than it solves won't last.' In the end, it's not about resisting innovation. It's about choosing it wisely. In a world shaped by AI, what we build is only as good as why we build it. ALSO READ: Brands Use Context Engineering to Appeal to Answer Engines Chandni is an Editor with a keen interest in customer-obsessed ideas. A journalist by profession and a writer at heart, she is committed to martech and CX content that resonates with readers across industries. View More
Martechvibe
a day ago
- Business
- Martechvibe
7 Hard Truths About Building AI Products That Actually Last
For years, product innovation has been fueled by excitement: a new framework, a breakthrough model, a rising trend. But in 2025, as AI weaves itself into every interface, the question that separates good AI products from enduring ones isn't 'What can we build?'—it's 'What should we build?' According to the 2025 State of Martech Report by Scott Brinker, product management is now one of the most pivotal roles in the Martech ecosystem tasked with balancing rapid innovation, data complexity, and the promise (and pitfalls) of AI. As boundaries between product, marketing, and customer experience blur, the need for clarity, focus, and intentional design has never been greater. Because behind every buzzword, be it LLM, Web3, or genAI , is a simple truth: if it doesn't solve a real customer problem, it's noise. To ground this exploration, we turn to insights from Sumaiya Noor, Product, AI & Technology Leader, who has built B2B, B2C, and B2B2C SaaS products across emerging tech domains like AI and Web3. Drawing from her product, engineering, and customer experience background, Sumaiya offers a refreshingly pragmatic lens, one focused not on hype cycles, but on human problems. Strategy Can't Be Built in Silos In high-velocity environments, roadmaps shift, features morph, and priorities blur. So how do you keep AI product and marketing aligned ? The answer lies in dissolving the silos entirely. 'We don't build and then inform,' says Sumaiya. 'We build together.' Cross-functional planning, with inputs from sales, marketing, CX, and engineering, not only improves go-to-market timing, it ensures that every feature is designed with customer communication in mind. And yet, even with cross-functional harmony, another trap remains: building for the tech instead of the problem. That's where disciplined product thinking becomes essential. The Case for Problem-First Product Thinking There's a temptation to fall in love with an idea or worse, a technology. But as Sumaiya puts it: 'Even the best idea is irrelevant if no one will pay for it.' The most impactful products today aren't those that chase AI for the sake of AI. They start with deep listening. They define the problem before prescribing the tech. And only then do they decide whether that shiny new model is the right tool. But what happens when customer needs evolve faster than the solutions built to serve them? When Customer Pain Points Evolve Faster Than You Build Technology is changing rapidly but so are customer expectations. The feature they needed last month might feel redundant next week. This is where continuous product discovery becomes non-negotiable. Beta feedback, prototype testing, and agile pivots must be baked into the build process. 'It's not about being right from the start,' says Sumaiya. 'It's about being flexible enough to shift fast, based on what your users actually tell you.' Flexibility is key. Not just in building, but in knowing when to stop building. Because holding on to outdated features can be just as risky as launching the wrong ones. Sunsetting Isn't Failure, It's Focus Great teams know when to quit. That feature your team launched with pride may no longer serve its purpose—maybe a competitor has done it better, or your users have outgrown it. The hardest part? Internal buy-in. 'You're not just retiring code,' Sumaiya notes. 'You're sunsetting people's effort, pride, and belief.' But with clear metrics and shared goals, this becomes a strategic move, not an emotional one. And as AI becomes embedded in more features, another layer of complexity emerges: unpredictability. Especially when the tech behaves in ways even its creators can't fully control. You Can't Eliminate AI Hallucinations But You Can Contain Them As large language models make their way into every workflow, a difficult truth remains: hallucinations are part of the system. 'If someone in the product world says that hallucination can completely be eliminated or can be mitigated, I think they don't understand the technological side of LLMs or AI, artificial intelligence or agents that much,' says Sumaiya. Rather than over-promise, product teams must scope narrowly, train models on proprietary data, and design safeguards to guide behaviour. 'You can't fully control LLMs but you can control how, where, and why you deploy them.' But responsible deployment isn't enough. You also need to know if your AI is actually adding value, which brings us to the challenge of building effective feedback loops. Feedback Loops in AI Products Are Twice As Hard Feedback is already tough in traditional product development. In AI, it's even more layered. 'You need two types of feedback loops. One for validating the feature itself, the service itself, whatever you are trying to provide to the customer. In terms of solving their problem or pain point. If it's an AI integrated or AI-based product, the additional feedback loop is also required to validate what type of value addition this AI integration is adding to your overall solution,' says Sumaiya. You're not only asking whether a feature works, you're asking whether AI is meaningfully improving the experience. This means comparing pre- and post-AI metrics, collecting real-time usage data, and isolating AI's impact on usability and satisfaction. Even with feedback in place, product teams still face a difficult judgment call: which technologies are worth betting on, and which ones are just noise? How to Tell If a Technology Will Stick or Fizzle When everyone's chasing the next 'platform shift,' how do you know what's real? Sumaiya's take: measure cost (financial, environmental, ethical), problem-fit, and long-term sustainability. Her critique of blockchain coin mining, versus her long-term belief in AI, isn't about trendiness. It's about impact. 'Tech that creates more problems than it solves won't last.' In the end, it's not about resisting innovation. It's about choosing it wisely. In a world shaped by AI, what we build is only as good as why we build it. ALSO READ: Brands Use Context Engineering to Appeal to Answer Engines Chandni is an Editor with a keen interest in customer-obsessed ideas. A journalist by profession and a writer at heart, she is committed to martech and CX content that resonates with readers across industries. View More
Time of India
08-06-2025
- Time of India
Woman slashes hubby's throat in sleep
Shamli: A 30-year-old woman was arrested on Sunday for allegedly slitting her husband's throat while he was asleep at their home in the Salehk Vihar area. Police said the incident took place early Sunday morning. Tired of too many ads? go ad free now Mohammad Khurshid, 35, who runs a biryani stall in Jammu, had returned to Shamli with his wife Sumaiya and their three children on June 5 to celebrate Eid. The couple has been married for eight years. Soon after the attack, Khurshid was rushed to a local hospital and later referred to a higher facility in Meerut due to the severity of his injuries. Before being hospitalised, he told police that his wife had been threatening to kill him for the past year. Sumaiya was arrested from the spot with the knife reportedly used in the attack. Preliminary investigation suggests she may have been distressed over alleged threats of abandonment by Khurshid. Later, Sumaiya's father filed a complaint against her, accusing her of attempting to kill her husband. Police have registered a case under Section 109(1) of the BNS. Further investigation is underway to ascertain the motive and gather evidence.
Time of India
14-05-2025
- Time of India
12 interstate fraudsters arrested for work-from-home fraud in Bengaluru
1 2 3 4 5 6 Bengaluru: Adugodi police busted a cyber syndicate involved in fleecing money from people by offering part-time work-from-home jobs and making quick money. Twelve interstate fraudsters were arrested, including the 24-year-old kingpin — a BTech graduate — and mule account DCP Sarah Fathima told TOI: "A 42-year-old woman residing in LR Nagar in Adugodi lost Rs 5 lakh between Jan 2 and 17 to a work-from-home fraud . A special team was formed to nab the accused. The team camped in Uttar Pradesh and Maharashtra and nabbed the 12 crooks." The team working under Fathima's guidance was headed by inspector Ravi Kumar victim, Sumaiya Banu, lodged a complaint on Jan 22, stating that an unknown person sent her a text message offering a 'work-from-home' project. The crook offered the homemaker a commission and she took the project. However, on completion, the crook told her the project work credit score showed negative in the balance sheet and asked her to register on a platform he'd provided to withdraw money. She did, and received Rs 800. Sumaiya was then assigned multiple Sumaiya was tricked into paying Rs 10,000 to withdraw the remaining money she had earned. She transferred the money to the unknown person's account and got Rs 20,000. The fraudsters then assigned her special projects and informed her that if her credit score was less, she could not withdraw the money she earned and made her transfer Rs 5 lakh, showing her balance sheet as Rs 10.8 lakh. When the miscreants asked her to transfer another Rs 3.2 lakh to withdraw money, Sumaiya suspected something fishy and called 1930 helpline, and later filed a complaint with obtained KYC details of the account — in a private bank in UP's Prayagraj — to which Sumaiya had transferred the money. Police issued a notice to account holder Sonu, who admitted that his passbook, debit card and sim card used to open the account were with a labour contractor based in contractor detainedWith Sonu's help, police went to Mumbai on April 14 and detained mule account provider Raj Mishra. He confessed that he got the accounts opened using the labourers' documents, collected their passbooks and debit cards, and gave them to three persons in Prayagraj, who paid him a commission of Rs 1,500 per account. Mishra had provided documents for 22 bank accounts. Call centre raided in Prayagraj Police took Mishra to Prayagraj on April 23, where they detained another accused who confessed that the syndicate kingpin, Harshavardhan Ojha, paid him Rs 18,000 to Rs 20,000 for providing a bank account from other states and Rs 3,000 for local accounts. Cops then raided a call centre run by Ojha in a house in Kamla Nagar, Prayagraj, on April 26. Ojha had hired 10 telecallers to run the scam. Police seized 400 sim cards, 140 debit cards, 17 cheque books, 27 mobile phones, 22 bank passbooks, a spiral-bound book in which income and expenses were mentioned, and Rs 15,000 in cash. All 10 were arrested from the call confessed to running the racket for the past year. "We are verifying the bank accounts used in the crime and the total money the accused earned from it," a police officer said.10 telecallers in custodyOther than Ojha, the mastermind, the others arrested are Sonu, 27, contractor from Mumbai; telecallers Akash Kumar Yadav, 23, BA graduate; Goraknath Yadav, 20; Sanjith Kumar Yadav, 25; Akash Kumar Singh, 19, BA graduate; Amit Yadav, 19; Gourav Pratap Singh, 22; Brijesh Singh, 20; Raj Mishra, 21, MA graduate; Tushar Mishra, 22; Goutam Shailesh, 25, all from UP.
