Indranil Chatterjee’s Post

Managing an #AI project is not easy. An organization needs more than just the right #technical #talent to build AI projects. It also needs a pervasive data #culture and strong #data literacy to catalyze AI adoption.

The future is here, it's just not evenly distributed. Deep pockets & Ingenuity win the game. Adoption of Generative AI technologies surprisingly follows a unique trend among laggards and leaders. It's a U-curve where large enterprises with deep pockets and small startups with grit and ingenuity are investing aggressively in these use cases. Mid-sized companies on the other hand are dealing with usual cost pressures and "inertia" to move forward. Many companies are still playing tactical here. We see the following use cases being widely adopted by Future leaders: ✨ Virtual Expert - Act as a personal coach in a specific domain - Summarize and extract insights - Information retrieval and problem-solving 👨💻 Sales & Customer Engagement - Co-pilot to navigate complex sales cycles and do research - Chatbots for personalized customer support - Customer life cycle management and insights generation - AI-generated product recommendation 📃 Content Generation - Create multimodal personalized messaging - Effective triangulation of information for targeted messages ⌨️Coding (Saving best for the last) - Co-pilot to generate code - Disruption in Software dev. life cycle. Newer frameworks - Synthetic data generation - Develop prototypes in hours for quick validation - LLMOPS What is it that you see widely adopted?

💡Why exactly is an AI proof of concept crucial to the success of your project? ✅ It helps you determine the best AI methodology. ✅ It allows you to test AI development partners. ✅ It lets you explore different solution variations and choose the best one. ✅ It adds credibility to convince stakeholders to support your project. ✅ It provides estimates of project time and cost. Check for more 👇🏼

You probably often wonder how to reduce risk and secure investments as you bring your AI idea to life. At Unicsoft, we believe an AI proof of concept approach is the way to go. Let me explain why. You face several challenges when starting to implement an AI solution, but one of the most critical is the significant amount of resources and investments required. 💼 In the healthcare sector, for example, AI projects typically span 6 to 12 months, involving data collection, model training, algorithm development, testing, and validation. Each phase demands expertise and resources. 💼On top of investing time and effort, you’ll also need high-quality data, robust computing infrastructure, a top-notch team of data scientists and AI experts, and data privacy and security compliance. 🔎No wonder that 50% of AI projects fail. However, by building an AI POC, you can avoid pitfalls and secure a significant portion of your initial investment.💰 💡So, why exactly is an AI proof of concept crucial to the success of your project? ✅ It helps you determine the best AI methodology. ✅ It allows you to test AI development partners. ✅ It lets you explore different solution variations and choose the best one. ✅ It adds credibility to convince stakeholders to support your project. ✅ It provides estimates of project time and cost. My team has written an article that explores, step by step, how to build an AI proof of concept, how much time you'll need for it and provides practical use cases. It's well-structured and properly written, so go ahead and read it – you won't regret it. 🔗 Read the full article here https://lnkd.in/e9FC89S5 🔍 If you have any questions or need guidance, feel free to DM 📩 Me and my team will be happy to help.

AI's transformative journey from science fiction to a vital force in our lives is a testament to the relentless efforts of scientists and engineers. In software development, AI's evolution opens new frontiers, serving as both a powerful tool and an object of development. Definition and Applications of AI in Software Development 🔻 AI's ability to solve complex problems, generate information, and learn has led to its integration into various aspects of modern life. In software development, AI serves as a valuable tool for tasks such as data analysis and classification, while also being an object of development for customized solutions. Applications in Software Engineering 🔷 Code Generation: AI can generate code based on parameters, potentially reshaping the role of developers. 🔷 Requirements Gathering: AI streamlines requirements compilation, reducing manual documentation. 🔷 Planning, Estimating, and Designing: AI assists in project planning, cost estimation, and team setup recommendations. 🔷 Quality Control and Feedback Management: AI scans for errors, enhances quality assurance processes, and manages user feedback. Industries and Areas Harnessing AI AI extends beyond software development, contributing to automation and enhancement in various sectors: ⚫ Automation: Fields like data mining, analysis, and classification operate autonomously. ⚫ Enhancement: AI elevates productivity in smart devices, autonomous driving, gaming, writing, and visual art. Benefits of AI for Developers 🔶 Precise Planning: AI aids in setting realistic project goals and timelines. Predictable Workflow: Developers can rely on AI for consistent workflow management. 🔶 Informed Decision-making: Data-driven insights enable informed decision-making. 🔶 Accurate Estimates: AI assists in precise cost, resource, and deadline estimation. 🔶 Better Security and QA: Automation improves security checks and vulnerability assessments, enhancing software quality. Challenges in AI Implementation 🔴 Demand for Specialist Skills: Effective AI operation requires skilled specialists. 🔴 Hardware and Resource Requirements: AI tools demand powerful computational resources. 🔴 Licensing Costs: Commercial use of advanced AI solutions may involve substantial licensing fees. The Future of AI in Software Development AI's integration in software development is set to grow as developers increasingly use specialized AIs to create more advanced solutions. While AI simplifies tasks, its successful integration demands a high level of skill, emphasizing the need for continuous learning in the evolving landscape of AI and software development.

Will AI replace Software Developers ? This is one of the common questions among developers, mainly due to the current downfall in the market and the revolution of Artificial Intelligence in the software industry since its inception. Yes, we may agree to an extent that Artificial Intelligence has a significant impact in every developer's day to day work. But, does that mean AI will replace developers ? DEFINITELY NOT. Developing any kind of software requires skills like Problem-solving, Decision Making, Analyzing issues and finding solutions, Project Management and Planning etc., which can be done better by humans than any machine in the world (at least for now 😅 ). Software Development is not just about writing code; It is about understanding the user's point of view and client's requirements to create a product from scratch. This requires team work and effective communication between people from various tech backgrounds, which cannot be replaced by AI. Also, humans are expert in providing creative and innovative solutions to the world compared to machines. We can say that AI speeds up the development process by giving quick solutions and codes, but it is the developers who fix and arrange them and provide the complete software to the world. So, AI can be used as a tool for faster development, but not as a cheaper version of human workforce. PS : This post is completely based on my thoughts and insights. Let me know your views in the comment section ! 😊

AI is not taking over jobs but it is making them exponentially efficient. Our AI agents of Expert Analyst generates perfect, complete and accurate product stories. Accurate stories lead to accurate product development, and not leaving anything to assumptions. The Project Manager AI Agent performs perfect product planning with meticulous detail. The project manager of your dreams is here. The Enterprise Pro Agent performs perfect product deployment and lifecycle management. They have all the answers ready for any upgrade, innovations, change and enhancements. Questions like "What it takes to ...", "How much time ...", "What is the impact..", are easily achievable with a flash. Our human capabilities are limited. The AI agents are making them go beyond human limits. Be a great analyst, PM & EAI with AI. #AI #Superhuman #SuperCode

Making the transition from traditional product management to AI has been fascinating! While the core of solving problems remains the same, the approach is a whole new game. Gone are the days of pixel-perfect design and endless feature debates. AI products are living organisms - they learn and adapt over time. This means one of the biggest hurdles is managing the possibility of mistakes. My secret weapon? Prioritizing accuracy (think 90%!), but also empowering users to course-correct any errors. This builds trust and keeps users invested in the product's journey. Here's where things get interesting: the design and execution stages differ drastically from traditional PM. Forget architecture discussions, AI design is all about reading white papers and understanding how these intelligent machines function. Think of it as training your AI teammate for success! Execution hinges on a strong foundation of data - the better the data, the smarter your AI becomes. Don't forget to assess the model's "muscles" - robustness and scalability are crucial for future growth. #AIProductManagement #ProductManagement #TheFutureisHere

AI's transformative journey from science fiction to a vital force in our lives is a testament to the relentless efforts of scientists and engineers. In software development, AI's evolution opens new frontiers, serving as both a powerful tool and an object of development. Definition and Applications of AI in Software Development 🔻 AI's ability to solve complex problems, generate information, and learn has led to its integration into various aspects of modern life. In software development, AI serves as a valuable tool for tasks such as data analysis and classification, while also being an object of development for customized solutions. Applications in Software Engineering 🔷 Code Generation: AI can generate code based on parameters, potentially reshaping the role of developers. 🔷 Requirements Gathering: AI streamlines requirements compilation, reducing manual documentation. 🔷 Planning, Estimating, and Designing: AI assists in project planning, cost estimation, and team setup recommendations. 🔷 Quality Control and Feedback Management: AI scans for errors, enhances quality assurance processes, and manages user feedback. Industries and Areas Harnessing AI AI extends beyond software development, contributing to automation and enhancement in various sectors: ⚫ Automation: Fields like data mining, analysis, and classification operate autonomously. ⚫ Enhancement: AI elevates productivity in smart devices, autonomous driving, gaming, writing, and visual art. Benefits of AI for Developers 🔶 Precise Planning: AI aids in setting realistic project goals and timelines. Predictable Workflow: Developers can rely on AI for consistent workflow management. 🔶 Informed Decision-making: Data-driven insights enable informed decision-making. 🔶 Accurate Estimates: AI assists in precise cost, resource, and deadline estimation. 🔶 Better Security and QA: Automation improves security checks and vulnerability assessments, enhancing software quality. Challenges in AI Implementation 🔴 Demand for Specialist Skills: Effective AI operation requires skilled specialists. 🔴 Hardware and Resource Requirements: AI tools demand powerful computational resources. 🔴 Licensing Costs: Commercial use of advanced AI solutions may involve substantial licensing fees. The Future of AI in Software Development AI's integration in software development is set to grow as developers increasingly use specialized AIs to create more advanced solutions. While AI simplifies tasks, its successful integration demands a high level of skill, emphasizing the need for continuous learning in the evolving landscape of AI and software development.