Black Basil Technologies’ Post

As a research engineer often using and studying AI, I've been delving into how AI can accelerate and optimize my work, and the journey has been nothing short of exhilarating. Here's a brief look at some of the key insights I've gained from Solita Crash Course: 🔍 Leveraging AI for Documentation: By feeding historical reviews and documentation into AI, we create a dynamic repository. This allows seamless future referencing and querying, enhancing both individual and team productivity. 🎯 Goal Setting with AI: AI can set up actionable goals and steps, but it's crucial to evaluate their business implications before execution. This ensures alignment with organizational objectives and maximizes ROI. 💡 Enhancing Code Quality: AI's ability to generate basic code structures allows us to focus on refining and customizing prototypes. For example, within just 50 minutes in one session, a basic prototype can be ready. Based on that basic prototype, multiple prototypes can be generated, significantly boosting the development speed and innovation potential. 🚀 Agile Methodology with AI: Integrating AI into Agile practices has been a game-changer. It's not about the hierarchy of guru, senior, or junior developers; it's about the collective attitude and collaboration. AI is possible to accelerate the iteration cycles as short as 12 hours, promoting rapid development and continuous improvement. 🤖 Set-Based Design: While programming robots can't yet combine different code blocks seamlessly, AI aids in set-based design, allowing us to explore various possibilities and converge on the best solution efficiently. 💼 Decision Making: AI supports informed decision-making by providing data-driven insights and predictive analysis, enhancing strategic planning and execution. Embracing AI in our work is not just about speed and efficiency; it's about fostering innovation, collaboration, and excellence. Excited for the future where AI continues to push the boundaries of what's possible in tech! 🚀🤖💡 #AI #SoftwareDevelopment #Agile #Innovation #TechTransformation #AIEngineering

🚀 Event Alert: "How AI Design and Product Development Differ from Traditional Software Processes" 🚀 🧐 If you're ignoring AI - are you missing out? Join us for a free live workshop where Rupa Chaturvedi and I will delve into the fascinating world of AI-powered product and design development. Uncover how AI is reshaping the landscape through: 🔹 Data-Driven Decision Making - Learn how AI harnesses vast data to revolutionize design choices. 🔹 Iterative Learning - Discover the iterative cycles of learning that enhance AI products, setting them apart from traditional methods. 🔹 Ethical Considerations - Explore the ethical nuances of AI, from bias issues to ensuring transparency and accountability. 🔹 User Trust & Explainability - Understand the importance of making AI understandable and trustworthy for users. 🔹 Scalability & Automation - See how AI paves the way for scalable and automated solutions in design and product development. Don't miss this chance to gain insights from industry experts and network with professionals who are at the forefront of integrating AI into creative and operational processes. ↗ This workshop is an fantastic precursor to our upcoming course, "Generative AI Products: How to Get from Idea to MVP." Whether you're a product manager, designer, startup founder, or AI enthusiast, this event will equip you with the knowledge to lead in the AI space. 🔗 Sign up now >> https://lnkd.in/gCU4JyHE 👥 Feel free to share this with anyone who might benefit from understanding the powerful impact of AI in product and design!

Top 10 Data & AI Trends for 2024: A Guide for Product Managers and Engineers As we embrace 2024, the data and AI revolution is shaping the future of product management. Here are the trends that will define the next wave of innovation: - Generative AI Goes Mainstream: From crafting unique product descriptions to generating code, generative AI is set to transform content creation. - LLMs at the Forefront: Large Language Models are revolutionizing tech, demanding new data architectures for enhanced user experiences. - Automation Takes the Lead: Say goodbye to manual analysis as automated insights become standard in delivering real value. - RAG Offers Competitive Edge: Retrieval-Augmented Generation is your new ally in supercharging AI products with unique data. - Focus on Explainability & Ethics: Transparent AI models and ethical data use are paramount for responsible product development. - Data Teams Adopt App Dev Practices: A shift towards agile development practices among data teams accelerates innovation. - Democratization of AI & ML: AI/ML tools are becoming more accessible, empowering teams without deep technical expertise. - Customized Generative Models Emerge: Tailored AI solutions are set to provide more relevant and efficient outcomes. - Enhanced ML Tooling: Streamlined workflows and improved tooling are speeding up AI/ML application deployment. - Talent Landscape Evolves: Bridging the skills gap through upskilling and partnerships is crucial as demand for AI and ML expertise soars. Let's navigate these changes together and lead the charge in innovation. The future of Data and AI is vibrant, and as Product Managers and Engineers, we are at the helm of this transformation. Embrace these trends to create groundbreaking products that stand out in the AI age. #artificialintelligence #data #productmanagers #engineers #trending

Cutting Costs in Generative AI Product Development: Essential Tips for Founders Developing generative AI products can be a costly endeavour, but with the right strategies, you can significantly reduce expenses while still delivering a high-quality product. Here are some key ways to keep costs under control: Leverage Open-Source Tools: Utilize open-source frameworks and libraries. These can provide robust functionalities without the high costs associated with proprietary software. Optimize Data Usage: Focus on using high-quality, relevant datasets rather than large volumes of data. This reduces storage and processing costs while improving model accuracy. Cloud-Based Solutions: Adopt cloud services for scalable and cost-effective computing power. Pay-as-you-go models allow you to scale resources up or down based on your needs. Automate Where Possible: Implement automation in your development and deployment processes. Automated testing and CI/CD pipelines can save time and reduce errors, lowering overall costs. Collaborate with Experts: Partner with experienced AI development firms who can provide guidance and avoid common pitfalls. This can streamline your development process and prevent costly mistakes. Prototype and Iterate: Start with a minimum viable product (MVP) to test your concept. Iterative development allows for incremental improvements and prevents overspending on unproven ideas. Energy-Efficient Models: Use energy-efficient models and hardware to reduce the operational costs associated with training and running AI models. By implementing these strategies, founders can effectively manage their budgets while developing innovative generative AI products. Ready to optimize your generative AI development? Black Basil Technologies has the expertise to help you reduce costs without compromising on quality. Connect with us to learn more! #GenerativeAI #AIProductDevelopment #CostOptimization #TechFounders #AIInnovation

In my view, prototyping new AI features is one of the most exciting activities out there - but developing and managing real-world AI products can quickly become overwhelming. Oftentimes, teams believe that training the best model and “plugging” it into the existing product is all it takes - but in reality, the complexity of an AI product by far exceeds traditional software products. As a PM, sooner or later, you will need to make the transition from playgrounds and fun features to the challenge of creating robust and reliable value with AI. You will find yourself confronted with the following questions: - Does our “raw material” - the available data - actually provide the right learning signals to solve the business problem? - How can we integrate AI models with the logic of existing business knowledge and processes? - How can our UX and communication guide users and manage their expectations, given the non-deterministic character, uncertainty, and creative failure modes of real-world AI models? - AI brings new roles into a product team - like data scientists, ML and MLOps engineers, data annotators, etc. How can we align all of these roles and make sure all of us share the same language and vision? My upcoming book 𝐂𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬 provides answers to these - and many other - questions, summarizing the experience I have accumulated from a large number of AI projects in different industries. It not only provides PMs with an intuitive theoretical basis for modern AI, but also introduces a systematic process to conceptualize different kinds of AI applications - incl. predictive, generative, and autonomous AI - with a focus on user experience, sustainable value creation, and successful communication. #artificialintelligence #productmanagement #generativeai #startups #automation #advancedanalytics https://lnkd.in/eZSrQJ9J

Generative AI is revolutionizing software development, with the potential to boost productivity by up to 40% and time to market by 5%, according to McKinsey. From automating routine tasks to generating innovative design concepts, AI is empowering product teams to achieve more in less time. As the global market for generative AI in software development is projected to reach a staggering USD 287.4 billion by 2033, it's clear that this technology is here to stay. To truly harness AI's power, organizations must invest in talent development, prioritize ethical considerations, and foster a culture of experimentation. Data privacy, bias, and ethical considerations must be at the forefront of the integration of AI in product development. Are you leveraging generative AI in your product development process? I’d love to hear from you. #GenerativeAI #ProductManagement #AI #Technology #Innovation

AI and Data-Driven Solutions: Transforming Industries and Unlocking Billions $$$$$ AI and data-driven technologies have the potential to benefit numerous industries, delivering billions of dollars in value over the next few years. However, ~80% of AI and data projects fail or remain unused within six months of launch. Here are some key reasons for these failures: - **Good Product, Poor Adoption:** Often, the people and processes are not aligned to use the product effectively. - **Misaligned Priorities:** Many projects suffer from a lack of broad strategy and wrong prioritization. - **Performance Issues:** Solutions may not meet user expectations, with UI/UX often overlooked in industrial settings. - **Wrong Teams, Wrong Problems:** Teams may lack the necessary context, leading to the illusion of completion without real problem-solving. MVP hence critical before a full product development. - **Continuous Improvement:** There's often a lack of understanding of the importance of continuous improvement. Current technologies require a human-in-the-loop approach, regular reviews of model performance, and consistent testing and updating of models and systems. Addressing these challenges can significantly enhance the success rate of AI and data-driven projects, ensuring they deliver the expected benefits. Keep trying and learning as an organization and as individuals… Want to talk and learn more, drop me a message…

🔍 Best Practices to Follow in Generative AI Product Development 🔍 In the rapidly evolving world of AI, developing generative AI products requires a strategic approach to ensure innovation, efficiency, and ethical integrity. As founders of a company specializing in AI product development, we have identified several best practices to follow: Define Clear Objectives 🎯: Set specific, measurable, and achievable goals for your generative AI project. Understand the problem you aim to solve and the impact you intend to create. Quality Data Collection 📊: Ensure access to diverse, high-quality datasets. Implement robust data cleaning and preprocessing techniques to maintain data integrity. Focus on Ethical AI 🌐: Incorporate ethical guidelines from the outset. Address biases and ensure transparency in your AI models. Iterative Development Process 🔄: Adopt an agile methodology to facilitate continuous feedback and improvements. Regularly test and validate your models to refine their performance. Cross-Functional Collaboration 🤝: Engage multidisciplinary teams, including data scientists, engineers, and domain experts. Foster a collaborative environment to leverage diverse perspectives. Scalable Architecture 🛠️: Design your AI system with scalability in mind. Ensure your infrastructure can handle increasing data volumes and computational demands. User-Centric Design 🎨: Prioritize the user experience in your product design. Gather user feedback to iteratively improve the product’s usability and functionality. Continuous Learning and Adaptation 📚: Stay updated with the latest advancements in AI research and technology. Be prepared to adapt your strategies based on emerging trends and insights. By following these best practices, you can navigate the complexities of generative AI product development and deliver innovative, reliable, and ethically sound AI solutions. At Black Basil Technologies, we are committed to excellence in AI product development. Connect with us to learn how we can help bring your AI vision to life! https://lnkd.in/dD32ycMJ #GenerativeAI #AIDevelopment #EthicalAI #AIInnovation #TechLeadership #AIProductDevelopment #BlackBasilTechnologies

🧠Continuous learning in a product leadership position is more crucial than ever, especially with AI revolutionizing our field in 2024. Let's dive into why we must stay on our toes and keep learning! 🤯 🤖 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗦𝗽𝗿𝗶𝗻𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 AI is set to transform how we manage sprints by predicting task completion, optimizing team assignments, and identifying skill gaps. This means more efficient and productive teams! 🔍 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗨𝘀𝗲𝗿 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 Using AI-powered NLP, we can now quickly analyze vast amounts of user feedback, helping us pinpoint trends and pain points to inform our product roadmaps. This makes our decisions more data-driven and aligned with user needs. 💡 𝗥𝗮𝗽𝗶𝗱 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗶𝗻𝗴 AI tools will allow us to create detailed mockups and prototypes from simple textual descriptions. This means less time spent on design iterations and more time focusing on strategic decisions. 🔧 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 In industries dealing with physical products, AI will help predict potential failures by analyzing sensor data, reducing downtime and maintenance costs. This ensures our products are always in top shape for our customers. 🎯 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 AI will drive personalized user experiences by analyzing user data and customizing interactions based on individual preferences. This leads to higher user satisfaction and engagement, making our products even more loved by users. 📈 𝗠𝗮𝗿𝗸𝗲𝘁 𝗧𝗿𝗲𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 AI provides real-time insights into market trends, competitor activities, and industry developments, enabling us to make informed decisions and stay ahead of the competition. It's like having a crystal ball for market trends! 🌐 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗔𝗜 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 As product leaders, it's our responsibility to ensure AI is used ethically. This means implementing certification programs and adhering to transparency and accountability standards to maintain user trust. 📅 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 Generative AI tools will help us with tasks like synthesizing user research, writing product requirement documents, and creating product backlogs, leading to faster time-to-market and improved productivity. How are you planning to incorporate continuous learning into your product management practices? Share your thoughts or experiences below! 👇 #ProductManagement #AI #ContinuousLearning #FutureOfWork

🚀 Unlocking the Power of Generative AI: Transforming Business Practices with Cutting-Edge Innovation! 🌟 Discover the game-changing impact of Generative AI in revolutionizing productivity and strategic planning for early adopters in the business realm. In a recent Wharton conference, experts delved into the profound implications of Gen AI, showcasing its transformative potential in processing unstructured data. Imagine harnessing the vast sea of unstructured information from emails, team communication platforms like Slack, and project management tools like Jira, and turning it into actionable insights! Key Talking Points: 🔍 Real-world examples of Gen AI streamlining decision-making processes, from predicting delivery times for software projects to gaining superior insights from raw patent texts. 📈 Case studies demonstrating how enterprises are utilizing Gen AI to distill massive volumes of data into actionable factors for strategic planning and resource allocation. 🎯 Success stories of early adopters like Automation Anywhere, leveraging Gen AI to automate workflows, enhance customer service, and improve cash flows. Join the conversation and discover how Gen AI can propel your organization to new heights of efficiency and innovation! #GenerativeAI #Innovation #StrategicPlanning #ai #pharma #business https://lnkd.in/e2_UFNg7