People often ask me, 'How should one decide which AI tools to pick for their product development lifecycle, especially in mid-sized to large B2B/SaaS enterprise companies?' Based on my observations, here’s an effective approach: 1. When exploring AI tools for product development teams, integration is crucial. Tools like GitHub Copilot and Microsoft Azure DevOps stand out because they fit seamlessly into existing workflows, minimizing disruptions, and ensuring continuity. ◘Important KPIs: Integration time, User adoption rate, Workflow disruption incidents. 2. Security is another top priority. Companies like Equifax and Target have faced significant challenges due to a lack of robust security, leading to data breaches and financial losses. This is why tools like AWS CodeGuru, with its high threat detection rate, are highly recommended for maintaining data safety and compliance. ◘Important KPIs: Threat detection rate, Compliance adherence rate, Security incident frequency. 3. Scalability also plays a vital role. As projects grow, the tools must keep pace. TensorFlow and Google Cloud AI are excellent examples, handling increased workloads efficiently, which ensures smooth operations regardless of project size. ◘Important KPIs: Performance under load, Scalability metrics (e.g., horizontal/vertical scaling efficiency), System availability. 4. Finally, the importance of ROI cannot be overstated. The right AI tools can significantly enhance efficiency and reduce costs. For example, using Google Cloud has been reported by some companies to increase development speed and reduce defect-related costs, demonstrating a clear return on investment. ◘Important KPIs: Development speed improvement (% increase in time to deploy rate), defect-related cost reduction (% reduction in defects), Overall cost savings (release by release comparisions) The ultimate North Star metric for selecting AI tools for product development teams should be Time to Market (TTM). This metric encapsulates the overall effectiveness of the AI tools in accelerating development cycles, improving productivity, and ensuring timely delivery of high-quality products. By focusing on these key criteria—seamless integration, robust security, scalability, and measurable ROI—and keeping the North Star metric in mind, product teams can make informed decisions that enhance their development lifecycle and drive overall success. #Leadership #AIStrategy #ROI #TimeToMarket #ProductDevelopment #ProductManagement
Mahuya Ghosh’s Post
More Relevant Posts
-
Customizable Video Analytics Engine for both real-time and prerecorded footage. Key Features: ➤ Zone of Interest Demarcation ➤ Advanced People and Object Tracking ➤ Accurate People Counting ➤ Face Recognition and Blurring for Privacy Compliance ➤ Customizable Deep Learning Models ➤ Use Cases Across Industries: ➤ Traffic and Public Space Analytics ➤ Retail Customer Flow Management ➤ Enhanced Law Enforcement and Public Safety ➤ Security in Educational, Industrial, and Residential Areas Custom Model Training: ➤ We tailor models for specific needs using Vision-Transformers and other advanced architectures. Flexible Deployment: ➤ Whether Cloud or on-premises, we ensure secure, scalable solutions with top-notch tools like Docker, Kubernetes, and Ray.io Proposed Deliverables: ➤ Fully Functional Source Code ➤ Bug Fixes (90-day support) ➤ Ready-to-Deploy Docker Image and Microservices ➤ Comprehensive Deployment on GCP, AWS, Azure ➤ DevOps Automation for seamless integration Enhance Your Operations: ➤ Harness the power of AI for improved decision-making and operational efficiency. Demo Video is available at: https://lnkd.in/gsaSvbKP For further details, reach us at: [email protected] #DreamAIIO #machinelearning #artificialintelligence #ai #startups #entrepreneurship #innovation #venturecapital #fundraising #leanstartups #digitalmarketing #business #inspiration #cloud #kubernetes #docker
Seeking an innovative video and image analysis solution? Look no further! DreamAI Software leverages advanced AI-powered Computer Vision technology, offering a customizable Video Analytics Engine for both real-time and prerecorded footage. Key Features: ➤ Zone of Interest Demarcation ➤ Advanced People and Object Tracking ➤ Accurate People Counting ➤ Face Recognition and Blurring for Privacy Compliance ➤ Customizable Deep Learning Models ➤ Use Cases Across Industries: ➤ Traffic and Public Space Analytics ➤ Retail Customer Flow Management ➤ Enhanced Law Enforcement and Public Safety ➤ Security in Educational, Industrial, and Residential Areas Custom Model Training: ➤ We tailor models for specific needs using Vision-Transformers and other advanced architectures. Flexible Deployment: ➤ Whether Cloud or on-premises, we ensure secure, scalable solutions with top-notch tools like Docker, Kubernetes, and Ray.io Proposed Deliverables: ➤ Fully Functional Source Code ➤ Bug Fixes (90-day support) ➤ Ready-to-Deploy Docker Image and Microservices ➤ Comprehensive Deployment on GCP, AWS, Azure ➤ DevOps Automation for seamless integration Enhance Your Operations: ➤ Harness the power of AI for improved decision-making and operational efficiency. Demo Video is available at: https://lnkd.in/gsaSvbKP For further details, reach us at: [email protected] #DreamAIIO #machinelearning #artificialintelligence #ai #startups #entrepreneurship #innovation #venturecapital #fundraising #leanstartups #digitalmarketing #business #inspiration #cloud #kubernetes #docker AI Accelerator Institute Google Cloud Amazon Web Services (AWS) Microsoft Azure
To view or add a comment, sign in
-
-
Seeking an innovative video and image analysis solution? Look no further! DreamAI Software leverages advanced AI-powered Computer Vision technology, offering a customizable Video Analytics Engine for both real-time and prerecorded footage. Key Features: ➤ Zone of Interest Demarcation ➤ Advanced People and Object Tracking ➤ Accurate People Counting ➤ Face Recognition and Blurring for Privacy Compliance ➤ Customizable Deep Learning Models ➤ Use Cases Across Industries: ➤ Traffic and Public Space Analytics ➤ Retail Customer Flow Management ➤ Enhanced Law Enforcement and Public Safety ➤ Security in Educational, Industrial, and Residential Areas Custom Model Training: ➤ We tailor models for specific needs using Vision-Transformers and other advanced architectures. Flexible Deployment: ➤ Whether Cloud or on-premises, we ensure secure, scalable solutions with top-notch tools like Docker, Kubernetes, and Ray.io Proposed Deliverables: ➤ Fully Functional Source Code ➤ Bug Fixes (90-day support) ➤ Ready-to-Deploy Docker Image and Microservices ➤ Comprehensive Deployment on GCP, AWS, Azure ➤ DevOps Automation for seamless integration Enhance Your Operations: ➤ Harness the power of AI for improved decision-making and operational efficiency. Demo Video is available at: https://lnkd.in/gsaSvbKP For further details, reach us at: [email protected] #DreamAIIO #machinelearning #artificialintelligence #ai #startups #entrepreneurship #innovation #venturecapital #fundraising #leanstartups #digitalmarketing #business #inspiration #cloud #kubernetes #docker AI Accelerator Institute Google Cloud Amazon Web Services (AWS) Microsoft Azure
To view or add a comment, sign in
-
-
How much should you charge for your AI product? Lenny Rachitsky and I reviewed how tech incumbents are pricing their AI features. The pricing for AI add-ons varies significantly, ranging from 25% of the base package price (Adobe) to as much as 4.75 times the cost of the standard SaaS product (GitHub). In absolute terms, the monthly price per user for these AI products spans from $4 to $30. Generally, AI products are priced lower than their non-AI add-on counterparts. A key takeaway from our analysis is that features should be priced accordingly to capture their worth. For example, Microsoft Copilot for Microsoft 365 charges the highest per-user price, at $30, exceeding the subscription cost for Microsoft 365. Similarly, GitHub Copilot charges $19 per user, which is more than 4.75 times the cost of their standard SaaS subscription. Those prices are high, but reports indicate that Copilot increases productivity by up to 70% and coders reported that they complete tasks 55% faster with GitHub Copilot. These examples illustrate the importance of aligning your pricing strategy with the perceived value of your AI features Enjoy this? Follow Palle Broe for startup & tech insights and the occasional meme. ✌️
To view or add a comment, sign in
-
-
🚀 How AI APIs are Transforming Software Development? Artificial Intelligence (AI) APIs are revolutionizing modern software development by providing easy access to sophisticated AI functionalities. In our post, we explore how these powerful tools are transforming the way developers approach problem-solving. From boosting efficiency and reducing development time to enabling advanced capabilities like personalized recommendations and predictive analytics, AI APIs are democratizing the use of AI across various industries. Here’s what you’ll find in the blog: ✅ How AI APIs Enhance Efficiency: Learn how AI APIs can streamline development processes and cut down on time-to-market. ✅ Advanced Capabilities Unlocked: Discover the innovative solutions made possible by AI APIs, such as predictive analytics and personalized recommendations. ✅ Key Players in the Market: An overview of popular AI APIs from Google Cloud, AWS, Microsoft Azure, and more, highlighting their key features and real-world applications. ✅ Best Practices for Integration: Tips and strategies for effectively integrating AI APIs into your development workflow. ✅ Future Trends in AI Development: Insights into what the future holds for AI in software development. 👉 Read the Full Post here: https://lnkd.in/d5gT8YBx #AI #APIs #SoftwareDevelopment #Innovation #TechTrends #AIinTech
To view or add a comment, sign in
-
-
How are enterprises adopting and consuming AI? Here is a framework to understand the consumption of AI in the enterprise. At the foundational level, Co-Pilots and Chatbots are the initial AI interactions and workflows, serving as frontline AI applications that enhance productivity and customer engagement. These are AI co-pilot and chatbot products from usual suspects: Anthropic, Microsoft, Google and others. Next up, Enterprise Applications, from in-house solutions to SaaS and Collaboration Platforms, now have embedded AI capabilities to drive smarter workflows, analytics, notifications and decision-making processes. These are traditional enterprise applications and SaaS players ranging from Atlassian, Salesforce, to Workday, and their peers. For organizations seeking a more tranformative approach, building a Custom AI Stack is becoming increasingly prevalent. This includes Commercial and Open Source LLMs (Large Language Models), which are providing unparalleled customization in AI applications. Data Pipelines and RAG (Retrieval-Augmented Generation) systems are vital for managing the vast inflow of data, while Hyperscaler Stacks ensure scalability and robust infrastructure. There is a ton of players in this space as opportunities abound, ranging from OpenAI to Mistral AI, and hyperscalers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. Each layer of this model represents a step towards AI maturity, from basic automation to strategic AI-driven innovation. It's a pathway that businesses are navigating with keen investment, reshaping industry paradigms and redefining what's possible. What are your thoughts on enterprise AI adoption? #GenAI #AIAdoption #EnterpriseTechnology #ArtificialIntelligence #BusinessStrategy #Innovation
To view or add a comment, sign in
-
-
DevOps Evangelist & Spiritual Coach | DevOps Ambassador, Speaker and Author | I help people 100x in Professional and Personal life through Spiritual Wisdom, Healing and Technology
AI PoC's are everywhere these days; Here are 5 AI project mistakes to avoid 1️⃣ What problems will AI solve? "AI" can be anything you want it to be. Be sure there's valid use cases; and it's not ONLY hype driven 2️⃣ Building isolated PoC's The AI PoC should ultimately become a part of your data pipeline and integrate with the overall systems. Isolated PoC's are cool, but where do you go from there? 3️⃣ Getting the "wrong" datasets Garbage in, garbage out - holds true even more with AI tools. How will you get the real/right data for your AI tools? What's the cost involved? 4️⃣ Measuring success Define how you will measure the success? Have a clear understanding on how to measure improved business decisions/sales/customer support or whatever else you want to do with AI 5️⃣ Building your own AI capability for everything AI tools are increasingly built into SaaS offerings such as Salesforce, Dynamics, Adobe, along with Cloud Providers. Leverage these to get started quickly and then consider which other models and tools you need to build from scratch ⁉️ What's some mistakes you've seen people make with AI projects? #AIPoC #LearningTogether #MakeAIGreat 🔄 If you liked this, follow Savinder Puri for more 📺 DevOps + Spirituality on YT: https://lnkd.in/dmArxVym
To view or add a comment, sign in
-
Slack is rolling out a native generative AI experience to enterprise customers this week, the company said Wednesday. Slack AI offers access to several previously announced capabilities, such as thread summaries and channel recaps. Enterprise customers can now gather AI-generated summaries for specific threads or channels when they return from PTO or need to catch up quickly. AI will also improve search results within the interface, enabling users to look up definitions and information related to enterprise or industry-specific terms and project titles. The AI upgrades are offered as an opt-in only, add-on enterprise offering. Pricing details are not public, Jackie Rocca, Slack’s VP, product, AI, told CIO Dive. Paul Young CPA CGA is a former IBM Customer Success Manager that has deployed over 300 data and AI solutions across geographies and industries for the past 8 years. Paul is also a lecturer and Consultant in many areas like ESG, Financial Close, Emerging Technologies, IoT, Emergency Planning, Crisis and Risk Management, and transforming the board of directors. [email protected] Blog – CIO challenges with Generative AI project adoption - https://lnkd.in/gCGfkPC9 Blog - How Generative AI Impacts the Business of Healthcare - https://lnkd.in/g4Wmhtru Blog – Technology – McDonalds – Automation and Generative AI - https://lnkd.in/gTc6uGwz Courses - https://lnkd.in/ga_S5dMR
To view or add a comment, sign in
-
AI Advisor | Author “From Data To Profit” | Course Instructor (Data & AI Strategy, Product Management, Leadership)
Yesterday, a student said they are working to build an AI platform to capitalize on a trillion-dollar opportunity. That’s a disruptive statement, but is it rational and feasible? Software pushed Microsoft’s initial growth curve past IBM’s. The web and mobile drove Google's rise and Apple’s redemption past Microsoft. The cloud drove AWS at a ludicrous speed past them all. AI-driven growth follows a different trajectory and will be faster than all prior technology waves. People and businesses at the forefront will understand why. There’s a thin line between disruption and hype. Disruptors see and articulate 4 details that hype can’t: 1️⃣ How AI breaks and replaces assumptions. 2️⃣ The opportunities it creates. 3️⃣ The customers who will benefit. 4️⃣ The products that will deliver new value. I explain AI-driven growth and how AI breaks an assumption about technology-driven growth to achieve its new trajectory. https://lnkd.in/gnXxqxSg #AI #Data #GenerativeAI
What Is AI-Driven Growth?
vinvashishta.substack.com
To view or add a comment, sign in
-
💫Meet Oscar AI, my latest AI project, an innovative chatbot application that harnesses cutting-edge AI and cloud services to offer a comprehensive suite of capabilities💫 💬 Content-Based Responses: Oscar excels in providing well-informed responses to your queries which are based on the content you have trained it ensuring accuracy and relevance. ☁️ AWS Integration: Seamlessly connect Oscar to your AWS account to access data, analyze metrics, and gain insights from your cloud infrastructure. 📧 Outlook Integration: Effortlessly send emails directly from Oscar's interface. Just provide recipient, subject, and message details, and Oscar will handle the rest. 🎫Jira Integration: Oscar AI can also create Jira tickets based on user input. 🔧 Technical Excellence: Oscar AI is powered by a robust architecture: - Google's Embeddings Model: Semantic understanding of text - Faiss: Efficient content retrieval - GCP's Text-Unicorn: Enhanced comprehension and generation of the output - Langchain: Modular and Customizable Framework for developing AI based applications - Langchain Agents and Custom Tools: Offering flexibility and extensibility to connect with 3rd party applications like Jira, Outlook, AWS etc. 🚀 Future Plans: My commitment to continuous improvement for Oscar AI includes: - Expanding the features of this chatbot by Integrating other useful applications - Exploring advanced AI models for enhanced capabilities 🎯 Conclusion: Oscar AI seamlessly blends AI and cloud prowess, offering personalized and intelligent assistance. Experience the power of Oscar AI today and witness the transformative nature of this innovative technology! If you want to discuss further on this application, please reach out to me on LinkedIn chat or via email. Note: The name Oscar AI is just a sample name used for this chatbot project. This is for educational purpose only. VC: Sowmyasree Venutharla #Chatbot #ArtificialIntelligence #CloudComputing #Technology #Innovation #gcp #aws #chatbotdevelopment #ai #bsh
To view or add a comment, sign in
-
While still nascent, AI testing frameworks are evolving rapidly alongside model operationalization capabilities. Organizations willing to invest time upfront evangelizing these practices position themselves as leaders in responsible AI. #predictiveanalytics #data #help #AI #machinelearning #machinelearningengineer #machinelearningalgorithms #microsoft #aws #ibm #google #productivity #gpt #salesforce #automation #analytics #enterprise
As AI models become integral to software applications, new challenges emerge around testing and validation. Traditional software QA practices require expansion when intelligent components introduce non-determinism. How can teams adapt their testing strategies to ensure AI quality? Incorporate proactive testing throughout model lifecycles while augmenting human oversight with automated guardrails. https://lnkd.in/e_MPeSse #predictiveanalytics #data #help #AI #machinelearning #machinelearningengineer #machinelearningalgorithms #microsoft #aws #google #productivity #gpt #power #automation #analytics #enterprise #nvidia #google #software #saas #industry #ml #security #business #technology #artificialintelligence #development #algorithms #retail #usercentricdesign
Rightsizing AI Testing for Software Quality - MLX Ventures
https://mlxventures.com
To view or add a comment, sign in