Marko Kohtamäki’s Post

🤖 Operational Excellence in AI/ML: A Strategic Approach 🤖 #IA #ML #DeepLearning Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the industry, driving efficiency and innovation. To fully realize their potential, companies need a strategic focus on operational excellence. This article explores how to define goals and measure success in this field. Introduction AI/ML is not just a technology; It is a transformation. Implemented correctly, it can optimize processes, reduce costs and improve decision making. But the key is operational excellence. How is success measured? What metrics are crucial? Objectives and Success Metrics To achieve operational excellence in AI/ML, it is essential to define clear and measurable objectives. These are translated into Objectives and Key Results (OKRs). Examples: OKR 1: Increase prediction accuracy in process X by 15% in the next 3 months. OKR 2: Reduce system Y response time by 10% by optimizing the ML model. Success Metrics: Precision: The accuracy of the model's predictions. (Accuracy = True Positives / (True Positives + False Positives)) Accuracy: The proportion of correct predictions. (Accuracy = (VP + VN) / (VP + VN + FP + FN)) Response time: The time it takes for the system to process a request. Efficiency: The relationship between performance and resources used. Cost per prediction: The cost associated with each prediction made by the model. Error rate: The proportion of incorrect predictions. Model performance: A general measure of model performance. Key KPIs and Formulas Accuracy: (True Positives / (True Positives + False Positives)) 100 Accuracy: ((True Positives + True Negatives) / (Total)) 100 Response time: Average system response time. Efficiency: (Performance / Resources) Cost per prediction: (Total cost / Number of predictions) Key Benefits of Operational Excellence in AI/ML Cost reduction: Task automation and process optimization. Increased productivity: Faster and more efficient data processing. Quality improvement: Identification of patterns and more accurate predictions. More informed decision making: Predictive analytics and real-time data. Innovation: Development of new products and services based on AI/ML. Scalability: Systems capable of adapting to growing data volumes. Conclusion Operational excellence in AI/ML is crucial to success in the digital age. Defining OKRs, establishing clear metrics and monitoring KPIs are essential steps to maximize the return on investment in these technologies. A strategic approach and a culture of continuous improvement are essential to achieving extraordinary results. #IA4Industry #MachineLearning #DigitalTransformation

Generative AI: The Packaging and Paper Industry’s Next Frontier The packaging and paper industry is undergoing a transformation, with Generative AI emerging as a key enabler of innovation and efficiency. From optimizing supply chains to driving sustainable practices and enhancing design processes, AI is reshaping the way businesses in this sector operate. This insightful article by McKinsey & Company delves into how generative AI can unlock unprecedented opportunities, such as: ✅ Accelerating product development cycles ✅ Optimizing manufacturing processes ✅ Enhancing customer personalization ✅ Driving sustainability initiatives The future of packaging and paper is not just about materials; it’s about smart solutions powered by technology. As industries embrace AI, those at the forefront will reap the benefits of agility, cost efficiency, and enhanced customer experiences. 💡How is your organization preparing for this AI-driven transformation? Let’s discuss in the comments! 📖 Read the full article. #GenerativeAI #Innovation #Sustainability #PackagingIndustry #DigitalTransformation #McKinseyInsights

Does AI adoption lead to greater productivity? Reading this latest report by the Dais, Canada who urge caution in asserting that AI adoption at the firm level result in short-term productivity gains. At Lighthouse3, we are studying the actual vs. predicted performance of popular AI systems deployed in business settings. DM me if you have an active or planned enterprise AI project and want to participate in our study on performance of AI systems at organization-level.

AI adoption can feel overwhelming with so many options to choose from; off-the-shelf, specialist, or custom-built. But the real question is: how do you identify the right solution to tackle your specific business challenge? In our latest article, we explore why focusing on the problem, not the technology, is key and how innovation cycles can help you navigate the AI landscape effectively. https://lnkd.in/evN944hC

🌟 What is AIO? AIO stands for Artificial Intelligence for IT Operations. It utilizes AI and machine learning to enhance IT systems, making them more innovative and efficient. With AIO, businesses can: Fix issues faster Predict problems before they happen Save time and improve workflows GenBe is here to help you understand how AIO is changing companies' IT management. It’s not just about automation—it’s about making IT smarter and more reliable. 💡 Want to learn more? Follow GenBe for simple tech insights! #AIO #artificialintelligence #ITsolutions #automation #techinnovation #genbe #genbeupdates #managedservices #digitalmarketing #innovation #brandbuilding 📎 Copy Link: https://lnkd.in/gVfAtwEQ Learn how AIO can help your business

🌟 Transform your business with AI-powered integration and Retrieval Augmented Generation! 🤖✨ Dive into part 3 of our whitepaper series to unlock the full potential of these cutting-edge technologies. Download the whitepaper now! ➡️ https://hubs.ly/Q02Dc7ww0 #AI #BusinessTransformation #Innovation #TechTrends #Adeptia

The role of Artificial Intelligence (AI) in shaping the future of BA work is gaining momentum. According to the IIBA whitepaper, AI holds the potential to revolutionize businesses by solving various challenges, enhancing customer engagement, and introducing innovative business models.   🔍 AI is not just about beating humans in games or composing music; it's about leveraging smart algorithms to address real-world business problems. However, there's a gap between industry expectations and realistic applications of AI. Organizations need to shift the conversation towards understanding AI's current capabilities and its potential to drive business outcomes.   🎯 This is where BA professionals play a crucial role. With their expertise in understanding business, technology, and customer needs, they can bridge the gap between AI capabilities and practical business applications. By specializing in digital business analysis and acquiring knowledge about AI concepts and methodologies, BA professionals can lead the way in unlocking AI's potential.   📈 As AI continues to evolve, BA professionals must stay updated on emerging technologies and best practices. By embracing AI and incorporating it into their skill set, BA professionals can become invaluable assets in driving successful AI solutions within organizations.   Check out the full IIBA whitepaper for insights into how AI is transforming the BA landscape and how BA professionals can seize opportunities in this rapidly evolving digital era! https://shorturl.at/oszD2  #BusinessAnalysis #ArtificialIntelligence #DigitalTransformation

🤖 Operational Excellence in AI/ML: A Strategic Approach 🤖 #IA #ML #DeepLearning Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the industry, driving efficiency and innovation. To fully realize their potential, companies need a strategic focus on operational excellence. This article explores how to define goals and measure success in this field. Introduction AI/ML is not just a technology; It is a transformation. Implemented correctly, it can optimize processes, reduce costs and improve decision making. But how do we ensure that this investment generates tangible results? The key is clearly defining objectives and accurately measuring performance. Objectives and Success Metrics To achieve operational excellence, we must establish specific and measurable Objectives and Key Results (OKRs). These OKRs must be aligned with the company's overall strategy. Examples: OKR 1: Increase prediction accuracy in process X by 15% in the next 3 months. OKR 2: Reduce system Y response time by 10% in the next 6 months. Success Metrics: Precision: Proportion of correct predictions. (Accuracy = Correct Predictions / Total Predictions) Accuracy: Measurement of the deviation of predictions from actual values. (Accuracy = √((∑(Prediction Actual Value)²)/n)) Response time: Time it takes for the system to process a request. Efficiency: Relationship between the result and the resources used. Cost per prediction: Cost associated with each prediction made. Key KPIs and Formulas KPIs (Key Performance Indicators) are essential for monitoring progress towards OKRs. Here are some examples: Prediction Accuracy (PP): (Correct / Total) 100% 📊 Error Rate (TE): (Errors / Totals) 100% ⚠️ Mean Response Time (TMR): (Sum of response times) / (Number of responses) ⏱️ Cost per Prediction (CPP): (Total Cost) / (Number of Predictions) 💰 Key Benefits of Operational Excellence in AI/ML Greater Efficiency: Automation of repetitive tasks and process optimization. Cost Reduction: Minimization of errors, optimization of resources and improvement of productivity. More Informed Decision Making: Predictive analysis to anticipate trends and make strategic decisions. Quality Improvement: Identification of patterns and anomalies to improve the quality of products or services. Innovation: Development of new products and services driven by AI/ML. Conclusion Operational excellence in AI/ML requires a strategic approach, clear definition of OKRs, and accurate measurement of performance. By implementing relevant KPIs and metrics, companies can maximize the return on their AI/ML investment and achieve their business objectives. #IA4Industry #MachineLearning #DigitalTransformation

🤖 Operational Excellence in AI/ML: Optimizing your Impact 🤖 #IA #ML #DeepLearning Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the industry, offering unprecedented opportunities for process optimization and strategic decision making. 🚀 But how to ensure that these technologies translate into tangible results and operational excellence? Introduction: AI/ML is not just a fad, it is a powerful tool to improve efficiency and profitability in any sector. From predicting demand to automating repetitive tasks, these technologies offer enormous potential. However, its implementation requires a strategic approach and clear definition of objectives. Objectives and Success Metrics (OKRs): To achieve operational excellence, it is crucial to set measurable and achievable goals. Here are some examples of OKRs: Objective: Improve the accuracy of demand predictions by 15% in the next 3 months. Metrics: Prediction accuracy (calculated with root mean square error) Predictive model response time Obsolete inventory reduction Goal: Automate 20% of manual data processing tasks in the next quarter. Metrics: Number of automated tasks Average processing time before and after automation Reduction of human errors Key KPIs and Formulas: To monitor progress toward OKRs, it is essential to define accurate KPIs. Here we present some examples: Model Accuracy (ML): (1 (Mean Square Error / Total Variance)) 100% Prediction Success Rate (AI): (Number of correct predictions / Total number of predictions) 100% Processing Time (AI/ML): Total processing time / Number of data processed Cost per Transaction (AI/ML): Total cost / Number of transactions processed Key Benefits: The implementation of AI/ML in operational excellence brings with it a series of benefits: Cost Reduction: Task automation, resource optimization and demand prediction. Increased Efficiency: Faster and more precise processes, reduction of human errors. Quality Improvement: Detection of patterns and anomalies, which allows for higher quality in products or services. More Informed Decision Making: Real-time data analysis for a better understanding of the market and trends. Scalability: AI/ML models can adapt and grow with the business. Conclusion: Operational excellence in AI/ML requires a strategic approach, clearly defining OKRs and KPIs, and constantly measuring progress. By implementing these technologies intelligently, companies can reach a new level of efficiency, profitability and competitiveness. Get ready for the future! 🚀 #IA4Industry #MachineLearning #OperationalExcellence

🤖 Operational Excellence in AI/ML: Optimizing your Impact 🤖 #IA #ML #DeepLearning Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the industry, offering unprecedented opportunities for process optimization and strategic decision making. 🚀 But how to ensure that these technologies translate into tangible results and operational excellence? Introduction: AI/ML is not just a fad, it is a powerful tool to improve efficiency and profitability in any sector. From predicting demand to automating repetitive tasks, these technologies offer enormous potential. However, its implementation requires a strategic approach and clear definition of objectives. Objectives and Success Metrics (OKRs): To achieve operational excellence, it is crucial to set measurable and achievable goals. Here are some examples of OKRs: Objective: Improve the accuracy of demand predictions by 15% in the next 3 months. Metrics: Prediction accuracy (calculated with root mean square error) Predictive model response time Obsolete inventory reduction Goal: Automate 20% of manual data processing tasks in the next quarter. Metrics: Number of automated tasks Average processing time before and after automation Reduction of human errors Key KPIs and Formulas: To monitor progress toward OKRs, it is essential to define accurate KPIs. Here we present some examples: Model Accuracy (ML): (1 (Mean Square Error / Total Variance)) 100% Prediction Success Rate (AI): (Number of correct predictions / Total number of predictions) 100% Processing Time (AI/ML): Total processing time / Number of data processed Cost per Transaction (AI/ML): Total cost / Number of transactions processed Key Benefits: The implementation of AI/ML in operational excellence brings with it a series of benefits: Cost Reduction: Task automation, resource optimization and demand prediction. Increased Efficiency: Faster and more precise processes, reduction of human errors. Quality Improvement: Detection of patterns and anomalies, which allows for higher quality in products or services. More Informed Decision Making: Real-time data analysis for a better understanding of the market and trends. Scalability: AI/ML models can adapt and grow with the business. Conclusion: Operational excellence in AI/ML requires a strategic approach, clearly defining OKRs and KPIs, and constantly measuring progress. By implementing these technologies intelligently, companies can reach a new level of efficiency, profitability and competitiveness. Get ready for the future! 🚀 #IA4Industry #MachineLearning #OperationalExcellence