Pedro Noe Mata Saucedo’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 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: 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 demand prediction accuracy by 15% in the first quarter of 2024. OKR 2: Reduce AI system response time by 10% in Q2 2024. 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 performance and resources used. Cost per prediction: Total cost of generating a prediction. Key KPIs KPIs (Key Performance Indicators) are essential for monitoring progress towards OKRs. Here are some examples: Prediction accuracy (%). Example: 95% Response time (seconds). Example: 0.5 seconds Error rate (%). Example: 1% Cost per prediction (USD). Example: $0.05 Volume of data processed (units). Example: 1 million units Key Benefits Operational excellence in AI/ML generates multiple benefits: Cost reduction: Automation of repetitive tasks and process optimization. Increased efficiency: Faster and more accurate data processing. Quality improvement: Identification of patterns and anomalies for better decision making. More informed decision making: Predictive analysis to anticipate trends and opportunities. Data-driven innovation: Development of new products and services based on insights. Conclusion Operational excellence in AI/ML requires a strategic approach, clear definition of OKRs, and accurate measurement of performance. By implementing relevant KPIs and monitoring progress, companies can maximize the value of their AI/ML investments and achieve their business objectives. #IA4Business #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

🤖 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: 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 in any sector. But the key is operational excellence. Objectives and Success Metrics To achieve excellence, it is crucial to define clear and measurable objectives. Objectives and Key Results (OKRs) are ideal for this. Examples: OKR 1: Improve demand prediction accuracy by 15% in the first quarter of 2024. OKR 2: Reduce AI system response time by 10% by Q2 2024. Success Metrics: Precision: Proportion of correct predictions. (Accuracy = Correct Predictions / Total Predictions) Response time: Time it takes for the system to process a request. Efficiency: Relationship between performance and resources used. Cost per prediction: Cost associated with each prediction made. Time/resource savings: Quantifiable difference in time or resources saved thanks to AI/ML. Key KPIs and Formulas KPIs (Key Performance Indicators) are essential for monitoring progress. Here some examples: Prediction Accuracy (PP): (Number of correct predictions / Total number of predictions) 100% Average Response Time (TRP): Sum of all response times / Total number of responses Model Efficiency (ME): (Model performance / Resources used) 100% Cost per Prediction (CPP): Total operating cost / Total number of predictions Time Savings (AT): Time saved thanks to AI/ML (in hours or days) Key Benefits Operational excellence in AI/ML generates multiple benefits: Greater efficiency: Automation of repetitive tasks and process optimization. Cost reduction: Optimization of resources and elimination of human errors. More informed decision making: Predictive analytics and real-time data. Quality improvement: Identification of patterns and anomalies for greater accuracy. Innovation driven: Development of new products and services based on data. Scalability: AI/ML systems can adapt to different data volumes and needs. Conclusion Operational excellence in AI/ML is critical to success in the digital age. Defining OKRs, monitoring KPIs and focusing on key benefits are crucial steps to harness the full potential of this technology. #AI #ML #OperationalExcellence #DigitalTransformation #Industry4.0

🤖 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 in any sector. But the key is operational excellence. Objectives and Success Metrics To achieve operational excellence in AI/ML, it is crucial to define clear and measurable objectives. Objectives and Key Results (OKRs) are essential: Objective: Implement AI/ML models to predict demand with 95% accuracy. Key Result 1: Reduce unsold inventory by 15% in the next 6 months. Key Result 2: Improve response time to service requests by 10%. Success Metrics: Model Accuracy: (Correct/Total) 100 Response time: Average request processing time. Inventory reduction: (Beginning inventory Ending inventory) / Beginning inventory 100 Customer satisfaction: Measured through surveys. Operational efficiency: Measured by cost reduction and cycle time. Main KPIs and Formulas KPIs (Key Performance Indicators) are crucial for monitoring progress. Here are some examples: Model Accuracy (KPIs): (Correct predictions / Total predictions) 100% Model error rate: (Incorrect predictions / Total predictions) 100% Model training time: Time in hours/days to train the model. Cost per prediction: Total cost of training and operating the model / Number of predictions. Return on investment (ROI): Benefits generated by AI/ML Implementation cost / Implementation cost. Key Benefits Operational excellence in AI/ML generates multiple benefits: Cost reduction: Process optimization and demand prediction. Increased efficiency: Automation of tasks and improved productivity. Quality improvement: Error detection and accuracy improvement. More informed decision making: Predictive analytics and real-time data. Improved customer experience: Personalization and proactive service. Driven innovation: Development of new products and services. Conclusion Operational excellence in AI/ML requires a strategic approach, defining clear OKRs, and constantly measuring progress. By implementing relevant KPIs and focusing on key benefits, companies can maximize the value of AI/ML and achieve their business objectives. #IA4Business #MachineLearning #DigitalTransformation

Artificial intelligence isn’t just a buzzword anymore; it’s a real force shaping industries! 🚀 Many are still skeptical about how it applies to their daily operations. The good news? The right strategies can help you seamlessly integrate AI into your business and simplify your workflows. Here are a few tips to get started: 1. Identify repetitive tasks in your day-to-day operations that can be automated. 2. Explore AI tools that fit your specific business needs—don’t just go for the most popular ones! 3. Start with pilot projects to see how AI can enhance your productivity without a full-scale commitment. By embracing these actionable steps, you can unlock productivity gains and improve decision-making in your team. Imagine having more time to focus on creative and strategic initiatives! 🙌 What are your thoughts on incorporating AI into your work processes? Share your tips or experiences below! #AI #BusinessStrategy #Productivity #Innovation #TechTrends https://lnkd.in/duFfxpx7

🤖 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 in any sector. But the key is operational excellence. Objectives and Success Metrics To achieve excellence, it is crucial to define clear and measurable objectives (OKRs). These must be aligned with the company's overall strategy. Examples of OKRs: Increase prediction accuracy: Reduce the error in demand predictions by 15% in the next 6 months. Automate repetitive tasks: Automate 20% of data analysis tasks in the first quarter. Improve process efficiency: Reduce data processing time by 10% in the next year. Success Metrics: Precision: Proportion of correct predictions. (Accuracy = Correct Predictions / Total Predictions) Accuracy: Measurement of the deviation of predictions from actual values. Response time: Time it takes for the model to process the information. Efficiency: Relationship between the result and the resources used. Scalability: Ability of the model to handle increasing volumes of data. Key KPIs These KPIs are essential to monitor the progress and effectiveness of AI/ML implementation: Model success rate: (Successes / Total predictions) 100 Model training time: Time required to train the model. Cost per prediction: Total cost of the prediction divided by the number of predictions. Classification accuracy: (True Positives + True Negatives) / (Total cases) False positive/negative rate: Proportion of incorrect predictions. Key Benefits Operational excellence in AI/ML generates multiple benefits: Cost reduction: Task automation and process optimization. Increased efficiency: Faster and more accurate data processing. Quality improvement: Reduction of errors and increase in accuracy. More informed decision making: Predictive analytics and real-time data. Driven innovation: Development of new products and services. Greater competitiveness: Adaptation to market demands. Conclusion Operational excellence in AI/ML is critical to success in the digital age. Defining clear OKRs, monitoring relevant KPIs, and focusing on key benefits are crucial steps to harnessing the transformative power of this technology. #IA4Business #MLStrategies #DataDrivenDecisions

Find attached a link to our newest study on: Artificial intelligence enabled product–service innovation: past achievements and future directions The paper explores the role of Artificial Intelligence (AI) in Product-Service Innovation (PSI), noting significant growth in literature since 2018 and the need for a structured review. The study identifies five clusters within the literature: 1. technology adoption barriers, 2. data-driven capabilities, 3. digitally enabled business model innovation, 4. smart design changes for sustainability, and 5. sectorial application. The study acknowledges challenges in integrating AI into business models and operations, and suggests future research directions to address these gaps. The findings highlight the transformative role of AI in business models, suggesting that managers can leverage AI to create value and gain competitive advantage. The paper calls for more inclusive research across sectors to guide AI applications in specific industries. Many thanks Rimsha Naeem for leading the development of this paper, and Vinit Parida for coauthoring. Many thanks also to Review of Managerial Science and the editor Sascha Kraus for your support. The paper is available as #openaccess