Your competitors are using AI to analyze sales, what do you do? Organizations today are very enamored with artificial intelligence capabilities to find hidden patterns and tease out issues in data and text. Each organization is trying to find an advantage over its competitors. This explosion of interest in AI poses a challenge to managers for effectively make sense of and use AI effectively.
The most nimble and adaptable companies and executives will thrive. Organizations that can rapidly sense and respond to opportunities will seize the advantage in the AI-enabled landscape. (Erik Brynjolfsson and Andrew Mcafee, HBR,) Understanding, organizing, integrating and delivering AI is a key issue today. Business must be clear about the use and value of AI to avoid chasing an unachievable and expensive dream. AI carries with it many implications. Jobs change dramatically, current skill become obsolete and displaced, there may exist resistance to change and unrealistic fear of robots taking over as well as other aspects of automation. This was true when automation hit the factory floor in the 1960’s and 1970’s. Union issues may pale by comparison with white-collar response.
Today pictures of auto assembly and other manufacturing plants show few workers. What detail work remains is slowly but surely giving way to automation. However, the positive side promises better paying work, newer and more interesting tasks and less physically demanding and boring operations. Managers and professionals should prepare for the coming changes. What happens when AI becomes part of the work process? What can we expect from AI capabilities today? What may happen in the future? Answers for these and other questions are part of the value of this course.
This course is tailored for any managers, directors interested in learning the application, current and future use of Artificial Intelligence.
Get your employees up to speed with Chat GPT, Gemini and CoPilot and explore other AI tools for your specific departments.
We run short, high-impact learning sprints either in-person at your office or virtually. These combine pre-analysis of your biggest challenges and opportunities, one or two-days practical learning sessions and follow-up AMA session to consolidate learning.
Outcomes include creation of your AI policy framework, data insight approach, use case identification and roadmap planning.
Your competitors are using machine learning (ML) to support sales forecasting, what do you do? Organizations today are applying machine learning capabilities to a wide variety of uses especially in improving decision making. Improving decision making translates directly to improved organization performance. The increased use of analytic techniques, data bases and algorithmic techniques means that managers need understanding of the value and effort relating to ML.
A 2011 Mckinsey report indicated that by 2018 1.5 million managers and analysts will be making decisions using analytical skills. How many managers and professionals today are capable of recognizing, understanding and using ML techniques for best value? Identifying, organizing, integrating and delivering ML capabilities to useful points in the organization is a key issue today. The good news is that organizations are already using some of the techniques without knowing they are part of ML. Machine learning is still a developing discipline. With over 50 different ML algorithms how do you know which one to use? Which one provides insight that you can use now? Which should you be looking at for the future?
There are however, many machine learning algorithms that are reliable, easy to use and have great value to the typical organization. Statistical algorithms like affinity analysis, correlation matrices and semantic keyword analysis are used daily to assess market opportunities and customer and citizen behavior. ML promises faster, more effective decision making. This enables the ability to respond to rapidly changing market conditions, waves of culture shock and shifting markets.
Managers and professionals should prepare for the coming changes in analytics. What happens when ML becomes part of automated decision making? What will ML do for us in the future? How much of this will be done by robotic processes? Answers for these and other questions are part of the value of this course. This course is key for managers, strategic planners, marketing analysts, data analysts and architects, planning managers, process analysts, business analysts, business, enterprise and IT architects.
What do you say when you are asked by management what these analytics are and can we use them? What if they ask about the field of AI and what it consists of? Should you implement one AI project or several? Should you focus on one type of AI or several? What happens if it is a large or ambitious project? Where do expert system and knowledge management fit in the AI framework?
Answering these and many more of these types of questions requires some basic knowledge and insight into the meaning, value, use and benefit of applying a technology like AI.
In reality, it’s all about decisions and the right data that feeds them. Any AI effort has an objective the AI is to achieve and inputs that help you predict the outcomes of several scenarios. So, this is also about planning, anticipation land correction with the intent of improving decision making.
Organizations apply AI types of solutions based on what they see or read in articles describing what other organizations are doing. That may or may not fit the specific needs of your organization. Improving decision making translates directly to improved organization performance. The increased use of analytic techniques, data bases and algorithmic techniques means that managers need understanding of the scope, value and effort relating to AI.
Understanding, organizing, integrating and delivering AI solutions to useful points in the organization is a key issue today. AI supports better, and more effective decision making leading to better performance.
Managers and professionals should prepare for the coming changes. This course is key path to knowledge about AI for business managers, strategic planners, marketing analysts, data analysts and architects, planning managers, process analysts, business analysts, business architects, enterprise and IT architects.
Retail organizations today are using AI to analyze customer preferences and behavior. Organizations today are very enamored with artificial intelligence capabilities to find hidden patterns and tease out issues in data and text. Each organization is trying to find an advantage over its competitors. This explosion of interest in AI poses a challenge to managers for effectively make sense of and use AI effectively.
The most nimble and adaptable companies and executives will thrive. Organizations that can rapidly sense and respond to opportunities will seize the advantage in the AI-enabled landscape. (Erik Brynjolfsson and Andrew Mcafee, HBR,)
The reality of AI in the retail space is that the machine learning parts of AI heve been used for at least 25 years. These take the form of statistical AI such as correlation matrices and affinity anaysis in marketing to understand aspects of consumer behavior. More recently, this has included sentiment analysis to understand what consumers think of the organization and certain applications of neural nets to help with choosing sizes of clothes. .
AI carries with it many implications. Jobs change and current skills become obsolete and displaced. Service bots such as chatbots replace customer service staff. Many aspects of automation now focus on AI. The impact of AI on operations is well known in service industries such as retail or hospitality. Today customers are using technology more and more, just ask any travel agent what happened to their jobs. So, what impact do these changes have on organizations that serve the retail segment?
Many organizations are moving into the use of artificial intelligence (AI) to support strategic impact, customer relations and operational performance, what should your organization do? Organizations today apply AI capabilities to a wide variety of uses especially in improving decision making and operational flow. Improving decision making translates directly to improved organization performance. The increased use of analytic techniques, data bases and algorithmic techniques means that managers need understanding of the value and effort relating to AI.
How many managers and professionals today are capable of recognizing, understanding and using AI techniques for best value? Identifying, organizing, integrating and delivering AI capabilities to useful points in the organization is a key issue today. The good news is that organizations are already using some of the techniques without knowing they are part of AI.
The hype today is around AI capabilities like deep learning, facial recognition, driverless cars and other sophisticated applications. The reality is these are expensive and do not directly benefit the operation of an organization. With many machine learning and neural net algorithms how do you know which one to use? Which one provides insight that you can use now? Which should you be looking at for the future? Many AI algorithms are simple, reliable, easy to use and have great value to the typical organization. Algorithms like affinity analysis for customer behavior, correlation matrices for preferences and semantic keyword analysis for attitudes are used daily to assess service and product opportunities from customer and citizen behavior. AI enables rapid response to changing market conditions, waves of culture shock and shifting markets. Managers and professionals should prepare for the coming changes in analytics.
This course is key for managers, strategic planners, marketing analysts, data analysts and architects, planning managers, process analysts, business analysts, business, enterprise and IT architects.
Organizations today are very enamored with artificial intelligence capabilities to find hidden patterns and tease out issues in data and text. Each organization is trying to find an advantage over its competitors or to improve operational performance. This explosion of interest in AI poses a challenge to managers for effectively make sense of and use AI effectively.
The reality of AI is that the machine learning parts of AI have been used for at least 25 years. Organizations today are using AI to analyze customer preferences and behavior, operational performance, strategic alignment and a host of other more elaborate applications. Today customers are using technology more and more, just ask any travel agent what happened to their jobs. So, what impact do these changes have on organizations that serve the retail segment?
Current use of AI is based on known AI algorithms such as correlation matrices and affinity analysis to understand and identify hidden relationships in an organization using factors such as for process performance. More recently, this has included sentiment analysis to understand what employees, the public or consumers think of the organization. AI carries with it many implications. Jobs change and current skills become obsolete and displaced. Service bots such as chatbots replace customer service staff in many industries. Key aspects of automation now focus on AI enablement of processes as part of digital transformation. The impact of AI on operations is well known in both product service industries.
Managers and professionals should prepare for the coming changes. What can we expect from applying AI capabilities to strategy? What may happen in the future? Responses to these questions are part of the value of this course. This course is key for senior managers, strategic planners, marketing analysts, data analysts and architects, planning managers, process analysts.
This course is key for managers, strategic planners, marketing analysts, data analysts and architects, planning managers, process analysts, business analysts, business, enterprise and IT architects.
You can also contact us at marie-louise@innoverto.com or call +971 4 338 5690.
