The technologies that fall under the rubric of AI include:<ul><li>"Statistical machine learning" - Many firms use machine learning to enhance their operations.</li><li>"Neural networks" - This more complex version of machine learning is useful for categorizing activities.</li><li>"Natural language processing" (NLP) - This is used to analyze text, translate, and recognize spoken language.</li><li>"Semantic NLP" - This form of AI involves graphing a language's rules and relationships.</li><li>"Robotics" - This technology includes both physical robots and "robotic process automation" (RPA).</li></ul>

AI  used for

“Cognitive technology is primarily about analyzing and extracting insights from content – data, information or knowledge.”

<ul><li>Process automation” – This common application uses robots or RPA for repetitive tasks, transferring data between systems, updating or reconciling systems, and so on. </li></ul>“Cognitive insight” – This involves using AI to generate apps that employ algorithms to find data patterns and help users interpret data. “Cognitive engagement” – Businesses provide AI for customers and workers, as in around-the-clock customer service; recommendations; answering employee

Organizations tend to use AI in three general areas:

Organizations can see workers go through five stages as AI is implemented:<ol><li>🔍 "Stepping in" - Monitoring & modifying AI apps</li><li>🏃‍♂️ "Stepping up" - Using AI for simple tasks & creating strategic visions</li><li>🤝 "Stepping aside" - Focusing on skills that humans excel at, like collaboration</li><li>🧐 "Stepping narrowly" - Specialized jobs that won't be automated</li><li>🚀 "Stepping forward" - Designing & building new AI</li></ol>Get ready for the future of work and the role of AI in the workforce!

The Five Stages of AI and Workers

Organizations need to create cognitive strategies for AI development.Why?<ul><li>Data analysis is a key aspect of AI.</li><li>Usage strategies should align with the organization's handled content.</li><li>Set goals for internal and external usage</li><li>Some strategies can evolve into AI-driven business models and drive future development.</li></ul>How?<ul><li>Develop strategies that cater to your specific organization and AI system.</li><li>Ensure alignment with the type of data being handled.</li><li>Take into consideration both internal and external goals.</li><li>Continuously evaluate and improve strategies as AI technology evolves.</li></ul>

Benefits of Cognitive Strategies for AI Development

<ul><li>AI is complex and challenging to integrate, but it will transform everything in the long run.</li><li>AI encompasses several different technologies that are used for different purposes.</li><li>Big companies and start-ups are leading the way in AI. They are using it for "process automation," "cognitive insight," and "cognitive engagement."</li><li>Organizations need to develop "cognitive strategies."</li><li>AI will change the workplace, eliminating some jobs and transforming others.</li><li>Applying AI carries technical, social, and ethical challenges. Firms should be open about how they use AI.</li></ul>

AI Affects

<ul>
<li>AI strategy leads to improved data analysis and utilization of AI technology.</li>
<li>It enables organizations to make efficient decisions and improve their decision-making capabilities.</li>
<li>AI strategy provides the potential to develop innovative and AI-driven business models.</li>
<li>It encourages adaptation and growth in a constantly evolving technology landscape.</li>
</ul>

The Benefits of AI Strategy ✨

How to Put the Artificial Intelligence Revolution to Work (Management on the Cutting Edge)

Cutting through the hype, a practical guide to using artificial intelligence for business benefits and competitive advantage.In The AI Advantage, Thomas Davenport offers a guide to using artificial intelligence in business. He describes what technologies are available and how companies can use them for business benefits and competitive advantage. He cuts through the hype of the AI craze—remember when it seemed plausible that IBM's Watson could cure cancer?—to explain how businesses can put artificial intelligence to work now, in the real world. His key recommendation: don't go for the “moonshot” (curing cancer, or synthesizing all investment knowledge); look for the “low-hanging fruit” to make your company more efficient.Davenport explains that the business value AI offers is solid rather than sexy or splashy. AI will improve products and processes and make decisions better informed—important but largely invisible tasks. AI technologies won't replace human workers but augment their capabilities, with smart machines to work alongside smart people. AI can automate structured and repetitive work; provide extensive analysis of data through machine learning (“analytics on steroids”), and engage with customers and employees via chatbots and intelligent agents. Companies should experiment with these technologies and develop their own expertise.Davenport describes the major AI technologies and explains how they are being used, reports on the AI work done by large commercial enterprises like Amazon and Google, and outlines strategies and steps to becoming a cognitive corporation. This book provides an invaluable guide to the real-world future of business AI.A book in the Management on the Cutting Edge series, published in cooperation with MIT Sloan Management Review.