With budget challenges post COVID, many companies are looking at alternatives to create a better Employee Experience with compelling AI results. Much of the success or failure of an initial AI effort has less to do with the tool selection, and more to do with creating a mindset around positive Employee experience.
The AI Myths and Misnomers becomes the biggest Success Factor
The most frequent barriers to successful AI come from not realizing the truth from the fiction, and not partnering with people who meet you where you are, and show the value you need to get started successfully.
AI is not a Do It Yourself (DIY) project if you have not created an Agile culture with an interest and commitment to making it work. The human factor and believe in limitations, becomes a barrier to a successful Start.
Gartner debunks the most common AI Myths that obscure progress and even “getting started” well. A capability depends on rules, knowledge and data management to be successful.
- AI is a buzzword and unnecessary luxury during the COVID-19 pandemic (AI evolved COVID Care)
- AI and Machine Learning are the same thing. (How AI and ML work together)
- AI Machines, if you turn it on, they will learn and deliver alone. (Why AI needs Human Intelligence)
- AI can be 100% objective. (Knowledge Centered Support improves human and AI outcomes)
- AI will replace jobs (in more cases it will evolve the future of work, and enable efficiency)
- Business does not need an AI Strategy. (Why and how to approach an AI First Strategy)
AI Capabilities are used more widely than most people realize. How many of these day-to-day AI Use Case solutions might you have already experienced? Companies that are using AI Capabilities for an improved consumer experience, realize the time and value to reimagine the support experience as well.
- Hey Siri How do you use AI? Push Button, voice Activated, “Hey Siri” becomes a virtual assistant for activities, actions, and search for information via your Apple products.
- Movie Selection via Netflix. Uses AI to understands your preferences and then creates a curated experience suggesting movies you might not be aware of, that you might like to watch.
- Immersive Shopping Experience with Amazon uses AI to improve search, recommend products. manage supply chain and shipping experience.
- How Google helps improve your search with AI.
- How IBM uses Watson to improve the customer support experience.
AI technologies have transformed the capabilities of businesses globally, enabling humans to automate previously time-consuming tasks, and gain untapped insights into their data through rapid pattern recognition.
Establish a Best of Breed Virtual Support Agent’s Top Use Cases
ARTIFICIAL INTELLIGENCE TERMS TO KNOW:
Algorithm: A set of rules that a machine can follow to learn how to do a task. Examples
Artificial intelligence: References the general concept of machines simulating human intelligence. AI can have a variety of features, such as human-like communication or decision making. Examples.
Autonomous: A machine is autonomous if it can perform its task or tasks without needing human intervention. Examples and pitfalls of autonomous things.
Backward chaining: A method where the model starts with the desired output and works in reverse to find data that might support it. Examples with children
Barista Espressive: AI-based Employee Self-Service Solution to Automate Processes. A compelling example of Automation for Post Covid Challenges. EXAMPLE: How Solar Turbines’ Managed Sudden Work From Home Mandate
Big data: Datasets that are too large or complex to be used by traditional data processing applications. Examples
Chatbot: A program designed to enable natural language communications with people through text or voice commands that mimic human-to-human conversation. Examples
Data mining: Process of analyzing datasets to discover new patterns with potential to improve the data insights that can be driven from the model. Examples
Dataset: A collection of related data points, using a standardized order and tags.
Deep learning: A function of artificial intelligence that learning from the way data is structured, to understand the more human related analytics process over an algorithm that’s programmed to only do or perform one specific task. Examples
General AI: Also known as Deep AI, AGI and Strong Intelligence. General AI allows machines to comprehend, learn, and perform intellectual tasks much like humans by emulating the human mind and behavior for complex problem solving. Everyday examples from Amazon
Intent: Commonly used in chatbot training data and natural language processing tasks, that defines the purpose or goal of what is spoken. Example, the intent for the spoken phrase “turn the volume down” could be “lower volume”. Alexa Examples.
Machine learning: This subset of AI is particularly focused on developing algorithms that will help machines to learn and change in response to new data, without the help of a human being. Examples
Machine translation: Text translation of text by an algorithm, without any human involvement. Fear of Machine Translation Stealing Jobs.
Neural network: A neural network is a computer system designed to function like the human that can perform tasks involving speech, and vision tasks. Examples
Predictive analytics: Merges data mining and machine learning producing analytics to determine what will happen based on historical data and trends. Examples
Predictive Intelligence: Predictive Intelligence provides four frameworks that you can use to create machine-learning solutions in your instance. Each framework delivers a different solution type for training the system to predict, recommend, and organize data outcomes. A trained solution can be invoked by any application through a prediction API to make a prediction. Consumer Examples
ServiceNow: Glossary of AI and Automation Terms – Workflow
Supervised learning: This is a type of machine learning where structured datasets, with inputs and labels, are used to train and develop an algorithm. Assembly AI Video for how Supervised Learning Works
Unsupervised learning: This is a form of training where the algorithm is asked to make inferences from datasets that don’t contain labels. These inferences are what help it to learn. SimpliLearn Lesson on Unsupervised Learning
Weak AI: a model using a narrowly defined range of skills focused on a particular set of tasks. Most used method is weak AI, unable to learn or perform tasks outside of its specialist skill set. Explanation
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