AI (Artificial Intelligence) is a field of computer science focused on creating machines that can think and act like humans. These machines are able to learn from their environment, recognize patterns, make decisions, and even communicate with people in an intelligible way. AI technology is rapidly evolving and being applied in numerous fields due to AI hardware and software advances. AI has been used for tasks such as natural language processing (NLP), facial recognition, robotics, autonomous vehicles, image recognition, video game playing, customer service chatbots, medical diagnosis, and many more.

At the heart of AI development is machine learning (ML). This is the process of teaching computers to solve problems without having to write explicit programming instructions. ML algorithms break down data into various features which a machine learning model can then use to answer questions or provide predictions in response to new information. The most common techniques used for ML include supervised learning where data scientists feed the computer labeled data sets so that it can determine how to generate accurate outputs based on previously known inputs; unsupervised learning where machines analyze datasets with no labels and look for patterns; deep learning where neural networks are employed; reinforcement learning where machines explore different scenarios within a given environment in order to maximize rewards; and finally transfer learning which uses pre-existing models as a starting point for developing new applications.

These techniques have enabled advancements not just in the field of AI but across all industries impacting how businesses operate and how people interact with technology today. In terms of the ethical implications of using AI, some critics worry about the misuse of private data, while others question whether it's appropriate to be replacing human decision-making with automated systems or if machines will eventually exceed human intelligence levels. Nonetheless, AI holds great potential for solving complex problems and reshaping the industry as we know it today.

Advantages of AI

NLP (Natural Language Programming) - NLP can speed up or increase a team's velocity by making it easier to interface with the system to make changes. Predefined capabilities are vital to making this happen. What tasks can a business user use NLP? Simple screen changes, data queries, and simple systems changes are worth considering having the business perform instead of the developer. Developers can utilize NLP to build a prototype of business logic or models of potential solutions.

Finding Patterns - AI is getting proficient at finding patterns in user behavior and complex data sets. Business decisions are driven by data, but that data can be overwhelming when harnessing it into something meaningful. Business Intelligence and Data Analysts should find NLP helpful in speeding up that analysis and finding trends in complex data sets.

Customer Response Automation - Although today's chatbots are very limited, further exploration into developing more complex AI decision-making capabilities is beneficial for cost reduction and responding to customers faster. Previously, companies used voice attendants asking you to press 1 for this or press 2 for that. AI in chatbots can create a more natural language interface for customers and quickly guide them through all the different paths of service. Business rules, or those rules a business has chosen to adopt in running its operations, will be vital in creating a better customer service model. Additionally, process flow models of the different paths a customer might need to follow.

Automating Data Suggestions and Dashboards - By understanding emerging patterns or patterns going away, the AI can make recommendations to business users by displaying patterns observed in a dashboard. The AI won't ultimately be making a business decision, but the information provided will give business users the ability to make more data-driven decisions.

Forecasting - AI has been very beneficial for predicting the weather. The ability of the AI to see patterns is one of its strong points. Application of its pattern recognition to see forecasts in business is still quite limited but has potential. Consider the many elements involved in

Disadvantages of AI

Empathy - The AI is not going to empathize with your stakeholder or customer problems. It lacks emotional awareness, so it does a great job with data but not stakeholder motivation. Empathy for a customer's needs gives a product or business a competitive edge by understanding and adapting to changing customer needs. The AI can discover a customer's patterns, but humans handle improving the customer experience better.

Data Bias - Although good at finding patterns in data, the quality of data and the bias of the engineers that developed the AI could provide insufficient or confusing data for decision-making. Keep in mind that humans train or develop the AI, and they have biases that can creep into the AI decision-making process. The key here is understanding what bias is in humans, how to be aware bias is occurring, and understanding the role of the ladder of inference in bias.

Shortage of Human Resources and Expertise - As an emerging technology, there are few experts in how AI is applied to business. Chatbots are just the beginning to help customers get through to the right person in the organization or perform a simple task like collecting information for further processing. Humans in customer service aren't going to be replaced. The AI will handle easy to moderately complex tasks, but humans will likely handle challenging, complex customer problems. Developing the AI is going to be difficult as you need experts in AI but also in eliciting, analyzing, modeling, and creating innovative solution designs.

Historical Blind Spot - AI can mimic authors writing styles to write a blog or juxtapose image composition to create a new image. The AI creates a blog or image based on historical data and images. The future is unpredictable. Innovative tectonic shifts in product capabilities aren't likely to be created by an AI anytime soon. That next new product sensation will be human-created with a supporting role by AI.

Human Versus AI Requirements Analysis and Modeling

AI is a tool that can be used to assist humans in many ways, but AI won't be replacing you anytime soon. Are jobs going to use AI more? Yes, but AI will leave tough decisions to humans. Learning how to use the AI tool is undoubtedly a skill all analysts should develop. The AI needs humans to keep it running, which means more jobs will open around keeping the AI operational.