What is AI Analytics?
AI analytics is the result of automating business analytics processes through artificial intelligence (AI) and machine learning (ML). AI analytics can improve the scale and speed at which data is analyzed compared to a human worker. It can be used to identify trends, provide insights, and discover relationships within data.
What is the Difference Between AI Analytics and Traditional Analytics?
Smart business leaders rely on data to make decisions. Traditionally, data analysts investigate business data by forming a hypothesis and then testing this hypothesis against available data until there is enough evidence. These findings are then summarized in a report. The process from start to finish can be time-consuming, and the risk of human error means that the answers they find may not always be reliable.
This is where artificial intelligence comes in. AI is employed in data analysis to automate this process. AI can look at larger amounts of data much faster than humans can, while combating the risk of inaccurate or misguided results.
What are the Four Types of AI?
Generally speaking, AI can be classified into four types:
- Reactive AI. This is the most basic and earliest type of AI. Reactive AI can respond to immediate requests but can’t store memory or learn from past experiences. It reacts the same to identical situations, and can’t function beyond the task it was created for.
- Limited-memory AI. This was the next development following reactive AI. Limited memory AI can observe actions or data and then make predictions. From pre-programmed information and historical and observational data, limited memory AI can learn from the past. This is the most widely used form of AI today.
- Theory-of-mind AI. This is the notion that AI that can and will develop an “understanding” of humans. Theory of mind AI develop an understanding of the entities they interact with, including their needs, thought processes, emotions and beliefs, then adjust behavior accordingly. This type of AI is yet to be fully realized. Today, conversational AI is used by businesses to improve on traditional chatbots and simulate more realistic human conversations.
- Self-aware AI. This is the most advanced type of AI, considered the ultimate objective of AI research. It’s the point where AI goes from understanding emotions to understanding and feeling emotions, needs, desires and beliefs of its own. When AI develops the ability to be aware of its emotions, it’s thought that it will have a level of human-like consciousness. Self-aware AI is not yet a reality.
How Does AI Analytics Work?
AI analytics uses machine learning (ML) to discover insights. Compared to traditional analytics, AI analytics only requires human input at the first instance, when the ML algorithms are fed training data. From this, it can learn how to spot patterns and analyze data. This enables algorithms to produce actionable insights and predictions from your business data.
4 Ways to Use AI Analytics
AI analytics can perform descriptive, predictive and prescriptive tasks. It can have multiple uses and applications throughout your business and its systems. Here are four ways you could use AI analytics. There are likely more.
As AI analytics has predictive abilities, it can forecast product demand. AI analytics can use available stock data, seasonal trends, and historical purchasing data to predict the future product demand for your business. This allows you to improve how you stock products and purchase inventory or materials.
The speed and scale of AI analytics mean that it can pull from across your various platforms and systems to provide a single, unified view. This is especially useful if you have disparate systems or wish to analyze customer data across various platforms.
Predict Business Outcomes
AI analytics can account for vast amounts of data and, from this, predict what works or is likely to happen. This can improve your business decision-making, as you can see the outcome of proposed courses of action.
Gain Audience Demographic insights
AI analytics can offer valuable insights into the demographics of your audience. This includes purchasing habits, location, age and gender. Getting a deeper and more specified view of your audience allows you to personalize your content and optimize your customer targeting.
Pros of AI Analytics
AI analytics brings with it several benefits. Its ability to expand the reach of your analytics can benefit businesses in various ways.
AI analytics free workers from the laborious and time-consuming task of analyzing data themselves. They can, instead, focus their time on more high-priority tasks. The speed and scope of AI analytics also means you can receive quality insights in near real time – meaning you can take action and make positive changes more quickly.
AI analytics can recognize and extract useful information from large amounts of data, even across multiple platforms. ML can use this to spot trends and patterns. A combination of AI and ML can improve your business insights and predictions.
ML algorithms mean that AI analytics is able to constantly learn and adapt. AI analytics can be applied to a variety of queries and data sources, simply needing to be fed the information.
Cons of AI Analytics
AI analytics can bring business benefits, but it may not be the right fit for every company. It can be:
AI analytics doesn’t come cheap, requiring time and resources. Implementing AI analytics might also mean upgrading your current systems to meet the requirements needed. Whilst the business benefits AI analytics generates will cover off this cost soon enough, this can create a large bill that might not be a realistic investment right now for some companies.
Prone to Error
Whilst AI analytics reduces the chances of human error, this doesn’t mean that the risk of errors is removed entirely. Human interaction is still needed for AI analytics, and user errors can arise. Design errors can also come from the training data inputted, such as biased, tainted, insufficient or incorrect data.
Disadvantaged by the Lack of Regulation in Some Industries
AI regulations are slowly being introduced around the world. For many industries, there is still a lack of regulations. It is up to companies to keep apace with regulations and ensure their AI analytics systems are in line with the evolving regulatory framework in their industry. It’s important to put risk frameworks in place to ensure that the risks of AI analytics are managed effectively.
Getting Familiar with AI-Driven Analytics
As data sets continue to grow, the use of AI in business analytics is also set to grow. Combining big data and AI analytics brings various benefits, not only to the data analysis process but to the business overall. It’s essential that companies planning to implement AI analytics make the necessary preparation. This means ensuring your systems are up to date, adapting your risk framework and training workers to lower the chance of user error.
About Sutherland CX360
Sutherland CX360 is a CX intelligence platform that uses AI to analyze 100% of customer interactions, rather than the mere 3% that gets audited manually. CX360 works to analyze the customer journey, monitor quality and give you predictive outcomes.
Sutherland CX360 features:
- Rich insights into agent behaviors.
- Sentiment, topic and DSAT analysis.
- Omnichannel interaction analysis into customer expectations.
- Automated QA reports.
- Predictive models for outcomes by interaction, agent and team.