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AI-driven analytics makes big promises. Analyzing large amounts of data, normalizing and preparing data for visualization, choosing the best visualization type for your goals… But can artificial intelligence be used safely when dealing with important information? Can you trust algorithms with decision-making?
Today, we take a look at AI-driven analytics and how it can function in the modern workplace.
AI-driven analytics is the use of artificial intelligence and machine learning to analyze data, uncover patterns, generate insights, and create visualizations based on available datasets. For modern businesses, AI-powered analytics helps with task automation and optimization, data preparation, and in general, getting actionable insights from raw data.
Using AI-driven analytics helps:
There are many advantages of using AI analytics solutions, but by far the biggest one is being able to analyze data and create dashboards without a team of data analysts and data scientists.
Machine learning algorithms are here to stay and for a good reason. Here are some of the biggest benefits of using AI for analytics.
Efficiency and speed: compared to traditional analytics, AI can analyze huge volumes of data more efficiently and quickly
Real-time analytics: AI tools allow stakeholders to analyze data in real-time instead of having to wait for a data scientist to deliver the finished analysis or dashboard
Pattern recognition: AI algorithms can spot patterns in data more quickly, uncovering new patterns that a data analyst may have missed
Risk management: AI models can not only forecast data but also foresee risks that can endanger business outcomes
Natural language processing: makes it possible for anyone to use business intelligence tools just by asking questions in plain English
You can get valuable insights more quickly and easily and eliminate some time-consuming tasks. However, let’s not forget some of the downsides of using generative AI for data analytics.
Technical expertise: AI apps have excellent user experience, once they’re set up and running. To get to this point, you have to get past implementing and setting up the tool, the data sources and the workflows
Dependence on data: for accurate results, you need superior data quality. With poor quality, unstructured data, your analytics platform won’t produce meaningful results
Bias: AI data analytics models are trained on data from the real world, which can be biased
Data privacy: depending on the AI models and APIs used by the analytics tool, there can be some issues with data privacy, i.e. who has access to customer data and how
Integration: to integrate AI tools in your ecosystem, you’re going to spend some time setting up integrations and data sources
Resistance to change: your team may resist using AI technologies for business analytics
We could talk about how AI is amazing if you want to streamline your operations and make a data-driven decision more quickly. But what does that look like in practice? Let’s take a look at some practical ways to use AI in the workplace.
Let’s say you run an e-commerce business selling a variety of products in one industry. You continuously create marketing campaigns but they’re targeting your entire audience.
You instruct AI to analyze your target audience with deep learning algorithms so the model can investigate who purchases from you and what their traits are. The AI analyzes the sales metrics and customer personas to segment your target audience.
You now have lists of segmented audiences and you can create more targeted campaigns (with paid ads, social media, etc.) that are personalized for specific audiences and pain points.
You run a factory that produces packaging for food and you want to learn more about your operations and performance. You give AI data access to large datasets and let it run in the background as you go about your work.
The AI analyzes all the data points to tell you which equipment you use the most, when it’s likely to fail, and when you should maintain it to prevent critical system downtimes.
You run a payment gateway that helps online businesses collect payments from their customers. Thanks to AI, you can analyze large data sets without any knowledge of data science.
The AI helps you detect unusual patterns and activities and spot fraud, both with the shops and their customers.
You run a healthcare clinic and you want to improve patient and customer satisfaction, all the while ensuring your staff are not overworked. You let AI analyze your data to make sure you’ve optimized your processes as well as to give you a competitive advantage over clinics in the area.
The AI model determines your clinic’s busiest hours, patients who are the most likely to come in during certain seasons, and which staff are frequently needed in peak hours. It also helps set early diagnoses for certain illnesses.
You run a brick-and-mortar store selling tires to businesses and consumers. You upload the data from your supply chain software to an AI algorithm to determine patterns and do prescriptive analytics.
The software determines which types of tires frequently go out of stock, when they are most likely to be in demand, which types of customers purchase them and when you should order them from the supplier.
You run a SaaS business and you want to improve your customer retention. Without sitting down for customer interviews, you want to understand why customers are leaving and what you can do to prevent that. Enter churn analysis.
The AI tool does sentiment analysis to determine which customers speak of you negatively in reviews and surveys. You also upload your app data to find out at which stage in the lifecycle customers are the most likely to churn. The end result is a list of customers that can be saved and retained before leaving your SaaS product for good.
On its own, AI analytics tools are still not at a point where they can be super useful to any business straight away. You still need to invest significant time in data cleaning, modeling and preparation. Also, you may need a data engineer or scientist on your team to at least set everything up for the rest of the stakeholders.
At the same time, AI tools can enhance your existing data analytics efforts. If you have the basics covered, you can use AI tools to find answers to key questions more quickly and easily. Not only that, but you can navigate your data in new and more engaging ways.
As for the right tool for the job, Luzmo can help. With Luzmo, you can add AI-based analytics to your app and ensure that your end-users can reap the benefits of using AI directly in your app.
Grab a free demo with our team to see what Luzmo AI can do for your software and your customers!
Experience the power of Luzmo. Talk to our product experts for a guided demo or get your hands dirty with a free 10-day trial.
