Data and AnalyticsStrategy & Operations

What Is AI Analytics, and What Can It Do for My Business?

Lauren Spiller profile picture
By Lauren Spiller

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6 min read
Header image for the blog article "What Is AI Analytics, and What Can It Do for My Business?"

Learn how you can boost your data analytics efforts with AI analytics.

According to Capterra’s 2022 Services Users Survey*, 70% of SMB leaders invested $50k or more in data analytics and AI services over the past 18 months, and with good reason. AI analytics is a must-have for driving revenue, cutting costs, and improving user experience. 

Infographic showing "Seventy percent of SMB leaders spent $50k or more on their most recent data intelligence services contract, which includes AI services." for the blog article "What Is AI Analytics, and What Can It Do for My Business?"

Don’t get left behind by your competitors. By investing in AI-powered business intelligence (BI) tools, you too can supercharge your data analysis in ways that positively impact your small business. This Gartner research-backed guide covers the basics of AI analytics as well as five use cases that show you what to expect when you add AI analytics tools to your tech stack. 

What is AI analytics?

AI analytics refers to a subset of BI that uses machine learning to extract insights from data. Tools that leverage AI analytics give your data analyst a leg up by processing higher volumes of data at a faster rate. In fact, Gartner[1] predicts that by 2025, 70% of public companies that outperform competitors on key financial metrics will also report being data-and-analytics-centric.

Infographic showing "Seventy percent of public companies that outperform competitors on key financial metrics will also report being data-and-analytics-centric." for the blog article "What Is AI Analytics, and What Can It Do for My Business?"

Unsurprisingly, then, industries or areas that benefit most from AI analytics tend to work with big data: high-volume, high-speed, or high-variety information that needs sophisticated processing and analysis. They also benefit from trend prediction or forecasting and the identification of patterns in their data. Examples include financial services, human resources, and retail.

While AI analytics doesn’t refer to any one software category, we’ll be discussing this capability and its use cases through the context of business intelligence tools, which can also be referred to as business analytics software. This will help narrow your search for an AI solution that will boost the effectiveness of your data analytics efforts and make better use of customer data.

What are some use cases of AI analytics?

1. Translating complex datasets into business impact

As we just discussed, artificial intelligence is extremely useful for processing big data. But when stakeholders ask you to report on different key performance indicators or metrics, you’re going to have a difficult time providing them with answers if you’re working with unstructured data.

Screenshot of analytics dashboard in NetSuite

Analytics dashboard shown in business intelligence platform NetSuite

An AI analytics tool saves both time and eye strain by drawing insights and patterns from large datasets. It does this through a machine learning model that applies labels to data quickly and at scale. It also uses natural language processing to translate those labels into answers, so you can tell stakeholders which marketing campaign resulted in the most conversions this year.

/ Feature spotlight

BI tools allow users to convert complex data into easily understandable visuals such as charts, graphs, infographics, and animations through features such as dashboards and visual analytics. These typically include drag-and-drop mechanisms, drill-down options, and self-service data preparation for easier use of your software. Find tools that offer both here.

Supply chain woes got you down? AI analytics can be used to identify patterns, risks, and trends to help you make better predictions of future obstacles and minimize downtime. Fifty-eight percent of SMB retail supply chain managers** have invested or plan to invest in technologies to provide better inventory insight to address existing supply chain bottlenecks.

Infographic showing "Fifty-eight percent of supply chain/inventory management managers in retail SMBs have already invested or plan to invest in technologies to provide better inventory insight." for the blog article "What Is AI Analytics, and How Can It Do For My Business?"

AI-powered business intelligence tools use predictive analytics to forecast demand based on inventory, seasonal activity, and historical data. Retailers, restaurants, manufacturers, and other businesses that rely on the global supply chain can invest in these tools to improve how they stock products, maintain inventory, purchase materials, and plan for future investments.

/ Feature spotlight

To find business intelligence tools that use predictive analytics and trend/problem indicators, click here. These features not only help you predict future data based on historical activity, but also alert you to areas that require attention. There are even vendors that offer free trials

When you’re ready for more advanced features, you can upgrade to tools that include predictive modeling, data mining, and profitability analysis. These features use advanced analytics to boost decision-making with statistical techniques and help you evaluate the degree to which your operations are making money. Learn more about them and our pricing tiers here.

3. Better data security and protection against fraud

Like supply chain issues, cyber attacks are another major hardship that businesses have faced in the past few years. And because SMBs don’t have the same resources to protect themselves as larger enterprises, their effects on your data security can be even more devastating.

Screenshot of security monitoring dashboard in business intelligence platform Splunk

Security monitoring dashboard in business intelligence platform Splunk

Through machine learning algorithms, AI analytics can identify patterns in how your data is accessed and report anomalies. This way, your business has an additional layer of protection against ransomware, DNS threats, and phishing scams. AI capabilities are so successful that even the US Treasury Department is investing further in AI to fight money laundering[2].

/ Feature spotlight

While you’re browsing BI platforms, look for a feature called security/user administration. This ensures secure access to reports using role-based permissions and logs of report access so that you can control who has access to your data.

4. Unifying data across multiple channels for better and more personalized customer service

Omnichannel customer service has grown in popularity over the past few years[3], which might mean the data you need to improve your service strategy doesn’t live in one place. But data analysis can be a trickier endeavor than usual when customers are reaching out through your social media accounts, email inbox, website contact form, and over the phone.

Screenshot of reporting dashboard in TOPdesk

Reporting dashboard in customer service platform TOPdesk

Luckily, AI analytics can be used to analyze data across platforms so you can have a unified view of all your customer information. Additionally, this capability can help you to create more personalized experiences for your customers through analysis of browsing and buying data. Examples include product recommendations, or suggesting relevant content based on location.

/ Feature spotlight

Did you know that most popular BI tools can be integrated with customer service or customer relationship management apps? This is a great way to ensure that business decisions reflect customer data. 

Make sure your tech stack includes the integrations that are important to your business goals. Start by making a list of the products you’re currently using and inquire with any potential new vendors about the depth and scope of integration with each of those systems. You can also check the websites of each product on your shortlist since integrations are often listed there.

5. Prioritizing customers in live chat queues

If you’ve ever plodded your way through an options menu on a customer service line, or on a website’s live chat feature, you know the relief of finally connecting with a real human being. But on the business’s side of that exchange, an AI chatbot is hard at work gathering information to determine how to best serve you–or whether it’s worth it to connect you to a live agent at all.

Screenshot of appointment scheduling in IBM Watson Assistant

Appointment scheduling through chatbot platform IBM Watson Assistant

The AI bot’s skill set boils down to gathering and forwarding data such as user intent and location. It responds to queries with relevant information through natural language processing. It can even direct agents toward which customer is most worth helping at a certain time based on data such as likelihood of conversion, or whether they’re chatting from a corporate IP address.

/ Feature spotlight

BI tools don’t typically offer chatbot features, but as we mentioned earlier, many BI platforms can be integrated with customer service apps. This integration would enable you to use your AI-powered BI tool to process and analyze data gathered by the customer service chat function.

Find the right service for your business in Capterra's list of Artificial Intelligence Companies in the United States.

What should I consider before implementing an AI-powered business intelligence tool?

While you might be drawn to more than one of the use cases above as reasons for investment in an AI-powered business intelligence tool, Gartner[4] recommends prioritizing those focused on your best return of investment (ROI). Ask yourself the following questions to determine the feasibility and business value of any AI-powered business intelligence tool you’re considering:

  • Is the business need well-defined?

  • Do we have the right people and experience on our team to implement this solution?

  • What is our performance expectation of the solution, and how will we measure it?

  • Is the solution similar to anything we’ve implemented previously?

  • Is the solution improving the efficiency of an existing business process?

  • Is the solution impacting customer experience?

Alternatively, you may decide you’d rather invest in AI-as-a-Service, or AIaaS. AIaaS is a great option for businesses interested in exploring ready-to-use AI solutions at a lower initial cost and commitment level than what it would take to deploy and manage an AI solution on your own. See below for a brief video outlining the pros and cons of AI-as-a-Service.

To get the same benefits that AI analytics tools offer, we recommend data for AI services companies. They focus on data quality, source identification, authority, and control to build data models. They can process both unstructured and semi-structured data and offer collected data sets for businesses to build data models. Check out our full collection of AI companies here.

Note: The screenshots of applications selected in this article are examples to show a feature in context and are not intended as endorsements or recommendations.


Survey methodology

*Capterra’s 2022 Services Users Survey was conducted between July 25 and August 23 among 1,078 Past Services purchasers (defined as those who have purchased or commissioned a qualifying service in the last 18 months) who work at an SMB (defined as a company with fewer than 1000 employees and between $5M and < $1B in revenue) in the US. Respondents must have spent at least $10,000 on their most recent service engagement to qualify.

**Capterra’s 2022 SMB Retail Supply Chain Survey was conducted in March among 305 U.S-based supply chain/inventory management managers in retail small/midsize businesses. Respondents were screened for size of business (1 - 1,000 employees), involvement in procurement and inventory management at their retail company (very to extremely involved), and that they had experienced at least minimal supply chain delays in the past 12 months.


Looking for Big Data software? Check out Capterra's list of the best Big Data software solutions.

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About the Author

Lauren Spiller profile picture

Lauren Spiller is a senior content writer at Capterra, covering sales and CRM with a focus on retail and customer experience. After receiving an MA in rhetoric and composition from Texas State University, Lauren has pursued a career that allows her to help others through writing.

Lauren previously taught college writing and served as writing center assistant director at Texas State University. She has presented at the European Writing Centers Association, Canadian Writing Centres Association, and the International Writing Centers Association conferences. She currently lives in Wimberley, Texas, with her husband and their three cat sons.

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