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I’ve adopted AI into many parts of my business, particularly in marketing and operations. Yet, I wasn’t making the most of what AI could bring to my business. I hadn’t considered AI’s role in business analytics.
I spoke to business founders, marketers, and data analysts to learn more about how AI business analytics allows for faster data analysis and delivers business findings faster than you’d do alone. I was pleasantly surprised by the response; there are a lot of ways that business analysis benefits from AI.
In this article, I’m sharing everything I learned about AI business analytics.
First, we’ll dig into the ways that AI can help with business analytics, followed by a step-by-step guide to getting started with it. Throughout, I’ve shared my favorite insights from professionals who are already using AI in business analytics.
Table of Contents
- How can AI help with business analytics?
- Expert Tips on Using AI for Business Analytics
- How to Use AI for Business Analytics
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How can AI help with business analytics?
I spoke to over 30 professionals about AI analytics and discovered four common ways that AI is helping businesses.
Automating Data Processing
I am already familiar with AI’s automation of data. Whenever I handle a large dataset, I turn to AI to process that data. I might ask the AI to condense data, collapse duplicates, or deliver findings from a report.
GA4’s analytical AI is brilliant at delivering information in an easy-to-read and efficient manner. For example, in the screenshot below, I wanted to know my site's traffic volume. Rather than clicking through to reports, the AI delivered the information I most wanted to read.
The above is one example of where AI saves time and accurately reports on data; the answer was instant.
What I like: This is a simple way to start with AI business analytics. It’s built into a tool that every website owner should be using. It’s easy and efficient.
Enhancing Predictive Analytics
Similar to the above, artificial intelligence enhances predictive analysis. I’ve talked about predictive analysis related to CRMs before. Many CRMs rely on AI to support users with predictive analysis. It makes sense since AI can analyze data quickly and accurately.
At the click of a button, AI can help you understand your audiences and make data-driven decisions to a) serve your audiences in the best way possible and b) get closer to conversion quicker.
Carmen Mendoza, account executive at Booking Agent Info, credits AI with detecting complex patterns and trends in data sets that would otherwise be imperceptible.
Booking Agent Info connects customers with celebrities.
Mendoza says, “AI allows us to create predictive analytics. Using historical data and current market trends, we can forecast when there might be a rise in demand for certain skills or talent types. This is useful information because it helps us identify talent early and cultivate relationships with them so that we can provide clients with the right people when they need them.”
What I like: While staff are busy working day-to-day, it’s easy to miss opportunities that are coming up. AI takes the predictive analysis off employees; AI won’t forget or miss opportunities. The predictive analysis then reports to the human, who does the important bit: building the human-to-human relationship.
Scheduling
I especially love the idea of using AI for scheduling. I love AI for scheduling my daily activities. There was a time when I used Motion, an AI project management tool, to manage myself, but I hadn’t considered the impact of AI when scheduling a team.
Szymon Skoneczny, a mathematical model specialist at Softinery, uses AI business analytics in manufacturing to optimize processes.
He said, “[AI] analyzes vast amounts of data from production-line sensors in real-time, enabling us to make decisions that lead to increased efficiency. The application of AI also allows us to predict machine failures, optimize production schedules, and minimize operational costs.”
AI becomes most helpful when it fulfills a role that a human can’t. It’s impossible for a human to monitor every machine in real time, nor can a human easily consider the production schedule for an entire manufacturing team efficiently.
What I like: Using AI to predict machine failures and optimize schedules directly impacts business profitability. Unlike humans, AI can monitor numerous machines at once. In this use case, AI offers many cost-saving opportunities.
Summarizing Data
Many who have adopted AI for business analytics use it to summarize data. A common way of using AI summarization is customer feedback.
Instead of manually picking through customer feedback and assigning findings, the AI does it all for you.
Dana Brown, head of marketing at Shortcut, uses AI summaries for customer feedback. Brown says, “We can use AI tools to help efficiently summarize large text documents, such as customer feedback from surveys, extracting key themes and sentiments without a whole bunch of manual work. This not only saves time but also ensures that no critical insights are overlooked.”
What I like: I think data summary is a fantastic use case for AI. Many of us are already using it for things like AI meeting notetakers. However, the benefits of summarizing business data are significant. Brown is saving hours and hours using AI to summarize and contextualize customer feedback surveys.
Expert Tips on Using AI for Business Analytics
It’s clear that AI for business analytics is beneficial and efficient, but before you get started, consider these tips from professionals already using AI in this way.
Monitor data on a real-time basis.
With AI, your data can be monitored all day, and significant findings can be reported to you.
Chris Roy, product and marketing director at Claimsline, uses AI to monitor the sales pipeline.
Roy says, “Timely data is crucial. Utilizing AI for real-time monitoring helps identify trends and issues as they happen, enabling swift adjustments.”
For instance, Roy notes, tracking inbound and outbound lead metrics in real-time has allowed the team to maintain a balanced approach in their lead generation strategy. This informs “decisions that directly impact our hiring and revenue-generation processes,” Roy says.
Joey Lowery, founder and marketing coach at Media Shark, also recommends using real-time data.
Lowery says, “Our AI system alerts us to unusual sales patterns instantly. Last month, it caught a sudden spike in a product line, letting us quickly restock and capitalize on a trend.”
What I like: AI can monitor data 24/7. It doesn’t need a break; it’s fast, accurate, and can spot trends and alert you to potential actions. Analysis like this is difficult and incredibly time-consuming for humans. Give AI this analysis so you can get to work on resolving AI’s findings.
Start small.
Lowery from Media Shark has some advice for getting started with AI. He warns, “Don't get caught up in the hype — look for practical applications that directly impact your bottom line.”
Instead, Lowery suggests starting small, focusing on one area where you need insights, and growing from there.
What I like: From HubSpot surveys, we know that teams can easily adopt AI within the tools they already use. Instead of trying to do everything and overwhelming teams with new processes and tools, I suggest focusing on one thing, ideally connected to something you’re already doing. It eases the mental load and increases the chances of AI adoption.
Don’t replace your humans.
AI should be used to complement your human workforce.
Mike Sadowski, founder and CEO of Brand24, says, “AI has evolved into a great tool for our business analytics, but I also want to emphasize that it doesn't replace human insight.”
Sadowski has first-hand experience of AI’s shortfalls.
He says, “When we first implemented AI tools, there was an initial wave of excitement as if we had discovered a shortcut to comprehensive understanding. Though, we soon realized that while AI excels at highlighting trends, it lacks contextual awareness. Human judgment remains key for correctly interpreting these insights and making informed decisions based on them.”
According to Sadowski, the solution is using AI to enhance analysis.
He says, “For those considering AI implementation, I advise against expecting it to solve every problem for you automatically. Instead, utilize it as a tool to enhance your analysis, not supplant it. Begin with specific areas where AI can provide an advantage, such as customer sentiment analysis or predictive sales modeling.”
What I like: While AI is a fantastic tool, it is just that: a tool. It’s easy to get excited by AI and become over-reliant on it. I think this is another benefit of starting small: You’ll get a better gauge of where AI excels and where its shortfalls are.
Be ethical.
Ethical AI is really important. We are all still in the very early days of using AI, yet there have already been AI lawsuits and questions on how and when AI usage is appropriate. This isn’t here to put you off using this amazing tool, but it should be used conscientiously.
Maggie Bolt, marketing manager at Forum Ventures, says, “Make sure you are ethically using AI. You should always respect customer privacy and use AI responsibly in line with data regulations.”
What I like: It’s easy to get excited by what AI can do for our business, but we must be careful. Business analyst consultants, for example, should be transparent about their AI usage.
Provide quality data.
As much as AI takes tasks off humans, it still depends entirely on humans to operate successfully.
Josh Bolstad, owner of Niche Ranker, recommends that teams provide quality data to their AI. Bolstad says, “I’ve learned that AI is only as good as the data it’s fed. Ensuring data accuracy and relevance is essential.”
What I like: Even in its simplest form, I’ve noticed that the AI output correlates with the input I give the tool. Bolstad is right: You get out of AI what you put in. Data input is a commitment, but it pays off.
How to Use AI for Business Analytics
If you’re new to using AI for business analytics, my simple step-by-step guide will get you from the consideration phase to using AI for business analytics.
Step 1. Choose one area to improve with AI business analytics.
Before investing time or money into AI for business analytics, determine which area of your business you will analyze with AI.
Consider:
- What will be most helpful to you and your team. Your team is more likely to invest the time and learn if the AI directly impacts their work in a good way.
- Areas where you’re already using tools or have good processes. AI adoption is more likely to stick if you use AI that exists within tools you’re already using.
- Measurable outcomes. Decide which KPIs will monitor the success of using the AI. Were you looking to save time, get more accurate data, or something else? Whatever it is, make sure you have a measured outcome.
Step 2. Start small.
Experts I interviewed suggested starting small and building out from there. You don’t have to do everything in one go. In fact, there’s going to be a period where you and your team need to get used to the AI.
Start by handling a few tasks with the AI and seeing how it responds. Then, analyze the output, tweak the input, and alter systems and processes accordingly.
Remember: You can always scale your AI operations as you go.
Step 3. Manage the AI with your team.
As you explore your new AI for business analytics, hold one member or a small team accountable for the execution.
Hilary Corna, a strategy coach, says, “Accountability is key to sustaining the success of any process improvement efforts.”
To help build accountability, Corna recommends framing expectations, setting realistic expectations, and cultivating a growth mindset.
Step 4. Human Analysis
As our experts said, no AI system is complete without human analysis. Once the AI has delivered its output, your team must add all the important human layers. This will be especially important in the early days of the adoption of AI for business analytics. You need to be sure that the system is doing what it should as accurately and efficiently as you need it to.
Step 5. Human Action
Three common end goals of bringing AI into your business analysis are saving time, analyzing large datasets accurately, and improving efficiency.
Remember that the AI is taking some of your team's work. Now, you must allow your team the time and freedom to do what they do best and add that all-important human layer.
Getting Started
Before I started learning about AI and business analytics, I knew it would be incredibly useful. Still, the scale to which AI can benefit business analytics surprised me greatly.
Insights from our AI and business analytics professionals have inspired me to level up how I use AI in my own business, and I hope it does the same for you.
Next, you just need to pick an area of business analytics to optimize with AI and get to work. The benefits are great; don’t sleep on it.
Free AI Agents Playbook
This practical guide reveals where to start, which applications deliver real value, and how to implement agents that transform workflows without replacing jobs.
- Marketing Workflow Automation
- Sales Acceleration System
- Operational Excellence
- Implementation Blueprint
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