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<\/a><\/p>\nI 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\u2019d do alone. I was pleasantly surprised by the response; there are a lot<\/em> of ways that business analysis benefits from AI.<\/p>\n In this article, I\u2019m sharing everything I learned about AI business analytics.<\/p>\n First, we\u2019ll 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\u2019ve shared my favorite insights from professionals who are already using AI in business analytics.<\/p>\n Table of Contents<\/strong><\/p>\n <\/a> <\/p>\n I spoke to over 30 professionals about AI analytics<\/a> and discovered four common ways that AI is helping businesses.<\/p>\n I am already familiar with AI\u2019s 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.<\/p>\n GA4\u2019s analytical AI<\/a> 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.<\/p>\n The above is one example of where AI saves time and accurately reports on data; the answer was instant.<\/p>\n What I like:<\/strong> This is a simple way to start with AI business analytics. It\u2019s built into a tool that every website owner should be using. It\u2019s easy and efficient.<\/p>\n Similar to the above, artificial intelligence<\/a> enhances predictive analysis. I\u2019ve talked about predictive analysis related to CRMs before<\/a>. Many CRMs rely on AI<\/a> to support users with predictive analysis. It makes sense since AI can analyze data quickly and accurately.<\/p>\n 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.<\/p>\n Carmen Mendoza<\/a>, account executive at Booking Agent Info<\/a>, credits AI with detecting complex patterns and trends in data sets that would otherwise be imperceptible.<\/p>\n Booking Agent Info connects customers with celebrities.<\/p>\n Mendoza says, \u201cAI 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.\u201d<\/p>\n What I like: <\/strong>While staff are busy working day-to-day, it\u2019s easy to miss opportunities that are coming up. AI takes the predictive analysis off employees; AI won\u2019t forget or miss opportunities. The predictive analysis then reports to the human, who does the important bit: building the human-to-human relationship.<\/p>\n 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<\/a>, to manage myself, but I hadn\u2019t considered the impact of AI when scheduling a team.<\/p>\n Source<\/em><\/a><\/p>\n Szymon Skoneczny<\/a>, a mathematical model specialist at Softinery<\/a>, uses AI business analytics in manufacturing to optimize processes.<\/p>\n He said, \u201c[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.\u201d<\/p>\n AI becomes most helpful when it fulfills a role that a human can\u2019t. It\u2019s 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.<\/p>\n What I like:<\/strong> 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.<\/p>\n Many who have adopted AI for business analytics use it to summarize data. A common way of using AI summarization is customer feedback.<\/p>\n Instead of manually picking through customer feedback and assigning findings, the AI does it all for you.<\/p>\n Dana Brown<\/a>, head of marketing at Shortcut<\/a>, uses AI summaries for customer feedback. Brown says, \u201cWe 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.\u201d<\/p>\n What I like: <\/strong>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.<\/p>\n <\/a> <\/p>\n It\u2019s 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.<\/p>\n Source<\/em><\/a><\/p>\n With AI, your data can be monitored all day, and significant findings can be reported to you.<\/p>\n Chris Roy<\/a>, product and marketing director at Claimsline<\/a>, uses AI to monitor the sales pipeline.<\/p>\n Roy says, \u201cTimely data is crucial. Utilizing AI for real-time monitoring helps identify trends and issues as they happen, enabling swift adjustments.\u201d<\/p>\n 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 \u201cdecisions that directly impact our hiring and revenue-generation processes,\u201d Roy says.<\/p>\n Joey Lowery<\/a>, founder and marketing coach at Media Shark,<\/a> also recommends using real-time data.<\/p>\n Lowery says, \u201cOur 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.\u201d<\/p>\n What I like: <\/strong>AI can monitor data 24\/7. It doesn\u2019t need a break; it\u2019s 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\u2019s findings.<\/p>\n Lowery from Media Shark has some advice for getting started with AI. He warns, \u201cDon’t get caught up in the hype \u2014 look for practical applications that directly impact your bottom line.\u201d<\/p>\n Instead, Lowery suggests starting small, focusing on one area where you need insights, and growing from there.<\/p>\n What I like<\/strong>: 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\u2019re already doing. It eases the mental load and increases the chances of AI adoption.<\/p>\n AI should be used to complement your human workforce.<\/p>\n Mike Sadowski<\/a>, founder and CEO of Brand24,<\/a> says, \u201cAI has evolved into a great tool for our business analytics, but I also want to emphasize that it doesn’t replace human insight.\u201d<\/p>\n Sadowski has first-hand experience of AI\u2019s shortfalls.<\/p>\n He says, \u201cWhen 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.\u201d<\/p>\n According to Sadowski, the solution is using AI to enhance analysis.<\/p>\n He says, \u201cFor 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.\u201d<\/p>\n What I like:<\/strong> While AI is a fantastic tool, it is just that: a tool. It\u2019s easy to get excited by AI and become over-reliant on it. I think this is another benefit of starting small: You\u2019ll get a better gauge of where AI excels and where its shortfalls are.<\/p>\n Ethical AI<\/a> is really important. We are all still in the very early days of using AI, yet there have already been AI lawsuits<\/a> and questions on how and when AI usage is appropriate. This isn\u2019t here to put you off using this amazing tool, but it should be used conscientiously.<\/p>\n Maggie Bolt<\/a>, marketing manager at Forum Ventures<\/a>, says, \u201cMake sure you are ethically using AI. You should always respect customer privacy and use AI responsibly in line with data regulations.\u201d<\/p>\n What I like:<\/strong> It\u2019s 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.<\/p>\n As much as AI takes tasks off humans, it still depends entirely on humans to operate successfully.<\/p>\n Josh Bolstad<\/a>, owner of Niche Ranker,<\/a> recommends that teams provide quality data to their AI. Bolstad says, \u201cI\u2019ve learned that AI is only as good as the data it\u2019s fed. Ensuring data accuracy and relevance is essential.\u201d<\/p>\n What I like:<\/strong> Even in its simplest form, I\u2019ve 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.<\/p>\n <\/a> <\/p>\n If you\u2019re 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.<\/p>\n Source<\/em><\/a><\/p>\n Before investing time or money into AI for business analytics, determine which area of your business you will analyze with AI.<\/p>\n Consider:<\/p>\n Experts I interviewed suggested starting small and building out from there. You don\u2019t have to do everything in one go. In fact, there\u2019s going to be a period where you and your team need to get used to the AI.<\/p>\n 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.<\/p>\n Remember: You can always scale your AI operations as you go.<\/p>\n As you explore your new AI for business analytics, hold one member or a small team accountable for the execution.<\/p>\n Hilary Corna<\/a>, a strategy coach, says<\/a>, \u201cAccountability is key to sustaining the success of any process improvement efforts.\u201d<\/p>\n To help build accountability, Corna recommends framing expectations, setting realistic expectations, and cultivating a growth mindset.<\/p>\n 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.<\/p>\n Three common end goals of bringing AI into your business analysis are saving time, analyzing large datasets accurately, and improving efficiency.<\/p>\n 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.<\/p>\n 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.<\/p>\n 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.<\/p>\n Next, you just need to pick an area of business analytics to optimize with AI and get to work. The benefits are great; don\u2019t sleep on it.<\/p>\n I\u2019ve adopted AI into many parts of my business, particularly in marketing and operations. Yet, I wasn\u2019t making the most of what AI could bring to my business. I hadn\u2019t considered AI\u2019s role in business analytics. 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How can AI help with business analytics?<\/h2>\n
Automating Data Processing<\/h3>\n
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<\/p>\nEnhancing Predictive Analytics<\/h3>\n
Scheduling<\/h3>\n
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<\/p>\nSummarizing Data<\/h3>\n
Expert Tips on Using AI for Business Analytics<\/h2>\n
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<\/p>\nMonitor data on a real-time basis.<\/h3>\n
Start small.<\/h3>\n
Don\u2019t replace your humans.<\/h3>\n
Be ethical.<\/h3>\n
Provide quality data.<\/h3>\n
How to Use AI for Business Analytics<\/h2>\n
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<\/p>\nStep 1. Choose one area to improve with AI business analytics.<\/h3>\n
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Step 2. Start small.<\/h3>\n
Step 3. Manage the AI with your team.<\/h3>\n
Step 4. Human Analysis<\/h3>\n
Step 5. Human Action<\/h3>\n
Getting Started<\/h2>\n
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