Why Is AI Becoming Important in Business Analytics?
Last Updated August 31, 2026
AI is becoming an increasingly common part of business analytics as organizations work with larger and more complex amounts of data. Businesses can use AI to identify patterns, analyze information, and support faster decision-making. That does not mean AI replaces the people responsible for making those decisions. Analysts and managers still need to determine whether an AI-generated result is accurate, relevant, and useful in a particular situation.
The value of AI in analytics is largely about helping people work with information more efficiently. It can take on certain repetitive tasks and help identify patterns that might otherwise take more time to find. The person using the result still needs to understand what it means and whether it should be trusted.
How Is AI Changing Business Analytics?
Traditional reporting remains useful for answering questions about past performance. A business might use a dashboard to compare quarterly sales, examine results by region, or track changes over time.
AI-assisted analytics can add other capabilities, including pattern recognition, anomaly detection, forecasting, and natural-language exploration of data. Some analytics platforms now allow users to ask questions about their data in everyday language and receive automated insights that can be investigated further. (ThoughtSpot)
This does not make traditional analytics obsolete. Instead, AI can add another layer to existing analytical processes. It can help analysts explore information, identify areas that deserve closer attention, and work through certain tasks more quickly.
If AI Can Do More of the Analysis, Why Do People Still Need to Learn Analytics?
An analytical result is only useful if someone can determine whether it makes sense.
An AI system can identify a pattern, produce a forecast, or summarize a dataset quickly. A person still needs to ask basic questions about the result. Is the underlying data accurate? Does the analysis address the business question? Could a data quality issue be affecting the outcome? What assumptions went into the analysis? How much confidence should someone place in the result?
AI adoption is also increasing across organizations. McKinsey reported that 88 percent of respondents in its 2025 global survey said their organizations regularly used AI in at least one business function. The same research found that many organizations were still working through experimentation and pilot programs rather than having fully scaled AI across the enterprise. (McKinsey & Company)
As businesses continue experimenting with AI, employees need enough analytical knowledge to evaluate the results they encounter. Understanding data and analytical methods can make it easier to recognize when an AI-generated answer needs additional investigation.
What’s the Risk of Using AI-Generated Analytics Without Understanding It?
One concern is relying on an analytical result without understanding its limitations.
AI-driven tools do not all work in the same way. Some provide more information about how they arrive at a result, while others can be harder to interpret. That difference matters when an analyst or manager needs to explain why a particular recommendation or prediction was accepted.
Organizations also need to consider how AI is governed. McKinsey’s research has examined the role of AI governance as companies move from experimentation toward broader adoption. Policies and processes for evaluating AI systems can help organizations manage issues related to accuracy, responsible use, and oversight. (McKinsey & Company)
For business professionals, the takeaway is straightforward. An AI-generated result should be treated as information to evaluate, not as an automatic answer.
Do You Need a Technical Background to Work With AI-Driven Analytics?
Not necessarily. Working with AI-driven analytics can involve interpreting results, asking questions about the data, and understanding the limitations of an analytical method. Those skills do not necessarily require someone to become a programmer or data scientist.
Michigan State University’s AI & Business Analytics series takes a business-focused approach to analytics and AI. The university describes the program as combining conceptual understanding with hands-on applied analytics, including interpreting machine learning outputs and evaluating generative AI. (Michigan State University Online)
That approach can be useful for professionals who need to work with analytical information without necessarily building every model themselves.
How Does MSU’s Online AI & Business Analytics Curriculum Address This?
Michigan State University offers online courses that cover both the practical and conceptual sides of AI & business analytics.
Mastering Data-Driven Management and Decision Making focuses on data-driven decision-making and the use of analytics and AI in business. The course addresses topics including data, information, insight, analytics, and generative AI, along with considerations related to interpreting AI outputs. (Michigan State University Online)
Applying Business Analytics, Using AI Techniques and Data Visualization takes a more hands-on approach. The course covers business analytics techniques and data visualization and includes tools and methods such as Excel, Power BI, customer segmentation, regression, classification, and A/B testing. (Michigan State University Online)
Neither course requires an advanced technical background, although comfort working with data is recommended. (Michigan State University Online)
Why Does Analytical Judgment Matter More as AI Use Grows?
As businesses use AI to analyze more information, employees may encounter more automated forecasts, recommendations, summaries, and other analytical outputs.
That makes analytical judgment an important part of using these tools responsibly. A professional who understands the basics of data and analytics is in a better position to question an unexpected result, identify information that may be missing, and determine when additional analysis is needed.
AI can make certain parts of analytics faster, but it does not remove the need to understand the information being analyzed.
The Bottom Line
AI is changing how businesses work with data, but it has not made business analytics less important to understand.
Organizations can use AI to explore data, identify patterns, and support analytical work. People still need to evaluate those results and consider how they apply to a particular business decision.
For professionals who want to build those skills, Michigan State University’s AI & Business Analytics online certificates offer coursework covering both applied analytics and the use of AI in business contexts. (Michigan State University Online)
