How Data Analytics and Artificial Intelligence Are Shaping Business Strategy

When Kroger customers place pickup orders, store associates don’t simply follow a shopping list from aisle to aisle. AI software analyzes 200,000 totes per second to group orders and map more efficient routes through the store. The system has reduced the number of steps associates take by 10%, helping Kroger fulfill pickup orders more efficiently.¹

Kroger’s approach illustrates a much broader shift in how businesses use data to make decisions. Artificial intelligence and analytics can help organizations process vast amounts of information, identify patterns and problems, evaluate possible solutions, and make strategic recommendations supported by stronger evidence.

Those capabilities are quickly becoming important across the workforce. In 2026, Indeed reported that nearly 45% of data and analytics job postings included AI-related terms.² For professionals who can combine analytics expertise with business judgment, that shift creates opportunities to play a more influential role in shaping strategy.

This post examines how business analytics and artificial intelligence work together, how organizations are applying them to strategic decision-making, and which skills can help you use these technologies more effectively in your current role or advance into more sophisticated analytics work.

What Is AI Analytics?

AI analytics is a form of data analytics that uses artificial intelligence to process and interpret large volumes of data. It can help businesses uncover patterns, investigate problems, and evaluate potential outcomes more quickly than manual analysis alone.³

Traditional analytics often requires analysts to define queries, clean and reconcile data, select appropriate methods, build models, and create reports or visualizations. These processes remain essential, but they can be time-consuming and susceptible to human error.⁴

AI-supported tools can automate parts of that work. Machine learning algorithms, for example, can rapidly process historical data, detect patterns, and generate predictions.⁴ The technology does not eliminate the need for skilled analysts. People still need to assess data quality, ask the right questions, determine whether business assumptions are sound, and interpret results in context.

Consider inventory planning. A retailer can use AI to analyze historical data and predict demand for individual products during the holiday season. In healthcare, hospitals use predictive AI to anticipate patient needs and support decisions about staffing and other administrative functions.⁵ Hotels can use AI analytics to forecast occupancy and schedule housekeeping staff accordingly.⁶

These applications demonstrate why AI literacy increasingly matters beyond strictly technical roles. As AI becomes more deeply integrated into business operations, professionals across functions may need to evaluate AI-supported recommendations, understand their limitations, and determine when the evidence warrants action.

The Impact of Artificial Intelligence and Analytics on Decision-Making

Speed is one of the most significant advantages of artificial intelligence in analytics. Instead of relying solely on periodic reports, businesses can use real-time analytics to identify needed changes sooner and adjust their strategies before small problems become larger ones.⁷ If new packaging creates a bottleneck in a shipping process, for example, managers may be able to identify the slowdown and respond before hundreds of orders accumulate.

AI can also surface evidence that challenges human assumptions. One study published in Electronics found that AI-supported decision-making reduced confirmation bias by 82% and overconfidence bias by 78%.⁸ At the same time, AI systems can reflect biases in their training data, design, or implementation. Their outputs still require critical evaluation rather than automatic acceptance.

That combination of speed and human judgment can be particularly valuable in complex operations such as supply chains. At Albertsons, AI models predict how many shipments will arrive at stores each day, helping managers determine appropriate staffing levels. The company also uses AI to analyze supplier documents for last-minute delivery changes. When a shipment is delayed or arrives earlier than expected, managers can adjust staffing accordingly.⁹

Used effectively, artificial intelligence and analytics give business leaders more timely information on which to base decisions. The technology can strengthen decision-making, but its value ultimately depends on professionals who know how to interpret the evidence and apply it to the business problem at hand.

Transforming Business Strategy With Data and Artificial Intelligence

Artificial intelligence and analytics are influencing more than individual decisions. They are changing how organizations approach functions ranging from customer experience to financial modeling.

Many companies, for example, use machine learning to analyze customer behavior in real time and deliver more personalized recommendations. AI-powered chatbots can also anticipate why customers may be seeking assistance and help them find relevant answers more quickly.¹⁰ In financial modeling, tools such as Microsoft’s Agent Mode use natural language processing to help users build models and explore financial questions.¹¹

For organizations, however, adopting AI is not simply a matter of adding new technology. Developing an AI-driven strategic framework can involve several steps, including:¹²

• Researching available AI tools and their potential uses
• Building datasets that combine relevant internal and external information
• Assembling teams with expertise in areas such as data science and business analytics
• Developing hypotheses and testing them with AI tools
• Using the resulting insights to inform business decisions

Responsible use must be part of that strategy. Professionals who select AI tools, interpret their outputs, or present AI-supported recommendations need to understand how data was collected, protect sensitive information, recognize and document important limitations, and be transparent about AI’s role in the analysis.¹³

The strategic advantage, then, does not come from AI alone. It comes from an organization’s ability to combine powerful analytical tools with reliable data, relevant expertise, responsible practices, and sound business judgment.

Preparing for a Future in AI and Data Analytics

As AI becomes more common in business analytics, technical ability alone will not be enough to use these tools effectively. Professionals will also need the judgment, communication skills, and business perspective to evaluate AI-generated insights and translate them into sound business decisions. Important capabilities include:¹⁴

• AI literacy
• Collaboration
• Continuous learning
• Critical thinking
• Data analysis
• Problem-solving
• Prompt engineering

Graduate education in business analytics can help professionals develop this combination of technical and strategic skills while keeping pace with rapidly evolving tools and practices. These capabilities can support careers in areas such as AI consulting, business intelligence analysis, and machine learning engineering.¹⁵

Turn Analytics Expertise Into Strategic Impact

Human judgment remains essential to business strategy, even as AI and data analytics become more powerful. Organizations need professionals who can do more than use sophisticated tools. They need people who can determine which questions matter, interpret results in a business context, communicate insights clearly, and apply data responsibly.

Santa Clara University’s Online Master of Science in Business Analytics program is designed to develop that combination of technical expertise and strategic thinking. The curriculum includes hands-on work with SQL, Python, R, machine learning, cloud computing, and data visualization, along with business acumen, ethical leadership, problem-solving, and communication skills. Students can also choose business-focused or technically oriented electives to shape the degree around their professional goals.

The flexible online program can be completed in as few as 15 months and includes an immersive three-day residency on SCU’s Silicon Valley campus. Students build connections with faculty, classmates, alumni, and professionals working at companies such as Google, Apple, Cisco, and LinkedIn.

If you want to strengthen your ability to turn data into consequential business decisions, explore the Online MSBA curriculum and admissions requirements. To learn more about the program and how it could support your goals, schedule a call with an admissions outreach advisor.