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Artificial intelligence (AI) seems like it’s everywhere you look these days: Your favorite email client wants to write your emails for you, your phone wants to save you the tedious work of actually reading your text messages, and your refrigerator wants to tell you what to cook with the ingredients it sees inside. Those are just three of the splashy consumer-facing AI use cases picked at random — there are plenty more.
While we could debate how truly useful some of those marketing-friendly consumer-facing features are, there’s no debate that AI is establishing itself as a powerful business technology, especially in the realm of data analytics or business intelligence (BI). Business applications of AI are decidedly less splashy but much more impactful, delivering significant advantages to businesses here in the real world.
In particular, the area of business analytics (BA) is increasingly relying on AI technologies to sort through vast troves of business data and enable better data-driven decision-making.
What Is Artificial Intelligence in Business Analytics?
Understanding AI’s place in business analytics starts with defining AI — something that’s not exactly simple to do. “Artificial intelligence” is a broad term for a family of technologies that enable computers to make human-like decisions, rather than just follow a prescribed (programmed) path. There are numerous types of AIs — generative AI and its large language models (LLMs) are at the center of all those consumer-facing tools and toys — and several different technologies fit under the “AI umbrella,” including machine learning (ML), natural language processing (NLP), and more.
So, what does all this have to do with business analytics? Before, analytics relied on a combination of two elements. One was human agents, who can make nuanced decisions and work from context but have a hard limit on speed and capacity. The other was “dumb” software tools: these could process huge stores of data much faster than humans, but they could only follow the instructions they were given. They couldn’t “think” in the way we think of thinking.
In broad terms, AI tools merge these two abilities: they retain the speed and capacity of machines and gain the ability to do something approximating reasoning, making contextual decisions and working autonomously within whatever guidelines an organization sets up.
With AI-powered BI, organizations gain the ability to process more data and extract more detailed, nuanced insights from that data, including the sorts of insights that traditional BI delivers and often unexpected insights that legacy methods wouldn’t have found.
Top Benefits AI Delivers in Business Analytics
AI stands to deliver numerous benefits for organizations looking to improve their business analytics efforts. These are a few of them:
- Better decision-making: AI can mine data for insights supporting business decisions, including real-time insights and predictive analytics.
- Efficiency and scalability: AI-powered BA tools work more intelligently than legacy tools, increasing the efficiency of their output. Plus, these tools are nearly infinitely scalable, limited only by your compute power.
- Expanded segmentation and personalization: With more raw power and more intelligence, AI-powered BA tools allow for analysis of smaller and smaller segments of data (e.g., data tied to specific demographics, regions, products, services, etc.).
- Cost savings: The cost of manually keeping up with what AI can do isn’t just astronomical; it’s impossible.
Of course, none of these benefits happens automatically. AI-driven BA systems still need to be configured properly, and AI can’t replace all human decision-making: organizations still need to chart a course and decide on plans of action. But when used properly, AI can support those decision-makers and course-charters with data and insights.
AI in Business Analytics: 6 Key Applications
Forward-thinking businesses are using AI to expand their capabilities in six key areas of analytics.
1. Predictive Analytics
Predictive analytics looks at current performance data, historical data, market trends, and other factors to project out future performance. It can give organizations a reasonable estimate of what outcomes they should expect from various courses of action (status quo, making changes, introducing new products/features, etc.).
Note that predictive analytics is just that: predictive. AI isn’t all-knowing (at least yet), and while it can do an effective job at showing where the data says you’re heading, future outcomes (both good and bad) aren’t guaranteed.
2. Prescriptive Analytics
Prescriptive analytics evaluates the data feeding into a specific area, element, or segment and suggests (or prescribes) actionable recommendations based on the data it ingests.
This is, essentially, your phone’s mapping app rerouting you around an accident: it (rather, its cloud servers) sees new data (the route ahead is blocked) and prescribes a new, better way to your intended goal. (Whether your map app is actually using AI or just pulling from a massive big-tech database is a separate discussion.)
This is the kind of work we humans do every day: we look at the information and variables available to us and decide what’s the next best step. But we can only take in so many points of data. Prescriptive analytics helps us do this at a scale far beyond what humans do in everyday decision-making.
3. Customer Analytics
Analyzing customer behavior and customer data is another area where AI-powered BA tools can help a business reach its goals. This type of analytics can map out customer behaviors and preferences, helping businesses stay current on customer trends and changes in the market.
4. Risk Management
Businesses can apply analytics to risk management as well, identifying what the data has to say about risks, risk likelihood, risk impacts, mitigation, and more.
For example, an AI-driven BA tool might identify anomalies in user behavior faster than legacy methods. Those quirks in user behavior could be related to a known or unidentified risk, helping organizations see what’s coming that much earlier.
5. Operational Analytics
No business process is perfect. Chances are, no business process will ever be completely perfect. There’s always room for operational improvement, and analytics can help here as well. AI-driven analytics can identify operational flaws that have otherwise gone undetected. Some solutions may even be able to help engineer the right answer to those operational challenges.
6. Sentiment Analysis
Sentiment analysis uses natural language processing (NLP) to analyze conversational text (unstructured data) like customer service chats, emails, surveys, and even transcriptions of phone calls, for user sentiment. This growing branch of analytics helps identify not just what is happening in a given conversation, but how users and customers are feeling about what is happening.
Think about a beloved local business near you. Chances are, it is beloved because the owner or manager understands and responds to the way customers are feeling. This is small-scale customer service success and doing it at a global scale just isn’t feasible.
Sentiment analysis is a tech-infused way of scaling up what that local store manager is doing, giving organizations the ability to understand how customers are feeling (and identify any issues leading to those negative feelings).
Current Challenges and Limitations
AI in business analytics offers a lot of promise, but it hasn’t quite ushered in business utopia. Organizations face many challenges in integrating AI into their BA/BI efforts, including these:
- Data concerns: Where do businesses get data? Is it the right data? Is it entirely relevant? Is it clean (i.e., formatted properly and free from extraneous information)? Data concerns like these can limit a business’s ability to execute BA or to trust BA insights/results.
- Technical complexity and personnel availability: Complex systems require sufficient staffing with the right set of skills. Such positions are in high demand, so talent acquisition remains difficult. And without the right skilled team members, the technical complexity of AI and BA may limit effectiveness.
- Cost: While AI tools promise to reduce costs in the long term, initial investment can be significant. Often, legacy infrastructure isn’t sufficient for new computing needs, requiring investment in infrastructure, the tools themselves, implementation, and training.
- Ethical concerns: AI systems are limited by the quality — and ethical soundness — of the data they consume (and of their programming). Bias in AI systems is real and can be detrimental to a company both in terms of image and outcomes.
Sample Use Cases
Here are a few ways various industries are leveraging AI in their analytics.
Retail
In the world of retail, large retailers are using AI-driven analytics to predict customer demand, understand customer sentiment and what’s driving that sentiment, and optimize their inventory and distribution models.
Finance
AI-powered tools are reshaping the way firms approach fraud detection and credit risk analysis (though the latter is a clear example of where training data bias could lead to undesirable outcomes).
Healthcare
Healthcare organizations are using AI to find operational weaknesses and look for trends in patient care, such as procedures or methods of care with comparatively high or low rates of desired outcomes.
Marketing
Marketing teams are diving deeper into customer segmentation and marketing personalization thanks to AI. They’re also using analytics to optimize campaigns, prioritizing elements that perform better in testing, for example.
Next Steps in Adopting AI in Business Analytics
If your organization is looking to adopt new approaches to BA or to expand your current use of AI in analytics, follow these next steps.
- Assess current capabilities: What are you currently able to do with analytics? Where are your analytics weak or insufficient?
- Determine your goals: AI for AI’s sake is how we got AI-powered toasters. (Maybe.) Instead, devise a set of goals, the outcomes you want to see achieved by your AI integration.
- Choose your weapons: The market has no shortage of analytics tools; identify and invest in the ones that make the most sense for your business, goals, and needed capabilities.
- Invest in training: Train and upskill current employees so they know how to effectively use and manage new systems.
- Start small: Identify smaller areas of analytics where you can afford to test and iterate. Once your implementation is stable and successful, scale to other areas over time.
