We at CHI Software are exploring AI from various perspectives, applying it in numerous domains. In this article, we look closely at AI-based decision-making.
In a Gartner survey,65 percent of respondentsagreed that their decisions now are more complex (involving more options and participants) than they were two years ago. Businesses have to keep up with the high pace of changing market conditions, but they struggle todoit effectively.
Read on to learnhow and when to apply advanced AI decision-making. We’ll guide you through basic terms and step-by-step integration instructions. Follow along!
What Is AI Decision-Making?
Let’s start with the essentials. AI decision-making is data processing completed fully or partially with the help of artificial intelligence algorithms to improve business process efficiency.
Today,company workers and tech innovations happily coexist in one environment:
● Decision support. Algorithms provide raw analytical insights by gathering big data sets in a meaningful form, while business employees make final decisions based on their experience, common sense, and market knowledge.
● Decision augmentation. In this form of interaction, the AI system can offer decision variations based on gathered analytics. Employee experience is less crucial to the final decision, so responsibility can be partially shared with the machine.
● Decision automation. AI almost or fully takes over everyday routine tasks and frees up employees’ time for more human-involved activities. It allows formaximum consistency in decision-making.
How Artificial Intelligence Helps in Decision-Making
It’s been years since companies started using the combination of artificial intelligence and decision-making in their daily activities. But how exactly does it look? Let’s review some use cases that are applicable to any industry.
1. Customer Relationship Management
AI algorithms can follow the user’s browsing history and draw up the client’s persona. Technologies monitor different aspects of the user’s behavior simultaneously and provide a full overview of the changing customer preferences.
2. Personalized Recommendations
A recommender system is a solution that follows the user’s history of viewed items and recommends similar products afterward. Such a personalized approach makes people stay on your website a little longer and turns them into your loyal customers later on.
3. Opinion Mining
Smart algorithms gather reviews and opinions from all sources and identify trends (e.g., what users mentioned most often). It would barely be possible with human-only efforts.
To havetechnologies solve issues directly, companies from various fields use expert systems. These are AI-based tools that replicate human-like problem solving.
5. Pricing Strategy
Smart technologies not only analyze vast amounts of data rapidly, but they also capture price relations that humans may not notice. A price change for some products, for example, may impact the sales of several other items.
6. Business Expansion
To decide where to open another branch office, AI analyzes the company’s growth, customer portrait and behavior, and alist of locations. It then offers location variations that are likely to bring the maximum ROI.
How Do You Integrate AI Decision-Making into Your Business?
All those examples of decision-making applications in artificial intelligence do sound wonderful, but what about your own business? The time for action has come.
1. Outline the most troublesome areas
What disturbs you the most needs all of your attention. You should focus on one or two issues to start with. Define where your company experiences the biggest challenges and start there.
2. Evaluate your options
The next question to ask yourself is who will do all the work? You should keep your budgeting and management capabilities in mind and act accordingly: buy a ready-made solution or build one yourself.
3. Prepare your data
We have a full guide on unstructured data management to help you get through data preparation, storing, and cleaning (checking spelling, analyzing formal and informal abbreviations, removing HTML tags, etc.).
4. Start with your goal in mind
Starting small will show you how close you get to your goal and protect you from high organizational risks. If the first movement is successful, you can gradually expand your efforts and cover the problematic issue in full.
5. Review the results
Think about the metrics that measure your AI achievements. Did you want to correct your pricing strategy? Check how sales numbers change. Did you want to improve your relationships with customers? Check the ratio of positive reviews to negative ones.
What else is there? We includeway more details and insights on artificial intelligence and decision-making in our original article. Follow this link to continue your reading.