AI in Decision Making

AI in Decision Making

What is Artificial Intelligence ?

Artificial Intelligence is the branch of computer science which focuses on building machines and algorithms which automate the tasks manually done by humans. This field focuses on providing human intelligence to machines. AI focuses on decision making and problem solving without involvement of human errors and hence, attempts to achieve the highest possible accurate result. In the current era, AI can be classified into 3 levels:

=> Assisted Intelligence :

Enables basic task automation

=> Augmented Intelligence :

This covers the combined effort of evaluation by humans and machines. Machines learn from human input whereas humans need to make optimal decisions based on the system generated information.

=> Automation Intelligence :

This enables complete automation with machines devoid of human involvement. Self driving cars are a great example for this category.

What is AI Decision Making ?

Decision making is a core part of modern management. A decision can be defined as a course of action purposely chosen from a set of alternatives to achieve managerial or organisational goals. Decision Making by an AI is the idea where AI itself makes the decision without involvement of human input either completely or partially and decides the best possible outcome to achieve desired state. AI can handle data crunching, anomaly detection, trend analysis, recommendation systems and many such tasks. The output of AI based decisions is either handled by humans or is completely automated to make the final judgement. The degrees of decision making involved with AI can be categorised into three parts :

=> Decision Support :

With the combined effort of predictive and diagnostic analysis of AI along with the common sense and expertise based insights of humans, we can generate the best outcome for AI in businesses

=> Decision Augmentation :

The synergy of AI based predictive analysis and human knowledge leads to rapid examination of large datasets to recommend multiple decisions.

=> Decision Automation :

In this process, humans rely completely on system based analysis and take advantage of the speed, consistency and reliability of the system.

AI Decision Making Factors.

We decide the category of the current decision making process based on the two most important factors: Time Limit & Complexity

The Cynefin framework aims to help leaders understand the circumstances and uniqueness of a given situation while making a decision. From placing an order for a stock which is a decision taken in milliseconds to yearly decisions regarding strategic acquisitions and merger of an organisation, decision making varies a lot in the context of time and circumstances. Based on this framework, there are 4 major kind of situations which determine the variability in the decision making process

=> Obvious situation :

Easy to predict and based on the simple Cause-Effect pattern.

=> Complicated situation :

Requires analysis to determine the Cause-Effect pattern.

=> Complex situation :

Requires a holistic and systematic approach because of multiple interdependencies and relationships among the determining factors. It may also require simulation to evaluate and analyse the impact of the decision.

=> Chaotic situation :

Requires more practical learning and experimentation. These situations have inter-dependent dynamics with vague interpretations. It also needs to account for events like natural calamities or stock market crashes in the evaluation process.

Applications of AI in Decision Making

=> AI in business decisions

Businesses put augmented intelligence into action which provides incredible insights and revolutionary models as the basis of the decision making process. This enables the organisations to make faster and more accurate decisions. We have developed many systems which provide great impact on the business organisations like :
○ Smart Weather Forecasting
○ Automating Recruitment processes
○ Personalising marketing and sales
○ Dynamic Pricing strategies

=> Marketing decisions

Customer driven market complexities are handled using AI by performing customer analysis which takes into account the needs and desires of the customer. Customer Analysis is the amalgamation of qualitative and quantitative research methods with the goal of better comprehension of the customer base. It focuses on the following stage of approach :
1. Identification of the customers based on traits like age, gender, location and other demographics.
2. Observing their requirements and their preferences
3. Grouping them based on similar traits and behaviours
4. Keeping a profile of ideal customers.

Advantages of Customer Analysis:
1. Lower Customer Acquisition Costs
2. Improved Customer Retention
3. Effective Customer Service
4. Increased sales and profits

Users are recommended similar and related items based on their preferences via the services. They are also recommended offers which may be useful for them. This approach leads to more pleasant user experience and helps in generating additional revenue.

Recommendation systems

Recommendation systems use decision making based on deep learning to recommend users similar content based on many different factors. They take into account user’s preferences, past searches and other such features to fetch the best possible recommendations. Benefits of Recommendation systems are :
○ Helps user find items of interest
○ Personalised content
○ Improve user engagement
○ Identify relevance of product to users

Customer Relationship Management

AI has improvised in the CRM field by automating simple tasks like :
○ Contact management
○ Data analysis
○ Data recording
○ Lead ranking
○ Predict customer’s lifetime value
○ Feedback analysis
○ Social Computing
○ Opinion Mining

With the support of AI, retailers can control and monitor the market and hence, respond to demands more accurately.

Advantages : AI and Decision Making

As a custom ai development company, we assist businesses in enhancing the decision making processes of businesses. Improved understanding of customer base for targeted marketing campaigns. Better decision making of companies dealing with complex or chaotic situations( with reference to Cynefin framework). Complete utilisation of all data parameters in the decision making process. Unbiased decision making and predictive analysis