How To Use AI in Decision Making

Advancements in artificial intelligence (AI) can help with the decision making process by evaluating data and variables in complex situations. This enables companies and organizations to make faster, more well-informed decisions than when humans tackle problems without assistance.

However, AI cannot use human wisdom and discernment, so some level of personal human involvement is still important. The purpose of integrating AI in decision making is not complete automation. Rather, the goal is to help humans make quicker and better decisions through streamlined processes and effective use of data.

In this article, we’ll dig deeper into the role AI can play in the decision making process. We’ll make you aware of some of the most common challenges individuals and companies face when they incorporate AI into their business decision making and problem-solving. 

Finally, we’ll share several real-world use cases and benefits of using AI in your decision making process, which should help you envision what AI's capabilities could do for your team or business.

How AI can improve decision making

AI can play a significant role in data-driven decision making, providing benefits such as:

• Enhancing accuracy. AI can use advanced algorithms and data science and analysis to provide accurate and objective insights repeatably, reducing the likelihood of human error and bias.
• Speeding up decision making. AI can process vast amounts of data at incredible speeds, enabling quick analysis and generating insights in real time. This ultimately leads to faster and more efficient decision making processes, especially when you’re able to incorporate automation in many components of the process.
• Improving efficiency. AI automates time-consuming and repetitive tasks in decision making processes, freeing up valuable human resources to focus on more complex and strategic aspects.
• Enhancing risk assessment and mitigation. AI can assess and analyze various risk factors, helping decision makers identify potential risks and devise effective mitigation strategies.
• Offering data-driven insights. AI leverages large data sets to uncover patterns, trends, and correlations that may go unnoticed by humans. Understanding data can be a complicated endeavor, but incorporating the computer science of AI into your analysis can simplify the process.

Challenges of using AI in decision making

While the help of AI and large language models is rapidly transforming the decision making process for many data scientists, some challenges must still be addressed. Be aware of the following issues that may arise:

• Data quality and reliability. AI heavily relies on high-quality and reliable data for decision making. Challenges arise when dealing with incomplete, inaccurate, or biased data, which can lead to flawed insights and decisions or false outputs, known as hallucinations.
• Lack of human understanding and context. AI applies complex algorithms to inputs and data to produce outputs that its algorithms predict from its training data. However, it lacks the nuances of human understanding and can’t grasp the context and subjective factors that play a role in decision making. This means some level of human intervention is always necessary.
• Ethical concerns. AI decision making systems can raise ethical concerns, particularly when it comes to issues such as privacy, fairness, transparency, and accountability.
• Interpretability and explainability. Some AI algorithms, such as deep learning neural networks, can be highly complex and difficult to interpret or explain. This lack of interpretability because of the black box these data systems work from may hinder trust in AI systems, particularly when making critical decisions.
• Overreliance and decision bias. Blindly relying on AI systems for decision making without proper human oversight can lead to overreliance and potential biases. The right balance must be struck between human judgment and AI assistance to avoid undue reliance or abdication of responsibility.
• Developing unwarranted trust. Unwarranted trust (or incorrect levels of trust) in AI machines can happen when we mistake complex outputs for intelligence and intent.

How is AI being used in decision making?

Below we’ll discuss a few ways that individuals and organizations are already using AI in decision making. As you consider these applications, you’ll learn how your management team could incorporate AI methods and solutions in the way that you make decisions.

Predictive analytics

Predictive analytics is a powerful AI-driven technique that analyzes historical data, identifies patterns, and forecasts future outcomes. By leveraging this technology, businesses can make data-informed decisions and gain a competitive edge. 

Here are some of the ways companies can use predictive analytics to enhance their decision making processes:

• Sales and marketing optimization. Businesses can forecast customer demand, identify potential leads, and personalize marketing campaigns based on predicted consumer behavior.
• Supply chain management. By predicting demand fluctuations and potential bottlenecks, companies can anticipate supply chain disruptions, optimize inventory levels, and improve logistics.
• Risk assessment and fraud detection. Financial institutions and insurance companies can evaluate credit risks, detect fraudulent activities, and make informed underwriting decisions using predictive models.
• Predictive maintenance. Manufacturing and industrial sectors can schedule equipment maintenance proactively by analyzing performance data and predicting potential failures before they occur.
• Human resources and talent management. Based on historical data and performance metrics, organizations can predict employee turnover, identify high-potential candidates, and optimize workforce planning.
• Health care outcomes. By analyzing patient data and treatment outcomes, medical professionals can more effectively predict patient risks, recommend personalized treatment plans, and allocate resources.
• Financial forecasting. Businesses can more accurately project future revenue, expenses, and cash flow, enabling better budgeting and strategic planning decisions.

By incorporating these predictive analytics applications, companies can make more informed decisions, reduce risks, and capitalize on emerging opportunities across various aspects of their operations.

Risk assessment and mitigation

Risk assessment is a critical process for companies to identify and protect against potential threats. Artificial Intelligence (AI) has revolutionized this field by enabling more comprehensive and accurate risk analysis. 

Here's how AI enhances risk assessment and management:

• Data analysis and anomaly detection. AI algorithms can quickly analyze large sets of complex data, detecting anomalies and patterns that humans might miss, thus improving the accuracy and completeness of risk assessments.
• Predictive threat identification. By processing historical and real-time data, AI can help companies predict future threats and plan appropriate responses, enhancing proactive risk management strategies.
• Credit risk evaluation. Financial institutions can use AI to assess credit risks more accurately, considering a wider range of factors and data points than traditional methods.
• Cybersecurity threat analysis. AI-powered tools can continuously monitor network activities, identifying potential security threats and vulnerabilities in real-time.
• Fraud prevention in banking. Banks can leverage AI for detecting and preventing fraudulent activities by analyzing transaction patterns and customer behaviors.
• Healthcare risk management. AI can be applied in healthcare systems for patient-specific disease prevention and community epidemic risk assessment.
• Support for decision makers. Risk managers and auditors can use AI tools to evaluate risks, identify vulnerabilities, and devise effective mitigation strategies based on a broader range of available data.

By incorporating AI into risk assessment processes, companies can enhance their ability to identify, evaluate, and mitigate potential risks, leading to more robust and effective risk management strategies.

Natural language processing (NLP)

Natural language processing (NLP) refers to a computer’s ability to automatically analyze and process language in a conversational manner. 

Conversational chatbots such as ChatGPT use NLP to analyze human prompts and questions to produce a coherent response. NLP techniques enable AI systems to analyze human language in ways that facilitate decision making processes that involve text data, such as sentiment analysis, contract review, or customer feedback analysis.

Here are a few of the main ways NLP can help with decision making:

• Sentiment analysis. NLP can provide insight into the sentiment (or emotional tone) of textual documents and data in addition to analyzing the actual information presented.
• Text classification. NLP can sort text into predefined labels or classes. This can help you organize large amounts of data into preset categories, making the information easier to understand and utilize.
• Information extraction. By extracting relevant information, you can better identify trends and patterns during the decision making process.
• Summarization. NLP can help you condense long documents into summaries so that you can have the relevant information without going through all the material yourself.

Many marketing organizations use AI to manage programs across channels to optimize revenue. Individuals can use these generative AI tools for wide-ranging decision making in activities such as planning trips, determining who to vote for, or simply creating menus from available ingredients.

Decision support systems

AI-powered decision support systems assist decision makers by providing relevant information, data analysis, and insights in real time. These systems empower well-informed decisions across various domains. 

Here's how they function and their applications:

• Real-time data processing. These systems use machine learning models and operational data to develop insights and access real-time information, enabling quick and consistent data analysis.
• Domain-specific applications. Decision support systems can be tailored for various fields, including healthcare, logistics, and supply chain management.
• Data validation. While these systems offer powerful insights, critical thinking is necessary to ensure the data being used is accurate and trustworthy.
• Continuous learning. AI-powered decision support systems can adapt and improve their recommendations based on new data and outcomes.

When implementing these systems, it's crucial to verify the data sources and understand how the system uses available information to validate its conclusions.

Recommender systems

AI-based recommender systems analyze user preferences, historical behavior, and contextual data to provide personalized recommendations. These systems leverage big data to offer insights that companies might not identify on their own. 

Here's how recommender systems contribute to decision making:

• Personalized product recommendations. By analyzing past purchases and user behavior, these systems can suggest products likely to interest specific customers.
• Content suggestions. In media and entertainment, recommender systems help users discover new content based on their viewing history and preferences.
• Targeted marketing campaigns. Marketers can use these systems to deliver personalized advertisements tailored to individual user tastes and interests.
• User experience enhancement. Recommender systems, like those used by Netflix, aim to improve user satisfaction by reducing decision fatigue and suggesting relevant content.
• Customer insights. These systems provide valuable data on customer preferences and behaviors, informing broader business strategies and decision making.

By leveraging recommender systems, businesses can make data-driven decisions to improve customer satisfaction, increase engagement, and drive sales.

Optimization and resource allocation

AI optimization algorithms enable decision makers to allocate resources efficiently, optimize processes, and solve complex problems. This technology has revolutionized various aspects of business operations. 

Here are key ways AI enhances optimization and resource allocation:

• Workforce scheduling. AI can analyze employee availability, skills, and performance to create optimal work schedules, improving productivity and employee satisfaction.
• Supply chain optimization. AI algorithms can streamline supply chain operations by predicting demand, optimizing inventory levels, and improving supplier selection.
• Route planning. Supply chain managers use AI to create the most efficient routes for drivers, considering factors like traffic and consumer demand to minimize costs and delivery times.
• Resource utilization analysis. AI quickly analyzes resource availability and utilization, identifying potential bottlenecks and ensuring team members focus on high-priority tasks.
• Process optimization. By analyzing complex data sets, AI can identify inefficiencies in business processes and suggest improvements for better performance.

By leveraging AI for optimization and resource allocation, businesses can significantly improve operational efficiency and decision making across various domains.

Fraud detection and prevention

AI algorithms have transformed fraud detection and prevention efforts by analyzing large volumes of data to identify anomalies and patterns associated with fraudulent activities. 

Here's how AI assists decision makers in this important area:

• Real-time transaction analysis. AI systems can process billions of transactions in real-time, quickly flagging suspicious activities for further investigation.
• Pattern recognition. Machine learning algorithms can identify complex patterns of fraudulent behavior that might be impossible for humans to detect manually.
• Anomaly detection. AI can spot unusual activities or transactions that deviate from normal patterns, potentially indicating fraud.
• Predictive modeling. By analyzing historical data, AI can predict potential future fraud attempts, allowing for proactive prevention measures.
• Adaptive learning. AI fraud detection systems continuously learn from new data, improving their accuracy and effectiveness over time.

For example, American Express developed an AI-based system that analyzes billions of transactions in real-time to identify patterns of fraudulent activity, significantly enhancing their fraud prevention capabilities.

Cognitive decision making

AI technologies, such as cognitive computing and machine learning, facilitate decision making processes by analyzing vast amounts of data, recognizing patterns, and recommending optimal solutions. Here's how cognitive decision making systems enhance various fields:

• Medical diagnosis. AI can analyze patient data, medical images, and research to assist doctors in making more accurate diagnoses and treatment plans.
• Strategic planning. In business, AI can process market trends, competitor data, and internal metrics to suggest strategic directions and initiatives.
• Risk assessment. Cognitive systems can evaluate complex risk factors in finance, insurance, and other industries to support more informed decision making.
• Customer service. AI-powered cognitive systems can analyze customer interactions to provide personalized support and recommendations.
• Research and development. In scientific fields, AI can process vast amounts of data to identify patterns and suggest new avenues for research.

While these AI-driven insights are valuable, it's crucial to remember that they should inform rather than replace human decision making. Decision makers should use their judgment to evaluate AI-generated recommendations, checking for potential errors or biases in the data or algorithms.

Examples of AI in decision making

Let’s introduce a few prominent companies already using AI to help with their decision making:

• Google uses a deep learning system to better understand search prompts and provide personalized results.
• IBM optimized its decision making to solve complex problems in a fraction of the time it once required. This innovation has saved customers significant time and money.
• Microsoft believes AI can help individuals tackle life’s biggest challenges with ease. Their philosophy is that AI can provide people with a wider range of information, but humans ultimately must make the decisions.
• Deloitte is working on creating automated processes that improve human decision making by predicting and simulating future outcomes.
• Salesforce incorporates AI to gain further insight into customer behavior and buying patterns. The company has improved its decision making by forecasting sales trends, which enables them to quickly respond to an ever-changing market.

Harness the decision making power of AI with Upwork

Involving AI in your decision making can streamline your operations and improve the quality of your decisions. However, you can’t (and shouldn’t) remove the human element from the decision making process. Keep in mind that AI models can help with the process, but AI applications are not a mechanism that can fully replicate or replace the need for human insight and discernment.

Perhaps you’d like support from a talented professional with experience in AI who can help you with your next project. If you’re looking for the best AI engineers, check out Upwork’s talent database. You can also find open AI jobs on the platform if you’re looking to put your AI skills to use.