In the dynamically changing business environment, management staff faces increasing challenges related to decision-making. The growing amount of data, the complexity of business processes, and the pressure to respond quickly require modern decision-support tools. Artificial intelligence (AI) offers solutions that can significantly support company management. This article will discuss how AI can support management staff in decision-making.

1. Definition and Scope of AI-Assisted Decision-Making

Explaining Decision Support Systems (DSS)

Decision Support Systems (DSS) use AI techniques to analyze data, identify trends, and provide recommendations to decision-makers. DSS can support various levels of decision-making, from strategic to operational, and encompass both simple and complex business processes.

“Since the late 1980s, “intelligent” DSS (IDSS) has been developing in various branches of the so-called high-risk industry, medicine, and for threat management, using artificial intelligence technologies, expert systems, and operational and cognitive modeling of decision-making processes. The purpose of these systems is to replace or support complex but already well-defined reasoning functions.”

Scope of AI application in decision-making

AI can be used in many areas of business management, such as market analysis, sales forecasting, inventory management, production process optimization, risk management, and many others.

Using AI to support executives is not a new trend. In 2022, Gartner predicted that by 2024, more than a third of large organizations would implement Decision Intelligence, a DDS variant involving using artificial intelligence (AI) to use data to help companies make business decisions—or even make them for them.

2. Types of DSS Systems

Expert Systems

Expert systems use knowledge bases and logical rules to provide recommendations. They are instrumental in domains with much historical data and experience.

Model-Based Systems

Model-based systems use mathematical and statistical models to predict outcomes and make recommendations. They are effective in analyzing complex relationships between data.

Data-Driven Systems

Data-driven systems analyze large data sets to identify patterns and trends. They are useful when rapid analysis of large amounts of information is needed.

Hybrid Systems

Hybrid systems combine AI techniques like machine learning and natural language processing to provide comprehensive decision support.

3. Examples of AI applications supporting decision-making

3.1. Predictive Analytics

Sales Forecasting

AI can analyze historical sales data, market trends, and macroeconomic factors to predict future sales results. This allows companies to plan marketing strategies better and manage inventory.

Inventory Management

AI systems can optimize inventory levels by predicting demand and minimizing the risk of stockouts or overstocks.

3.2. Process Optimization

Supply Chain Management

AI can analyze supplier, production, and logistics data to identify areas for optimization. This can lead to faster delivery times and reduced costs.

Human Resources Management

AI systems can analyze HR data such as employee performance, turnover, and training to support workforce management decisions.

3.3. Risk Analysis

Operational Risk Management

AI can analyze historical and current data to identify high-risk areas and propose strategies to manage them.

Credit Risk Management

AI systems can analyze customer financial and market data to assess credit risk and make lending decisions.

4. Benefits of using AI in decision-making

Major consulting firms agree that, with proper planning and implementation, the potential for profits from using AI to support decision-making processes can be huge.

In one of its reports, PwC emphasizes that artificial intelligence can increase productivity and contribute to $15.7 trillion in profits globally by 2030.

Meanwhile, according to a McKinsey study, companies that use AI-powered analytics see productivity increases of up to 25%. This is not surprising, as AI systems process massive data sets at unprecedented speeds, uncovering insights that were once out of reach.

Some examples of companies that have increased their profits in this way include:

• Aeon Co., a Japanese retail giant that implemented AI-powered demand forecasting and reduced food waste by 20% (according to MIT Sloan Management Review),
• Amazon, which uses machine learning algorithms to personalize recommendations, achieved a 35% increased sales in some categories (according to Forbes).

The main benefits of implementing AI in a company as a decision-making support include:

4.1. Speed ​​and accuracy

AI can process vast amounts of data in a fraction of a second, providing fast and accurate analyses that support real-time decisions. This allows for more flexible and effective company management.

4.2. Objectivity

AI systems are free from subjective biases and emotions, which ensures an objective approach to data analysis and decision-making. This reduces the risk of human errors and increases the reliability of the decision-making process.

4.3. Scalability

AI can scale with the company’s growth, providing decision-making support at various levels of management and across multiple business areas. This allows for flexible adaptation to the changing needs of the company.

4.4. Cost reduction

Automating decision-making processes can significantly reduce operating costs and increase the company’s efficiency. AI can replace some tasks humans perform, saving time and resources.

5. Challenges and constraints in implementing AI as a decision-making support

5.1. Trust in AI

Management must have trust in AI systems, which requires proper training and understanding of how these systems work. Decision-makers need to understand how AI processes data and what its limitations are.

5.2. Ethics and privacy

The use of AI in decision-making raises significant ethical and data privacy challenges. Companies must ensure that AI systems comply with applicable laws, such as the General Data Protection Regulation (GDPR) in the European Union.

This includes:

• transparency – users and stakeholders must understand how AI processes their data and what decisions are made based on it,
• consent – companies must obtain informed consent to process personal data,
• data minimization – only collect data that is necessary to achieve the AI’s goals,
• data security – it is necessary to ensure that appropriate security measures are in place to protect data from unauthorized access.

5.3. Integration with existing systems

Integrating AI with current IT systems can be a technical challenge and require significant investment.

In this regard, companies must:

• Identify interfaces – Determine how AI can communicate with existing systems,
• Ensure compatibility – Make sure data can be transferred between systems without losing quality,
• Migrate data – Transfer existing data to new AI systems,
• Train staff – Train employees to use the new systems.

6. How to implement AI as a support for decision-making in a company

6.1. Analysis of needs and priorities

To effectively implement AI in decision-making, companies should:

• identify areas for optimization – determine which business processes can benefit from AI support,
• assess available data – check what data is available and whether it is sufficient for AI implementation,
• define goals and KPIs – determine what performance indicators (KPIs) will be measured after AI implementation and what level of ROI should be achieved by implementing AI.

6.2. Selecting the right technologies

According to Gartner’s Top Strategic Technology Trends for 2023 study, 65% of CIOs are uncertain which AI-based DSS will best meet their organization’s goals. The report emphasizes the importance of aligning technology choices with business strategy.

Choosing the right AI technologies requires:

• Business Needs Analysis – Understanding what features and capabilities are needed in a specific case,
• Comparing Available Solutions – Assessing Different AI Systems for Their Performance, Scalability, and Cost,
• Consulting with Experts – Using the Opinion of AI Specialists to Choose the Best Solution.

6.3. Management training

Proper management training is critical to the success of AI implementation. It should include:

• understanding technology – employees should have a basic understanding of AI and its applications,
• development of analytical skills – management should be able to interpret the results of AI analyses and make decisions based on them,
• change management – employees should be prepared for changes related to AI implementation.

Summary and our recommendations

In summary, implementing AI in decision-making can bring many benefits, such as speed, accuracy, objectivity, and cost reduction.

A PwC study found that 61% of executives believe incorporating AI into their daily work is essential to staying competitive. In a rapidly changing competitive landscape, understanding the definitions and fundamental concepts of AI and ML is a strategic imperative for companies looking to thrive in a data-driven future. The statistics back this up – 73% of companies currently invest in AI to increase productivity (source: McKinsey).

Artificial intelligence is becoming an increasingly important tool supporting management in decision-making.

Although implementing AI is associated with specific challenges, proper planning and preparation can benefit the company significantly.

We encourage you to explore the topic of AI in company management further on our blog to maximize its potential and ensure competitiveness in the dynamically changing business world.

This post is also available in: Polski (Polish)