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AI Planning and Decision Making

Cornell University_011122B
[Cornell University]


- Overview

Artificial intelligence (AI) uses machine learning (ML) and deep learning (DL) to make decisions based on data and models. AI can help with decision making when humans can't handle the amount of data, variables, and parameters involved. AI can also provide benefits such as:

  • Accuracy: AI can use algorithms and data science to provide objective insights, reducing the chance of human error and bias.
  • Speed: AI can process large amounts of data quickly, allowing for real-time analysis and insights.
  • Efficiency: AI can help make decision-making processes faster and more efficient, especially when automation is incorporated.

AI can train itself to build large language models of data collections, which can then make accurate decisions and categorizations. AI can also use past data to make future decisions, similar to a student learning from mistakes. 

However, AI is still not able to make decisions when emotions are involved, as AI models are driven by algorithms that respond to data and models, not morality and ethics. Some also question whether AI is trustworthy, as it lacks the qualities that would make it worthy of trust.

AI can handle anomaly detection, data crunching, complex analysis, optimized decision making, and spotting trends. The final decisions are then either completely automated or taken over by the human end.


- AI and Planning

AI planning is a branch of artificial intelligence (AI) that uses autonomous techniques to solve planning and scheduling problems. It involves assessing the current situation, identifying the desired outcome, and developing a strategy to achieve it. 

AI planning tools use time series data to estimate future developments in many industries, such as: Sales, Healthcare, Financial services, Manufacturing. 

AI planning can also automate repetitive, labor-intensive tasks in urban planning. This can allow for more efficient decision-making and free up planners' time to focus on higher-value work. 

The three most common AI planning approaches are: Rule-based, Goal-based, Utility-based. 

Some mature industrial applications of planning technology can be seen in various fields, such as: 

  • Dialog systems
  • Cybersecurity
  • Transportation and logistics 

The following four-part framework is used to build an effective AI strategy:

  • Define the problem and identify opportunities
  • Consider your timeline
  • Create a roadmap
  • Data, algorithms, and infrastructure


- AI in Decision Making

Artificial intelligence (AI) can help businesses make decisions faster, more accurately, and consistently. AI can analyze large amounts of data without error, and can help decision makers in complex scenarios. 

Here are some ways AI can help with decision making:

  • Cognitive load: AI tools can help leaders make better decisions under pressure by improving tracking and simulation, and offering real-time decision advice.
  • Data-driven decision-making: AI algorithms can process large datasets at speeds that humans can't. This allows organizations to identify patterns, extract insights, and make decisions with more accuracy.
  • Automated processes: AI can make decisions more quickly and accurately than humans by automating certain processes. For example, AI can improve hiring efficiency by streamlining the screening and selection process.
  • Complex decision-making: AI-powered decision support systems can help with complex decision-making processes by considering multiple factors and applying predefined rules. This can reduce the risk of bias and enhance objectivity.

Some challenges in implementing AI-assisted decision-making include: 

  • Poor business case for AI
  • Data quality
  • Bias and fairness
  • Trust and adoption
  • Regulation and compliance
  • Technical expertise

While AI can replace some tasks, it cannot replace human problem-solving skills.


- AI Decision-Making Processes

AI decision-making processes can help businesses make faster, more accurate, and consistent decisions. AI can use technologies like machine learning and cognitive computing to: 

  • Analyze large amounts of data
  • Identify patterns and trends
  • Predict outcomes
  • Minimize human biases
  • Offer impartial insights
  • Automate specific tasks
  • Make decisions more quickly than humans

AI can help decision makers in complex scenarios, such as strategic planning or medical diagnosis. 

Here are some steps to an AI strategy for a business: 

  • Start with the right problems
  • Define the business outcomes
  • Collect and organize data
  • Choose the right technology

AI can also help business teams focus better on work relevant to their field.


- AI at Scale

AI at scale refers to the use of artificial intelligence (AI) and machine learning (ML) algorithms to perform daily tasks at the speed that business needs. It involves using data models, algorithms, and infrastructure that can increase or decrease complexity, size, or speed to handle different situations.

To implement AI at scale, systems need to have robust infrastructure and large amounts of data to maintain speed and scale. Data from different parts of the business should be integrated and complete enough to provide algorithms with the information they need to produce the desired results. It’s also important to prepare people to use AI output in their work.

Changing company culture may be a key challenge in scaling AI across the organization. However, companies across many industries are using artificial intelligence to drive business value. For example, The Dow Chemical Company used machine learning to speed up the development process of polyurethane formulations by 200,000 times. 

Gartner predicts that by the end of 2024, more than 75% of organizations will move from piloting AI technologies to implementing them.

[More to come ...]

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