Personal tools

AI Planning and Decision Making

Cornell University_011122B
[Cornell University]


- Overview

Artificial intelligence (AI) is an important technology of the future. Whether it’s smart robots, self-driving cars, or smart cities, different aspects of artificial intelligence are used! ! ! But planning is very important in making any such AI project.

Even planning is an important part of AI, dealing with problem-specific tasks and domains. Planning is considered the logical aspect of action. 

Everything we humans do is done with a clear purpose, and all of our actions are aimed at achieving our purpose. Likewise, planning also applies to AI. 

For example, planning is required to reach a specific destination. Finding the best route in Planning is essential, but it's also important to know what to do at a particular time and why.

AI has greatly increased decision making. It makes the process clearer, faster and more data-driven. With AI, you can make small (micro) decisions, solve complex problems, initiate strategic change, assess risk, and assess overall business performance anytime, anywhere. 


- 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.


San Francisco_California_050921A
[San Francisco, California - Civil Engineering Discoveries]

- AI and Micro-Decisions

Your business's use of AI is only going to increase, and that's a good thing. Digitization enables businesses to operate at the atomic level and make millions of decisions every day about a single customer, product, supplier, asset or transaction. But those decisions can't be made by people working in spreadsheets. 

We call these AI-driven fine-grained decisions "micro-decisions." They need a complete paradigm shift, from making decisions to making "decisions about decisions." 

You have to manage at a new level of abstraction with rules, parameters, and algorithms. This shift is happening in every industry and in every kind of decision-making. 


- Micro-Decision and Automation

The nature of micro-decisions requires some degree of automation, especially for real-time and high-volume decisions. 

Automation is enabled by algorithms (rules, predictions, constraints, and logic that determine how to make micro-decisions). And these decision-making algorithms are often described as artificial intelligence (AI). The key question is how human managers manage these types of algorithm-driven systems. 

Autonomous systems are very simple in concept. Imagine a driverless car without a steering wheel. The driver just tells the car where to go and hopes for the best. But once you have the steering wheel, you have a problem. 

You must inform drivers when they may want to intervene, how they can intervene, and how much notice you will give them if intervention is required. You must carefully consider the information you will provide your driver to help them make appropriate interventions.


- AI at Scale

Accelerating AI integration across the enterprise can lead to positive business growth. 90% of enterprise AI initiatives are struggling to break out of beta. 

Organizations are maturing in data science, but still unable to integrate and scale advanced analytics and AI/ML into day-to-day, real-time decision-making, so they fail to capture the value of AI. 

The new world of remote work requires accelerated digital transformation, and AI/ML can be used to achieve this faster. They can lead to more efficient business operations, more compelling customer experiences and more insightful decision-making. 

Businesses can leverage AI to reap significant benefits across the value chain, but organizations must get it right from the start or risk fines, penalties, errors, corrupted results, and general distrust from business users and the marketplace.


[More to come ...]

Document Actions