AI planning and forecasting is a field of artificial intelligence used to make scientific predictions about the future without requiring oversight. AI planning tools use time series data to estimate future developments for many industries, such as sales, healthcare, financial services, and manufacturing. Planning and scheduling problems are easily predicted with AI forecasting.
Artificial intelligence vs. augmented intelligence
How does augmented intelligence differ from the similarly-named artificial intelligence?
Artificial intelligence is a field combining computer science and datasets to solve problems without human interaction. Machine learning and deep learning are both subfields of artificial intelligence.
Augmented intelligence, also known as intelligence amplification, cognitive augmentation, and machine augmented intelligence, is strictly limited to computers and software that augment the abilities of a human mind and cannot operate independently.
Artificial intelligence is enabling machines to think and act like humans, whereas augmented intelligence is using those same machines to assist humans with specific tasks.
Why is AI planning important for your business?
AI planning and forecasting uses algorithms to make predictions and forecast trends without human judgment, which leads to far less error and often outperforms data scientists and experts. Studies comparing AI predictions with expert predictions from humans almost always showed artificial intelligence as the victor. While algorithms and AI will not replace human intelligence in the future, their ability to analyze data will always be a welcome aid to data scientists and forecasters.
Since businesses know many pieces of specific data, like how long a consumer waits on average or how many products can be manufactured in a day, but with millions of bytes collected daily, and much of that data being unstructured, it’s nearly impossible for a human to analyze data as precisely and efficiently as AI.
What are the approaches for AI planning?
Three approaches dominate artificial intelligence today, including models and methods for types of AI planning and forecasting in business:
Bayesian Networks, one of the oldest forms of AI, scale extremely well for many types of problems and are probably the most widely deployed and influential AI technologies. They aptly perform many highly-diverse AI tasks, such as spam filters that protect your email inbox, the order planning tools in cutting-edge POS systems, and military hardware used in detecting threats to national defense.
Though the inspiration for evolutionary algorithms is ancient, evolutionary algorithms are one of the newer AI technologies. Mimicking the natural evolution process of mutation, recombination, and performance competition to determine the best adapted to continue evolving. Evolutionary algorithms are less heavily represented in AI planning because they are a more natural fit for engineering problems where conditions are complex but very well-understood. They are still very useful in some forecasting and planning tasks, though, as they are innovative and novel optimizers that can find solutions humans typically don’t consider.
This is the youngest AI discipline but by far the most computationally-intensive of the AI disciplines. What it lacks in the refinement of age, it often more than makes up for in its close correlation with the processes found in the true, naturally-occuring intelligence of humans. It takes unstructured data and passes it through a network of specialized processes that work on smaller parts of the data before recombining their individual characterizations until the full data set has been analysed. Because each component in a deep learning system progressively refines its task by “learning” every time it runs, it is very well-suited to judgements on tasks that are not easily defined by simple rules. This can be a critical strength when forecasting or planning around human behavior or preferences.
How do different industries use AI planning and forecasting?
A wide range of industries use AI planning and forecasting every day to make concrete scientific forecasts in their business.
Some examples include:
Every part of the global manufacturing supply chain depends on AI planning and forecasting to balance supplies and sales in an equilibrium that maximizes profits. The numerous suppliers necessary for even the simplest manufacturing have far exceeded the number a human mind can manage unaided, let alone optimize precisely. Without AI planning and forecasting, none of the modern large manufacturing companies could even exist, let alone dominate their industry.
High tech industries depend on AI planning and forecasting when developing cutting-edge technologies because human intuition is not well-suited for forecasting novel things but reliable planning is still essential to success.
Healthcare is rapidly adopting AI planning and forecasting to overcome the biases of doctors, researchers, and support staff to better understand illness and adapt treatments using data-driven approaches, saving lives and improving quality of living.
In a global retail management system, forecasting when to swap inventory from summer to fall is critical. It must be considered for both hemispheres separately, however, or heavy winter coats will be shipped to Australia as temperatures begin to rise.
What should I look for in AI forecasting tools?
Turning raw data into scientific forecasts with clarity and ease are key in AI prediction tools. They should have a two-prong approach, both forecasting models and evaluating them on historical data as well as predicting future trends beyond your data.
A lack of AI forecasting in your business could lead to massive losses in efficiency. Studies have shown that a majority of business planners said the future dominant technology for demand planning will be artificial intelligence.