What is predictive AI?
Predictive artificial intelligence (AI) involves using statistical analysis and
machine learning (ML) to identify patterns, anticipate behaviors and forecast
upcoming events. Organizations use predictive AI to predict potential future
outcomes, causation, risk exposure and more.
Analysts have long used predictive analytics within organizations to make
data-driven decisions. However, predictive AI technology speeds up statistical
data analysis and can make it more accurate due to the sheer volume of data
that machine learning algorithms have at their disposal. Predictive AI reaches its
conclusions by analyzing thousands of factors and potentially many decades of
data. These predictions can help organizations prepare for future trends.
Predictive AI is sometimes confused with descriptive or prescriptive analytics;
descriptive analytics helps organizations understand why something happened in
the past, while predictive analytics helps them anticipate what is likely to occur.
Prescriptive analytics recommends actions an organization can take to guarantee
those outcomes happen.
Predictive AI is widely used to gain insights into customer behavior and optimize
decision-making across industries. It can predict anything from customer churn to
supply chain disruptions to mechanical failures, enabling proactive planning by
producing reliable, accurate forecasts.
How predictive AI works
The accuracy and performance of predictive AI models largely depend on the
quality and quantity of the training data. Rigorous data governance practices,
data cleaning, validation and consistent updates to the data sets, guarantee that
the data used is reliable, which in turn enhances the accuracy of the predictive
models.
Building a predictive AI application requires a business to gather relevant data
from various sources and clean it by defining missing values, outliers or irrelevant
variables. The data is then split into training and testing sets, with the training set
used to train the model and the testing set used to evaluate its performance.
Predictive AI uses big data analytics and deep learning to examine historical
data, patterns and trends; the more data provided to the machine learning
algorithms, the better the predictions are.
It is also essential that organizations address ethical considerations and mitigate
biases in predictive AI models. Biases in data or algorithms can lead to unfair or
discriminatory outcomes. Ethical AI practices protect against harmful impacts and
build trust with users and stakeholders.
Algorithm choice in predictive AI:Once the data is ready, data scientists can
train the predictive AI model. Various machine learning algorithms, such as linear
regression, decision trees and neural networks, can be used. The choice of
algorithm depends on the nature of the data and the type of prediction being
made.
Predictive AI employs a subset of machine learning and AI algorithms to
generate accurate forecasts.
Neural networks: Neural networks are commonly used for various tasks
because they can learn complex patterns from large datasets.
Linear and logistic regression: Linear regression is a technique primarily used
to identify correlations between variables, while logistic regression is practical for
classification tasks such as helping to categorize data into distinct groups.
Support vector machines: Support vector machines are also used for
classification, offering robust performance in scenarios with clear margin
separations.
Decision trees: Decision trees estimate outcomes by splitting data into branches
based on feature values, improving classification accuracy.
K-means clustering: K-means clustering is employed to sort data into groups
based on similarity, aiding in the discovery of underlying patterns within the data.
Data diversity: Regardless of the algorithm an organization uses, during
training, the model learns relationships and patterns in the data and adjusts its
internal parameters. It tries to minimize the difference between its predicted
outputs and the actual values in the training set. This process is often iterative,
where the model repeatedly adjusts its parameters based on the error it observes
until it reaches an optimal state.
Models trained on more diverse and representative data tend to perform better in
making predictions. Also, the choice of algorithm and the parameters set during
training can impact the model's accuracy. Given enough data, a machine learning
model can learn to sort through the information and process data, yielding more
accurate outcomes.
Embeddings in predictive AI:
Predictive AI can query databases quickly and efficiently by using embeddings.
Embeddings are a way to store information that allows the AI to identify
similarities and relationships. Created by unsupervised neural network layers,
embeddings turn information into vectors and place them within a mathematical
space that relates to all other information in the dataset. Embeddings that cluster
together are considered relevant to each other, allowing the AI to rapidly "read"
all relevant data and make a prediction.
Explainability and transparency: Explainability and transparency in AI models
are critical for building trust and protecting regulatory compliance. Explainable AI
helps stakeholders understand how predictions are made; providing
transparency is crucial for gaining user trust and meeting legal and ethical
standards, especially in sensitive areas like finance and healthcare.
Big data analytics and predictive models: Predictive analytics applications
involve feeding structured data like sales figures, sensor readings and financial
records into machine learning algorithms such as regression or decision trees to
provide real-time analysis. The algorithms analyze historical correlations between
variables that preceded outcomes. These patterns inform quantitative models to
predict events under new conditions. Precision keeps improving as models ingest
more relevant, clean data over longer time horizons to refine correlations.
Predictions become more trusted as successes pile up.
Because external factors can impact it, predictive AI measures potential
outcomes, not certainties. However, heavily relying on forecasts and removing
human judgment can open risks of bias. Predicting human behaviors also raises
ethical issues and organizations should be wary of over relying on these
predictions.
Ways predictive AI delivers value
For predictive AI to deliver maximum value, it must be integrated into existing
business processes and workflows. This integration helps to ensure that the
insights and predictions generated by AI systems are actionable and can provide
value. Organizations should focus on aligning predictive AI with their strategic
goals and operational needs to fully benefit from it.
Inventory management
Predictive AI can help identify when consumer demand is highest and a store
should have more items in stock. For example, in the case of a natural disaster
like a hurricane, a store can make sure they have essentials in stock.
Supply chain management: Predictive AI can determine when road congestion
will most likely help trucks meet spikes in user demand for goods.
Personalized user experiences: Predictive AI can help service providers
anticipate user requests, enhance customer experiences and predict behavior
based on customer data and past activity.
Healthcare: With enough data, predictive AI can help forecast potential health
conditions based on a patient's medical history.
Marketing: Predictive AI can help marketing develop content, products and
messaging prospective customers may be interested in by anticipating user
behavior.
Finance: Predictive AI can predict market movements and analyze transaction
data for enhanced fraud detection, such as an unusual device sign-in, a new
location or a request that doesn't fit within the usual behavior of a specific user.
Retail and e-commerce: Predictive AI can examine sales data, seasonality and
nonfinancial factors to optimize pricing strategies, forecast consumer demand or
predict upcoming market trends.
Insurance: Predictive AI can streamline claims management and forecast
potential losses.
Predictive maintenance: By monitoring vibration, temperature and other sensor
data from machinery, predictive AI pinpoints equipment at risk of failure so it can
be proactively serviced and avoid downtime.
Recommendation systems: Streaming platforms apply predictive models to
suggest personalized content that matches users' tastes based on their viewing
and listening histories.
Freeing time for employees:
Automating processes in the workplace with predictive AI can accomplish
short-term tasks when analyzing data, further enhancing automation and allowing
employees to focus their energy on decision-making and creative choices.
