Azure Artificial Intelligence
The creation of systems that learn, adapt and potentially act autonomously has become a challenge for technological developments for the coming years.
What is AI (Artificial Intelligence)?
AI can be defined as technology that tries to emulate mental functioning based on the development of algorithms to learn, think and take decisions. AI combines several fields, such as robotics, expert systems and others, which have the same objective: to create systems that can think for themselves.
The use of AI aims to improve decision making and reinvent business models and ecosystems. Such tasks reduce costs and risks in human handling in hazardous areas and improve performance and quality control.
Most AI investment follows one of three patterns:
Automated Process Optimization
Technologies: Predictive Analytics, Prescriptive Analytics, Reinforcement learning, Machine Learning, Deep Learning
Cognitive Expertise
Technologies: Sentiment Analysis, NLP, Unestructured Data
New Interfaces
Technology: Bots
Detect Hidden Patterns
Spot Deviating behavior
Anomalies Detection
Correlation between process variables
Efficiency
Workflow Optimization
Consumer behavior in real-time
Chronometrics
Increase Security
Facial Signature in Forms
Anomalies Detection in access methods
Help Users
Case Auto Classification
Problems detection with potential customers
Outcome Prediction along process life
Actionable Insights
Informs company strength
Bots for internal/external user
Predict Future
Process Simulation for Bottlenecks
Business Metrics Predictions
Predict Future consumption trends
Customer Churn
AuraPortal uses Microsoft Azure’s Artificial Intelligence to extend the capabilities that offers to the user.
Watch a real DEMO to see the potential:
Actionable Insights
A few examples where Artificial Intelligence can be useful:
Example #1
Outcome Prediction
Prediction models can be used to obtain a prediction of a specific result of a process, and update this prediction in light of new events, or the passage of time. It can be used to detect when an opportunity is at risk, when a process is likely to be relevant and relevant.
Example #2
Detection of Anomalies
The automatic detection of outliers and anomalous patterns is one of the most common scenarios of application of the Machine Learning / Deep Learning models. Although its implementation by means of multi-variational time series is well known, the last years are being surpassed by models based on Recurrent Neural Networks (RNN).
Example #3
Recommendation Engine
Although AuraPortal already implements its recommendation mechanism for actions based on the outcome of the latest processes, the AI can provide the System with more advanced model combinations, anomaly detection and automated data pre-treatment.
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