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Intelligent Automation
Predictive analytics, optimisation, enhanced capabilities
“Don’t leave any money on the table.”
Challenges
Business workflows commonly suffer from ineffective or inappropriate, non-optimal, and reactive utilisation of business resources.
Due to inconsistent and partially unpredictable performance of human operators, business processes suffer, as common human error introduces issues with the quality of service, in addition to being an unreliable production resource and potentially ineffective in comparison to algorithmic approaches to task performance.
Furthermore, there is generally a reactive management or operational mechanism in place, where problems are dealt with as they occur, potentially ignoring signals that would have indicated imminent issues or process breakdown.
Solution and benefits
When incorporating automated information processing, businesses can take advantage of cost-effective, fast, and highly consistent quality of delivery on product or service. By doing so a firm can optimize the reliability and accuracy of service. Through automated information processing, the workforce is partially relieved from repetitive work, thereby decreasing cost for the firm. In addition, such resource-optimized delivery through automated information processing is faster, more reliable, and more consistent.
In addition, when dealing proactively with impending business process breakdown by detecting the appropriate signals, delivery can be kept on target and without interruption of service. Not just detection of known signals, but also new signals can be discovered through machine learning, where detection automatically becomes a learned part of the solution. By dealing proactively with impending issues, resource allocation is optimized, by ensuring that support processes are performed under circumstances of the organisation’s choosing: When and where is convenient rather than under duress. This implies that business process maintenance is performed more cost-effectively and accomplishing higher service quality through decreased downtime and service issues.
Our Clients
Case: Liberty Global
Case: Proximus
Case: Deutsche Telekom
Case: Telenet
Case: API Restauration
Case: BNP Cardif Airbus
CONTACT
AI Plan Workshop (1 week)
A major goal is to understand how to prepare and plan the implementation of your first AI Project.
Description of the AI Plan Workshop
At the end of this 1-2 week workshop we concretise the outcomes of the Discovery Workshop and head from data exploration towards either a minimal viable model or recommendations towards addressing the bottlenecks.
If the data allows it, we want to move towards a minimal viable product, otherwise we deliver recommendations to address the bottlenecks and plan the next steps. In addition, we train employees to handle the state of art adoptions.
Data exploration
The Workshop start with exploring the data to assess the possibilities of the data assets.
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Assessing data quality and quantity
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Validate feasibility and usefulness of data for intended goals
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Guidance towards improved data
Selection of a suitable ML/AI model
Study the state-of-the-art approaches and choose an algorithm / model to apply to the selected use-case.
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Research for suitable models
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Select the appropriate open-source framework / implementation
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Define the scope of the MVP (minimal viable product)
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Identify relevant features and if applicable recommend new features
Initial model training & evaluation
First training of the model with the selected customer data.
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Split the data into 3 subsets: training, validation, test.
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Launch and monitor training
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Evaluate and visualize results
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Recommendations to improve the model (performance metrics)
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Recommandations to integrate the model with your existing tools
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Identify outsourcing/partner selection criteria
Other phases
