Data support services, AI data, machine learning
The Importance of Artificial Intelligence, Data, and Hyper-automation
Artificial intelligence (including Machine Learning), data, and hyper-automation (RPA and cognitive technologies) are essential for transforming businesses. When used effectively, these technologies can significantly improve business performance.
Our Mission at Althéa
We simplify the understanding of emerging technologies and make them accessible to your teams. Working alongside you, we identify “QuickWin” opportunities to demonstrate how these technologies optimize your business processes.
Multi-Domain Expertise
We work with all business departments and IT divisions, with particular expertise in the following areas:
- Human Resources (HR)
- Finance
- Procurement
- Supply Chain
- Healthcare
Boost your performance through new AI-powered processes!
We support you every step of the way
Our Support Services
- Digital Strategy
Smart Diagnostic: In-depth analysis of your business processes and data to identify improvement opportunities.
- Audit of Existing Systems
Analysis of Automation and AI Initiatives: Evaluation of automation and AI solutions in production to maximize ROI.
- Solution Prototyping
Pilot Development: Rapid creation of a prototype to validate the benefits of AI, data, and automation.
- Organizational Development
Solution Deployment: Assistance with the rollout of AI and automation projects, including project management, change management, and technical expertise.
- Flash Support
Rapid Opportunity Identification: Quick analysis to identify 1 or 2 AI, data, or automation opportunities tailored to your specific challenges.
- Training
New Technology Training Sessions: Workshops and sessions to understand and use GenAI tools in HR, Finance, and Supply Chain.
Why Choose Althéa?
Althéa stands out for its pragmatic approach and sector expertise, guaranteeing personalized, high-value-added solutions. Our goal is to transform your technological challenges into sustainable growth opportunities.
Key Figures
of HR leaders say they are piloting, planning, or have already implemented generative AI in their processes, according to a Gartner survey.
of finance teams use AI tools, and adoption is increasing rapidly.
of companies have already adopted low-code platforms to automate and manage their workflows effectively.
of companies use AI solutions to improve their processes. AI adoption in procurement functions remains limited.
References
For a major retail player
Payroll Control
AI-powered payroll control tool
For a leading DIY retailer (30,000 employees): design and development of an AI-based tool for payroll auditing, as part of an innovation project for the Shared Services Center (SSC) consisting of 65 payroll managers.
Result:
Three modules were developed to prioritize audits on high-risk payrolls, reducing processing time by at least 20% and enabling complex audits such as those for RGCS amounts.
For a player in the transport sector
Data Reliability Improvement
Data Quality Control Tool
Our client called upon Althéa for the migration of its HRM/Payroll information systems. The first requirement was to verify data integrity before proceeding with the migration. The second requirement was to verify data consistency following the migration to ensure that no data was altered during the process.
An automation and data quality tool for HR/Payroll was created by our teams, enabling:
- Data auditing across different types/sources
- The creation of comprehensive and automatic custom reporting for anomalies
- Cross-referencing of any data format
- Audits that can be structured by administrative division and/or scope
For an energy sector player
Intelligent Ticketing Management
ML Module for Customer Ticket Management
Our client requested Althéa’s help with a diagnostic of customer support activities to find optimization levers (organization / RACI / indicators).
As part of the mission, an overload in ticket analysis and prioritization was identified within their ServiceNow tool.
Development of an AI-based program to automatically classify tickets.
Ultimately, this project resulted in:
- Automatic ticket detection
- Classification by theme
- Prediction of ticket priorities (1 to 3)
- Repositioning of 2 FTEs
- A 30% increase in customer satisfaction
Challenges
Operational Efficiency
Automation and optimization of HR processes.
Strategic Decisions
Advanced data analysis for informed decision-making.
Competitiveness
Rapid adaptation to trends and effective talent management.
Contacts
Frequently Asked Questions about AI Innovation
How can AI innovation improve HR, Finance and Supply Chain performance at the same time?
Generative artificial intelligence is revolutionizing business transformation by enabling an integrated approach to repetitive tasks. Within an innovation strategy, process automation via language models and machine learning allows for real time optimization.
For example, in HR, intelligent assistants designed for natural language processing can manage recruitment and training at scale. In finance, AI algorithms, supported by significant computing power, analyze data in real time for more effective decision-making. As for the supply chain, AI enables autonomous inventory management and flow optimization, with a controlled environmental impact.
What are the most common AI quick wins for Finance and Supply Chain functions?
The most frequent QuickWins in AI for Finance and Supply Chain revolve around concrete applications that have proven themselves in daily operations.
In finance, the automation of bank reconciliation tasks and invoice processing provides an immediate productivity gain, with a visible return on investment within a few months. In the supply chain, the integration of voice recognition tools for warehouse management and the use of big data for stock forecasting represent major advances that can be implemented quickly.
These solutions, often developed in partnership with players like Microsoft or public research laboratories, leverage cloud computing power to transform time-consuming processes into efficient automated systems. For example, a simple language model applied to supplier communications can reduce tender processing times, while predictive analysis of transport data optimizes delivery routes with a positive impact on the ecological transition.
How can you accelerate AI adoption by your teams?
To accelerate AI adoption within your company, an effective three-point strategy is required. First, it is essential to demystify new technologies by starting with simple, consumer-grade tools like ChatGPT or DALL-E, allowing teams to become familiar with AI in a concrete way and without pressure.
Secondly, implementing a booster program should create an experimental space where each user can test concrete applications related to their daily work. Finally, the gradual integration of technological advances must rely on a mentoring system where those most comfortable with digital tools support their colleagues, creating a multiplier effect. This human-centric approach, supported by targeted training and progress reviews, enables massive adoption within a few months.
