Co-designing and accelerating algorithms for the energy transition

Embedding Decision Science within the Digital Excellence unit to validate predictive and prescriptive prototypes at industrial scale.

Markets

Energy

Solutions

The challenge

For global energy players, the green transition and operational efficiency demand turning massive data flows into stable industrial decisions.

Within elite units such as Eni’s Digital Excellence team, the critical challenge is bridging the gap between advanced theoretical research and its practical application in core processes. Internal innovation teams often face a twofold obstacle: the need for highly specialist mathematical and data engineering skills, and the need to validate models quickly without slowing operational cycles.

Without a strategic partner able to prototype and test complex algorithms promptly, valuable insight risks being lost, delaying the adoption of the predictive and prescriptive solutions needed to stay competitive in fast-changing markets.

How we create value

With our AI & Data Science Accelerator consultancy service, we work alongside Eni’s unit as a co-design partner for algorithms. The collaboration focuses on translating complex industrial challenges into applied mathematical models.

Optit’s team works in close integration with Eni’s in-house specialists to design and validate highly specialised predictive and prescriptive tools, covering data modelling, machine learning and advanced process optimisation.

The value of this partnership lies in an agile, results-driven approach: Optit enables Eni to quickly test the mathematical stability and scalability of complex models, accelerating time to market for new solutions and dramatically reducing the risks and costs of industrialising core technologies.

Impact

Deploying the AI & Data Science Accelerator service has delivered:

Shorter prototyping times for data science models.
Scalability validation for algorithms before deployment in company systems.
Access to elite, highly specialised expertise in Decision Science, strengthening in-house teams’ know-how.
Methodological flexibility in testing prescriptive approaches on complex industrial processes.