Artificial Intelligence Living Lab


The Artificial Intelligence living lab (AI LL), which has been entrusted to the Spanish State Secretariat for Digitalisation and Artificial Intelligence (SEDIA), addresses the primary objective of producing a full picture of the Spanish STI-AI ecosystem to optimise policy action by providing policymakers from different Public Administrations with information to understand the array of sectoral/technological/institutional potential driven by AI. As such, it would be possible to gain useful insights into how the different stakeholders making the AI ecosystem have evolved over the last few years, what their main prospects, main strengths, and weaknesses are, and how they are interrelating and cooperating. This approach is very much circular-based and considers innovation as a systemic phenomenon, where innovation outcomes and impacts are the results of multiple interactions and back-and-forth loops.

Additional to this primary goal, the AI LL has two complementary (secondary) goals:

  • Fully and effectively involve the four different types of stakeholders making up the “quadruple helix” thus pushing forward new synergies.
  • Serve as a useful instrument to meet the main Spanish strategic policy guidelines on AI, such as the Spanish National Strategy on Artificial Intelligence (ENIA).


The first two years of the AI LL activity have been basically devoted to the following activities:

  • Shape and streamline the use case described to experiment with the IntelComp functionalities. This implied the full definition and design of a set of policy questions that were both measurable and aligned with the nature and scope of the AI LL
  • Create and engage a strong base of motivated stakeholders coming from the different components of the quadruple helix: Academia, Public Administration, industry (business sector), and civil society.  
  • Nurture the project by incorporating new datasets to be ingested into the platform. An example has been de Data Lake omnia.
  • Organise the kick off event (October 2022) at FECYT, where more than 50 stakeholders were shown the great potential of the IntelComp tools.
  • Act as “project evangelists”, both internally within SEDIA and externally.


2023 is the year when the AI LL will be fully implemented. The main foreseen tasks are the following:

  • Be involved, as Product Owners, in subsequent sprints organised by the IntelComp technical teams and to be delivered throughout the year. The goal is to provide feedback on different IntelComp tools (IMT, STI Viewer, STI Portal…) to optimise final output.
  • Keep on strengthening the base of stakeholders to facilitate interaction and two-way dialogue.
  •  Organise both online events and in-person sprints with stakeholders to facilitate co-creation practices so as to improve IntelComp usability, finetune IntelComp tools, and suggest new functionalities.
  • Report all AI LL activity in the project´s deliverables and collaborate to design protocols and tools in order to streamline co-creation practices. 
  • Work on the project´s sustainability by exploring to what extent IntelComp may be used in the Spanish Public Administration once the project has finished by figuring out alternative use cases.

Discover this use case

Related events

IntelComp AI Living Lab - Final Event

IntelComp AI Living Lab - Final Event

Get acquainted with all the activities that have been developed within the AI LL related to the project, to practically demonstrate some newly designed tools (such as the STI Participation Portal), and to assess the potential real-world applications of the IntelComp project in the near future.
IntelComp AI Living Lab - STI Viewer workshop

IntelComp AI Living Lab - STI Viewer workshop

Last September 27th a session of the STI Viewer tool took place in the Spanish Foundation for Science and Technology (FECYT), under Artificial Intelligence Living Lab which is leading by t

LL AI IMT workshop

Artificial Intelligence Living Lab: Interactive Model Trainer workshop

One of Intelcomp's tools is the Interactive Model Trainer (IMT), which is the heart of the platform. The IMT is a tool specifically designed for training Artificial Intelligence models, enabling a user-in-the-loop approach that allows the incorporation of domain-specific expert knowledge into the models.