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Strategic Analysis
A winning proposal under this two-stage call must pioneer an industry-validated, worker-centric paradigm that blends bidirectional human-AI co-learning with adaptive cognitive hardware/software interfaces to elevate workforce capability rather than replace human labour. The consortium must integrate real-world factory pilot lines across diverse industrial sectors, proving seamless knowledge capture, cultural adaptability, and enhanced shopfloor decision-making while rigorously complying with the two-stage blind evaluation rules.
TRL 4 → 6
Human-AI Co-Learning and knowledge capture to share competences, capture expert knowledge, provide interactive mentoring to up-skill the workforce, and support re-qualification and continuous training – leading to increased knowledge at factory level and avoiding loss of know-how.
Human-AI teamwork thanks to innovative natural interaction models (considering the e.g. related hardware interfaces and/or collaborative machine tools), enabling to control complexity in cognitive cooperating production systems, including planning activities at shop floor level.
Interfaces with automation which automatically adapt to the need of the humans including different abilities and different cultural needs.
Industrial jobs are transformed through AI-based human-machine interactions (and skills linked to them) which enhance flexibility, inclusion, well-being, up-skilling, career evolution and knowledge sharing;
Increased competitiveness and sustainability of advanced manufacturing industries by means of knowledge formalization and adaptability of the machines to workers and markets based on different cultures.
No expected impacts identified for this destination.
Apply AI Strategy
lowThe Apply AI Strategy is central to this call, focusing on the deployment of AI in healthcare to improve screening, diagnosis, and patient outcomes. It emphasizes the creation of a European network of AI-powered screening centres.
Evaluators will prioritize proposals that align closely with the Apply AI Strategy, demonstrating how the project will contribute to the European network of AI-powered screening centres, share best practices, and ensure scalability and sustainability of AI solutions in healthcare.
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Applicants submitting a proposal for a blind evaluation (see General Annex F) must not disclose their organisation names, acronyms, logos nor names of personnel in the proposal abstract and Part B of their first-stage application (see General Annex E).
In order to include a business case and exploitation strategy, as outlined in the introduction to Destination 'Leadership in materials and production for Europe', the page limit in part B of the General Annexes is exceptionally extended by 3 pages (for second-stage proposals).
described in Annex A and Annex E of the Horizon Europe Work Programme General Annexes.
Proposal page limits and layout: described in Part B of the Application Form available in the Submission System.
described in Annex B of the Work Programme General Annexes.
A number of non-EU/non-Associated Countries that are not automatically eligible for funding have made specific provisions for making funding available for their participants in Horizon Europe projects. See the information in the Horizon Europe Programme Guide.
described in Annex B of the Work Programme General Annexes.
described in Annex C of the Work Programme General Annexes.
The first-stage proposals of this topic will be evaluated blindly.
are described in Annex D of the Work Programme General Annexes.
are described in Annex F of the Work Programme General Annexes and the Online Manual.
described in Annex F of the Work Programme General Annexes.
The granting authority may, up to 4 years after the end of the action, object to a transfer of ownership or to the exclusive licensing of results, as set out in the specific provision of Annex 5.
Eligible costs will take the form of a lump sum as defined in the Decision of 7 July 2021 authorising the use of lump sum contributions under the Horizon Europe Programme – the Framework Programme for Research and Innovation (2021-2027) – and in actions under the Research and Training Programme of the European Atomic Energy Community (2021-2025) [[This decision is available on the Funding and Tenders Portal, in the reference documents section for Horizon Europe, under ‘Simplified costs decisions’ or through this link: https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/guidance/ls-decision_he_en.pdf]].
described in Annex G of the Work Programme General Annexes.
described in the [specific topic of the Work Programme]
Application form templates — the application form specific to this call is available in the Submission System
Stage 1: Standard application form (HE RIA IA Stage 1 BLIND)
Stage 2: Standard application form (HE RIA, IA)
Evaluation form templates — will be used with the necessary adaptations
Stage 1: Standard evaluation form (HE RIA, IA and CSA Stage 1 BLIND)
Stage 2: Standard evaluation form (HE RIA, IA)
Guidance
Model Grant Agreements (MGA)
Call-specific instructions
Information on financial support to third parties (HE)
Information on clinical studies (HE)
Guidance: "Lump sums - what do I need to know?"
HE Main Work Programme 2026-2027 – 1. General Introduction
HE Main Work Programme 2026-2027 – 7. Digital, Industry and Space
HE Main Work Programme 2026-2027 – 14. Horizontal Activities
HE Main Work Programme 2026-2027 – 15. General Annexes
HE Framework Programme 2021/695
HE Specific Programme Decision 2021/764
EU Financial Regulation 2024/2509
Decision authorising the use of lump sum contributions under the Horizon Europe Programme
Rules for Legal Entity Validation, LEAR Appointment and Financial Capacity Assessment
EU Grants AGA — Annotated Model Grant Agreement
Funding & Tenders Portal Online Manual
Evaluators will rigorously assess stage-1 compliance with blind evaluation rules (strictly no identifiable consortium or institutional names). For the core technical evaluation, they will prioritize genuine bidirectional co-learning and workforce upskilling over pure automation, demonstrability from TRL 4 to 6 in complex shop-floor environments, and explicit focus on inclusion, diversity, and worker well-being across varying technical backgrounds and cultural needs.
Everything the call asks for, seen from the call's point of view. Each line shows what answers it, and which partner carries it.
This matrix lists everything the call asks for: outcomes, impacts, scope, the requirements buried in the call text, and policy alignment. Sign up free and GrantForge tracks each line against the concept you build.
| Requirement | Covered by | Carried | Status |
|---|---|---|---|
| Scope activities | |||
| SC1Human-AI Co-Learning and knowledge capture to share competences, capture expert knowledge, provide interactive mentoring to up-skill the workforce, and support re-qualification and continuous training – leading to increased knowledge at factory level and avoiding loss of know-how. | · | · | Sign up to track |
| SC2Human-AI teamwork thanks to innovative natural interaction models (considering the e.g. related hardware interfaces and/or collaborative machine tools), enabling to control complexity in cognitive cooperating production systems, including planning activities at shop floor level. | · | · | Sign up to track |
| SC3Interfaces with automation which automatically adapt to the need of the humans including different abilities and different cultural needs. | · | · | Sign up to track |
| Expected outcomes | |||
| EO1Industrial jobs are transformed through AI-based human-machine interactions (and skills linked to them) which enhance flexibility, inclusion, well-being, up-skilling, career evolution and knowledge sharing; | · | · | Sign up to track |
| EO2Increased competitiveness and sustainability of advanced manufacturing industries by means of knowledge formalization and adaptability of the machines to workers and markets based on different cultures. | · | · | Sign up to track |
| Other requirements | |||
| REQ1Gender perspective and non-discrimination in AI systemsProposals should integrate a gender perspective and avoid any type of discrimination in the design and deployment of AI systems and human-machine interaction models, addressing differences in user needs such as persons with disabilities, physical and cognitive ergonomics, and training pathways. | · | · | Sign up to track |
| REQ2Contribution of Social Sciences and Humanities (SSH) and Digital HumanismBecause human/AI collaboration requires sensitivity to human values and ethical principles as represented in Digital Humanism, proposals must give appropriate consideration to the contribution of SSH. | · | · | Sign up to track |
| REQ3Bias mitigation and inclusive design in AI systemsProposals are expected to identify and address potential biases in AI systems to promote inclusive design that ensures safe and effective use by all workers. | · | · | Sign up to track |
| REQ4Collaboration with Apply AI Strategy initiativesAs the topic is linked to the Apply AI Strategy, proposals should seek collaboration with relevant initiatives. | · | · | Sign up to track |
| REQ5Synergies with past Extended Reality (XR) projectsProposals are invited to build on the results of past projects on Extended Reality Technologies (XR), such as HORIZON-CL4-2021-HUMAN-01-13, -14, -25, -06, and -28. | · | · | Sign up to track |
| REQ6Alignment with Made in Europe and AI, Data and Robotics PartnershipsThis topic implements the co-programmed European Partnerships Made in Europe and AI, Data and Robotics. | · | · | Sign up to track |
| Expected impacts | |||
| No expected impacts in this call. | |||
| Underlying policies | |||
| No underlying policies in this call. | |||
The binding rules of this call. Items marked auto are verified by GrantForge from the call and the template. The others are yours to confirm.
LMIC entities auto-eligible
Low/middle-income country entities are automatically eligible for funding.
Page limit Stage 1
Part B Section 1+2+3 combined is typically capped at 10 pages at Stage 1. Pages exceeding the limit are invisible to evaluators.
Consortium continuity
The consortium declared at Stage 1 is expected to submit Stage 2. Substantial changes must be justified.
EU space data infrastructures
If the project uses satellite-based Earth observation, positioning, navigation or timing data/services, beneficiaries must use Copernicus and/or Galileo/EGNOS.
Civil applications only
Exclusive military or dual-use applications are excluded.
5 key insights you must internalise before writing. Each is grounded in the call text and tells you what evaluators will actually look for. Share these with your consortium before drafting.
Stage 1 proposals are evaluated blindly. Any inclusion of organisation names, acronyms, logos, or personnel names in the abstract or Part B will result in immediate rejection before scoring. Consortium capabilities must be described purely through functional roles and technical capacities.
Source: Eligibility
The proposal architecture must explicitly address at least two of the three specified fields: Human-AI Co-Learning, Human-AI teamwork, or adaptive automation interfaces. Attempting to cover all three without sufficient budget depth risks diluting the technical focus, while covering only one will disqualify the proposal.
Source: Scope
Evaluators are explicitly instructed to penalize proposals that treat AI merely as an automation tool to replace human tasks. The technical narrative must prove genuine bidirectional co-learning, demonstrating how the AI system captures tacit shopfloor knowledge while simultaneously upskilling the human worker.
Source: Evaluation_pre_award
Stage 2 proposals receive an exceptional 3-page extension in Part B specifically to detail the business case and exploitation strategy. Evaluators will expect a highly mature commercialization pathway for the cognitive interfaces, not just standard dissemination activities.
Source: Eligibility
Because this is a lump sum grant, payments are triggered exclusively by the completion of work packages. The consortium must design a work plan where major technical milestones, especially the TRL 6 shop-floor validations, are distributed across reporting periods to prevent severe cash flow bottlenecks.
Source: Eligibility
Talk to the Grant Coach to build your concept. There is no set order: start wherever your project starts. The sections below mirror what the conversation produces, and your coverage tracks the progress. You can refine everything once your project workspace is created.
The problems this call frames, and who they affect. Your concept and plan address them.
Aging workforces and high turnover in advanced manufacturing cause rapid loss of expert craftsmanship and tacit procedural knowledge, which conventional static documentation cannot preserve.
Current robotic and automated machine tools force operators to adapt to rigid computational processes, causing cognitive fatigue, high error rates, and barriers for workers with diverse abilities.
Lack of explainability, trust, and cultural/ergonomic personalisation in automated shopfloor systems fosters worker skepticism and resists widespread digital transformation.
Manufacturing line operators, technicians, and assembly workers who directly engage with AI-driven collaborative tools and adaptive interfaces.
Discrete and continuous manufacturing facilities seeking to preserve expert know-how, improve operational flexibility, and adopt cognitive AI collaboration.
Machine tool developers, robotics manufacturers, and industrial software providers integrating adaptive human-AI interaction modules.
European research organisations and scholars advancing human-robot collaboration, cognitive ergonomical systems, and ethical AI in manufacturing.
Organisations overseeing worker safety, ergonomics, labor transition policies, and continuous professional training standards.
The long-term impacts your project should drive, and the policies they serve.
Systematic adoption of AI mentoring frameworks accelerates onboarding and qualification times while elevating digital skills across diverse industrial roles.
Adaptive, inclusive human-AI interfaces decrease cognitive workload, physical strain, and operational hazards on the factory floor.
Digital formalisation of tacit expertise and real-time collaborative planning mitigate production downtime and boost competitiveness of EU manufacturers.