A competence the survey did not measure
The ESG4GLAM competency framework has 31 competences, but the survey that inspired it rated only 29. The other two did not come from a questionnaire item: they emerged strongly and consistently from the focus groups and expert interviews, as priorities the survey could not fully capture.
One of them is digital sustainability and responsible use of AI and data (CC-06). Both focus-group participants and interviewees identified it as the most rapidly emerging ESG competency need for the sector. This article looks at what it covers and what it asks of people at every level, from volunteer to director.
Why digital belongs in an ESG framework for GLAM
Galleries, libraries, archives and museums now run on digital tools: digitisation projects, cataloguing systems, online services, data storage and, increasingly, AI. None of these is free of consequences.
On the environmental side, large digitised collections need energy-intensive, ongoing data storage. On the ethical and social side, AI tools used for cataloguing, description or public engagement raise questions of accuracy, bias and trust, which matter a great deal for institutions whose authority rests on being reliable.
CC-06 asks institutions to look at both sides together: the footprint of digital infrastructure, and the ethical and trust-related implications of AI and data.
Is digital sustainability on your institution’s radar yet? read more about the framework here
What the competence covers
In the framework’s words, CC-06 is the ability to understand and manage the environmental footprint of digital infrastructure, and the ethical, social and trust-related implications of AI tools used in collections management, cataloguing, digitisation and public engagement.
It belongs to the cross-cutting domain, the foundational layer that determines whether ESG becomes an integrated way of working or stays a set of isolated activities. That domain recorded the largest average gap in the survey, with importance at 4.23 out of 5 and current level at 2.55. CC-06 itself was not one of the rated items, so we have no survey score for it: its place in the framework rests on what practitioners and experts told us in discussion.
The framework aligns it with GDPR, GRI 302 on energy and emerging EU AI governance principles.
Five levels, from awareness to sector standards
Like every competence in the framework, CC-06 is described at five levels aligned with EQF 3 to 8. The progression moves from noticing that digital tools have costs, to setting the rules for the whole institution, and finally to shaping standards for the sector.
| Level | Typical roles | What it looks like in practice |
|---|---|---|
| L1 Associate | Trainee, volunteer, new staff member | Understands that large digitised collections require energy-intensive, ongoing data storage |
| L2 Professional | Librarian, curator, archivist, education officer | Optimises file formats and storage choices when digitising or managing collection records |
| L3 Senior professional / manager | Department head, senior specialist | Assesses an AI cataloguing or description tool for accuracy, bias and energy use before adoption |
| L4 Lead professional / senior manager | Deputy director, head of institution | Develops an institutional policy on verifying and correcting AI-generated content and on sustainable digital storage |
| L5 Principal | Director, sector-network coordinator | Advises national or international bodies on AI governance and trustworthiness for archives, libraries and museums |
At L4, the framework’s assessment criteria point to concrete evidence: AI and digital-sustainability guidance documents. In other words, the competence is shown through decisions and documents, not attendance at a course.
Where to start
CC-06 does not stand alone. It sits close to three other competences: using basic ESG data and indicators (CC-05), data protection and privacy under GDPR (GOV-06) and data management for ESG purposes (GOV-07). An institution that already handles personal data carefully has a head start on the ethical side.
The L3 example is also a practical first test for any institution, whatever its size: before adopting an AI cataloguing or description tool, ask how accurate it is, whether it introduces bias, and how much energy it uses. The framework links CC-06 to a digital sustainability module and an AI governance checklist to support exactly this kind of decision.
Your turn
This is the part of the framework where practice is moving fastest, and where your experience matters most. If you work with digitised collections, cataloguing systems or AI tools, tell us whether CC-06 and its five levels reflect what you see, and what is missing. You do not need to be a sustainability specialist.