Visibility and value: As AI-generated outputs become more prominent, many of the human contributions that enable them remain behind the scenes.
Artificial intelligence is advancing at remarkable speed. New tools are reshaping how we create, analyse, and decide—often faster than organisational norms, contracts, and governance structures can adapt. In this environment, it is tempting to focus on efficiency gains or disruption metrics alone.
Yet a more fundamental question deserves attention: as AI becomes more capable, how do we ensure progress continues to serve shared human values—rather than narrowing who receives credit, agency, and reward?
A signal from the creative industries
Recent debate around AI-generated performers in the film industry offers a useful signal. Public debate has included examples such as AI “actress” Tilly Norwood, presented as a digital performer rather than a behind-the-scenes production tool.
What matters here is not novelty, but the structural pattern the debate highlights. As AI moves from supporting human work to standing in for it, attribution becomes more complex. Public narratives often centre a visible creator or studio, while enabling contributions—technical teams, training data, cultural inputs, and prior human labour—remain largely invisible. When attribution is unclear, so too is the distribution of reputational and economic value.
This pattern is not unique to film. Similar dynamics are beginning to surface across knowledge-intensive sectors, from education and media to professional services and life sciences.
Beyond disruption: how value is defined and shared
Much discussion of AI remains framed around job displacement. While workforce impact matters, it is only part of the picture. The deeper issue is how value is defined, credited, and shared in AI-enabled systems.
AI makes it easier to concentrate recognition and reward at the most visible point in a complex system, while obscuring the distributed human and data contributions that make outcomes possible. Without deliberate design, this can become the default.
This raises questions organisations and ecosystems may need to address sooner rather than later:
- How should invisible or indirect contributors be acknowledged in AI-enabled work?
- If AI amplifies productivity using collective inputs, who should capture the upside?
- What practical frameworks—contractual, institutional, or cultural—might help avoid repeating historic patterns of value concentration at scale?
These are not purely technical challenges. They are questions of organisational design, governance, and trust.
Why human capability matters more, not less
A common misconception is that focusing on people is a defensive response to AI. In practice, the opposite may be true.
As tools become more powerful, human capability becomes the determining factor in whether outcomes are responsible, trustworthy, and socially sustainable. Technical capability without corresponding human judgement risks accelerating problems rather than solving them.
Academic work on human capability—including research associated with Professor Rose Luckin—emphasises the importance of strengthening durable capacities alongside technological deployment. These include:
- Judgement and insight: critical thinking, ethical reasoning, and contextual understanding in AI-supported decisions
- Resilience and adaptability: enabling learners and professionals to navigate uncertainty and evolve as roles change
- Collaboration and networks: building trust-based connections across education, industry, and ecosystems to address shared challenges
Underlying this is a simple principle: innovation and care must advance together. Scaling technological power without equal attention to human capability and governance risks eroding confidence, legitimacy, and long-term value.
From reflection to practice
At EFEC, our work focuses on translating these principles into practical pathways where education, talent, and innovation ecosystems intersect. This work is being developed through pilots and partner conversations, with a focus on evidence, delivery quality, and responsible scaling over time.
Current areas of focus include:
- Early-stage talent development: programmes that give young people real sector exposure while building transferable capabilities relevant to an AI-shaped economy
- Workforce upskilling pathways: we are exploring employer-aligned routes grounded in real organisational workflows, focused on applied and responsible capability building. This work will be developed after the FutureReady pilot is launched and early evidence is available
- Ecosystem facilitation: supporting well-governed collaboration, moving beyond purely transactional engagement
These initiatives are not presented as final answers. They are practical experiments—designed to learn, adapt, and contribute to a wider conversation about how institutions can respond thoughtfully to technological change.
A small number of operating disciplines
To translate principles into practice, three operating disciplines guide our approach:
- Evidence before scale: prioritising pilots, learning loops, and validation before expansion
- Governance before velocity: embedding clear boundaries and responsible practices from the outset
- Capability before tools: focusing on human judgement and resilience, not tool-use alone
These disciplines help protect quality, trust, and long-term relevance as AI continues to reshape how value is created.
An invitation to the ecosystem
The challenges raised by AI—around credit, value, and human capability—are too complex for any one organisation to address alone. They require shared inquiry, disciplined collaboration, and openness to new institutional models.
We would like to leave the UK and international life sciences and innovation community with two questions:
What kind of future are we collectively building?
And how might each of us contribute to shaping it responsibly?
This article was first published on Cambridge Network and is reposted for wider discussion. The views expressed reflect EFEC‘s perspective and do not represent the official position of Cambridge Network or any affiliated institutions
