Quantum companies
Keep brilliant minds at the centre of AI adoption
A quantum company wanted AI to extend scientists' capacity while sustaining the original thinking, expert judgement and knowledge continuity behind its competitive advantage.
01 / The context
The situation.
A quantum company was expanding AI use in literature analysis, experimental planning and code development. Its strongest scientists contributed much more than documented procedures: they recognised misleading results, questioned assumptions and connected ideas across unfamiliar problems.
The business question extended beyond the amount of work AI could assist. Scientific judgement was itself part of the company's advantage. The development of that judgement, and its transmission to newer researchers, mattered alongside access to existing knowledge.
02 / The challenge
The decision that matters.
AI assistance promised useful capacity, but experts increasingly reviewed outputs. Junior researchers could retrieve answers without necessarily developing the judgement behind them. The company had to decide how to invest in human capability alongside AI, while making essential knowledge less dependent on a few individuals.
Expert review and independent thinking drew on the same scarce attention. More accessible answers did not necessarily give a researcher the experience needed to challenge them. Leadership had to consider immediate assistance together with the longer-term development of people able to handle unfamiliar scientific questions.
03 / The strategic perspective
Treat scientific judgement as a capability to develop.
The value of a scientist's contribution extends beyond the information they can retrieve or the procedures they can describe. Recognising an implausible result, questioning an assumption and making an unfamiliar connection depend on judgement developed through scientific work. Access to a useful answer and the ability to assess it are related, but distinct, capabilities.
This gives human development a direct place in AI investment. Assistance should be considered alongside the opportunities people need to build expertise and the attention experts need for demanding questions. Knowledge continuity matters most when it supports that capacity to think, interpret and develop new understanding.
04 / DELSOL Group’s contribution
How DELSOL Group contributed.
Human capability as an investment priority
DELSOL Group's Cognitive Capital Protection Program helped leaders give critical thinking, creativity and informed judgement a deliberate place in AI adoption discussions, with attention to experienced scientists and emerging talent. The programme connected human development with the company's scientific ambitions. The discussion treated opportunities to think, challenge and learn as part of the capacity the business needed to sustain alongside AI assistance.
Expertise that remains usable
DELSOL Group supported knowledge continuity planning around carefully governed access to expert explanations and decision context, including suitable AI support, with clear human responsibility for interpretation and scientific decisions. The distinction between an accessible explanation and the ability to exercise judgement remained central. Knowledge continuity planning connected the availability of expertise with the context needed to interpret and use it responsibly.
Leadership attention where it matters
Through Fractional CIAO support, DELSOL Group connected specialist capacity, learning opportunities and knowledge dependence with business priorities, helping the board assess useful assistance alongside potential effects on essential human capabilities. This kept scientific capability within the investment conversation. Attention to expert availability and learning needs complemented the discussion of useful AI capacity, linking immediate work demands with the people on whom future development depended.
05 / Intended value
The business objective.
The objective was to gain useful AI capacity while continuing to develop the people behind the company's scientific edge. The aims were to create more room for experts' demanding work, support newer researchers' own judgement, and maintain access to critical knowledge as teams, responsibilities and technologies changed.
The intended value was a more coherent relationship between AI adoption and scientific capability. Decisions about assistance were meant to support both the work of today's experts and the development of the people who would carry the company's knowledge forward.