What could support more mature AI adoption?
The research reveals significant variation in AI maturity across the respondent base. While some respondents described coordinated approaches and expanding use cases, many reported experimentation or team-level adoption. Housemark’s interpretation is that maturity depends not only on technology, but also on the organisational foundations that support it.
Although the survey was not designed to identify definitive characteristics of mature adopters, several themes emerge consistently throughout the responses and provide useful indicators of what may support progress.
Clear leadership and ownership
Leadership engagement varies considerably across the respondent base.
Only eight respondents described AI adoption as executive-led and actively championed. Nineteen reported limited executive involvement and 13 said there was no visible executive sponsorship.
Leadership understanding was also mixed: 14 respondents, 21%, rated it as good, while 20, 30%, rated it as limited or very limited. Responsibility for AI was spread across IT, data and analytics, executive leadership, transformation teams and individual business units, with some respondents reporting shared responsibility or no clear owner.
The findings suggest ownership structures remain highly varied, reflecting the relatively early stage of AI adoption across much of the sector.
How actively does your executive team sponsor AI adoption?
Limited executive involvement
Supported but delegated
No visible executive sponsorship
Executive-led and actively championed
Don’t know
Leadership team’s understanding of AI opportunities and risk
Moderate
Good
Limited
Very limited
Don’t know
Strategy provides direction
Only seven respondents, 11%, reported having a formal AI strategy in place. However, a further 27 said they were currently developing one and 15 said they planned to introduce one in future.
This shows that most respondents either had a formal strategy or were developing or planning one.
Housemark’s view is that a strategy can help organisations connect AI initiatives to wider organisational priorities, define ownership and identify which activities should be developed, measured or scaled.
Stronger data foundations
Data quality appears repeatedly throughout the survey as both a challenge and a priority.
Twenty-six respondents identified data quality as a barrier to adoption and 18 highlighted poor internal data quality specifically. At the same time, improving data quality was selected by 39 respondents, 59%, making it one of the most frequently cited priorities for the next 12 months.
The responses show that organisations recognise data quality as an important issue for AI adoption. As AI use becomes more sophisticated, Housemark believes reliable, accessible and well-managed data will become increasingly important.
Investment in skills and capability
Skills emerged as the most commonly cited barrier in the survey, selected by 35 respondents, 53%.
Importantly, upskilling staff was also selected by 39 respondents, 59%, making it one of the most commonly identified priorities for the year ahead. By comparison, only eight respondents selected investment in new tools.
This suggests workforce capability is a more pressing priority among respondents than expanding access to technology. The findings indicate that many organisations are still developing the confidence, knowledge and practical experience required to support wider adoption.
Governance that supports confidence
Respondents reported a broad range of governance controls, including AI policies, approved tool lists, training, risk assessments, human oversight and AI usage monitoring.
Governance was identified as a barrier by 16 respondents, while 27 selected strengthening governance as a priority for the next 12 months.
Housemark believes proportionate governance can support wider adoption by providing colleagues with clearer guidance, safeguards and confidence in how AI should be used.
Measurement and evaluation
Perhaps the most significant maturity gap identified in the research relates to measurement.
Sixty-five per cent of respondents agreed or strongly agreed that AI had improved productivity and 62% agreed or strongly agreed that it was delivering measurable value. However, only 21% agreed or strongly agreed that they could demonstrate return on investment, while 70% were not measuring AI impact at all.
For Housemark, stronger measurement will be essential if organisations are to identify valuable use cases, make informed investment decisions and scale successful activity.
Improved productivity (agree or strongly agree)
Delivering measurable value (agree or strongly agree)
Can demonstrate ROI (agree or strongly agree)
Not currently measuring the impact of AI
Housemark expert insight:
Characteristics of mature adopters
AI maturity is not defined by the number of tools an organisation has access to. The research shows that technology is only one part of the equation.
Taken together, the responses point to several foundations that could support more mature adoption: leadership engagement, workforce capability, reliable data, proportionate governance and clear organisational priorities.
Housemark’s view is that organisations are most likely to make sustainable progress when AI is treated as a business capability rather than a standalone technology initiative. Mature adoption is less about doing more AI and more about applying it with greater clarity, coordination and purpose.
As the sector moves from experimentation towards wider adoption, organisations that strengthen these foundations are likely to be better positioned to identify value, scale successful use cases and realise long-term benefits from AI.
Jonathan Cox,
Chief Data Officer, Housemark