Which barriers hold back AI maturity? A survey of 108 specialists

At a glance

Source

van Veen, Zhu and Ravesteyn (2026). How AI Implementation Barriers Shape AI Maturity? The Interacting Roles of Skilled Employees, Technological Infrastructure and Data Quality. CONF-IRM 2026 Proceedings, 17. aisel.aisnet.org

How it was done

A survey analysed with a statistical model that tests several links at once. Survey. People answered questions once. It shows what goes together, not what causes what.

Who took part

108 specialists from different sectors. The sample is 108 people, not 108 organizations.

Where

The Netherlands.

“Barriers related to skilled employees exert the strongest influence: they directly hinder AI maturity and intensify perceived barriers in technological infrastructure and data quality.”

What it found

• A lack of skilled employees was the barrier most strongly tied to low AI maturity.
• Infrastructure barriers and data-quality barriers did not have a direct effect of their own larger than chance.
• A skills shortage also made the other two barriers feel bigger.

When the result holds

The answers are the views of specialists who work with AI. They rated both the barriers and the maturity of their organization.

Limits

• A conference paper, which gets a lighter review than a journal article.

Practices that use this study

This study supports:

• Budget for people and process, not just tools Licences are the small part of the cost. Plan most of the time and money for training, redesigning work, and helping people through the change. Do first · Effort: low (days, one person) Evidence: Emerging Strategy · Adopt

Assessments linked to this study

• What will help or block the rollout? Consolidated Framework for Implementation Research (CFIR) People at several levels, in interviews · Interviews on the factors you choose, out of 48 Free to use, ask before changing Testing: Strong Interview guides and checklists
• What makes this technology project hard? NASSS-CAT: complexity assessment tools for technology projects The project team, and staff in interviews · Two checklists, a 35-question form and an interview guide Free to use, ask before changing Testing: Moderate Interview guides and checklists
• Is everything in place before an AI system goes live? AI readiness pre-implementation checklist (SID and ADE) The team responsible for the deployment · A checklist in 8 areas Free to use and adapt Testing: Emerging Interview guides and checklists

Claim checks that cite this study

• “MIT Sloan shows that the barriers to digital transformation are mainly cultural” No support found