Skill predictions for the data industry

With 74% of organisations already reporting that they’ve adopted AI in some form, and 67% saying their data usage has improved year-on-year, the foundations are clearly being laid. The focus for 2026 won’t be adoption, it will be accountability, scalability and ownership. We looked at the themes within the report to predict where we think the industry is heading next year.

Prediction 1: Model ownership becomes a board level question

The report shows AI product ownership, MLOps and model governance becoming part of everyday language in data teams.

Next year, we expect this to contribute to more AI Product Owner and Data Product Owner roles as well as clearer lines of accountability between engineering, data science and the business. For data professionals, that will potentially mean more time documenting assumptions and limitations and explaining models to non-technical stakeholders.

Skills wise this means that more than ever those soft skills will come into play. If you feel like you are very technically capable but maybe lack confidence around soft skills like communication or influencing etc, 2026 might finally be the year you decide to invest in yourself and really double down on these crucial business skills.

Prediction 2: Data quality finally moves from “problem” to “platform requirement”

Poor data quality is still the number one blocker to AI progress so next year sorting out the data quality and governance issue will become a strategic advantage.

By next year, data professionals will start to think more about creating stronger data contracts between source systems and downstream consumers. Data quality checks will need to be embedded into pipelines, not added as an afterthought and if they are not already, data governance teams will work more closely with engineers, not just reporting into compliance.

If you work in engineering, analytics or governance, the ability to design and enforce “quality by default” processes will become a key differentiator.

Prediction 3: AI skills expectations will broaden, not just deepen

Lack of in-house AI skills remains one of the top three blockers today. Next year, the skill ask is likely to shift from “Can you build a model?” to “Can you deploy and maintain a model?” “Can you co-design an AI solution with product, risk and operations?” or  “Can you translate capabilities and constraints to stakeholders?”

The most valuable data professionals will blend technical literacy, operational thinking and communication skills and that’s where we expect career progression and salary growth to concentrate.

Download the full report here