The AI Skills Needed to Drive Business Growth

AI Skills Competency Framework for every business, by Innovate UK Bridge AI

AI programmes rarely fail due to a lack of algorithms; they fail because organisations lack people who know how to use them effectively.  The government-backed AI Skills for Business Competency Framework (v2, Jan 2024) arrived at a critical moment: 8 in 10 UK executives cited capability, not capital, as the biggest barrier to AI adoption. The framework translates that barrier into a practical skills roadmap that every company can act on to achieve AI-driven business growth. 

The framework also aligns directly with the National AI Strategy’s ambition to widen talent pipelines and raise data maturity across sectors.  This article highlights the main takeaways from the framework, and will gives you the reference points to dive deeper into the new AI skills landscape.

Meet the Four New AI Learner Personas.

To keep training practical and targeted, the framework groups the UK workforce into four ascending personas:

• AI Citizen - Any member of the public who encounters AI

• AI Worker - Employees in non-data roles who use AI tools day-to-day

• AI Professional - Specialists who design, build, or maintain AI systems

• AI Leader - Senior decision-makers accountable for ethical, strategic deployment.

While these personas can overlap, they provide HR and L&D teams with a clear lens for training-needs analysis, and talent development pathways.

Understand the Five Competency Dimensions

Across each persona, five dimensions map the AI project lifecycle. Namely;

• Privacy & Stewardship (A) - Protect sensitive data and apply FAIR standards

• Data Engineering (B) - Collect, store, and curate data securely at scale

• Problem Definition & Communication (C) - Frame the question, manage stakeholders, and surface bias early

• Problem-Solving & Modelling (D) - Apply statistical, ML, and “off-the-shelf” AI tools to deliver value

• Evaluation & Reflection (E) - Embed governance, risk management, and continuous learning

• Dimensions A–D track the project phases, while Dimension E underpins them all with ethics and assurance.

Why This Matters Now - understanding the five competency dimensions

Across every persona, five dimensions map the AI project lifecycle, this is key to understanding how to implement the framework across your business.

• Privacy & Stewardship (A) - Protect sensitive data and apply FAIR standards.

• Data Engineering (B) - Collect, store, and curate data securely at scale.

• Problem Definition & Communication (C) - Frame the question, manage stakeholders, and surface bias early.

• Problem-Solving & Modelling (D) - Apply statistical, ML, and “off-the-shelf” AI tools to deliver value.

• Evaluation & Reflection (E) - Embed governance, risk management, and continuous learning.

• Dimensions A–D track the project phases, while Dimension E underpins them all with ethics and assurance.

Why This Matters Now

Unlike earlier frameworks, DSIT’s model requires everyone to reach at least awareness across all five dimensions.  Workers are expected to be “working” in data privacy and governance, while Leaders should be “expert” in those same areas.  This shift reframes AI from an tactical initiative, typically, led by single team to a company-wide capability discipline.

The framework also aligns directly with the National AI Strategy’s ambition to widen talent pipelines and raise data maturity across sectors.

Government’s Priority Recommendations - 3 key areas

• Bake privacy into every role (dimension A) and report against it

• Build cross-functional data platforms that respect fair principles and secure architectures (dimension B)

• Adopt systematic risk management throughout the AI lifecycle - document decisions and engage stakeholders early (dimension E).

Following these steps contributes to a nationally agile, ethical, and innovation-ready workforce.

Action Steps You Can Start This Week

• Run a rapid skills heat-map - Tag each key role to a persona; rate competence across dimensions A–E.

• Focus on the 2 areas - Prioritise privacy (A) and governance (E); regulators and customers watch these closely.

• Launch a “problem-definition” sprint - Pair a domain expert with a data scientist to re-frame one high-value challenge using dimension C guidelines.

• Update exec scorecards - Add KPIs for model assurance, audit readiness, and continuous-learning hours.

Ready to put skills - not hype -at the heart of your AI strategy?

Book a discovery call today