AI Literacy

Imagine trying to navigate today’s world without the ability to read and write. Now imagine trying to navigate the world of tomorrow without a fundamental understanding of artificial intelligence. That’s the challenge currently presented to workforces across the globe.

Just as basic literacy unlocked mass participation in education, economy and democracy, AI literacy can unlock society’s ability to harness, question and shape intelligent technologies for collective progress.

And with a projected £79.3 billion contribution to the UK economy by 2035, there is no turning back the age of AI.

In this page, we’ll explore what AI literacy really means, how it intersects with data literacy and what frameworks you can use to measure and increase AI literacy across your workforce to drive transformative change.

What is AI literacy?

AI literacy represents the technical knowledge, durable skills, and future-ready attitudes required to thrive in a world influenced by AI. It enables learners to engage, create with, manage, and design AI, while critically evaluating its benefits, risks, and ethical implications.

AI literacy, at its core, is the capacity to engage intelligently with artificial intelligence.

While definitions may vary slightly across academic, industry and international bodies, a broad consensus exists: AI literacy is the ability to understand, use and think critically about AI technologies and their multifaceted impact on society, ethics and everyday life.

Closely aligned with the principles of data literacy, AI literacy empowers a more effective workforce. This understanding is crucial for enabling better decision-making across all levels of an organisation. By knowing how AI systems function and what their capabilities and limitations ar , AI -literate teams can more accurately interpret AI-driven insights, critically evaluate outputs and leverage AI tools responsibly. This leads to improved problem-solving, enhanced strategic planning, and operational efficiencies.

Modern AI literacy also includes generative AI literacy: knowing how to use, prompt, and evaluate large language models and generative tools effectively. This empowers teams not just to consume AI outputs, but to actively shape them for business impact.

Why does AI literacy matter?

Commercial impact of AI literacy

An April 2025 report by Amazon found that more than half of UK businesses now use some form of AI. It also found that an incredible 92% of adopting businesses were reporting increased revenue as a result. Further to this point, as recognised in DataCamp's 2025 report, Mature AI literacy programmes correlate with a 92% likelihood of revenue increases vs. 75% for less mature programmes, better innovation (95% vs. 85%), improved customer experience (93% vs. 76%) and even decreased costs (91% vs. 75%).

Early adopters of AI are recognising the benefits, using it to build competitive advantage, drive operational efficiencies and ultimately increase revenue. AI has quickly moved past a nice to have feature and is becoming an imperative part of business strategy. However, only 52% of leaders are realising value from GenAI beyond cost cutting, so the full value is yet to be explored.

But the commercial case is clear: organisations with role-based AI learning paths and measurable, outcome-driven metrics see greater ROI. AI literacy is not a “nice to have”; it’s a measurable business enabler.

Cost of AI illiteracy

Without AI literacy, people become passive users rather than empowered participants. They are more likely to fall for AI-generated misinformation, accept biased outputs without question or lose opportunities to use AI as a tool for creativity and growth.

And as AI capabilities continue to expand rapidly, the gap between the AI-literate and AI-illiterate will likely widen, building on existing skill and communication gaps that exist and lead to further collaboration concerns.

And that skills gap is tangible. Multiverse reports that about half of workers have had fewer than five hours of AI training, and only 34% of FTSE100 annual reports even mention AI training. Yet 91% of employees want to improve their AI skills.

In their Skills Intelligence Report, they also found out that workers spend 2.8 hours per day on data tasks (13.8% of the week). Yet 33.9% of that time is used inefficiently. This results in approximately 27 days lost per employee per year. That’s a whopping £61.94bn annual drag on the UK economy. But, when employees are empowered to use AI for repetitive data tasks, they can reduce this time dramatically.

Who needs AI literacy?

Short answer: every role that makes decisions, serves customers or builds processes in an AI-enabled organisation. AI literacy is a critical enabler of productivity, competitiveness, and innovation, especially in non-technical roles. But the required depth varies by responsibility.

Multiverse reported that they see an increase of above 38% efficiency if employees are skilled to use AI well.

On a macro level, AI literacy empowers:

• Employees: to use AI tools confidently, interpret results critically, and protect data.
• Leaders: to govern AI strategy ethically, foresee emerging technology impacts, and align AI with business goals.
• Training providers: to design relevant, sector-specific AI skills programmes.

On a more granular level, AI literacy is essential across every department who wants to remain competitive and future-proof.

• Executives and senior leaders: set direction, risk appetite, governance and investment.
• People managers: redesign workflows, set standards and coach teams on safe, effective use.
• Business functions (Finance, Operations, Marketing, Product, Sales, Service): apply AI to analysis, forecasting, automation and customer experience.
• Data, analytics and engineering teams: ensure model quality, documentation, observability and responsible AI practices.
• Risk, Legal, Compliance and Security: assess privacy, bias, IP, regulatory and third-party risks.
• HR and L&D: define role-based competencies, policies and training pathways.
• Frontline and customer-facing roles: use copilots and generative tools safely to improve speed and service.

AI literacy is both role- and competency-based. Build a common baseline for all employees, then deepen by function to drive adoption, reduce risk and unlock measurable business impact.

What are the four core capabilities of AI literacy?

At Data Literacy Academy, we see AI literacy as having four essential capabilities:

Understanding AI: grasping key concepts, understanding it’s strengths and limitations.

Interacting with AI: using tools such as LLMs, Generative AI and Predictive Analytics effectively to enhance your work.

Evaluating AI: questioning outputs to ensure accuracy and fairness, avoiding bias and hallucinations.

Acting responsibly: applying ethical, secure and compliant practices, in line with general and organisation specific guidance.

What is an AI literacy & skills framework?

Various frameworks have been developed to identify and lay out the core competencies required for AI use. Below, we look at three of them that stand out from a research and data-driven perspective.

The AI Skills for Business Competency Framework

First, let’s look into the AI Skills for Business Competency Framework (developed by The Alan Turing Institute and Innovate UK’s BridgeAI programme). The framework is the first in a series delivering on the UK National AI Strategy’s skills research commitments.

It was developed via public consultation with broad sector representation and geographic reach. Informed by 16+ existing standards and frameworks, it provides a solid basis to review your organisational personas and competencies.

Four learner personas

The framework breaks down four different key learner personas:

1. AI citizens: General public/customers; critical consumers of AI; aware of ethical and security issues.
2. AI workers: Non-AI specialists in AI-adjacent roles; capable of using AI tools and assessing outputs.
3. AI professionals: Data and AI specialists; design, deploy, and maintain AI systems.
4. AI leaders: Senior decision-makers; govern AI adoption, ensure compliance, lead organisational AI strategy.

Five competency dimensions

Next, it maps out five different competency dimensions, which have a

Dimension A: Privacy & Stewardship

• Secure and ethical data management throughout its lifecycle.
• Compliance with legal and regulatory frameworks.
• Applying FAIR data principles for interoperability.

Dimension B: Specification, acquisition, engineering, architecture, storage & curation

• Data collection, integration, and engineering.
• Choosing infrastructure (cloud/on-premise) to meet business needs.
• Deploying AI systems securely and sustainably.

Dimension C: Problem definition & communication

• Framing AI projects with clear success criteria.
• Managing diverse stakeholder expectations.
• Translating technical outputs for non-technical audiences.

Dimension D: Problem solving, analysis, modelling, visualisation

• Applying statistical, ML, and AI methods.
• Understanding model limitations and uncertainty.
• Human-AI teaming, prompt engineering, and architecture selection.

Dimension E: Evaluation & reflection

• Governance and ethical assurance throughout the AI lifecycle.
• Transparency, explainability, and sustainability practices.
• Ongoing professional development and reflective practice.

Data Lab AI Skills Framework

We also like how The Data Lab, Scotland’s innovation centre for data and AI, put their framework together, leaning on similar personas. You can find an image below of how they break up key competencies across personas.

The framework also emphasises the importance of foundational skills to establish a strong base in data and AI. These skills equip learners to navigate and leverage data effectively, setting the stage for more advanced studies and professional roles. Focusing on data and AI ethics and governance as part of strategic development equips individuals to tackle ethical implications, bias mitigation, and responsible AI deployment.

Digital Education Council AI Literacy Framework

And finally, let’s take a look at the framework from the Digital Education Council.

This framework takes a distinctly human-centered approach compared to many of the other frameworks we’ve reviewed. While the Alan Turing Institute’s AI Skills for Business Competency Framework focuses heavily on technical proficiency, workplace application, and business value, the DEC model builds AI literacy across five balanced dimensions:

1. Understanding AI & Data
2. Critical Thinking & Judgement
3. Ethical & Responsible AI Use
4. Human-Centricity (emotional intelligence and creativity)
5. Domain Expertise.

It maps these dimensions across these across three progressive stages of competency. Its adaptable design means it can be applied in any organisation, supporting a gradual, scalable approach to AI literacy development.

Other frameworks to take a look at include:

• Empowering Learners for the Age of AI - An AI Literacy Framework for Primary and Secondary Education. This framework was set up by a joint initiative of the European Commission (EC) and the Organisation for Economic Cooperation and Development (OECD).
• This framework by the AI Literacy Institute

How Bloom’s taxonomy shapes AI literacy

Bloom’s Taxonomy was first developed in 1956. It’s a classic framework for describing different levels of learning, from simple knowledge recall to higher-order skills like analysing, evaluating, and creating.

While it wasn’t originally designed for AI literacy, many modern AI literacy frameworks mirror its structure.

For example:

• Remember – Knowing key AI terms and concepts.
• Understand – Explaining how AI works in plain language.
• Apply – Using AI tools in real-world tasks.
• Analyse – Breaking down AI outputs to understand their accuracy or bias.
• Evaluate – Deciding if an AI solution is ethical or effective.
• Create – Designing new ways to use AI for solving problems.

Because Bloom’s model is already the foundation for countless school and university competency frameworks, it naturally informs how AI literacy is defined and measured today. Many AI literacy scales build on these levels, ensuring they cover not just technical know-how, but the critical thinking and problem-solving skills needed to use AI effectively.

Why data literacy matters for AI literacy

Yes, of course we would say that…

But ultimately, you can’t be AI literate without being data literate.

Why?

AI tools are only as good (or as dangerous) as the data they’re using. That’s why data literacy; the ability to understand, work with, analyse, and communicate with data; is foundational to understanding AI. If you can’t question where and how the data was collected, how it’s processed or what biases it may contain, then you can’t critically evaluate it’s outputs.

In short: data literacy helps you question the input. AI literacy helps you question the output.

How to measure AI literacy in your organisation?

When it comes to measuring AI literacy, there’s no single universal test. Most tools have been designed for schools or specific training programmes, meaning they’re often too narrow to work in a broad workplace context.

Traditionally, there are two main approaches:

• Knowledge tests: Multiple choice or open-ended questions to check what someone knows before and after training. These can be accurate but often focus only on the exact content taught, which limits how well they apply in other contexts.
• Self-assessments: Questionnaires where people rate their own skills and confidence using AI. These are easier to run at scale and remove the need for subjective marking, but they depend on honest self-reflection.

Some researchers use both methods together. However, most existing tools have two big problems in a business setting:

1. They measure very specific knowledge rather than general, adaptable AI skills.
2. They don’t separate the different facets of AI literacy, like ethics, usage, and problem-solving, which are crucial for training programmes to be targeted and effective.

Examples of AI literacy measurement tools

Although most scales are tied to specific sectors, a few stand out:

• AI Literacy Scale (Wang et al., 2022) – A 12-item tool covering Awareness, Usage, Evaluation, and Ethics, inspired by digital literacy models.
• Workplace AI Literacy Scale (Pinski & Benlian, 2023) – Focuses on both explicit knowledge (what you know) and tacit knowledge (your hands-on experience).
• AI Literacy Item Bank (Laupichler et al., 2023) – A collection of 38 questions designed for non-experts, created through expert consensus.

These tools provide starting points but don’t fully capture the bigger picture of AI literacy in a professional, cross-industry context.

That’s why Data Literacy Academy provides both organisation-wide data and AI literacy assessments ahead of launching any programme. Because benchmarking is key to getting a birds-eye view of what the current state of affairs is before allocating learning budgets and curriculums. This allows us to take a customised approach, instead of deploying one-size-fits-all education.

Why self-perception matters in AI literacy

From a psychological perspective, how capable people think they are with AI (their self-efficacy) is just as important as their actual skills.

Research in behavioural science shows that this “perceived control” plays a direct role in whether someone chooses to use AI, and whether they keep using it over time.

If people believe they can:

• Learn new tools quickly,
• Solve problems when things go wrong
• Manage their own emotions around change

…they’re far more likely to stick with AI in the long run, even as technology evolves.

How to build AI literacy in your organisation?

So far we have covered at it’s core what AI literacy is, the importance of AI literacy, key frameworks and how to measure it. But now you ask, how can you embed AI literacy in your business so you can begin to drive innovation, decrease costs, improve customer experience and fuel further revenue growth?

As Stephanie Gradwell at Pendle LLP outlines in her AI Strategy: The Blueprint for Business Success in 2025, organisations ready to thrive in the AI-driven economy focus on three pillars:

• AI fundamentals training: Equip employees with essential knowledge and responsible AI practices.
• AI Centre of Excellence: Create a collaborative hub to accelerate adoption and innovation.
• Early employee involvement: Reduce resistance and build trust by including staff from day one.

Training for AI Literacy across every role

AI literacy starts with a baseline understanding for all employees, regardless of technical expertise. At Data Literacy Academy, we’ve built tailored data and AI literacy Certification programmes designed for different learning personas and industry contexts.

These programmes enable teams to:

• Recognise AI’s current and potential impact in their sector
• Spot AI opportunities and limitations
• Apply AI concepts to their daily workflows for immediate value
• Confidently explore and use AI tools to boost productivity

Collaboration & knowledge sharing for scalable AI adoption

Knowledge is a force multiplier. An AI Centre of Excellence serves as the central hub for:

• Sharing AI use cases, toolkits, and prompt libraries
• Enabling departments to build on each other’s successes instead of starting from scratch

We also recommend building communities of practice: regular meetups, workshops, and virtual forums where technical specialists and business experts co-create AI solutions. This cross-functional collaboration ensures that innovation is both technically sound and commercially viable.

Driving cultural change for sustainable AI use

The success of AI in business depends as much on mindset as on technology. Our change management approach is all about changing hearts and minds, and finding the “why” of every individual. It’s a proven way to make learning initiatives up to 5x more likely to succeed.

We guide organisations through:

1. Awareness – Clearly explain how AI can solve real pain points and align to business goals. You need to bring your teams into the conversation from the outset, not just to understand what AI is doing, but to understand why it matters to them. That means highlighting how AI can solve real pain points, align to business goals and deliver meaningful Return on Investment (ROI).
2. Desire – Engage employees with campaigns, workshops, and champions who make AI relevant to their role. Tailored communication efforts are launched via compelling campaigns, workshops and one-to-one discussions that make AI relatable to each role. You need to engage the early adopters and internal champions not just as trainees, but as co-creators, helping refine content and resonate your message across teams.
3. Knowledge & Ability – Assess current skills, then deliver tailored learning paths, interactive sessions, and expert guidance. Assessments accurately map current understanding, and help you tailor learning paths based on those insights. Live, interactive sessions enable deeper learning, while OnDemand education caters to those who need to grasp the foundational learning in a self-paced manner. Peer engagement and expert-led guidance drive both uptake and retention.
4. Reinforcement – Cultural change sticks when there’s recognition. Celebrate individuals who apply AI in impactful ways. Share success stories widely. Use performance milestones to maintain momentum and commitment to ongoing AI literacy.

Conclusion: AI Literacy as a competitive advantage

AI is no longer a distant future concept. It’s here, reshaping the way we work, compete and create value. The question is most likely not whether your organisation will pay the big bucks for AI, but instead whether your people will have the skills, confidence and critical judgement to use it well. AI literacy is the bridge between potential and performance: the difference between passively consuming AI outputs and actively shaping AI-powered outcomes.

Organisations that invest in AI literacy today are future-proofing their workforce, protecting their brand and unlocking opportunities for innovation and growth that others will miss. And while intelligent technologies will keep evolving daily, the real competitive advantage isn’t the AI itself, but the literacy of the people who wield it.

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