datacamp-dlr-report-2025-v2

2 0 2 5 L I T E R A C Y T H E S T A T E O F Data + AI REPORT -- 1 of 68 -- The State of Data & AI Literacy 2025 Contents Introduction Data & AI Literacy in 2025 — A Generational Transformation The Data & AI Skills Gap Revisited Data and AI Skills: The Engines of Better Performance The Data & AI Skills Ecosystem The State of AI Adoption in the Enterprise The Who, What, and How of AI Adoption The Gains and Challenges of Early Adopters The Data and AI Skills Agenda The State of Data & AI Upskilling The Challenges Leaders Are Facing in Filling the Data and AI Skills Gap The ROI of Data and AI Upskilling 5 Lessons to Effectively Upskill & Reskill Your Teams in the Age of AI 1: Align Learning ROI with Business Objectives 2: Focus on Change Management 3: Make Learning Interactive and Useful 4: Make Sure Data & AI Literacy Go Hand-in-Hand 5: Personalize Learning at Scale The 2025 Data and AI Competency Framework What the Future Holds for Data and AI Literacy Conclusion and Methodology Panel of Experts 1 2 3 7 16 21 22 27 31 32 36 40 46 47 50 53 56 58 61 62 63 64 -- 2 of 68 -- In this era of change, one factor remains constant: skills. Now in its third edition, The State of Data & AI Literacy Report continues to track the skills agenda amidst an ever-shifting landscape, shedding light on how organizations are adapting to the AI era. This year, we asked 500+ leaders in the US and the UK and explored key questions shaping the future of data and AI literacyV  How has the data and AI skills agenda evolved over the past 12 months  How are organizations adopting AI  How are organizations adapting to the data & AI skills gap  What best practices can help organizations future-proof their workforce? To add to their perspectives, we have collected insights from industry thought leaders and DataCamp for Business customers on many of the themes and trends covered in this report and those for the decades to come. Join us as we navigate the road ahead. We stand at the cusp of a new industrial revolution—driven by the power of data and AI. Just as electrification reshaped industries long after the discovery of electricity, the full potential of AI will take time to unfold. We are in a multi-year transformation in which awe-inspiring research will continue to outpace real-world adoption. Introduction The State of Data & AI Literacy 2025 1 -- 3 of 68 -- The State of Data & AI Literacy 2025 Data and AI Literacy in 2025 A Generational Transformation SECTION 1 In this first section, we revisit the data and AI literacy skill gap, uncover how AI literacy is outgrowing data literacy, and unpack the most important data and AI skills leaders need from their teams. -- 4 of 68 -- 3 69%of leaders believe AI literacy is important for their teams’ daily tasks, up 7% from last year. 86%of leaders believe data literacy is important for their teams’ daily tasks, Stable since last year. Over the past three years of running this report, we have established just how important data and AI literacy skills have become for leaders. This year’s insights do not buck the trend. Today, more than 86% of leaders across the US and UK identify data literacy as an important skill for their team, and 69% identify AI literacy as an important skill for their team. While we see AI literacy increasing in importance and data literacy remaining stable year over year, the fact is: the majority of leaders believe that data and AI literacy remain essential for the future. The Data and AI Skills Gap Revisited The State of Data & AI Literacy 2025 -- 5 of 68 -- We also surveyed leaders about the growing importance of data and AI skills over the last five years. The same question was posed to leaders in 2023 and 2024, and the results show an interesting trend: the demand for AI literacy skills is outgrowing that of data literacy. We learned that two of the five fastest-growing skills leaders needed from their teams over the past five years are data and AI skills—47% of leaders equally point to AI literacy and business intelligence as the fastest-growing skills they need from their teams. Furthermore, while AI literacy scored 7% higher on this question compared to last year, data literacy and data science skills decreased by 2% in the same time frame. Interestingly, this is even more pronounced among the C- suite, who see AI as the fastest-growing skill they need from their organizations—with 55% of executives pointing to AI literacy as the fastest-growing skill-set over the past five years versus 41% for data literacy. Is AI literacy outpacing data literacy? The State of Data & AI Literacy 2025 While this is not surprising given the democratization of AI over the past 12 months, treating AI literacy in isolation from data literacy would be short-sighted. As we explore in the final section of this report, AI literacy and data literacy go hand-in-hand. Effective AI adoption requires a strong foundation in data skills, from understanding data governance to framing analytical questions. Organizations that invest in both areas will be best positioned to navigate the complexities of the AI era. 4 “Having some sort of data literacy—understanding what you can do with data and what data insights mean, understanding what correlation is, what regression is—is going to be a skill that's fundamentally useful even more in the future than today.” Michael Berthold CEO at KNIME Listen to Podcast -- 6 of 68 -- The State of Data & AI Literacy 2025 Four of the top seven fastest-growing skills were data AND AI skills BI and AI skills have grown more in importance for leaders in the past 12 months “In the past five years, which skills have grown most important for your team (or department)? Rank by order of importance.” “In the past five years, which skills have grown most important for your team (or department)? Rank by order of importance.” 5 -- 7 of 68 -- 6 50%of leaders believe their organization has a data literacy skill gap, Down 7% from 2024. 60%of leaders believe their organization has an AI literacy skill gap, Down 2% from 2024. Despite growing awareness of the importance of data and AI skills, progress in closing the data and AI skills gap remains slow.‡ j 50% of leaders in the US and UK report a data literacy skills gap, marking a 7% improvement from last yeari j 60% of leaders say there is an AI literacy gap, a modest 2% decrease from the previous year. While these figures indicate some progress, the overall picture remains roughly the same. Year over year, the data shows that half of leaders still struggle with a data literacy gap, while three out of five report an AI literacy gap. The question begs here: given how important data and AI are for leaders, why do we not see improvement in the skills gap? Let’s explore this in more detail. The data and AI skills gap: Slow gains The State of Data & AI Literacy 2025 -- 8 of 68 -- 7 80%of leaders agree that AI will make it easier for their teams to work with and draw insights from data. We've already seen how the demand for data and AI skills continues to rise and how the skills gap persists. Unsurprisingly, when leaders are asked why these skills matter, we hear the same response: data and AI literacy are engines of better business performance. Specifically, data literacy powers better decision-making, while AI literacy fuels innovation. Moreover, as you’ll see throughout this report—there is a strong interplay between data and AI literacy, as 80% of leaders agree that AI will make it easier for their teams to work with and draw insights from data. As a result, instead of being just an innovation engine, AI literacy is also a force multiplier for better decision-making. Data and AI Skills: The Engines of Better Performance The State of Data & AI Literacy 2025 -- 9 of 68 -- 8 The State of Data & AI Literacy 2025 We also surveyed leaders about the growing importance of data and AI skills over the last five years. Once again, US and UK leaders point to poorer productivity and decision-making as the main risks of not developing data literacy skills. When asked, 40% cited decreased productivity, while 39% highlighted inaccurate decision-making as the principal risks of not developing adequate data literacy skills. Beyond individual decision-making, 31% of leaders report that a lack of data literacy stifles innovation, and 26% say it makes it harder to keep pace with competitors. The consequences aren’t just operational; they affect workplace morale, too. 15% of leaders cite burnout and employee attrition as direct outcomes of poor data literacy, underscoring that data skills are not just a business necessity but a key factor in employee satisfaction and retention.
 The impact extends to both customer experience and the ability to meet goals—23% of leaders believe a lack of data literacy negatively affects customer service. In comparison, 21% say it leads to missed team or departmental targets. Data literacy: The engine for better decision-making -- 10 of 68 -- The State of Data & AI Literacy 2025 9 Data literacy skills are seen as engines of improved decision-making AI literacy skills are seen as engines of improved innovation “What risks is your department or team facing if your people do not have adequate AI skills?” “What risks is your department or team facing if your people do not have adequate data skills?” -- 11 of 68 -- The State of Data & AI Literacy 2025 10 While data literacy underpins improved decision-making, leaders point to AI literacy as a key driver of innovation. When asked about the risks of not developing AI literacy skills, 37% of leaders say it slows innovation, while 34% report decreased productivity due to ineffective AI adoption. The competitive risks are equally pressing—32% of leaders believe their teams struggle to keep up with competitors without AI skills, and the same percentage say it leads to slower decision-making. 20% highlight poor customer experience, while 17% cite missed internal targets, reinforcing AI literacy as a critical factor in business performance. Like data literacy, AI skills gaps also impact workplace morale. 16% of leaders cite poor employee experience, and 15% highlight burnout and attrition. As AI adoption accelerates, closing the AI skills gap is no longer optional; it’s necessary for long-term success. AI literacy: The engine for better innovation -- 12 of 68 -- The State of Data & AI Literacy 2025 11 65%of leaders report that AI-literate employees drive better results than those without AI skills. 78%of leaders report that employees
 with strong data literacy skills consistently outperform their less data-literate counterparts. We can also glean the value leaders see in building data and AI literacy skills by looking at early adopters. With any new technology, there are always individuals within organizations who push adoption forward, setting the standard for others to follow. When asked about the performance of these individuals, leaders overwhelmingly agree that those with stronger data and AI skills consistently outperform their peers. According to this year’s survey, 78% of leaders report that employees with strong data literacy skills consistently outperform their less data- literate counterparts. Similarly, 65% of leaders say AI-literate employees drive better results than those without AI skills, further showcasing the growing importance of these competencies in today’s workforce. When looking at how employees with data and AI literacy skills outperform those without them, we see measurable improvements across key dimensions. Employees with stronger data literacy skills make faster, more accurate decisions and drive better business outcomes. Meanwhile, employees with AI literacy skills excel at innovation and creating novel customer experiences. Beyond technical skills, leaders also perceive data- and AI-literate employees as more engaged, resilient, and likely to stay with their organizations longer. The early adopter advantage -- 13 of 68 -- The State of Data & AI Literacy 2025 12 Early adopters of data and AI literacy are already outperforming those without data and AI skills “What value do data-literate employees provide over those with insufficient data skills? (Rank them by importance)” “What value do AI-literate employees provide over those with insufficient AI skills? (Rank them by importance)” -- 14 of 68 -- The State of Data & AI Literacy 2025 13 This trend is also apparent in hiring dynamics. Leaders are increasingly willing to pay a premium for talent with strong data and AI skills? I 79% of leaders now say they would offer higher salaries for candidates with strong data literacy skills, up from 66% in 2023 and 72% in 2024G I Similarly, 71% of leaders are prepared to pay more for AI-skilled employees, a significant jump from 60% in 2024. Beyond the willingness to pay, the salary premiums are also significant. For data literacy, 21% of leaders would offer an extra 10-15%, while 20% are prepared to pay 15-20% more. Additionally, 14% would pay 20-30% extra, and 15% are willing to offer 30-40% higher salaries for employees with better data literacy skills. The numbers for employees with AI literacy skills are equally striking. 19% of leaders would offer a 10-15% premium, while 18% would pay 15-20% extra, and 16% are willing to go as high as 30-40%. Most notably, 12% of leaders say they would increase salaries by 40-50% for candidates with AI skills. These figures make one clear: those who invest in data and AI literacy outperform their peers and command higher salaries in a market that increasingly values these skills. -- 15 of 68 -- The State of Data & AI Literacy 2025 14 Leaders are willing to pay a premium for data literacy skills Do you agree or disagree with the following statement: “When hiring someone new, I’m willing to pay a higher salary to a candidate who has good data literacy skills over a candidate who does not.” If you answered yes to the previous question, what salary premium will you pay to a candidate with high data literacy skills? -- 16 of 68 -- The State of Data & AI Literacy 2025 15 Leaders are willing to pay a premium for AI literacy skills Do you agree or disagree with the following statement: “When hiring someone new, I’m willing to pay a higher salary to a candidate with good AI literacy skills than a candidate without?” If you answered yes to the previous question, what salary premium are you willing to pay to a candidate with high AI literacy skills? -- 17 of 68 -- The State of Data & AI Literacy 2025 16 As leaders continue to value data and AI competencies, the specific data and AI skills they prioritize remain consistent with trends from the past two years. Once again, leaders emphasize that the most valuable skills today are at the foundation of the data and AI literacy spectrum—practical, non-technical competencies that enable employees to work effectively with data and AI in daily workflows. The Data and AI Skills Ecosystem -- 18 of 68 -- The State of Data & AI Literacy 2025 17 Watch Here When asked about their teams’ most critical data skills, leaders prioritize data-driven decision-making, analysis, and storytelling over highly technical skills like programming or machine learning. At the top of the list, 84% of leaders identify data-driven decision-making as the most important skill, followed by data analysis and manipulation (81%) and interpreting data visualizations and dashboards (80%). Business intelligence tools also remain a key focus, with 77% of leaders emphasizing their importance, alongside the ability to create visualizations and dashboards (76%). Given the importance of communicating with data, it’s no surprise that 69% of leaders highlight data storytelling as essential. While more technical skills like databases (68%), data engineering (65%), machine learning (61%), and programming (58%) hold value, they tend to rank lower in importance than skills that are applicable to everyone. As in previous years, equipping all teams with descriptive analytics skills is central to allowing effective data utilization throughout an organization. The most important data skills leaders need from their teams today “Nearly every business function is working with data—clear, evidence-based communication isn’t just for data teams—it’s a vital skill for everyone.” Paulina Davila VP, Analytics Insights & Storytelling, JPMorganChase -- 19 of 68 -- The State of Data & AI Literacy 2025 18 Uthman Ali, Global Head of Responsible AI at BP Listen to Podcast "What does every organization want? They want all of their employees to use AI safely, with awareness of the risks and how to mitigate them. If you want AI scaled up and used appropriately, your organization needs to train and upskill its employees on Responsible AI." When asked about the most critical AI skills they need for their teams, leaders continue to prioritize foundational AI knowledge, responsible AI practices, and practical business applications over deep technical expertise. 77% of leaders identify a basic understanding of AI concepts as the most essential skill. Close behind, 74% emphasize AI ethics and responsible AI best practices, which we'll explore in depth later.Similarly, 72% of leaders stress the importance of understanding AI applications in business, reinforcing the need for employees to identify relevant AI use cases and recognize where AI adds value—and where it does not. As AI-powered tools like ChatGPT become embedded in workflows, 68% of leaders cite prompt engineering and the ability to guide AI-generated outputs as a valuable skill. Meanwhile, developing AI systems from scratch ranks significantly lower (56%), underscoring the reality that most organizations are more focused on leveraging AI effectively rather than building it themselves. The most important AI skills leaders need from their teams today -- 20 of 68 -- The State of Data & AI Literacy 2025 19 skills at the foundation of the data and AI literacy spectrum are seen as most important by leaders "How important, if at all, are the following data skills for the day-to-day tasks of employees in your organization?” -- 21 of 68 -- The State of Data & AI Literacy 2025 20 Data and AI skills at the foundation of the data and AI literacy spectrum are seen as most important by leaders "How important, if at all, are the following data skills for the day-to-day tasks of employees in your organization?” -- 22 of 68 -- The State of Data & AI Literacy 2025 The State of AI Adoption in the Enterprise FROM EXPERIMENTATION TO WIDESPREAD ADOPTION SECTION 2 In this section, we examine the current state of AI adoption in the enterprise, who are the biggest users of generative AI, the gains and challenges coming out of early adopters, and a lot more. -- 23 of 68 -- The State of Data & AI Literacy 2025 22 As AI continues to reshape the enterprise, we set out to understand the current state of AI adoption—who is using AI, how frequently, and for what purposes. Three key insights emerged. First, AI adoption is already widespread, with the majority of organizations actively using it. Second, technical teams, especially IT, data, and R&D, are leading the way in AI adoption. Finally, coding and data analysis are, so far, the killer apps of AI, reinforcing our assertion that AI will lower the barrier to working with data. The Who, What, and How of AI Adoption -- 24 of 68 -- The State of Data & AI Literacy 2025 23 “Does your team or any other team within your organization use one or more AI tools in their work?” “How often does your team/teams within your organization use AI tools for their tasks?” AI adoption is already at healthy levels We first wanted to understand how many teams within organizations are actively using AI, and our research revealed a striking statistic: only 9% of leaders report that no one in their organization uses AI. This means that for most organizations, AI has already become part of workflows in some capacity. Expanding on adoption patterns, 64% of leaders say their teams actively use AI, while an additional 49% report that other teams in their organization also leverage AI. Beyond mere adoption, AI is being used frequently. 82% of leaders state that AI is used at least once a week, with 39% saying it is used daily, 27% reporting use two to three times per week, and 16% stating it is used once a week. These numbers clearly show one thing: AI is here to stay. AI adoption is growing rapidly -- 25 of 68 -- The State of Data & AI Literacy 2025 24 When looking at specific tools, we can see AI adoption is primarily driven by widely available generative AI systems, with 77% of leaders reporting that their teams use ChatGPT, making it the most commonly adopted AI tool in the enterprise. 58% use Google Gemini, 14% use Midjourney, and 13% use Anthropic’s Claude, reflecting growing adoption across both productivity and creative AI applications. Beyond consumer-facing applications, 33% of organizations have integrated Microsoft or GitHub Copilot. Additionally, 21% of organizations have built custom AI tools, a trend particularly common in large enterprises that prioritize security, privacy, and compliance. CHATGPT, GEMINI, AND COPILOTS LEAD THE ADOPTION WAVE “Which AI tools are currently being used by your team/ teams in your organization?” -- 26 of 68 -- The State of Data & AI Literacy 2025 25 Eran Yahav CTO at Tabnine Listen Here As AI adoption expands across enterprises, we also wanted to understand who uses AI the most within organizations. The results were both predictable and counterintuitive. Unsurprisingly, technical teams are the biggest adopters of AI, particularly those focused on coding, data, and research. 60% of IT teams, 54% of data & analytics teams, and 43% of R&D teams actively use AI for coding, research, and experimentation. This finding should not be surprising, as coding has become one of the killer apps of LLMs. As Eran Yahav, CTO at Tabnine, said on DataFramed: Technical teams are leading AI adoption, but functional teams lag “AI will accelerate all activities across the software development lifecycle. Code generation, documentation generation, test generation, code review and deployment to production. At all stages of the software development lifecycle, you already see assistance providing significant value and significant acceleration." -- 27 of 68 -- The State of Data & AI Literacy 2025 26 However, what stands out is the relatively low adoption in business functions that could greatly benefit from AI-assisted workflows. Marketing (34%), operations (34%), customer support (33%), and finance (31%), all functions where AI could enhance productivity, decision-making, and customer interactions, show lower-than-expected adoption. Even sales teams (25%), which could leverage AI for personalized messaging, better prospect research, and more, are among the slowest adopters. This gap suggests that while technical teams are leading the charge, many functional areas have yet to fully embrace AI’s potential, presenting a significant opportunity for broader enterprise AI integration. Technical teams lead adoption—but a big opportunity for functional teams remains Question answered: “Which departments are currently using AI in your organization?” -- 28 of 68 -- The State of Data & AI Literacy 2025 27 When looking at the impact of AI on teams that have already adopted it, leaders are seeing measurable gains in productivity. When asked about the effects of generative AI tools on their teams, 84% of leaders agree that these tools have improved team productivity. More significantly, only 3% believe AI has had no impact on productivity, reinforcing that AI is already making a tangible difference in workplace efficiency. While full-scale AI adoption still has a long way to go, especially among functional teams, these early gains are welcome news. The clear productivity boost is creating momentum and excitement around AI adoption, encouraging more teams to explore how they can leverage AI effectively in their workflows. The Gains and Challenges of Early Adopters “AI adoption has to be seen as a tide that lifts all the boat together. So if you really want to scale adoption, you have to have the leadership setting the agenda and making sure that they are engaged. It's really a question of making sure that you are watering all the plants at the same time.” Tathagat Varma Global TechOps Leader at Walmart Global Tech Listen to Podcast -- 29 of 68 -- The State of Data & AI Literacy 2025 28 With the benefits of AI adoption clear, we also asked leaders to report on any downsides they’ve encountered when implementing the tools. While 36% of leaders say AI has had no negative impact on their teams, concerns remain. For example, 30% report an increase in falsehoods and hallucinations in AI-generated outputs, highlighting the risks of relying on AI content without human oversight. Similarly, 30% cite increased bias in work, while 25% highlight a rise in low-quality outputs, reinforcing the need for stronger AI literacy and responsible AI practices. “It's crucial for everyone to grasp issues like bias and fairness. And now with the rise of generative AI, our attention has shifted toward challenges like hallucinations, misinformation, toxicity, and intellectual property concerns. Unless a business truly understands these complexities, it’s hard to navigate the risks in this space effectively.” Ranil Boteju Chief Data and Analytics Officer at Lloyds Banking Group Watch Here -- 30 of 68 -- The State of Data & AI Literacy 2025 29 “To what extent do you agree or disagree with the following statement: “AI tools like ChatGPT have increased productivity in my team?” “What negative effects have you noticed from your team/ teams in your organization using AI tools daily? (Select all that apply)” AI is driving productivity gains, but the risks cannot be ignored -- 31 of 68 -- The State of Data & AI Literacy 2025 30 “What challenges do employees in your organization face when using AI tools effectively?” We also wanted to understand the challenges associated with AI adoption, and leaders identified several key barriers to scaling the use of AI throughout their organization. 37% point to a lack of clear use cases, making it difficult for teams to determine where AI can be effectively deployed. 32% cite a lack of formal training, underscoring the need for structured AI upskilling programs to ensure employees use AI effectively and responsibly. Adjacent to training, 26% find AI tools too complex, making them less accessible for non-technical employees. Adoption challenges are also cultural. 32% of leaders report resistance to AI adoption within their teams, highlighting the importance of change management and cultural shifts in successful AI adoption. Looking at these findings from a high level, the challenges organizations face are fundamentally people issues. From lack of clear use cases to resistance and insufficient training, the biggest barriers to AI adoption stem from how teams understand, interact with, and implement AI. In the next section, we’ll explore how organizations are approaching the data and AI skills agenda and how upskilling and reskilling initiatives have changed since our last report. The challenges in scaling AI adoption The majority of challenges teams encounter with using AI effectively are all people-based -- 32 of 68 -- The State of Data & AI Literacy 2025 The Data and AI Skills Agenda STEADY GAINS SECTION 3 In the last section, we saw that a third of leaders cite lack of formal training as the biggest challenge teams face when using generative AI effectively. Now, let’s examine how organizations are addressing this problem and how data and AI upskilling efforts have evolved over the past year. -- 33 of 68 -- The State of Data & AI Literacy 2025 32 Robin Sutara Field Chief Data Strategy
 Officer at Databricks Watch Here Leaders report significant changes in how their organizations address the data and AI skills gap compared to last year. The good news is that organizations are making significant improvements in workforce training. When asked about the state of data literacy training, 46% of leaders now report having a mature, organization-wide data literacy program, a significant increase from 35% last year. Meanwhile, 29% of leaders say that only technical roles receive data training, marking a 5% decrease, while 20% report that only non-technical roles also receive training, reflecting a 6% improvement. Encouragingly, the percentage of organizations that do not provide any data training has dropped to 7%, down from 12% last year. AI training has seen even greater growth. 43% of leaders now report having a mature AI literacy program for all employees, a substantial jump from 25% last year. The State of Data and AI Upskilling Meanwhile, 23% of organizations limit AI training to technical roles (down from 26%), and 16% extend AI training to non-technical roles (down slightly from 18%). Most notably, the number of organizations that do not upskill employees on AI has dropped from 26% to 15%, highlighting a clear shift toward recognizing AI literacy as a critical workforce skill. This increase in upskilling efforts is further supported by an improved perception of access to learning resources. 74% of leaders now say their teams have access to training resources for data skills (up 9% since last year), and 69% report that their teams have resources for AI skills (up 13%). This reflects a growing recognition among leaders that organizations are beginning to invest in their workforce’s skills. “Our roles as leaders is to think about how can we empower people across an organization to effectively use the data product or service we're building. When we approach this through a data maturity lens, it's important to also consider the different user personas within each domain. The skills and capabilities you need to develop for technologists will differ from those required on the business side.” -- 34 of 68 -- The State of Data & AI Literacy 2025 33 The data and AI upskilling agenda is gaining momentum “What would best describe the state of data training at your organization?” “What would best describe the state of AI training at your organization?” -- 35 of 68 -- The State of Data & AI Literacy 2025 34 Key features of the program> \ DataCamp courses as pre-requisites for live sessionL \ Online sessions delivered by Bloomberg in-house expertL \ Capstone projects built with Bloomberg data “With our blended learning program, learners were able to go from never writing a line of code in their entire life to completing a data- driven news analysis.” Key features of the program> \ One-week immersive session designed and curated by the DataCamp tea \ Sessions delivered to different teams by DataCamp instructorL \ Meant to kickstart a wide-ranging skills transformation project “Together with DataCamp, we created custom projects using company- specific data, allowing participants to apply their new skills in a practical, business-relevant way.” Sheil Naik Senior Technical Trainer and Technical Program Manager for Global Data, Bloomberg Hannah Cuypers Project Leader Digitalisation & IT at Porsche AG Blended learning is the most widely used form of upskilling When it comes to how organizations are approaching data and AI training, blended learning (a mix of online-based training and instructor-led sessions) remains the most popular method. 42% of leaders report using blended learning as their main upskilling methodology. We see this first-hand with DataCamp for Business customers—who combine the use of DataCamp courses and live sessions to effectively drive learning outcomes. Bloomberg combines online learning with instructor-led sessions to drive learning outcomes Volkswagen kickstarts a learning culture with a deeply immersive week of training Learn More Learn More -- 36 of 68 -- The State of Data & AI Literacy 2025 35 Other approaches include internally created online-based training (18%), instructor-led sessions (13%), and third-party online-based training providers (11%). While external training providers remain a smaller piece of the puzzle, the fact that only 12% of organizations report not providing any data and AI training is a positive sign that upskilling is becoming a higher priority. Blended learning and online learning are the most popular ways of addressing the data & AI skills gap "How do you upskill your workforce on data & AI skills?” -- 37 of 68 -- The State of Data & AI Literacy 2025 36 Beyond budget and resources, leaders also face structural challenges in implementing training programs. 25% say they struggle to understand where to start, and 24% cite a lack of executive support, making it difficult to drive company-wide adoption. Additionally, 23% point to a lack of ownership over training programs, further reinforcing the need for a playbook on how to approach building data and AI literacy. Interestingly, 20% of leaders say they face none of these challenges, indicating that some organizations have already built mature, structured training programs. However, for the majority, overcoming these barriers is essential for closing the skills gap and ensuring long-term AI and data literacy. Despite progress in upskilling, many organizations still face significant challenges in closing the data and AI skills gap. From budget constraints to employee resistance and lack of executive support, these barriers continue to slow down large-scale training initiatives. When asked about the biggest obstacles to data and AI training, 33% of leaders cite a lack of budget as the primary challenge, making it the most frequently reported barrier. Employee engagement also remains a concern. 27% of leaders report resistance to training, while 27% also highlight inadequate training resources, suggesting that organizations are still struggling to provide effective, accessible, and engaging learning experiences. The Challenges Leaders Are Facing in Filling the Data and AI Skills Gap -- 38 of 68 -- The State of Data & AI Literacy 2025 37 LACK OF BUDGET, EMPLOYEE RESISTANCE, AND INADEQUATE TRAINING RESOURCES TOP THE LIST OF CHALLENGES "What challenges have you faced or are you currently facing when improving your workforce's data and AI skills? Please select all that apply.” -- 39 of 68 -- The State of Data & AI Literacy 2025 38 Despite data and AI being primarily active skills, many education providers have yet to adapt training methods to reflect this reality. Leaders report three key barriers that limit the effectiveness of third-party training providers: lack of applicability, lack of personalization, and difficulty measuring impact; : Lack of applicability: Many people struggle to apply AI and data training in real-world scenarios. 31% of leaders say video-based courses alone are insufficient, and 27% report that employees don’t know where to start, highlighting the need for structured, hands-on learning paths; : Lack of personalization: Training programs often fail to align with job-specific needs. 23% of leaders say the skills taught are not relevant to individuals’ roles, while 18% cite a lack of certifications, indicating demand for credentials that validate expertise and encourage participation; : Difficulty measuring impact: Many organizations struggle to assess the effectiveness of training programs. 30% of leaders say they cannot measure ROI, making it hard to justify ongoing investment. Online learning resources do not provide the needed level of personalization and interactivity -- 40 of 68 -- The State of Data & AI Literacy 2025 39 Online learning resources do not provide the needed level of personalization and interactivity "If you use a third-party online training provider, what challenges have you faced? Please select all that apply.” Take Hint (-30XP) Submit Answer 100 XP Answer the question You've been hired by a media company reporting on a new AI model called vidAIo. Use ChatGPT to write a template post providing this prompt:

 [Create a social media post about the release of vidAIo.] Which limitation means this prompt is unlikely to produce an accurate response? ChatGPT was potentially trained with biased data press 1 ChatGPT can struggle to track conversation context if the focus shifts press 2 ChatGPT has a knowledge cutoff press 3 D aily XP 5 00 Scale experiences with deeply personalized interactive learning DataCamp Whether data and AI literacy for beginners, prompt engineering with ChatGPT, or machine learning for developers, your teams will put their skills into action with hands-on interactive exercises right in the browser. Get Started -- 41 of 68 -- The State of Data & AI Literacy 2025 40 Despite some organizations struggling to measure ROI, leaders overwhelmingly report improved business performance from data and AI upskilling efforts. When asked to rank how their organization has benefited from data and AI training, leaders point to tangible benefits across decision-making, innovation, customer experience, and financial performance. Let’s break it down: The ROI of Data and AI Upskilling V 86% say training has improved decision-making, with 40% seeing significant improvements and 28% experiencing a complete transformationW V 83% report faster decision-making, with 38% seeing significant improvements and 29% reporting a complete transformationW V 85% cite better innovation, with 41% seeing significant improvements and 26% reporting a complete transformationW V 76% say training has improved customer experience (CX), with 32% seeing significant improvements and 24% reporting a complete transformation. V 75% highlight better employee retention, with 36% seeing significant improvements and 22% reporting a complete transformationW V 75% say training has increased revenue, with 32% seeing significant improvements and 25% reporting a complete transformationW V 75% report decreased costs, with 31% seeing significant improvements and 20% reporting a complete transformation. -- 42 of 68 -- The State of Data & AI Literacy 2025 41 "How has your organization benefited from data & AI training?” Leaders are already seeing the ROI of data and AI upskilling -- 43 of 68 -- The State of Data & AI Literacy 2025 42 What’s interesting here is that these are aggregate numbers. When we segment the results based on data and AI upskilling program maturity. When looking at results from leaders who have either a mature organization-wide data literacy program or a mature organization-wide AI literacy program, we can see significant increases in the benefits received from data and AI training. The difference is striking. When we isolate responses from leaders whose organizations have mature data and AI literacy programs, the ROI from training is even more pronounced across every metric. For example, 83% of all leaders saw improvements in faster decision making—but that number jumps to 95% among those with mature programs. Similarly, while 75% of the broader group reported increased revenue, that rises to 92% for those with mature initiatives. This trend continues across the board: better innovation (95% vs. 85%), improved CX (93% vs. 76%), and even decreased costs (91% vs. 75%). The data clearly shows that as organizations invest in maturing their data and AI literacy programs, the benefits scale significantly. This is especially the case when looking at leaders who report “complete transformations” across these different dimensions. “Our goal is to upskill everyone within our organization, because we know how important a common data and AI understanding is for everyone.” Markus Rolle Chief Financial Officer at Telefónica Germany Watch Here -- 44 of 68 -- The State of Data & AI Literacy 2025 43 Leaders with mature data and AI literacy training programs see even higher ROI "How has your organization benefited from data and AI training? (Mature data and AI literacy programs only)” -- 45 of 68 -- The State of Data & AI Literacy 2025 44 While AI and data literacy are critical for business performance, efficiency, and innovation, leaders also recognize their broader societal impact. The findings suggest that AI literacy is increasingly seen as a safeguard against automation risks, misinformation, and ethical concerns4 3 63% of leaders agree that generative AI will lead to job automation within their organization, reinforcing the potential problems AI may introduce into the labor market. However, 59% believe that individuals with AI literacy skills are less likely to be impacted by automation, highlighting AI literacy as a protective measure against workforce displacement4 3 75% of leaders believe that countries and organizations are responsible for ensuring their people have the necessary data and AI skills, emphasizing the role of governments and businesses in preparing society for the AI-driven future. Beyond business performance: Data and AI literacy as social safeguards 3 79% agree that the responsible and ethical use of AI should be taught to every employee, showing a strong consensus that AI education must go beyond technical skills and include ethical considerations and responsible AI use4 3 73% say AI literacy is fundamental for combating online misinformation, reinforcing the idea that data and AI skills are not just about productivity but also about ensuring informed, responsible digital citizens. These findings underscore that AI and data literacy are not just competitive advantages—they are essential tools for navigating the risks of automation, misinformation, and ethical AI deployment. Organizations that invest in AI literacy today are improving workforce resilience and contributing to a more informed and responsible society. -- 46 of 68 -- The State of Data & AI Literacy 2025 45 “To what extent do you agree or disagree with the following statement” Data and AI literacy are essential tools for navigating the risks of automation, misinformation, and ethical AI deployment -- 47 of 68 -- The State of Data & AI Literacy 2025 5 Lessons to Effectively Upskill and Reskill Your Teams in the Age of AI LEARNINGS FROM THE FRONTLINE SECTION 4 While AI adoption is accelerating, success hinges on how well the workforce is prepared to use data and AI effectively. In this section, we share five best practices from DataCamp for Business customers on building scalable, high-impact literacy programs that drive real business value. -- 48 of 68 -- The State of Data & AI Literacy 2025 47 In the previous section, we saw that 30% of leaders struggle to measure the ROI of their training programs. One of the biggest pitfalls when launching an upskilling initiative is failing to align learning objectives with business objectives. When training is disconnected from real business needs, it becomes difficult to track impact, gain leadership support, and drive meaningful change. To bridge this gap, organizations should move beyond skill- based learning objectives and adopt an outcome-driven approach. This means working closely with functional leaders to ensure that upskilling efforts are aligned with business priorities and measurable success metrics. 1—Align Learning ROI with Business Objectives -- 49 of 68 -- The State of Data & AI Literacy 2025 48 Skill-based vs. outcome-based learning objectives Many upskilling programs focus on learning a specific tool or skill, such as “Learn ChatGPT” or “Master Power BI.” While valuable, these skill-based objectives often lack direct business impact. Instead, leaders should shift to outcome-based goals that directly tie learning to measurable efficiency, accuracy, or innovation improvements. For example, suppose you want to upskill a finance team on Power BI. A skill-based goal might be to simply learn Power BI. An outcome-based goal, on the other hand, would be to reduce manual reporting time by transitioning from Excel to Power BI. This approach not only provides a clear business rationale for adopting Power BI, making change management easier but also creates a measurable success metric: time saved on reporting. By linking upskilling efforts to tangible business outcomes, organizations can drive greater adoption, justify training investments, and ensure learning translates into real-world impact. A great example comes from Rolls Royce, which worked with DataCamp for Business to upskill engineers in Python to automate engineering design processes. By focusing on a business process instead of a skill, Rolls Royce was able to curate the right learning resources, upskill their engineers, and deliver a 100x improvement in the speed of design engineering processes. Learn More Akin Keskin Chief of Design Systems,
 Rolls-Royce How Rolls Royce the speed of their design engineering processes with 100x’ed DataCamp Key features of the programO v Outcome-based goal of improving design engineering processes with Python skill_ v Worked with DataCamp for Business to build custom learning pathways that support these skill_ v Engineers upskilled were able to 100x the speed of their design engineering processes “At Rolls-Royce, we’re dealing with complex data sets related to airlines and engines. Through DataCamp, our engineers gained valuable experience and learned how to automate multiple data- handling processes that previously had to be completed manually.” -- 50 of 68 -- The State of Data & AI Literacy 2025 49 If you are a data or learning leader looking to build data and AI literacy skills across the organization, the best way to define outcome-based goals is by building interlocks with the business. Rather than offering generic AI and data training, work closely with functional leaders to ensure upskilling efforts directly address business challenges and drive measurable impact. Here’s how to do it effectively7 = Collaborate early: Meet with department heads to understand the specific pain points that AI and data skills can help solve, such as automating reporting, improving decision-making, or enhancing customer insights> = Create role-specific learning pathways: Different teams require different skills. Finance may need Power BI training, while marketing might focus on ChatGPT for marketing copy creation.H = Define KPIs and measure impact: Set clear business-aligned success metrics (e.g., "reduce manual reporting time by 30%") and track progress to demonstrate ROI. By embedding learning into business priorities and maintaining ongoing alignment with functional leaders, organizations can ensure upskilling drives real outcomes, adoption, and competitive advantage. Build interlocks with the business “To truly understand learners' needs, it’s essential to engage in direct conversations—not just with learners, but also with their managers. These business leaders provide crucial context about how data and AI skills are applied today and what’s expected in the future. Communication with both groups ensures training aligns with real business impact.” Janice Burns Chief Learning Officer, Degreed Watch Here -- 51 of 68 -- The State of Data & AI Literacy 2025 50 We’ve already seen that resistance to change is one of the biggest barriers to data and AI adoption and upskilling. This should come as no surprise, as the workforce naturally feels uncertainty and fear when new technologies emerge. Proactively addressing this resistance is key to successful AI and data training initiatives. 2—Focus on Change Management -- 52 of 68 -- The State of Data & AI Literacy 2025 51 People are more likely to embrace AI and data upskilling when they see how it directly aligns with their current role and career ambitions. When your workforce understands how these skills can enhance daily operations, streamline tasks, and open new growth opportunities, they are far more likely to engage with training programs. However, simply announcing upskilling initiatives isn’t enough— leaders must think like marketers when communicating about AI and data literacy, ensuring that messaging is clear, relevant, and compelling to every employee. This approach means going beyond standard email newsletters with training announcements and instead reaching employees in meaningful ways with messaging that resonates. To drive engagement, excitement, and adoption, organizations must treat upskilling like any internal product launch, delivering the right message at the right time through the right channels. Here are four tactics you can adopt today to effectively communicate the importance of AI and data literacy: Communication, communication, communication "People are busy and organizations face constant change. To make your upskilling program stand out—especially at scale—it must be engaging and memorable. Strong change management, with clear communication, branding, and personal marketing, is often what turns a good initiative into a successful one." | Use diverse and creative communication channels: Engage employees through internal AI events, podcasts, office hours, and town halls to spark interest and dialogue} | Focus on the “What’s in it for me”: Show how AI helps in daily tasks—saving time, reducing manual work, and unlocking new possibilities—rather than using abstract terms} | Build a decentralized network of champions: Empower AI advocates in each department to share wins, guide peers, and build excitement locally} | Create a centralized knowledge hub: Provide a single resource with FAQs, case studies, and learning paths to make AI training easy to access and apply.
 By treating AI and data upskilling as a company-wide movement and adopting a marketer’s mindset, organizations can boost engagement, build trust, and drive meaningful adoption of AI literacy across the workforce. Emily Hayward Transformation Team Manager at Financial Conduct Authority Watch Here -- 53 of 68 -- The State of Data & AI Literacy 2025 52 How Colgate upskilled employees with 14,000+ DataCamp A great example comes from Colgate, which used DataCamp for Business to upskill 14,000+ employees, with active participation from the CEO and leadership team. By leading by example, Colgate reinforced AI and data literacy as a company-wide priority, driving engagement and making upskilling a strategic imperative. Have leaders lead by example The best way to create excitement for data and AI literacy is to incentivize leaders to lead by example. Executives and managers should not just endorse AI upskilling—they should actively participate. When leadership commits to learning AI, it reinforces AI adoption as a strategic priority rather than a passing trend. Leaders who engage in training, share their learning experiences, and integrate AI into their workflows help normalize AI adoption across the organization. The following are three tactics to help leaders lead by example: êâ Make leaders the first learners: Have executives and managers complete AI and data literacy training before rolling it out to the broader workforce· Íâ Encourage leaders to share their learning journeys: Feature leadership stories in town halls, LinkedIn, internal newsletters, or company-wide discussions to show data and AI upskilling in action· â Enable leaders to use data and AI in daily work: Help leaders identify real data and AI use cases for their roles—whether it is leading with the data during meetings or building automations with AI. -- 54 of 68 -- The State of Data & AI Literacy 2025 53 We saw earlier that one of the biggest challenges with online learning is that passive, video-based learning does not drive learning ROI. While videos can be useful, they often fail to build practical, job-ready skills. Instead, organizations should focus on creating memorable, pedagogically sound learning experiences that engage learners and drive real-world application. 3—Make Learning Interactive and Useful -- 55 of 68 -- The State of Data & AI Literacy 2025 54 Use interactive learning if you’re using online training Data and AI skills are active by nature, requiring practice, experimentation, and application. We’ve seen firsthand how transformative active learning experiences can be, ensuring that learners consume information and apply it in real-world scenarios. A great example of this comes from Just IT, which runs a data expert apprenticeship program designed to help organizations upskill teams on Python, R, SQL, Power BI, and Tableau. Despite offering Pluralsight courses and instructor-led training, instructors found themselves spending too much time helping beginners catch up instead of focusing on higher-level learning objectives. To solve this, Just IT leveraged DataCamp for Business’ custom learning tracks to provide a modern, engaging learning experience that helped learners develop prerequisite skills before entering the program. As a result, instructors spent less time on beginner fundamentals and more time on advanced topics, leading to a 100% pass rate in their certification program. Learn More Scott Worland Service Delivery Manager at Just IT How Just IT accelerated its program with blended learning DataCamp Key features of the program# N Personalized learning paths with 100% active learning experiences. N A blended learning program where learners complete prerequisites on DataCamp and Just IT instructors provide in-person training. N 100% certification pass rate for Just IT learners “If we compare other providers to DataCamp, they are polar opposites. Most learning providers are video-based, and lack that crucial interactivity needed to learn data skills. So for us, choosing DataCamp was a no-brainer." -- 56 of 68 -- A mix of online training and instructor-led sessions can ensure your teams learn new AI and data skills and apply them effectively. However, building a true learning culture requires more than structured training. Organizations that foster collaboration, engagement, and friendly competition create an environment where upskilling becomes a shared experience rather than an isolated task. Here are three practical ways to build a strong learning culture  Competitions: Organize data and AI challenges, hackathons, and leaderboard-based competitions to create excitement and engagement around learning  Lunch and learns with experts: Bring in internal and external experts for informal knowledge-sharing sessions where employees can ask questions and discuss real-world applications of AI and data. Use blended learning as the stepping stone for a learning culture The State of Data & AI Literacy 2025 55 ¬ Internal conferences: Host company-wide learning summits to showcase AI and data use cases, employee success stories, and emerging trends—making data and AI literacy a strategic priority. Specsavers boosted engagement in their upskilling program by using DataCamp’s gamification features like XP and leaderboards. To address skill gaps in Power BI and Databricks, they launched tailored learning paths and competitions that drove adoption. The result: 115+ employees upskilled, each saving three–four hours per week—showcasing how blended learning and gamification drive real business impact. “We've begun to build a "ritual" using DataCamp to set fun challenges followed by presentations for our internal data community. So far, we've had around 45 completed challenges. Over time we'll build an expectation around these challenges, which we're running on an approximately monthly schedule. Using DataCamp's gamification, we’re able to generate some real energy for data learning.” Lorraine Pocklington Data Community Manager at Specsavers Read The Case Study -- 57 of 68 -- Earlier in this report, we saw that the demand for AI literacy skills is now outpacing the demand for data literacy. This is not surprising, especially given the urgency around the adoption and deployment of AI within the enterprise. That said, organizations should not make the mistake of deprioritizing data literacy. 4—Make Sure Data and AI Literacy Go Hand-in-Hand The State of Data & AI Literacy 2025 56 -- 58 of 68 -- In many ways, data literacy and AI literacy are two sides of the same coin; one cannot exist effectively without the other. While generative AI has recently dominated public discourse, it is just one piece of the broader AI landscape. Traditional machine learning and predictive modeling— deeply rooted in data literacy—continue to be some of the most valuable AI applications in organizations today. Additionally, understanding data collection, quality, and structure is essential for making sense of generative AI models. More importantly, as AI reduces the barrier to working with data, having strong data literacy is essential for preventing errors and ensuring responsible AI-assisted data work. That’s why data and AI literacy must be mutually supportive. Ultimately, data literacy enables employees to: 2 Frame the right questions: AI can process data, but employees must define what to measure and why4 2 Understand data quality: AI is only as good as its data. Employees need to identify poor-quality or biased data before trusting AI-generated insights4 2 Assess bias in AI models: AI can amplify biases in the data it’s trained on. Employees must critically evaluate AI- driven decisions to avoid reinforcing inaccuracies. Why data and AI literacy are two sides of the same coin The State of Data & AI Literacy 2025 57 “AI is everywhere now, so it’s critical employees use it responsibly. At Salesforce, AI safety training is central to our data literacy efforts. We've run live demos of AI "hacks" to highlight risks and teach safe, effective use.” Anjali Samani Senior Director of AI Engineering, Salesforce Watch Here -- 59 of 68 -- As we’ve seen throughout this report, data and AI literacy are not one-size-fits-all skills. Employees interact with data and AI differently based on their roles, responsibilities, and technical expertise. A finance executive may need to understand how AI impacts risk modeling and regulatory compliance, while a product manager might focus on integrating AI into product development. Similarly, marketers may need to become proficient in AI- driven content generation, while engineers require deeper knowledge of AI model development. To ensure relevance and effectiveness, organizations must design learning experiences that cater to these varying needs. This is where learning personas come into play. 5—Personalize Learning at Scale The State of Data & AI Literacy 2025 58 -- 60 of 68 -- A learning persona is a well-developed archetype representing a specific segment of your workforce based on their data and AI learning needs. Personas help you structure AI literacy programs in a way that makes training more engaging, relevant, and practical for different teams. At DataCamp, we have identified four distinct learning personas for data and AI literacy programs, each requiring different levels of competency, as laid out in our Data & AI Competency Framework. You can download and edit this framework to customize personas for your own organization. Find your learning personas How to create your own learning personas The State of Data & AI Literacy 2025 While we recommend using the Data & AI Competency Framework to get started, every organization is unique. Below are best practices for developing your own learning personas from scratch: 59 -- 61 of 68 -- Understand your learners Start by gathering insights into your workforce’s knowledge, usage patterns, and learning preferences related to data and AI. Use surveys, interviews, focus groups, or assessments to collect data on  Current role: How do individuals use data and AI in their daily tasks  General data and AI understanding: What is their baseline knowledge  Current data and AI usage: What tools are they using, and how comfortable are they  Learning needs and preferences: How do people prefer to learn—self-paced courses, hands-on projects, or workshops  Barriers to adoption: What challenges prevent individuals from using data and AI effectively  Motivation to learn: What incentives would drive engagement in an data and AI literacy program? Summarize and identify patterns Once you’ve collected data, analyze it to identify common themes. Look for patterns in h How people interact with AI and data h Their learning preferenceT h The barriers they fac_ h Their common learning goals Build your personas Using these insights, create fictional profiles that represent each learning persona in your organization. These profiles should capture key characteristics, such as h AI and data literacy levei h Common use cases for AI and data in their rol_ h Preferred learning methodT h Challenges they face with AI adoption To simplify this process, we recommend using the DataCamp persona framework alongside the Data & AI Competency Framework to build personas tailored to your organization. Iterate with learner feedback Once your learning personas are in place, validate them with employee feedback. Conduct follow-up surveys or interviews to refine personas and ensure they accurately reflect your workforce’s needs. By continuously iterating, you’ll develop a more effective and scalable data and AI literacy program that evolves with your organization. By leveraging learning personas, organizations can deliver personalized, role-specific data and AI training, ensuring that every employee receives training that is practical, relevant, and aligned with their responsibilities. The State of Data & AI Literacy 2025 60 -- 62 of 68 -- 11 The 2025 Data & AI
 Competency Framework Access the Data & AI Competency Framework Introduced in the State of Data Literacy 2023 Report, DataCamp’s Data and AI Competency Framework has evolved significantly over the past two years to reflect the rise of generative AI, growing demand for domain-specific data skills, and the need for cross-functional collaboration. Despite these changes, it remains a key tool used by DataCamp for Business customers and learning leaders worldwide to assess skill gaps, guide upskilling, and build data- driven cultures across roles and industries. Data & AI Competency Framework Communicating with Data & AI Working with Data & AI ± Data storytelling: The art of effectively communicating insights and findings from data analysisÀ ± Understanding data science concepts: Being knowledgeable and conversational about the methods, theories, and tools used in the field of data scienceÀ ± Understanding data engineering concepts: Being familiar with the processes and technologies involved in designing, constructing, and maintaining data pipelines and infrastructureÀ ± Understanding machine learning concepts: Being knowledgeable about the possibilities and limitations of machine learning and the techniques used to train and operate predictive modelsÀ ± Understanding AI concepts: Being familiar with key AI technologies, such as ChatGPT, Large Language Models, and Generative AIÀ ± AI applications for business: Understand how to utilize AI and Large Language Models to extract business value from AI. ± Data wrangling and manipulation: Transforming and organizing data for analysisÀ ± Predictive modeling and machine learning: Training and using predictive models to make predictions about future eventsÀ ± Data engineering: Designing and building the infrastructure and processes for collecting, storing, and analyzing dataÀ ± Programming: Mastery of programming languages to perform data -related tasks.D ± Importing and cleaning data: Reading data from various sources and ensuring they are free of data quality issuesÀ ± Data visualization and dashboard design: Creating graphical representations of data and designing interactive dashboards for data exploration and analysisÀ ± Developing AI systems: Create production -ready AI applications, build and fine-tune LLMs for specific use casesÀ ± Responsible AI: Understand the ethical implications of using AI and leverage AI responsiblyÀ ± Working with and steering AI systems: Leverage ChatGPT and other LLMs to automate routine tasks and drive workflow efficiencies. Curriculum Cheat Sheet For Every Skill Level 02 Reasoning with Data & AI ± Business analysis: Using data and analysis to understand and improve business processes and operationsÀ ± Statistical analysis: Using statistical methods to analyze and make inferences from dataÀ ± Reporting with data: Presenting data-based findings and insights clearly and concisely. Reading Data ± Interpreting data insights and visualizations: Understanding and making sense of data-based findings and their representationsÀ ± Data-driven decision-making: Using data and analysis to inform business decisions. Below you will find an updated competency framework including key
 data AI literacy skills covered throughout this report with updated curriculum recommendations. We highly encourage you to use this when building your data and AI upskilling and reskilling programs. Data & AI Competency Framework This framework oversees the entire spectrum of data and AI skills you may look to grow within your workforce. On the next page, we will introduce key personas and roles and will match skills and learning paths for each of them . At DataCamp, we identify four key groups of competencies : and with data and AI. communicating, reading, reasoning, working Curriculum Cheat Sheet For Every Skill Level 01 Responsible AI Working with and steering AI systems Understanding AI concepts AI applications for business AI systems modeling & N/ A B eginner Intermediate Ad v anced The State of Data & AI Literacy 2025 61 -- 63 of 68 -- The State of Data & AI Literacy 2025 What the Future Holds for Data and AI Literacy ON THE CUSP OF THE NEXT INDUSTRIAL REVOLUTION SECTION 5 We opened this report by exploring how data and AI are poised to usher in a future of abundance—transforming industries, accelerating innovation, and unlocking new opportunities at scale. But to truly seize this abundance, data and AI literacy must become a non-negotiable. -- 64 of 68 -- The work on this report began in early January 2025, with the goal of uncovering how organizations are navigating the shifting dynamics of data and AI literacy. Throughout this report, we’ve explored the growing demand for AI skills, the persistent need for data literacy, the challenges organizations face in upskilling, and the best practices that drive meaningful learning outcomes. At DataCamp, we’ve had the privilege of collaborating with organizations worldwide to bridge the data and AI skills gap. Through these partnerships, we’ve witnessed firsthand how leaders champion data and AI literacy initiatives, drive workforce transformation, and prepare their teams for an AI-driven future. This journey is just beginning. If you’re looking to equip your workforce with the skills needed to thrive in the AI era, speak to our team today. Let’s build a more data- literate, AI-empowered world—together. Conclusion This research was conducted using an online interview administered to members of YouGov Plc UK and a USA panel of individuals who have agreed to participate in surveys. In this research, the survey features the opinions of a sample of 533 business leaders in the UK and the USA. The data was collected between 22.01.25— 11.02.25. The qualitative interviews referenced with thought leaders in the data and education space were featured from the DataFramed podcast and DataCamp’s Webinars and Conference Series. Methodology 39 Subscribe to the
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 RADAR recordings Listen Here Watch Here Watch Here The State of Data & AI Literacy 2025 63 -- 65 of 68 -- Panel of experts Ellie Fields
 Chief Product & Engineering Officer at Salesloft Lorraine Pocklington Data Community Manager at Specsavers Uthman Ali Global Head of Responsible AI at BP Anjali Samani
 Senior Director of AI Engineering at Salesforce Tathagat Varma Global TechOps Leader at Walmart Global Tech Janice Burns Chief Learning Officer, Degreed Eran Yahav CTO at Tabnine Ranil Boteju Chief Data and Analytics Officer at Lloyds Banking Group Michael Berthold CEO at KNIME Markus Rolle Chief Financial Officer at Telefónica Germany Paulina Davila
 VP, Analytics Insights & Storytelling at JPMorganChase Robin Sutara Field Chief Data Strategy Officer at Databricks Hannah Cuypers Project Leader Digitalisation & IT at Porsche AG 40 Emily Hayward Transformation Team Manager at Financial Conduct Authority Sheil Naik Senior Technical Trainer and Technical Program Manager for Global Data at Bloomberg 64 The State of Data & AI Literacy 2025 -- 66 of 68 -- The State of Data & AI Literacy 2025 41 Empower your business with world-class data and AI skills DataCamp is the go-to data and AI transformation platform. Equip your workforce with the skills and tools to work with data and AI at scale. Make data-driven decisions—at scale Empower your workforce to make faster and better decisions with data. Drive technology adoption Drive technology ROI by equipping your workforce with the necessary skills to work with modern data and AI tools. Accelerate growth and innovation Drive and sustain innovation and enable your workforce with a common data and AI language. Retain and attract talent effectively Make skills the key to retaining and attracting top talent with tools to easily drive learner engagement. The DataCamp Edge A hands-on interactive learning experience that works From conceptual to coding exercises, your teams will put their skills into action with hands-on interactive exercises, right in the browser. A curated data and AI curriculum for everyone Whether aimed at executives or machine learning engineers, each DataCamp course is curated and handcrafted by a team of experts to help you get started quickly. Beyond online learning—scale effective blended learning programs* We know that online learning is not the only way to bridge the data and AI skills gap. This is why we combine online learning on DataCamp with instructor- led sessions tailored for your exact industry and audiences. * Available as an added-value service Learn why 16M+ learners and 6,000+ organizations prefer DataCamp for data and AI upskilling. With DataCamp, build everyone’s data & AI skills, from busy executives to front-line workers. Keep your team's data skills sharp at all career stages with DataCamp Certifications, an industry-aligned credential that validates their expertise during onboarding, transitions, or promotions. The best data and AI certifications for your team Deep personalization and reporting capabilities Build tailored learning paths and custom capstone projects with our track editor. Uncover learning ROI with DataCamp’s admin platform. Read the case study Read the case study Read the case study Read the case study Read the case study Read the case study More than organizations use DataCamp to transform how their teams use data and AI 6,000+ 65 -- 67 of 68 -- Speak to Our Sales Team Here Thank you for reading Are you an organization interested in scaling
 your data and AI literacy skills? -- 68 of 68 --