How to Build Data and AI Literacy in Your Organization

How to Build Data and AI Literacy in Your Organization

By Patrick Wolf

How to Build Data and AI Literacy in Your Organization

By Marta Teneva

July 12, 2024

Building data and AI literacy, along with cultivating a culture that embraces these technologies, is critical for the successful implementation of AI initiatives. This involves comprehensive training and development programs and effective change management strategies. Drawing insights from our webinar Build a Robust AI Data Strategy: Readiness Assessment and Implementation Framework featuring AI expert Dr. Patrick J. Wolf and B EYE’s CEO Dimitar Dekov, as well as the “State of Data & AI Literacy 2024” report, this article outlines why and how organizations can enhance data and AI literacy and establish a supportive culture. 

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What Is AI Literacy

AI literacy is the ability to efficiently, ethically, and responsibly comprehend, use, and guide AI systems. This involves not only knowing how AI technologies work but also understanding their implications, potential biases, and ethical considerations. AI literacy equips individuals with the knowledge to leverage AI tools effectively while ensuring that these tools are used in a manner that aligns with organizational values and societal norms. By building AI literacy, organizations can empower their workforce to make informed decisions and drive innovation responsibly. In a nutshell: 

  • AI literacy is an extension of data literacy: AI literacy builds on data literacy, emphasizing the ability to understand and use AI tools and techniques. 
  • AI literacy has immense business Impact: Improved data and AI literacy translates into better business performance, driving innovation and efficiency. 

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The Importance of Data and AI Literacy 

The importance of AI and data literacy cannot be overstated, as it remains at the heart of improved business performance. According to the State of Data & AI Literacy 2024 Report, a lack of adequate data and AI skills can lead to several critical issues within organizations: 

  • Poor Decision-Making: Inaccurate (42%) and slow (38%) decision-making are among the top risks leaders face due to insufficient data skills. This highlights the crucial role of data in modern decision-making processes and overall business success. 
  • Competitive Disadvantages: Organizations may struggle to keep up with innovation (30%) and broader competition (23%). Additionally, decreased productivity (36%) due to poor data skills can stall progress and efficiency. 
  • Employee Experience: Poor data skills are linked to increased burnout and turnover (17%), and overall poor employee experiences (15%), underscoring the broader impact on organizational health. 

When focusing on AI skills specifically, leaders perceive several significant risks associated with a lack of AI literacy: 

  • Deficit in Innovation (36%): Without strong AI skills, organizations struggle to innovate and stay competitive. 
  • Reduced Productivity (31%): A lack of AI skills can lead to inefficiencies and reduced overall productivity. 
  • Keeping Pace with Competitors (30%): Falling behind in AI capabilities can make it difficult to keep up with market leaders. 

Other concerns include inaccurate decision-making (25%), slow decision-making (22%), unmet team or departmental targets (18%), poor customer experience (18%), burnout and attrition (18%), and subpar employee experience (16%). 

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Reflecting the high value placed on data and AI literacy, there’s been a notable rise in leaders willing to pay a premium for employees with these skills.  

  • In 2023, 66% of leaders were prepared to offer higher pay for data literacy, a figure that has climbed to 72% today.  
  • Of these, 80% are ready to offer at least 10% extra, and 40% would pay 20% or more.  
  • While fewer leaders (60%) are inclined to pay a premium for AI skills compared to data literacy, those who do are prepared to offer even higher premiums.  
  • Among these, 85% would pay at least an extra 10%, and 46% are willing to increase salaries by a minimum of 20%.  
Infographic showing percentages of leaders willing to pay for AI skills: 72% of leaders pay premium for data literacy skills, 60% of leaders pay premium for AI skills, and 46% of leaders would increase salaries of employees with AI skills by a minimum of 20%.

In addition, 62% of leaders believe AI literacy is important for their teams’ day-to-day tasks, 86% of leaders believe data literacy is important for their teams’ day-to-day tasks, and 40% of leaders identify AI literacy as a critically growing skill. 

The data is clear — organizations must equip their workforce with the necessary skills to leverage these technologies effectively. Here’s how to do it. 

How to Build AI and Data Literacy in Your Company: A Framework 

Here is the fundamental framework you need to successfully upskill your teams and build enterprise-wide AI and data literacy. 

Infographic titled 'How to Build AI and Data Literacy in Your Company' with steps under categories such as Data and AI Literacy Training and Development, Change Management, and Building a Culture of Continuous Improvement. Steps include Assessing Skill Gaps, Developing Customized Training Programs, Communication Strategy, Inclusion of Stakeholders, Monitoring AI Performance, and Iterative Improvement

Data and AI Literacy Training and Development 

Developing data and AI literacy involves targeted training programs that address current skill gaps and future needs. This involves: 

Assessing Skill Gaps 

The first step in building AI and data literacy is to assess current skill levels and identify gaps. This assessment helps in tailoring training programs to meet the specific needs of the organization.  

  • 57% of leaders believe their organization has a data literacy skill gap 
  • 62% of leaders believe their organization has an AI literacy skill gap 

According to recent findings, a significant percentage of leaders recognize the need for a robust understanding of AI concepts and responsible AI use within their teams: 

  • Understanding AI Concepts: 70% of leaders identified a basic understanding of AI concepts as the most important skill for their teams. This foundational knowledge is essential for grasping the core principles of AI technologies. 
  • AI Ethics and Responsible Use: 69% of leaders highlighted the importance of AI ethics and best practices, emphasizing the need for ethical AI use to avoid negative impacts and damage to reputation. 
  • Application of AI in Business: 65% of leaders acknowledged the importance of understanding how AI can be applied in business contexts to drive innovation and efficiency. 
  • Prompt Engineering and AI Systems: Despite the popularity of generative AI tools like ChatGPT, 60% of leaders believe that prompt engineering and steering the outputs of AI systems are crucial skills. 
  • Developing AI Systems: 52% of leaders deemed developing AI systems from scratch as an important skill, underscoring the need for technical proficiency in AI development. 

These statistics highlight the critical need for organizations to invest in AI and data literacy to address skill gaps and enhance overall business performance.  

And the way to do it is to conduct regular skill assessments to understand current capabilities and identify areas needing improvement. Once you’ve narrowed those down, you can use this information to design customized training programs that address these gaps. 

Developing Customized Training Programs 

Customized training programs are essential for developing the necessary skills for AI and data initiatives. These programs can include e-learning modules, workshops, certifications, and continuous learning opportunities. 

To be effective, it is crucial to develop a comprehensive training curriculum that includes various learning formats to cater to different learning preferences. Offer certifications and continuous learning opportunities to ensure ongoing skill development. 

Current State of Training 

Many organizations are still in the early stages of implementing data and AI training programs. According to recent data: 

  • Data Training: 12% of organizations do not offer any form of data training, while 29% provide training only to employees in technical roles. Only 35% have a mature, organization-wide data literacy upskilling program for all employees. 
  • AI Training: 20% of organizations do not offer any AI training, and another 20% limit training to technical roles. Only 25% have a comprehensive AI literacy upskilling program across the organization. 
Preferred Training Methods 

Blended learning, which combines online learning with conventional instructor-led sessions, is the most popular approach for bridging the skills gap, preferred by 30% of leaders. This method effectively addresses the varying needs of employees and ensures better learning outcomes. 

  • Online Training: 18% of organizations rely exclusively on third-party online training, while 17% develop their own online learning materials. 
  • Instructor-Led Training: 10% of organizations emphasize instructor-led sessions as the key method for upskilling their teams. 
Challenges in Upskilling 

Leaders face several challenges when trying to bridge the data and AI skills gap: 

  • Lack of Budget and Resources: 35% of leaders cited a lack of budget as the primary obstacle, followed closely by inadequate training resources (33%). 
  • Unclear Starting Points: 31% of leaders find it challenging to know where to start with data and AI training, which hinders effective upskilling. 
  • Employee Resistance and Lack of Executive Support: 28% of leaders reported employee resistance to training, and 26% cited a lack of executive support as significant hurdles. 
Specific Challenges with Online Learning 

Many leaders find that online learning resources do not provide the needed level of personalization and interactivity: 

  • Application of Learned Skills: 29% of leaders find it difficult to apply skills learned from video-based courses in real-world scenarios. 
  • Employee Confusion: 29% also struggle with employees not knowing where to start their learning journey. 
  • Relevance and ROI: 24% of leaders indicate that the skills learned are not relevant to their roles, and they find it challenging to track the return on investment (ROI) of data training. 

To overcome these challenges, it is important to create a structured and supportive learning environment. This includes clear guidance on where to start, relevant and interactive learning content, and mechanisms to track and measure the impact of training programs. By addressing these issues, organizations can ensure that their employees are well-equipped with the necessary AI and data skills to drive business success. 

Data and AI Literacy Training Example: Colgate-Palmolive’s Upskilling Program 

A prime example of successful data and AI literacy training is Colgate-Palmolive’s upskilling program. Initially focusing on data literacy, the company’s program later expanded to include AI literacy, providing employees with the skills needed to leverage AI tools effectively. This initiative not only improved operational efficiency but also built a culture of continuous learning and innovation. 

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Infographic titled 'Build AI & Data Literacy and Culture' with sections for Training and Development, and Change Management, highlighting steps like Assessment of Skill Gaps, Customized Training Programs, Communication Strategy, and Feedback Mechanisms.

Change Management 

Change management is crucial in establishing a culture that embraces AI and data. It involves clear communication, inclusion of stakeholders, and effective strategies to manage the transition. 

Communication Strategy 

Effective communication is critical for managing change and establishing a culture that embraces AI and data. Clear communication strategies help in articulating the benefits of AI initiatives and addressing any concerns. 

Develop a communication plan that includes regular updates, open forums for discussion, and feedback mechanisms. Highlight the benefits of AI initiatives and how they align with organizational goals. 

Inclusion of Stakeholders 

Including stakeholders from different departments ensures that AI initiatives are well-rounded and address diverse perspectives. This inclusion promotes collaboration and support for AI projects. 

Engage stakeholders early in the planning process and involve them in key decisions. This approach ensures that AI initiatives have broad support and are aligned with the needs of various departments. 

Keep Reading: How to Build a Data-Driven Culture in Your Organization 

Building a Culture of Continuous Improvement 

Promoting continuous improvement is essential for maintaining the momentum of AI and data literacy initiatives. 

Monitoring and Evaluating AI Performance 

Regularly monitor and evaluate AI performance to ensure that the initiatives are on track and delivering the expected benefits. 

Implement real-time monitoring tools, automated alerts, and regular performance reviews to assess the effectiveness of AI initiatives. 

Iterative Improvement 

Adopt an iterative approach to improvement, making adjustments based on feedback and performance data. 

Use A/B testing, stakeholder feedback, and impact assessments to refine AI initiatives and ensure they are meeting organizational goals. 

Learning from Experience 

A continuous improvement culture is about learning from experience and iterating on processes and technologies. This can involve setting up dedicated teams to review AI projects, gather feedback, and implement changes. 

Infographic titled 'Monitor, Evaluate, Iterate, and Improve Continuously' with sections on Monitoring AI Performance, Evaluating AI Outcomes, Iterative Improvement, and Fostering Continuous Improvement. Detailed steps include Performance Metrics, Real-time Monitoring Tools, Impact Assessment, A/B Testing, Learning Organization, and Innovation Feedback Loops.

Data and AI Literacy FAQs

Build Data and AI Literacy with B EYE 

Building data and AI literacy and promoting a supportive culture are critical for the successful implementation of AI initiatives. Comprehensive training and development programs, effective communication strategies, and stakeholder inclusion are essential components of this process. By enhancing AI and data literacy and promoting a culture of continuous improvement, organizations can leverage these technologies to drive business success. To learn more about building data and AI literacy and culture, watch our webinar Build a Robust AI Data Strategy: Readiness Assessment and Implementation Framework on demand.