July 28, 2025

How Leadership Teams Build AI Literacy in 5 Steps

AI literacy is critical for leadership teams to make informed decisions, align AI with business goals, and manage risks. Here's how leaders can build AI knowledge in five practical steps:

Assess Current Knowledge: Use surveys, audits, and performance metrics to identify gaps in AI understanding.
Set Clear Goals: Define specific, measurable learning objectives tied to business outcomes like productivity or cost savings.
Run Tailored Training Programs: Create workshops and hands-on sessions focused on practical AI applications.
Apply AI to Leadership Tasks: Integrate AI tools into decision-making and workflows to improve efficiency and data-driven insights.
Track Progress and Expand Training: Measure results using KPIs like time savings or revenue growth and scale training across the organization.

Why this matters: AI can boost workforce performance by 40%, drive revenue growth up to 10%, and reshape job roles. Yet, only 23% of Americans feel confident about their AI knowledge. Leadership teams must act now to close this gap and stay competitive.

Step 1: Check Current AI Knowledge Levels

Before diving into AI training, it’s important to gauge where your team stands in terms of AI knowledge. While 96% of professionals are at least somewhat aware of AI, 71% lack a solid grasp of how to apply it in practical scenarios. Leaders who possess a deeper understanding of AI are 2.8 times more likely to achieve organizational benefits compared to those with limited knowledge.

According to an IBM survey, nearly half of executives admit their teams lack the skills and knowledge needed to implement and scale AI technologies. Despite this, only 25% of organizations have introduced company-wide AI training programs, even though 62% of leaders acknowledge a significant gap in AI literacy within their teams.

AI literacy isn’t just about technical skills - it’s a broader concept that includes six dimensions: basic knowledge of AI, conscious application, ethical and social awareness, critical thinking, attitudes toward AI, and the ability to collaborate effectively. Assessments should align with your organization’s goals, technological maturity, and leadership profiles. These evaluations can be led by HR, IT, leadership teams, or external AI consultants, using a mix of tools to create a comprehensive understanding of your team’s capabilities. Once you’ve assessed the baseline, AI audits can provide structured insights into your organization’s readiness.

Running AI Audits

AI audits are a great way to measure your leadership team’s current readiness to adopt and leverage AI. These audits help align team skills with business objectives. Research shows that companies with a clear, top-down AI strategy are 3.5 times more likely to see positive ROI from AI initiatives.

Professional audits, such as those offered by Alex Northstar, take this a step further by evaluating leadership’s practical understanding of AI tools and their limitations within the context of specific business needs. The audit process typically includes analyzing how AI tools are currently being used, identifying repetitive tasks that could benefit from automation, and assessing leaders’ ability to make informed decisions about AI investments. The results provide a clear roadmap for development, showing which leaders are ready to lead AI projects and which might need foundational training before stepping into AI-focused roles. To enhance these audits, consider using surveys and simulations to gain additional insights.

Using Surveys and Performance Metrics

To complement the findings from AI audits, surveys and performance metrics can help pinpoint specific knowledge gaps across your leadership team.

Surveys: Use self-assessment questionnaires to allow leaders to evaluate their AI knowledge without pressure. For example, ask questions like, “How would you measure the ROI of an AI project?” or “What ethical challenges might arise when implementing AI in customer service?”
Interviews and Focus Groups: These methods can uncover deeper insights into how leaders perceive AI and address any misconceptions. Focus groups, in particular, encourage peer learning and collaboration during the assessment process.
Performance Metrics: Analyze how effectively leaders are using existing AI tools, how quickly they make tech-related decisions, and their ability to spot automation opportunities. According to McKinsey, embedding AI skills can lead to a 20–25% boost in operational efficiency.

Finally, practical simulations can provide a real-world test of AI readiness. Present leaders with scenarios that require AI-driven decision-making, such as evaluating whether an AI chatbot would improve customer support. These exercises reveal not just knowledge gaps but also areas where targeted training can make a difference.

Step 2: Set Clear AI Learning Goals

After assessing your team's current understanding of AI, the next step is to establish specific and measurable learning objectives that align directly with your business goals. Even though 90% of companies anticipate revenue growth from AI, only 1% have fully integrated it into their workflows. This disconnect often arises from vague goals that fail to prioritize strategic needs.

Effective AI literacy programs tie learning objectives to real-world business challenges. Research shows that organizations with higher AI adoption rates are more likely to see their advanced AI projects deliver strong returns on investment compared to those with lower adoption levels. The secret lies in creating goals that are precise, time-bound, and measurable, while staying aligned with your company’s broader strategy. These goals serve as the foundation for ensuring training efforts contribute to tangible business outcomes.

Connect Goals to Business Results

AI learning goals should lead to clear, measurable business benefits, such as boosting productivity, reducing costs, or driving revenue growth. For instance, companies that integrate generative AI deeply into their operations are twice as likely to achieve measurable results.

Consider Urban Airship, which focused its AI literacy efforts on improving customer contact - a critical business need. Their AI-powered customer contact optimization tool now drives 40% of new business deals. Similarly, Company Nurse used AI training to enhance operational efficiency and security. Their efforts led to a speech-to-text system that reduced average handling times by over 10% while improving the protection of sensitive healthcare documents.

When defining your AI goals, start by identifying key business challenges that AI can address. According to IDC, global spending on AI-related technology is expected to reach $337 billion by 2025 and more than double to $749 billion by 2028. However, these investments only pay off when learning objectives are designed to tackle specific problems. Use frameworks like OKRs (Objectives and Key Results) or SMART goals to ensure clarity and focus. For example, instead of a vague aim like "improve AI understanding", set a targeted goal such as: "Train department leaders to identify three automation opportunities by Q2 2025, reducing manual processing time by 15%."

These well-defined objectives ensure AI training directly impacts operational efficiency and strategic decision-making.

Getting Leadership Support

Once goals are tied to measurable outcomes, securing leadership support becomes crucial. Leadership buy-in is one of the strongest predictors of success when scaling AI initiatives. However, only 13% of senior business leaders report feeling confident in making AI-driven decisions without help from technical teams. This confidence gap often hinders organizational AI adoption, making leadership engagement essential.

Despite recognizing the importance of AI, only 16% of executives have moved beyond the experimental phase, even though 84% acknowledge the need to scale AI across their organizations.

"Less than 2 in 5 respondents said senior leaders understand how technology can create value for the business."

McKinsey

To gain meaningful leadership support, connect AI literacy goals to executive priorities and demonstrate clear potential for ROI. The Gartner 2025 CIO Agenda reveals that over 80% of CIOs are prioritizing investments in areas like cybersecurity, generative AI, business intelligence, and data analytics. Align your AI literacy goals with these priorities to capture leadership attention.

A great example of leadership engagement comes from Infineon. In Spring 2022, they launched their "AI Heroes" program, a nine-week initiative combining learning with real-world application. This program resulted in four funded AI projects. The success stemmed from active leadership involvement, with CEO Sabine Herlitschka emphasizing the importance of AI knowledge:

"Grow your knowledge and best practices in AI innovation! AI is about creating change and you can be a change ambassador for our organization with all your learnings. This know-how will get you far – and the last mile you will walk by radiating your enthusiasm and convincing allies."

Sabine Herlitschka, CEO of Infineon

To secure leadership commitment, craft a compelling narrative about AI's role within your organization. As researchers from the London School of Economics point out, “AI strategies without a compelling ‘why’ rarely survive first contact with reality”. Present specific scenarios where AI literacy can improve decision-making, reduce costs, or unlock new revenue streams. With 74% of advanced generative AI initiatives already meeting or exceeding ROI expectations, the case for investment becomes even stronger.

Finally, establish clear ROI metrics and milestones to keep leadership engaged. With only 48% of digital initiatives meeting their business goals enterprise-wide, tracking progress is essential. Regularly report on how AI literacy efforts are driving measurable business outcomes, ensuring ongoing executive support throughout the process.

Step 3: Create and Run Custom AI Training Programs

Once you’ve defined clear objectives and secured executive support, the next step is to craft training programs tailored to your organization’s specific needs. While 92% of executives plan to increase AI spending in the next three years, only 1% of leaders believe their companies have fully matured in AI deployment. Meanwhile, nearly half of employees are seeking more formal AI training, and 47% of C-suite leaders report delays in rolling out generative AI tools due to skill gaps. This highlights the urgency of developing training programs that go beyond theory and focus on practical, hands-on learning.

A successful training program begins with a thorough analysis of organizational needs. This involves setting clear goals, identifying the skills required, assessing current employee competencies through surveys and interviews, and prioritizing training areas that will have the greatest impact on performance. Leveraging AI tools to track real-time performance data can further refine these programs, ensuring they remain effective and relevant.

From tailored workshops to user-friendly tools and collaborative learning teams, there are several ways to make AI training both accessible and impactful.

Custom Workshops and Hands-On Training

Custom workshops are a powerful way to build AI literacy by tailoring the content to your team’s experience level, industry requirements, and specific challenges. These sessions focus on practical applications, demonstrating how AI tools can be seamlessly integrated into daily workflows rather than relying on abstract concepts.

Workshops that succeed in building trust are especially important. Leaders need to see how AI tools will simplify their work rather than add complexity. This trust can be built through workflow analysis, which identifies repetitive or time-consuming tasks that are ideal for automation. Such workshops empower leaders to use AI insights to make informed decisions, aligning with the organization’s strategic goals.

Alex Northstar, founder of NorthstarB LLC, specializes in creating customized training programs. His approach combines AI audits, leadership consulting, and tailored automation strategies to help organizations pinpoint specific opportunities. By focusing on tools like ChatGPT and practical automation workflows, his training delivers immediate, measurable value.

Incorporating realistic simulations into workshops provides immediate feedback, helping leaders refine their skills. Additionally, using design-thinking techniques and promoting cross-functional collaboration encourages innovative problem-solving. Effective workshops also define clear success metrics and action plans, prioritizing use cases based on their potential impact, cost, and feasibility.

Starting with Easy-to-Use AI Tools

When introducing AI to leadership teams, it’s important to start small. Begin with simple, user-friendly tools that can be quickly integrated into everyday tasks before progressing to more advanced applications. Research from McKinsey shows that leaders often underestimate how much employees already use AI - employees are three times more likely to apply AI to at least 30% of their daily work.

Tools like ChatGPT and Grammarly are great starting points. ChatGPT can assist with drafting emails, creating meeting agendas, and summarizing reports, offering immediate value. Similarly, Grammarly enhances written communication, a benefit that resonates with most leaders. These tools not only build confidence but also pave the way for broader AI adoption by establishing foundational skills.

The ultimate goal is to integrate AI tools into leadership tasks in a way that enhances productivity without undermining human judgment. Sixty-three percent of HR leaders believe AI improves productivity, but this benefit is fully realized only when tools are used consistently and effectively.

Cross-Department Learning Teams

To embed AI into everyday operations, promote collaboration through cross-departmental learning teams. These teams bring together representatives from various departments, providing diverse perspectives and breaking down silos. By focusing on specific use cases - such as improving customer communication across sales, marketing, and customer service - these teams ensure that training translates into tangible business outcomes.

Cross-functional teams also play a key role in coordinating AI strategies and identifying training needs. They can foster a culture of open feedback, evaluate existing training resources, and explore modern approaches like microlearning and gamification to keep participants engaged.

Personalized learning paths tailored to different roles can significantly boost engagement and retention. McKinsey research shows that personalized learning increases retention by 30%. By addressing the unique needs of each role while maintaining a shared foundation in core AI concepts, cross-departmental teams can drive the organization toward widespread AI literacy.

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Step 4: Apply AI Knowledge to Daily Leadership Tasks

After gaining foundational AI skills through training, the real challenge for leaders is weaving AI into their everyday decision-making. This shift from learning to doing requires combining data-driven insights with human judgment. Leaders who embrace this approach can make quicker, more informed decisions while freeing up time for long-term strategic planning.

To start, focus on areas where AI can immediately improve workflows and decision-making. Prioritizing these high-impact opportunities ensures that AI delivers meaningful results right away.

Using AI for Decision-Making

Today’s leaders face an overwhelming number of decisions each day. According to Harvard Business Review, 85% of leaders report experiencing decision stress, and 75% have seen their daily decision load increase tenfold over the past three years. AI tools can ease this burden by analyzing massive datasets quickly, uncovering patterns that humans might miss.

AI doesn’t replace human decision-making - it enhances it. By automating routine processes, leaders can focus on bigger-picture initiatives. For instance, AI can analyze customer feedback, market trends, and operational data all at once, helping leaders prioritize resources or plan new products. It also enables proactive decision-making by simulating scenarios and reducing unconscious bias, leading to fairer outcomes.

"AI is not replacing human decision makers. It is simply a highly advanced tool for marshalling information in vast quantities to inform a decision. The insights gleaned from such data must still be interpreted by human leaders, who must choose a strategy to move forward."
– MTD Training

Effectively integrating AI into leadership involves three key steps: development, execution, and evaluation. In the development phase, AI can assess market conditions and competition. During execution, AI monitors progress and offers real-time adjustments. Finally, in the evaluation stage, AI measures outcomes and identifies areas for improvement.

Best practices for AI-driven decision-making include:

Ensuring data quality by using reliable, up-to-date sources and regularly cleaning datasets.
Building cross-functional teams that combine AI expertise with business knowledge.
Simplifying AI outputs into clear, actionable insights for easier communication.
Continuously refining AI models based on feedback and new data.

Additionally, leaders should establish AI governance frameworks to guide ethical use, ensuring compliance with both organizational standards and legal requirements. Real-world examples from U.S. businesses highlight the transformative potential of this approach.

Learning from U.S. Business Examples

Several U.S. companies are leading the way in integrating AI into their daily operations:

Colgate-Palmolive uses AI to streamline market intelligence gathering. By employing retrieval-augmented generation with large language models, employees can directly query consumer research, third-party data, and Google search trends. AI also accelerates the development of new product concepts, enabling teams to create copy and imagery in minutes, boosting creativity and efficiency.
JPMorgan Chase leverages AI-driven predictive analytics to detect fraudulent transactions and optimize investment strategies, improving risk management and resource allocation.
Walmart applies AI to its supply chain, accurately forecasting demand and guiding decisions on inventory, distribution, and planning.
StarKist Foods implemented an AI-powered planning platform, cutting planning cycles from 16 hours to under one hour. This 94% improvement allows leadership to focus on strategic goals.
Sanofi uses AI to help managers make objective decisions, avoiding sunk-cost bias and optimizing investments.
Nationwide employs GPT-4 to automate response letter generation, reducing response times from 45 minutes to 10–15 minutes. This 66% efficiency gain enables leaders to redirect resources toward higher-value tasks.

These examples offer valuable lessons for leadership teams:

Start with specific use cases: Identify clear challenges where AI can make a measurable impact.
Invest in training and change management: As seen at Colgate-Palmolive, comprehensive training ensures employees use AI effectively and responsibly.
Focus on complementing human skills: AI should enhance, not replace, human decision-making.
Measure and communicate results: Quantifiable successes, like StarKist’s reduced planning time, can build organizational support.
Establish ethical guidelines: Governance frameworks maintain transparency and ensure responsible AI use.

The broader implications are immense. Research from IBM shows that 42% of enterprises with over 1,000 employees used AI in 2024. McKinsey estimates AI could add $13 trillion to global economic output by 2030, increasing GDP by 1.2% annually. Similarly, PwC projects AI could contribute $15.7 trillion to global GDP by 2030.

These examples demonstrate that successful AI integration requires strong leadership, clear goals, and a focus on practical applications. By learning from these U.S. business cases, leaders can develop strategies that incorporate AI into daily operations while avoiding common pitfalls.

Step 5: Track Results and Expand AI Training

Once you've established a solid training framework, it's time to focus on measuring outcomes and broadening AI literacy across the organization. After integrating AI into daily workflows, the next step is evaluating success and scaling efforts to ensure lasting impact.

Measuring Key Performance Indicators (KPIs)

Tracking the right metrics is crucial to understanding whether your AI literacy programs are making a meaningful difference. As the saying goes, "You can't manage what you don't measure". Choose KPIs that align with your strategic goals and provide actionable insights.

Research highlights that 35.52% of respondents identified customer satisfaction as the most critical KPI for measuring AI success, followed by revenue growth (34.72%) and time saved on repetitive tasks (32.80%). A well-rounded AI literacy program should emphasize both operational improvements and business outcomes.

Organizations should monitor both direct and indirect metrics. For example:

Direct metrics: Productivity gains, cost savings in dollars, and time reductions on specific tasks.
Indirect metrics: Leadership confidence with AI tools, employee engagement, and smoother cross-departmental collaboration.

Take Wayfair's approach as an example. They redefined their lost-sales KPI by shifting from item-based calculations to category-based retention analysis. This adjustment allowed them to provide better furniture recommendations tailored to customer preferences, such as price and shipping timelines.

Key areas to measure include:

Financial Impact: Track cost savings, revenue growth, and ROI in measurable terms.
Operational Efficiency: Analyze time saved, automation rates, and faster decision-making.
Leadership Confidence: Regularly survey leaders to gauge their comfort with AI tools and their effectiveness in decision-making.
Adoption Rates: Monitor how frequently AI tools are used, which features are most popular, and any challenges faced.

Improving Based on Feedback

Feedback is a powerful tool for refining your AI training initiatives. Regularly gather input through surveys, reviews, and usage analytics to identify what’s working and what’s not. AI can process feedback from diverse sources - like employee surveys, performance reviews, and social media - to uncover trends and areas for improvement.

The impact of AI-driven feedback analysis is impressive. 71% of organizations believe AI tools are essential for enhancing employee experience and performance management. Additionally, natural language processing (NLP) in performance management has been shown to boost employee engagement by 25% and reduce turnover by 15%.

Smart feedback strategies include segmenting responses based on demographics, prior AI experience, and departmental roles. This segmentation helps pinpoint specific training needs. AI can also track participation in training programs, feedback sentiment, and performance trends, highlighting areas that need immediate attention. For instance, AI-powered tools have been shown to improve knowledge retention by up to 60% and increase engagement by 72%.

To effectively implement feedback:

Use multiple collection channels, such as pulse surveys, one-on-one interviews, and AI tool analytics.
Analyze feedback in real time to identify positive trends and areas of concern.
Address specific challenges. For example, if finance leaders struggle with a tool that marketing leaders excel at, create targeted support sessions or pair successful users with those needing extra help.

These insights can guide a phased approach for scaling AI literacy throughout your organization.

Expanding Across the Organization

Once you've measured performance and analyzed feedback, the next step is to scale AI training across the company. While over 70% of employees express strong interest in learning about AI, less than a third of organizations currently require AI training. This gap presents a significant opportunity for companies willing to invest in comprehensive training programs.

Projections suggest that by 2025, 60% of corporate training programs will incorporate AI. Early adoption of scalable AI training frameworks could give organizations a competitive edge.

Tony Bingham, President and CEO of ATD, underscores the urgency:

"I think AI training should be mandatory. The adoption is widespread enough that training should be required because everyone who interacts with it has the opportunity to learn, but they also have the opportunity to share data that they maybe shouldn't be sharing."

Strategies for successful expansion include:

Role-Based Training: Customize programs for executives, operational staff, and technical teams, tailoring learning objectives to specific responsibilities.
AI Champion Networks: Identify and empower advocates within each department to drive adoption and address resistance.
Blended Learning Approaches: Offer a mix of online and in-person training to cater to different learning styles. Use live sessions, self-paced modules, and gamified content to boost engagement.
Shared AI Glossary: Develop a common vocabulary for onboarding and training to ensure consistency and reduce confusion.
Community Building: Foster collaboration through internal forums, lunch-and-learn sessions, and cross-departmental projects.
Career Integration: Link AI literacy achievements to promotions and career growth opportunities, turning training into a valuable development tool.

Scaling should be gradual and strategic. Start with departments that showed the highest engagement during the initial training phase, then expand to other areas. Currently, 59% of organizations report advanced or proficient AI literacy, showing that systematic approaches are becoming more common and effective.

Tony Bingham also reminds us:

"Today is the slowest rate of AI change - it just keeps accelerating, so we need to keep adjusting our training to keep up with those changes."

For organizations seeking expert guidance, partnering with experienced AI consultants like Alex Northstar can help navigate the complexities of scaling AI literacy across diverse teams and structures. Their expertise can provide the frameworks and strategies needed for success.

Conclusion: Building a Future-Ready Leadership Team

AI literacy is no longer just about staying current with technology - it’s about preparing your leadership team to excel in an AI-driven world. By following the five outlined steps, your organization can move from being curious about AI to becoming fully capable of leveraging it.

The urgency is clear. Despite widespread acknowledgment of AI’s potential, only 1% of leaders consider their companies "mature" in AI deployment. This gap is both a challenge and an opportunity for leadership teams willing to act decisively.

The process - from assessing knowledge levels to implementing AI solutions - demands dedication and strategic planning. By evaluating current expertise, setting clear objectives, creating tailored training programs, integrating AI into everyday operations, and monitoring progress, leadership teams can build the skills needed to truly embrace AI.

Bill Gates captures the essence of AI’s rapid evolution:

"We should keep in mind that we're only at the beginning of what AI can accomplish. Whatever limitations it has today will be gone before we know it."

Bill Gates, GatesNotes

This underscores the importance of viewing AI literacy as essential as basic computer skills. Leadership teams that prioritize AI education today will be better positioned to take advantage of tomorrow’s breakthroughs.

But achieving AI literacy requires more than enthusiasm. Effective leadership, clear goals, and a solid data strategy are essential for successful AI adoption. The organizations that succeed treat AI integration as a transformative business initiative.

For leadership teams ready to take the next step, working with experienced consultants can provide the expertise and tailored solutions needed to turn AI literacy into actionable results. Alex Northstar offers customized training programs, strategic consulting, and hands-on workshops designed specifically for business leaders looking to enhance their AI capabilities.

Whether through internal efforts or expert guidance, taking action now lays the groundwork for long-term success in an AI-driven future.

FAQs

What steps can leadership teams take to evaluate their AI knowledge and pinpoint areas for improvement?

Leadership teams can begin by evaluating their current understanding of AI. This can be done through methods like team surveys, analyzing recent projects, or conducting skills gap assessments. These steps help pinpoint both strengths and areas where improvement is needed.

To take it a step further, structured frameworks like AI literacy maturity models can be incredibly useful. These models offer a clear pathway for assessing proficiency and identifying knowledge gaps. By using such tools, teams can prioritize the most important areas to develop a solid grasp of AI concepts and tools.

How can AI be used in everyday leadership tasks to improve decision-making?

AI plays a crucial role in transforming leadership decision-making by offering real-time insights and simplifying intricate processes. Take predictive analytics, for instance - it can anticipate market trends or customer behavior patterns, giving leaders the ability to act ahead of time. This proactive approach means decisions are not only faster but also more informed.

AI-powered tools also excel in risk assessment, pinpointing potential obstacles and opportunities far quicker than traditional methods. Beyond that, natural language processing (NLP) tools can condense lengthy reports or analyze feedback, saving valuable time while enhancing communication. Recommender systems can propose effective strategies or resources, and optimization algorithms can fine-tune resource allocation, ensuring leadership efforts are directed toward areas that deliver the most impact. With these tools in place, leadership teams can embrace smarter, data-driven decision-making with greater efficiency.

How can organizations evaluate the success of their AI literacy programs and ensure they support business goals?

To gauge the effectiveness of AI literacy programs, it's essential to look at both measurable results and how well these initiatives align with organizational goals. Metrics to consider include how proficient employees become with AI tools, how seamlessly AI is integrated into daily operations, and measurable outcomes like reduced costs or increased efficiency.

Start by setting clear KPIs tied to your company's strategic objectives. Track progress through tools like surveys, performance evaluations, and observing how AI skills are applied in real-world scenarios. This ensures your efforts not only enhance AI understanding but also deliver real value to the organization.