How to Build a Learning Organization

Part 1: The Disconnect Between Employers and Employees on AI Usage

Employer Expectations vs. Employee Reality

Employers have implemented AI with specific goals in mind: supporting initial research (62%), managing workflow (58%), and analyzing data (55%). However, employees have other ideas. The report shows that nearly two-thirds (63%) of employees primarily use AI to double-check their work, rather than for the intended purposes outlined by their employers. This disconnect suggests that employees may not fully understand the potential functions of AI, or they may lack the confidence to use AI in more complex ways.

The Role of Transparency and Education

A key reason for this disconnect appears to be a lack of transparency and education around AI. Only 23% of employees feel completely educated and trained on AI, despite 72% of employers believing that they have provided adequate training. This disparity is further highlighted by gender differences, with 66% of males feeling adequately trained compared to only 44% of females. Clearly, there is a need for more comprehensive and inclusive training programs that cater to all employees.

Part 2: AI’s Impact on Productivity – Perception vs. Reality

Employer Optimism vs. Employee Experience

Employers are optimistic about AI’s impact on productivity, with 60% believing that AI is significantly boosting their employees’ efficiency. However, employees report a different experience. Only 44% feel that AI is significantly improving their productivity, with 10% stating that AI has not increased their productivity at all. This discrepancy points to a potential overestimation of AI’s benefits by employers or a lack of effective AI integration at the employee level.

Time Savings: A Mixed Bag

While AI is saving time for employees—79% say they save at least 1-2 hours a day—there is a question of how this time is being used. Although 63% of employees are using their saved time to reduce work overload, 26% are spending it on non-work-related tasks. This raises concerns about whether AI is being used to its full potential and whether employees are being guided on how to best leverage the time saved by AI.

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This whitepaper provides a comprehensive overview of self-service embedded BI and its transformative impact on business intelligence. We’ll explore the challenges and functionalities of self-service embedded BI platforms, explore the benefits for organizations, and guide you through considerations for successful implementation.

Part 3: The Critical Barrier – Data Readiness

The Importance of Data in AI

Data is often referred to as the lifeblood of AI. In the context of artificial intelligence, data serves as the foundation upon which algorithms are built, decisions are made, and insights are generated. Without access to accurate, comprehensive, and well-organized data, AI systems cannot function effectively.

The purpose of using data in AI is to enable machines to learn from patterns, predict outcomes, and automate processes that would otherwise require human intervention. For instance, AI can analyze customer data to predict buying behaviors, streamline supply chain operations by forecasting demand, or even personalize marketing campaigns to improve customer engagement. Using natural language queries, you can ask questions to AI around your data and get immediate insights and answers. The possibilities are endless, but they all hinge on one critical factor: the quality and readiness of the data being used.

The Data Dilemma

One of the biggest hurdles to AI adoption is data readiness. Nearly half (45%) of employers report that their company’s data is not ready to support AI, with 19% citing it as the top reason for not implementing AI. This issue often stems from data being siloed across departments and platforms, making it difficult for AI to access and process the necessary information. Without centralized and clean data, AI cannot function effectively.

Employee Perspectives on Data Readiness

Employees are also aware of the data readiness issue. One-third of employees believe that their company’s data needs to be combed through for accuracy before AI can be fully implemented. Additionally, 32% of employees feel that more training around data and AI is necessary before their company can be considered AI-ready. This suggests that data readiness is not just a technical challenge but also a matter of employee education and engagement.

Part 4: Recommendations for Bridging the Gap

Enhancing AI Training and Education

To bridge the gap between employer expectations and employee reality, companies must prioritize comprehensive AI training programs. These programs should not only cover the technical aspects of AI but also provide practical examples of how AI can be integrated into daily workflows. Training should be inclusive, ensuring that all employees, regardless of gender or background, feel adequately prepared to use AI.

Centralizing and Cleaning Data

Data readiness is critical to the success of AI initiatives. Companies must invest in centralizing their data and ensuring it is accurate and accessible. This may involve breaking down data silos, implementing data governance practices, and providing employees with the tools and training they need to manage and utilize data effectively.

Aligning AI Goals with Employee Needs

Finally, employers need to align their AI strategies with the actual needs and workflows of their employees. This means involving employees in the decision-making process, gathering feedback on AI tools, and adjusting strategies based on how employees are using AI. By doing so, companies can ensure that their AI investments are truly enhancing productivity and driving business success.

Conclusion

The 2024 Digital Work Trends Report by Slingshot highlights the challenges and opportunities of AI in the workplace. While AI holds immense potential for improving productivity and driving efficiencies, there is still a significant disconnect between how employers intend for AI to be used and how employees are actually using it. By addressing the gaps in education, data readiness, and alignment of AI goals, companies can unlock the full potential of AI and create a more productive and engaged workforce.

This whitepaper should serve as a guide for organizations looking to utilize the power of AI. By understanding the current landscape and taking proactive steps to address the challenges, businesses can ensure that their AI initiatives are successful and impactful.

Survey Methodology and Source of Data

The insights and findings presented in this whitepaper are based on data from Slingshot’s 2024 Digital Work Trends Report, which was conducted in partnership with Dynata, a global leader in first-party data collection and insights. Dynata surveyed 253 full-time U.S. employees and managers across a range of industries and demographics, gathering critical insights on how AI is being implemented and utilized in today’s workplace.

The survey respondents were selected to ensure a balanced representation of different age groups, roles, and organizational levels to capture a comprehensive view of AI’s impact on both employees and employers. Data was collected from respondents located in all 50 states, providing a broad view of the current landscape of AI adoption and its associated challenges.