Introduction

A survey was conducted to assess the impact of AI tools on the work processes of IOL Inc. employees across different teams, including Backend, Frontend, and Business Services. The survey aimed to gather insights on AI tool usage, efficiency, quality of work, challenges faced, training and support, and future improvements. This report presents a comprehensive analysis of the survey results, considering the roles (developer vs. non-developer) and teams of the respondents.

Methodology 

The survey was administered using Google Forms, and responses were collected from 9 employees across the Backend (3), Frontend (2), and Business Services (4) teams. Among the respondents, 5 were developers (Backend and Frontend) and 4 were non-developers (Business Services). The survey included multiple-choice, short answer, and paragraph questions to capture both quantitative and qualitative data.

Key Findings

AI Tool Usage

1. ChatGPT (100%), Claude (100%), and Gemini (67%) were the most commonly used AI tools across all teams.

   

2. Developers heavily relied on Code Whisperer (80%), Co-pilot (60%), and Cursor (60%), while non-developers frequently used Adobe Creative Cloud (75%), Firefly (50%), and Capcut (50%).

3. 89% of employees (8 out of 9) reported using AI tools on a daily basis, with no significant difference between developers and non-developers.

   

Work Efficiency

1. 78% of employees (7 out of 9) indicated that AI tools have significantly improved their work efficiency, while the remaining 22% (2 out of 9) reported moderate improvements.

2. Developers mentioned faster debugging, code generation, and research assistance as key efficiency improvements, while non-developers highlighted document creation and content generation.

Quality of Work

1. 89% of employees (8 out of 9) reported that AI tools have positively influenced the quality of their work, with 56% (5 out of 9) indicating a significant improvement and 33% (3 out of 9) noting a moderate improvement.

2. Developers emphasized code optimizations and bug fixing, while non-developers mentioned high-quality document generation and enhanced marketing content.

Challenges and Limitations

1. The most common challenges were ensuring data quality and availability (67%), integrating AI tools with existing systems and workflows (56%), and addressing algorithmic bias (44%).

   

2. Developers faced challenges with steep learning curves (60%) and building trust in AI-generated insights (60%), while non-developers struggled with the lack of human touch and creativity (75%).

   

Training and Support

1. 89% of employees (8 out of 9) were satisfied with the training and support provided for using AI tools, with no significant difference between developers and non-developers.

2. Developers expressed interest in workshops (80%), Q&A sessions with experts (60%), and access to online learning resources (60%), while non-developers preferred video tutorials (75%) and peer-to-peer knowledge sharing (75%).

   

Future Improvements

1. Developers prioritized the integration of AI tools for data analysis (80%), predictive maintenance (60%), and automating repetitive tasks (60%), while non-developers focused on personalized customer support (75%) and streamlining project management (50%).

   

2. Developers desired advanced natural language processing (80%), computer vision (80%), and predictive analytics (60%), while non-developers sought sentiment analysis (75%) and recommendation engines (50%).

   

Additional Insights

1. Both developers and non-developers highlighted significant time savings and reduced mental effort in their respective tasks using AI tools.

2. Concerns regarding data privacy, learning curves, and job security were prevalent across all teams.

Recommendations

1. Tailor AI tool training and support based on the specific needs of developers and non-developers, focusing on workshops and online resources for developers and video tutorials and peer-to-peer knowledge sharing for non-developers.

2. Prioritize the integration of AI tools in areas specific to each team's requirements, such as data analysis and predictive maintenance for developers and personalized customer support and project management for non-developers.

3. Invest in advanced AI capabilities that cater to the needs of both developers (e.g., natural language processing, computer vision) and non-developers (e.g., sentiment analysis, recommendation engines).

4. Address data privacy concerns and provide clear guidelines on data handling tailored to the specific roles and responsibilities of developers and non-developers.

5. Regularly assess the impact of AI tools on job roles and provide targeted upskilling opportunities for developers and non-developers to ensure they can adapt to the evolving work landscape.

Conclusion

The survey results reveal the positive impact of AI tools on work efficiency and quality across IOL Inc. teams, with variations in tool usage, challenges, and future improvements based on the roles of developers and non-developers. By tailoring training, support, and AI integration strategies to the specific needs of each group and addressing common concerns, IOL Inc. can optimize its work processes and maximize the benefits of AI tools for all employees.