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54% of Companies Have Adopted Generative AI. Have You?
Integration of AI deeper into core businesses and workflows across departments is inevitable - it is expected to add up to $4.4 trillion annually to the global economy. To stay relevant and competitive, you need to get a headstart and learn how to prompt AI tools correctly.
Mastering the art of effectively prompting AI tools emerges as a critical skill set, offering a strategic advantage to leverage AI's full capabilities efficiently and creatively.
Business Leaders, Hiring Managers, and Candidates are Increasingly Accepting of AI-Generated Content
Companies and individuals are seeing significant advantages such as efficiency gains and innovation potential, fueling rapid adoption of generative AI. However, this acceptance goes further than just the benefits. There's a growing sense of trust in AI's capabilities, with advancements leading to factually accurate, well-written, and even creative content. This allows humans to focus on their strengths – strategic thinking, creative problem-solving, and building relationships. The collaborative environment, where AI acts as an assistant, is driving a new era of productivity and innovation for businesses and individual career journeys.
60%
Hiring managers are okay with candidates using AI in their job applications
Forbes79%
Business leaders are comfortable with AI being used to write responses to colleagues
Tech.co90%
Job seekers have used generative AI to build, update, or improve their resumes
Canva90%
Canva’s AI-powered Magic Write tool was used for CV template design
CanvaGenerative AI Boosts Work Quality by 18% in Writing Tasks
This study was published by MIT (Massachusetts Institute of Technology) in March 2023, conducted by Shakked Noy and Whitney Zhang.
Aim of the study:
Can AI tools like ChatGPT make experienced professionals redundant or will it complement their skills thus improving efficiency? The experiment was particularly designed to examine the productivity effects of ChatGPT on mid-level professional writing tasks.
Methodology:
- Candidates: 444 experienced, college-educated professionals were recruited from various occupations such as marketing, grant writing, consulting, data analytics, human resources, and management.
- Task: Participants were given 20-to-30-minute tasks to write PR, short reports, emails, and analysis plans. Once completed, task quality was evaluated by blinded professionals within the same occupations.
- Procedure: To study the effects of ChatGPT on productivity and work quality, half of the participants were randomly chosen and assigned to work with ChatGPT. It is to be noted that before the experiment, these participants had zero to minimal knowledge of ChatGPT and how to work with it.
Results and findings:
- Level playing field: The study showed improved productivity and output of work, with a conclusive reduction in inequalities between professionals. This was because ChatGPT can fill the gaps for average-skilled workers and significantly improve their productivity.
- Task execution: For tasks concerning content creation, it reduced the time spent on creating a rough draft by more than 50%. However, it increased the time taken to complete final edits by 50%.
- AI adoption: Participants exposed to ChatGPT during the study were 2x more inclined to adopt ChatGPT in their real jobs than the ones who were not.
AI & Prompting Lingo: Unlock the Power of Gen AI with Our Glossary
Explore terms related to prompting techniques, AI biases, and more.
Generative AI Can Help Developers Complete Their Tasks 56% Faster
The experiment was conducted by GitHub and Microsoft by Sid Peng, Eirini Kalliamvakou, Peter Cihon, and Mert Demirer in 2022 and published on the GitHub blog and Microsoft Research.
Aim of the study:
Can GitHub Copilot - a generative AI tool designed to assist in software development tasks - make developers more efficient? In particular, the study explores how the tool impacts the speed and quality of task completion among professional software developers.
Methodology:
- Candidates: A controlled experiment was conducted to measure how long it took 95 professional developers recruited via Upwork
- Task: Participants were asked to implement an HTTP server in JavaScript. They were divided into two groups: one group completed the implementation with the assistance of GitHub Copilot and the other didn’t.
- Process: The group that was using GitHub Copilot received a brief video tutorial on using it. Performance metrics included task success rate and completion time. These parameters were evaluated using GitHub Classroom to ensure accurate timing and assessment.
Results and findings:
- Productivity: The group that used GitHub Copilot was able to complete the task 56% faster than the one that didn’t use the AI tool.
- Task completion rate: The group using GitHub Copilot achieved a higher task completion rate of 78%, compared to the 70% completion rate of the group that didn’t use AI assistance.
- Sense of fulfillment: 60-75% of AI users had higher job satisfaction and were less frustrated when completing the task or coding. This sense of accomplishment allowed them to focus more on the work.
- Bridging the skill gap: During the heterogeneous treatment effects, it was revealed that developers with less programming experience, those dedicating more hours to coding daily, and older developers, experienced significantly greater benefits from AI assistance (GitHub Copilot).
The developments of the last few months are a real breakthrough and that we are entering a golden age of AI.
AI's Golden Age: A Timeline of Generative AI Milestones Since ChatGPT Launch
Generative AI platforms have been evolving rapidly since the launch of ChatGPT, especially with the number of LLMs launched and the rapid upgrades released by tech giants such as OpenAI, Google, and Meta. The surge in interest and advancement of AI technology has led to more funding and growth in AI startups like Perplexity AI, which grew 1,000x in a year.
Infact, in a WEF (World Economic Forum) podcast in March 2024, Aakrit Vaish, Co-Founder & CEO of Haptik mentions,
Here is a look at a timeline of significant events and AI model/tool releases following the launch of ChatGPT:
