Discover Top Generative AI Digital Badges and Skills for Future Success

According to Google Cloud, Generative AI (GenAI) refers to the use of artificial intelligence to create new content, such as text, images, music, audio, and videos. Unlike traditional AI, Generative AI relies on sophisticated machine learning models, each tailored to perform specific types of tasks based on their design and training.

Generative AI is revolutionizing industries by delivering content and solutions that were once thought to be uniquely human. Research by Pearson highlights the transformative impact of GenAI, predicting that it could help UK workers save up to 19 million hours a week by 2026. The study identifies tasks where GenAI can be particularly effective, including record maintenance, data collection, and information compilation. For instance:

• Maintaining current knowledge in an area of expertise: 679,000 hours
• Developing educational programs, plans, or procedures: 665,000 hours
• Creating visual designs or displays: 525,000 hours
• Maintaining operational records: 512,000 hours

Chances are, you’re among the millions using tools like ChatGPT, Microsoft’s Copilot, or Google’s Gemini to excel in job interviews, work projects, creative writing, or travel planning. These tools exemplify the growing field of generative AI, which is booming in popularity and demand.

“In nearly every workplace, people spend their day on common, time-consuming tasks that eat away at productivity or their work-life balance. If those tasks could be augmented with generative AI, employers and their workers could reallocate time to the things that needs a more human touch and mean more to their customers...”

- Oliver Latham, VP of Strategy and Growth for Pearson Workforce Skills

Given this context, you might be curious about the most valuable GenAI skills for your organization. In this blog post, we’ll highlight key GenAI skills crucial for staying ahead in technology. Plus, explore thousands of courses and badges on the Credly platform to recognize and advance the GenAI skills within your team!

Top Generative AI Skills and Badges on Credly

1. Prompt Engineering

Prompt Engineering is the process of designing and optimizing AI language models, like OpenAI's GPT-3, to efficiently generate human-like responses by providing a well-structured input that guides the model's behavior and maximizes its performance.

It involves understanding how these generative AI models, built on transformer architectures, interpret and react to different prompts. By carefully structuring and refining prompts, you can influence the AI’s output, specifying context, constraints, or desired outcomes to improve quality and relevance.

Prompt engineering is versatile and applicable across various fields. In marketing, it generates engaging content for campaigns, articles, and social media. In software development, it assists with code generation and problem-solving, streamlining workflows and saving time.

2. Model Evaluation

Assessing the performance of a machine learning or generative AI model by using metrics to compare its predictions or outputs against ground truth data.

In a business setting, model evaluation helps ensure that AI-generated content meets quality standards and aligns with user expectations. For example, evaluating a text generation model’s output for consistency and relevance can help improve the quality of automated content.

3. Deep Learning

Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning.

Unlike traditional machine learning models which typically use simple neural networks with one or two layers, deep learning models have three or more layers—often hundreds or thousands—allowing them to handle complex data patterns.

Deep learning is prevalent in everyday life, powering voice assistants like Google Assistant, Alexa, and Siri, which interpret human commands to perform tasks like scheduling appointments or ordering items. In healthcare, deep learning enhances medical imaging by helping radiologists and specialists analyze images faster and more accurately, improving diagnostic efficiency. 

4. Machine learning 

Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data.

Machine learning is comprised of different types of machine learning models, using various algorithmic techniques: supervised, unsupervised, semi-supervised, and reinforcement learning, each using various algorithms depending on the data and desired outcomes.

One of the most common applications of ML is recommendation engines, which analyze past user behavior to suggest relevant products, boosting sales and customer engagement. In e-commerce, recommendations drive up to 31% of revenues and account for 12% of overall purchases. Streaming services like Netflix also rely heavily on ML, with 80% of watched content driven by their recommendation system, contributing significantly to revenue growth, including a $1 billion increase in 2021.

5. Natural Language Processing  

Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data.

NLP includes Natural Language Understanding (NLU), which enables computers to comprehend language, and Natural Language Generation (NLG), which allows them to produce human-like text, often summarizing complex data into readable narratives.

NLP automates tasks like customer service via chatbots, improves search accuracy by understanding context and synonyms, and enhances search engine optimization. It can also organize large document collections, support legal discovery, and analyze social media to gauge real-time customer sentiment. NLP also helps businesses gain market insights and moderate user content based on tone and intent. 

NLP is already part of everyday life for many. For example, large insurance companies use NLP to sift through claims documents, automating and speeding up their processes.

6. Computer Vision 

Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos.

7. AI Ethics

AI Ethics is the study and application of moral principles and values to ensure the development, deployment, and use of artificial intelligence technologies benefit society while minimizing harm and ensuring fairness, accountability, and respect for human rights.

Lauren Bergnes, Strategic Initiatives Analyst at Pearson, shared key tips and insights on the limitations of Gen AI and how we can use it responsibly in the workplace.

As companies increasingly rely on big data to drive automation and decision-making, unintended consequences can arise, often due to biased datasets and poor research design. These issues highlight the need for ethical considerations in AI development.

Countries, cities, and regions are establishing their own AI ethics regulations, requiring businesses to navigate varying laws. For example,  Many countries have ethical frameworks and AI principles aligned with the OECD AI Principles, with 17 included in the OECD database as of May 2023. 

Canada and the EU propose comprehensive frameworks for all sectors while the US and UK prefer a sectoral approach, using cross-sectoral, non-binding principles, leaving enforcement to individual sector regulators.

The Role of Digital Credentials for Gen AI Skill Development

In today's rapidly evolving landscape, digital credential platforms like Credly are essential tools for managing credentialing needs, particularly in emerging fields like AI. Digital badges and credentials offer numerous benefits:

• Increased Employee Engagement and Retention: Recognizing and rewarding employees with digital credentials for their achievements fosters a culture of appreciation and motivates employees to excel. This, in turn, boosts morale, enhances job satisfaction, and contributes to higher employee retention rates.
• Streamlined Compliance and Certification Processes: Digital credentials offer a more efficient and secure way to manage compliance and certification processes within organizations. They provide verifiable proof that employees' certifications are up-to-date, simplifying audits and ensuring regulatory compliance.
• Enhanced Brand Reputation: Organizations that invest in digital credentialing demonstrate their commitment to employee development. This enhances their reputation as an employer of choice and attracts top talent in the industry.
• Facilitated Knowledge Sharing: Digital credentials provide a common language for skill recognition across departments and teams within an organization. This fosters knowledge-sharing and collaboration among employees, leading to increased innovation and productivity.
• Improved Decision-Making: By leveraging data from digital credentials, organizations can gain insights into their workforce's skills and competencies. This enables better strategic decision-making regarding talent management, workforce planning, and resource allocation.

Credly: The Leading Network for AI Digital Credentials

Credly is the leading digital credential network, with 93 million badges issued to over 46 million earners. Trusted by over 3,600 active issuers, including top organizations like Microsoft, IBM, Google Cloud, and AWS, Credly is the #1 ranked credential management software on G2.

In the AI field, Credly offers over 2 million AI-related badges, covering various AI topics, including Generative AI. This extensive network validates and showcases AI expertise, helping professionals stay competitive and address the global skills gap.