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Last week Skillsoft unveiled a new generative AI training program in partnership with Microsoft. The initiative aims to equip organizations with the skills needed to use Microsoft's AI tools, including Copilot and Azure OpenAI.
The partnership comes at a time when AI moves into seemingly every element of the digital workplace. And as happens when any new technology is introduced, workers need to learn how to use it.
There are two elements to this process. The first is developing an organizational strategy for what adoption will look like across the organization. The strategy should consider multiple elements, but particularly the impact introducing such technology will have on existing business strategies and processes.
Skillsoft won't help with the first one. But the second element — upskilling — is where it comes in.
A Mix of Learning Options With Positive Early Results
The program, built on Skillsoft's AI Skill Accelerator platform, offers a mix of on-demand courses, coaching, live training and practical labs. According to Skillsoft, early results show 98% of participants can immediately apply their new skills at work. Some of the key features of the 90-day program include:
- Skills assessment
- Data-driven talent mapping
- AI-powered coaching using Microsoft's Azure Open AI
- Post-training evaluation
In Skillsoft statement, Jeana Jorgensen, Microsoft's corporate VP for learning, said of the program, "This program aligns with our goal of driving AI adoption across industries. We believe it will spark innovation and give our customers a competitive edge." So what is it that workers need to learn?
Related Article: What AI Upskilling Looks Like at Every Level of the Organization
Data Analysis Skills
Genius Solutions president Jean Magny told Reworked that first and foremost, workers need to develop strong data analysis and machine learning skills. In the case of Genius, he said, the use of AI to extract insights from client data and provide recommendations has already boosted productivity between 15% and 20%.
However, he also noted that learning should extend beyond just how to use generative AI, to understanding how these technologies can help achieve business goals via automation.
“We had clients analyze how employees engaged with systems, then used machine learning so our AI could adapt to contexts and improve,” he said. Here he cites the example of chatbots. With routine customer service inquiries, they were able to use this information to determine what could be automated based on how people asked questions. AI is advancing fast, but the skills to leverage it matter more.
The key is developing an AI strategy aligned with business objectives, Magny said. With the right approach, AI boosts productivity. But it requires working with the tech, not expecting magic solutions.
Related Article: How Generative AI May Fit in Your Organization
Skills and productivity
Skillsoft's initiative in GenAI skilling could potentially enhance both productivity and innovation within organizations at all levels, Rivermate founder Lucas Botzen said. He believes now is a critical time to foster an innovation culture with a workforce skilled in AI technologies. Such a workforce can devise and implement new solutions and advance their company's technological capabilities.
Here he shares the example of using Microsoft Copilot to automate routine HR tasks. But he was quick to add that integrating AI into HR is not just about automation. Rather, it is about augmenting human capabilities and creating a more agile and responsive workforce.
"Creating a continuous learning environment where employees can experiment with AI technologies and learn from their experiences is essential,” Botzen said.
The level of AI skills required by digital workplace workers depends on the role they play within the organization and the specific applications of AI being used, Daniel Wood co-founder of the Swedish Wealth Institute, added.
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To justify the investment in generative AI technologies, employees need a foundational understanding of AI principles and hands-on experience with key tools to use them effectively. Here Wood recommends some core skills and knowledge:
- Basic AI Literacy: Understanding the fundamentals of AI, machine learning, and data science is essential. Employees should be familiar with concepts such as:
- Neural Networks: Understanding the structure, function, and types of neural networks (e.g., convolutional, recurrent).
- Data Preprocessing: Techniques for cleaning, normalizing, and transforming data to ensure it is suitable for training AI models.
- Model Training: Knowledge of algorithms for training models, including supervised, unsupervised, and reinforcement learning methods
- Proficiency With AI Tools: Familiarity with specific tools like Microsoft AI, Copilot, and Azure Open AI is vital. Employees should be able to:
- Integrate AI Tools: Seamlessly incorporate these tools into their daily workflows to boost productivity and innovation.
- Use APIs: Leverage APIs provided by these tools for custom applications and automation.
- Data Analysis and Management: Workers need skills in data collection, cleaning and analysis. They should be capable of:
- Data Collection: Using various methods to gather data, including web scraping, APIs and IoT devices.
- Data Cleaning: Applying techniques to handle missing values, outliers and ensure data integrity.
- Data Analysis: Using statistical and machine learning methods to derive insights and make data-driven decisions.
- Problem-Solving and Critical Thinking: AI can automate many tasks, but human oversight is necessary to ensure the technology is applied correctly. Employees should be able to:
- Identify Problems: Recognize issues that can be addressed with AI.
- Analyze AI Outputs: Evaluate the accuracy and relevance of AI-generated results.
- Decision Making: Make informed decisions based on AI insights while considering ethical and practical implications.
- Continuous Learning and Adaptability: The AI field is continuously evolving. Encouraging a culture of continuous learning and adaptability within the workforce is essential to keep up with:
- New Advancements: Staying updated on the latest AI research, tools and best practices.
- Skill Enhancement: Regularly updating skills through training programs, workshops and online courses.
“By investing in these skill areas, organizations can maximize the benefits of GenAI technologies, driving improved business productivity and fostering innovation,” Wood said.