Many people compare the impact of generative AI on society to the way the Internet democratized information access at the turn of the century. The Internet provided a digital gateway to information discovery, ecommerce and social connections, creating millions of jobs.
GenAI is poised to do likewise, but on an exponential scale. Some smart people claim that genAI be even bigger than the Internet. More certain is that genAI’s transformative function—automating content creation—will alter how people work.
The implications to changes for knowledge workflows are staggering. Enterprises will use personalized technology skills development to drive $1 trillion in productivity gains by 2026, according to IDC research.1
This productivity bump won’t come courtesy of magic pixie dust. Organizations must educate staff on how to incorporate genAI into their daily workflows. Education starts with prompt engineering, the art and science of framing prompts that steer Large Language Models (LLMs) towards desired outputs. Eighty-seven percent of IT leaders Dell surveyed2 said they would like prompt engineering training for themselves, their teams, or both.
A few examples of how various employee roles might leverage prompt engineering and other skills that involve genAI include:
Knowledge workers
This includes marketing personnel and business analysts, as well as HR specialists and managers across all business lines. Crafting prompts with relevant terminology, context and constraints can spur LLMs to create more accurate and relevant responses that may help them create marketing materials or work with a chatbot to better support customers.
Business leaders must improve core training programs by installing prompt engineering experts to run workshops that balance theory with practice to help upskill their team members. Online learning platforms such as Coursera, Udacity and edX offer courses taught by experts for organizations who prefer to go that route. Staff proficient in the practical application of AI tools in the context of enterprises will elevate their organizations’ capabilities.
Software developers
Software programmers regularly produce software code, the lingua franca of the digital world. It turns out that LLMs are pretty good at generating code. Learning the proper coding prompts can help software developers use LLMs to create and debug software, as well as increase their skills working with natural language processing (NLP).
Learning about NLP fundamentals such as tokenization, part-of-speech tagging, named entity recognition can round out developers’ education. Leaders should encourage developers to experiment with LLM frameworks such as Google’s BERT, Meta’s LLama 2 or Hugging Face’s Transformers libraries. They’ll use these tools and tutorials to build small projects and experiment with fine-tuning pre-trained models. Developers who can work with genAI systems will be able to build innovative digital products and services, becoming more valuable to their organizations.
Data engineers
Data engineers can supercharge their careers by becoming conversant in genAI systems. For instance, most data engineers may be familiar with working with diverse data sources, but companies require specialists who can collect, preprocess and manage the large datasets required for training models.
This requires data engineers to master concepts and techniques germane to NLP, as well as container orchestration and scaling strategies. Leaders must encourage data engineers to expand their horizons and provide them with the resources they require to branch out, helping them become proficient in manipulating data to suit genAI models.
The path forward for upskilling staff will vary by organization. Building consensus on business use cases for genAI will help inform the direction of in-house training, as well as online educational platforms and expert workshops organizations elect to provide.
Organizations must regularly communicate the availability of these courses and upskilling opportunities to all employees as well as promote the benefits of genAI tools in improving speed and efficiency. Moreover, the freedom to innovate is a talent magnet, as 78% of employees join their companies because they believe they will be empowered to innovate, according to a Dell survey.3
Partners can assist in upskilling
Coupling technological innovation with investment in upskilling employees will be paramount for a smooth transition into the future of human-plus-AI work. Regardless of their innovation capacity, many organizations will need help to leverage the growing open ecosystem of LLMs, software libraries and associated technologies.
Partners are moving in lockstep with evolving technologies to advise organizations on the best technologies, tools and processes. From figuring out how to run LLMs (or smaller SLMs) on laptops to leveraging validated reference designs, partners are cultivating expertise on building, operating and refining genAI systems.
Organizations needn’t take the genAI leap alone.
- 1 Workforce Upskilling for the AI Era, IDC, Jan. 2024
- 2 Generative AI Proficiency and Use Case Insight, Dell internal survey, Jan. 2024
- 3 Dell Technologies Innovation Index, Dell, February 2024
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