I work at Microsoft and teach a Stanford Online course on AI. These are my tips for non-technical workers.
Building a Career in Generative AI: Insights from a Stanford Instructor
As someone who has spent the last decade working in AI—from data science at Uber to AI consulting at McKinsey, and now working on Microsoft Copilot—I've gained valuable insights into how professionals can build successful careers in this field. Drawing from my experience teaching at Stanford and co-creating the "Mastering Generative AI for Product Innovation" course, which launched in August 2024, I've identified two main pathways for professionals looking to enter the generative AI space.
## Technical Skill Development Track
Fortune 500 leaders consistently express their greatest need: professionals who can bridge business and technical requirements. You don't necessarily need to become a programmer, but technical literacy is crucial. Here's how to progress:
### Beginner Level: Master Prompt Engineering
While it might sound basic, becoming proficient in prompt engineering across text, audio, and image platforms makes you immediately valuable in business settings. Understanding the capabilities and limitations of different AI tools allows you to generate actionable ideas before involving technical teams.
### Intermediate Level: System Design Understanding
At this stage, you should familiarize yourself with how generative AI systems operate within organizational constraints. Understanding concepts like data boundaries—the limits within which companies can use customer data—becomes crucial. For instance, a bank needs to ensure customer data doesn't leave its agreed-upon boundaries when interacting with AI systems like OpenAI.
### Advanced Level: Specialization
The advanced path typically splits into two directions:
- For professionals outside major tech companies: Focus on developing coding skills
- For Big Tech employees: Concentrate on system architecture, including detailed understanding of data boundaries and flow diagrams
## Industry Expertise Track
This track leverages business professionals' existing knowledge while building specialized AI expertise within their domain. For example, in finance, this means understanding:
- What data can be used for model training
- Privacy and security regulations for AI applications
- Industry-specific implementation challenges
This expertise is highly valued—so much so that professionals who master it often become sought-after consultants. I've seen operations managers transition into specialized AI consultants, commanding premium rates for their industry-specific AI knowledge.
## Practical Tips for Success
### Embrace Active Learning
The most successful practitioners actively integrate AI tools into their daily lives. Regular use of platforms like ChatGPT or Claude helps develop an intuitive understanding of AI capabilities and limitations.
### Think Abundantly
Experienced AI users understand the "abundance of intelligence" concept. Rather than asking for a single solution, they might request multiple options (e.g., 20 different responses to a message) and use their judgment to select the best one.
### Expand Use Cases
Don't limit yourself to work-related applications. Use AI as a thought partner for various activities—from communication and general planning to personal projects like trip planning. This broader exposure helps develop a deeper understanding of AI's practical applications.
The field of generative AI offers exciting opportunities for professionals willing to invest in their development. Whether you choose the technical track or focus on industry expertise, the key is to maintain hands-on engagement with AI tools while building your understanding of their practical applications in your chosen domain.