👨‍💻 Role Evolution in the Era of Gen AI

DTP #33: PLUS 6 lessons for enterprise GenAI adoption

An AI survey conducted by Foundry has also pinpointed various positions that organizations are actively seeking to fill in order to facilitate the seamless integration of Gen AI into the corporate environment.  

While it’s still hard to say where the chips will fall when it comes to AI-related job roles, we have an idea of what roles are in the spotlight right now. 

Below are 8 roles that companies are looking to fill, or are planning recruitment for, to cater to their evolving strategies involving next-generation AI. 

💼 AI in Business

6 lessons for enterprise GenAI adoption

Enterprises need a purpose-built GenAI approach to properly deploy and mitigate risks, and commercially available generative AI models may not suit enterprise settings due to data access and security concerns. A post from CIO explores 6 learnings for seamless Gen AI adoption. 

Lessons Learned - Overview: 

  • Avoid Training from Scratch: Fine-tuning pre-trained models is more viable and cost-effective than training from scratch. 

  • Diverse Applications of LLMs: LLMs excel in various natural language processing tasks beyond text generation. 

  • Limitations of Open-Source LLMs: Despite advancements, open-source LLMs have limitations; combining multiple LLMs can mitigate these constraints. 

  • Input Data Quality Matters: Quality input data is crucial for refining LLM outcomes; emphasis should be on preparing data. 

  • Cost Management: Training LLMs is expensive; exploring alternatives and optimizing computing resources is essential. 

  • Tailoring Solutions to Specific Needs: Customizing generative AI for unique enterprise needs maximizes the potential of these models. 

Practical Recommendations: 

  • Use prompting engineering techniques for pre-trained LLMs. 

  • Explore diverse applications beyond text generation. 

  • Conduct due diligence with open-source LLMs and consider combining multiple models. 

  • Prioritize input data quality over model parameter adjustments. 

  • Manage costs by optimizing resources and exploring alternative infrastructure. 

  • Tailor AI solutions to specific enterprise needs and consider non-AI solutions when appropriate. 

Enterprise Transformation Potential: 

  • Generative AI and LLMs offer transformative potential for enterprises. 

  • Customizing approaches and leveraging these models creatively can lead to significant advancements in various sectors. 

According to research by Velents AI, the role of AI research scientist is the most sought after, boasting 17,940 available positions on Glassdoor. The average salary for this position is $132,000, and there has been a 42% increase in interest in this career over the past year.  

Following closely is the position of machine learning engineer, with 6,817 openings and an average annual salary of $160,000. AI engineers secured the third spot with 4,352 available jobs and a lower average salary of $105,000 compared to the top two. 

Prompt engineers, responsible for structuring text for generative AI models, have the highest average yearly salary at $300,000. However, they rank sixth on the list due to a limited number of available positions, totaling 2,123 on Glassdoor. 

Despite the perception of tech job losses, industry experts like Michael Gibbs emphasize the ongoing desirability and ample opportunities in the tech sector.

💻 Platform Highlight 

Robin AI: Gen AI assistant for contracts. Recently raised $26m in series B funding. 

GenHealth AI: Gen AI foundation model trained on medical events. Brought in $13m in early funding 

Perplexity AI: AI powered search engine. Raised $73.6m in funding, valuing the company at $520m. 

🌐 AI Weekly News Roundup 

A Pew Research Center survey highlights public concerns about Gen AI, with 52% more worried than excited. Job security tops worries, especially among educated workers. For restructuring professionals, Gen AI mirrors earlier tech changes, enhancing efficiency and productivity but potentially replacing tasks, posing questions about professional skill development and dependence on AI. 

AI is revolutionizing the $1.4 trillion IT services industry, promising 20% to 50% boosts in worker efficiency. While new-gen AI bots disrupt traditional work processes, integrating AI into existing systems and workflows is key. Leaders must leverage AI's value through partner demands, human-AI collaboration, software integration, measurable productivity, and CFO-led innovation for sustained competitive advantage. 

Red teaming is crucial for identifying and managing risks in generative AI. It involves simulated attacks by trusted actors to find flaws. Red teaming for AI models varies, demanding clear red team definitions, standardized testing, and codified findings. This practice, while crucial, faces challenges in implementation, requiring customized approaches based on risk assessment and degradation objectives for different AI models. 

💬 Social Highlight

Is imposter syndrome in data analytics/science common? - A Reddit thread 

AI and Real Estate – A tweet  

🤖 Prompt of the week 

Act as a data analyst and preprocess the [raw data] in [dataset] by removing [duplicate records] and [missing values]. 

See you next week,


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