35 New AI Roles to Watch Out For

From Webmasters to Virtual World Builder, and AI Ethics Officers: The Evolution of Tech Careers

The Human-AI co-creation model from ‘Human-Computer Interaction’ (Wu et al, 2021)

This week I’d like to start a new column that is focused on new job roles and skills that AI will be creating. Some are already existing, some are educated hypotheses, or even better - ideas and demands that should be part of future regulations and governance.

Let’s dive in!

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There’s a big rush to build (or hire) agents, as we discussed in our Will Digital Workers Revolutionise Work? article. But as AI starts to be more integrated and ubiquitous in companies, we’re going to start to see five macro-trends:

Specialization, Human-AI Coordination, Augmentation / Innovation and Cross-Discipline.

This will lead to completely new job roles, like ‘Chief AI Officer’ (Companies including Boeing, NASA, PwC, Pfizer have recently appointed one) but also to augmented/enhanced existing roles.

We’ll dive into each with some examples both current and upcoming.

1. AI Specialized Roles

These are roles that focus on very specific parts of AI, similar to how web development has evolved over the past two decades. In the early 2000s, a ‘webmaster’ was often responsible for all aspects of a website. But as web technologies became more complex, we saw the emergence of specialized roles like front-end developers, back-end developers, UX designers, and DevOps engineers. The same will apply to AI.

Example Roles:

• AI Ethicist: Responsible for ensuring that AI systems are developed and used ethically, AI Ethicists address concerns related to bias, privacy, and accountability. This role is crucial in building trust in AI systems among employees and stakeholders, and includes a focus on data (how it’s obtained, how it’s used)

• Prompt Engineer: Often referred to as “AI translators,” Prompt Engineers optimize user interactions with AI systems by crafting effective prompts that guide AI responses. This role bridges the gap between human communication and AI comprehension.

• AI Security Specialist: Focused on protecting AI systems from cyber threats, this role requires knowledge of cybersecurity principles and AI-specific vulnerabilities. As AI systems become more central to operations, this role will be critical in maintaining system integrity.

• Conversational AI Developer: They create chatbots and virtual assistants that can engage users in natural language, enhancing customer service experiences. This role is at the forefront of human-AI interaction.

• AI Solutions Analyst: This role involves analyzing business needs and developing tailored AI solutions to improve operational efficiency across various sectors. It requires a blend of business acumen and AI expertise. Currently being catered to by consulting firms, but I predict it’s going to be an internal role soon.

• AI Policy Advisor: This new role addresses the unique regulatory challenges posed by AI. Issues like algorithmic bias, AI transparency, and the societal impacts of automation are at the core of this job. It might help shape regulations that ensure AI systems are deployed responsibly, balancing innovation with ethical concerns.

• AI Auditor: This one needs to assess whether an AI system’s decisions are explainable, check for potential biases in training data, or ensure that AI systems comply with data privacy regulations. This role is crucial in building trust in AI systems across industries.

• AI Data Quality Specialist: Unlike traditional data quality roles, these specialists focus on the unique data needs of AI systems. They ensure that training data is not just accurate, but also representative and free from biases that could skew AI outputs. They might develop new methodologies for assessing data quality specifically for machine learning applications

2. Human-AI Roles

These are going to be completely new type of roles that will fall into different kinds of categories, but all aimed at improving the way that humans interact, guide and improve AI. AI-Human Collaboration and AI-Human Co-Creation are going to be extremely hot topics for the next few years, so more to come on this space.

• AI-Human Interaction Designer: This role goes beyond traditional UX design, focusing on creating interfaces where humans and AI can collaborate effectively. They design systems where AI enhances human capabilities rather than simply automating tasks. For instance, they might create an AI writing assistant that doesn’t just correct grammar but offers creative suggestions, adapting to the user’s writing style over time.

• AI Trainer/Curator: This role involves training AI models by providing them with high-quality data and refining their algorithms to improve performance. The skills developed here are essential for ongoing AI optimization, but it’s data labeling from humans. Startup Pareto seems to be doing this, claiming to have ‘The top 0.01% of AI data labelers’ and work with ‘elite teams’. Honestly, this looks to be like the new ‘back-office’ kind of work we thought would go out of the window, but would be happy to know if it’s otherwise.

• AI Explainability Specialist: This role emerged from the need to understand AI’s “black box” decision-making processes. Unlike traditional systems analysts, they work on making complex, often opaque AI algorithms interpretable to humans. They might develop tools to explain why an AI made a particular medical diagnosis, ensuring doctors can trust and verify AI-assisted decisions.

• AI Agent Manager: Similar to the Interaction designer, this role will automate workflows at first but then manage a swarm of agents and their performance. Setting them up, and making them work together efficiently.

A lot of new C-level roles will need to focus on Human-AI Collaboration. We’ll see both entirely new positions and existing roles with expanded responsibilities. New roles might include the ‘Chief AI Officer’ (CAIO), focused on AI strategy and implementation. Existing C-suite positions will evolve: CEOs will need to leverage AI for competitive advantage, CTOs will integrate AI into core infrastructure, CHROs will prioritize AI talent management, and CDOs will enhance data governance for AI initiatives. Additionally, the Chief Innovation Officer (CINO) and Chief Experience Officer (CXO) will take on critical roles in driving AI-related innovation and using AI to enhance customer experiences, respectively.

• Chief Human-AI Integration Officer: Senior role with focus on integrating AI into human workflows. They oversee the development of human-AI collaboration strategies, ensuring that AI augments rather than replaces human capabilities. They might develop programs to reskill employees for AI-enhanced roles, design human-AI teamwork protocols, and measure the impact of AI integration on workforce productivity and satisfaction.

3. Innovation and Augmentation: AI Enhanced Roles

As AI roles become more specialized, AI will make other roles more generic again (an application of the ‘bundling/unbundling’ cycle theory to the job market) therefore eliminating the need of having ultra-specific roles. This happens through the augmentation and automation of skills and tasks. While I don’t think we’ll get back to the ‘webmaster’ as above, many roles that have become too narrow will need to be more generalist and broad / eclectic again: think of a Social Media Manager (role didn’t exist in 2010) who may be very narrow in their capabilities - AI will enable that role to also be more self-sufficient with design, copywriting, paid media and so on. Some roles will be simply ‘enhanced’ by AI, meaning some parts of the job will be led by AI, but the remit and goals won’t be hugely different.

• AI-Augmented Creative Director: Unlike traditional Creative Directors, those in this role leverage AI as a creative partner. They use AI to generate and iterate on ideas at a scale impossible for humans alone. For example, they might use AI to generate thousands of ad variants, then apply their human creativity to refine and select the best options, blending machine efficiency with human insight.

• AI-Powered Financial Analyst: These analysts go beyond traditional data analysis, using AI to process vast amounts of unstructured data and identify patterns invisible to human analysts. They might use AI to analyze social media sentiment, satellite imagery, and traditional financial data simultaneously to make investment decisions, a task too complex for traditional methods.

• AI-Enhanced Customer Service Representative: New customer service reps will work in tandem with AI systems. They handle complex, empathy-requiring situations while using AI to access vast knowledge bases instantly through advanced Knowledge Management Systems. For instance, they might use real-time AI analysis of a customer’s tone and history to tailor their approach, providing a level of personalized service even if they hadn’t spoken with that customer beforehand.

• AI-Powered Content Creator: These entrepreneurs use AI tools to produce, optimize, and distribute content at scale. They might use AI to generate article drafts, create variations for different platforms, and even predict trending topics. For example, a YouTuber might use AI to generate script ideas, edit videos, and optimize thumbnails, allowing them to produce high-quality content at a pace previously requiring a full production team.

• Innovation through One-Person Businesses - we mentioned this recently, and stand our ground that this type of role will grow, both as a desire and also as a necessity as some jobs are displaced and GenZ look for their purpose and mission (or loyalty to corporates lowers, layoffs become more frequent and so on). I guess it’s a stretch to call this a new role, it’s merely another word for entrepreneur, but I do think it will have a different description and it’s mentioning. AI will empower this category further by enabling further automations and skill development (we wrote about this in Building Your Own Digital Workforce)

Another category of innovative role is going to focus on virtual worlds and avatar / fake personality creation. This emerging category represents a fusion of AI technology, content creation, and social media influence, and these roles involve creating, managing, and monetizing AI-generated personalities that can interact with audiences across various platforms.

• AI Personality Designer: These professionals craft the personas, backstories, and visual representations of AI-generated characters. Unlike traditional character designers, they must consider how these personalities will evolve through AI-driven interactions. For example, they might create a virtual influencer for a fashion brand, designing not just their look, but their personality traits, interests, and how they’ll respond to different scenarios.

• Virtual World Builder: As AI influencers become more sophisticated, these professionals will create immersive digital environments for them to “live” in. They might design virtual homes, workspaces, or even entire cities for AI personalities to inhabit, enhancing their perceived realism and engagement potential.

Challenges and Final Consideration

Yes, reskilling and upskilling will be a challenge. However, unlike in other technological revolutions, we must consider one big difference: the ability to do all of this through natural language. In the past, when a traditional accountant needed to upskill to work on Excel, they had to learn a new interface, understand formulas, and grasp complex functionalities. It was a steep learning curve that often required formal training or courses.

But with AI, particularly large language models, the barrier to entry is significantly lower. An accountant today can interact with AI tools using natural language, asking questions like “How do I create a pivot table for this data?” or “Can you help me optimize this budget spreadsheet?” The AI can provide step-by-step instructions, explanations, and even generate code or formulas, all in plain language.

This accessibility doesn’t mean that deep expertise isn’t valuable - it absolutely is. But it does mean that the initial hurdle of adopting new technologies is much lower. It’s like having a patient, all-knowing colleague always ready to help and explain things in a way you can understand.

So, while AI presents a more accessible path to upskilling than previous technological revolutions, it also requires a shift in how we approach learning and skill development. That’s why the role of HR will be even more important than before. The focus will be less on memorizing procedures or mastering specific tools, and more on developing adaptability, critical thinking, and the ability to effectively collaborate with AI systems. Plus, we will need to do a lot of change management to adapt culture, mission and values to new organizational models too. We need to develop new frameworks for performance evaluation, compensation, and career progression that align with an output-based model.

For example, one of the most crucial shifts required is moving from a time-based performance model to an output-based one. This transition is not just a matter of changing metrics; it represents a fundamental reimagining of how we value and measure work. This shift is already being modeled by AI-driven systems and AI Agents, which are typically compensated based on their outputs rather than the time they take to complete tasks.

Businesses are starting to use AI language models or automated design tools and not paying for the milliseconds of compute time, but for the quality and quantity of the output produced. This output-centric model is going to influence how we think about human work as well.

Lots more to come on this topic!

Ciao, Matteo