What are non-technical AI roles?

Non-technical AI staff are often responsible for managing the more ‘human’ side of the technology. Non-technical staff roles are commonly found in:

• Marketing
• Sales
• Administration
• Content creation
• Human resources management
• Operations
• Product
• Customer services

You can hire employees with AI skills in content or sales roles to boost productivity. A marketing professional at an AI startup, for instance, translates complex technology into simple benefits for customers. A product team candidate with prompt generation skills may add value by turning raw customer data into actionable insights.

Why is hiring for non-technical AI roles important?

Non-technical AI professionals complement your business’s technical AI specialists. Technical AI specialists have the expertise to develop and refine AI-powered products and their features.

Meanwhile, non-technical professionals are able to communicate these product features to customers clearly and free of jargon. By engaging with customer insights and personal experience, they can also suggest creative new use cases for your AI products.

Employees with legal backgrounds may create AI ethics and compliance policies. They might also instruct your teams on how to follow them.

Non-technical staff can also use the AI tools your technical specialists create. This can involve managing daily workflows with AI project software or leveraging generative AI to produce content.

Hiring strategy for non-technical AI roles

Hiring for non-technical AI roles requires identifying the right combination of hard skills and soft skills. Compared to technical roles, non-technical AI skills are less about developing the technology and more about leveraging it.

Different non-technical AI roles may require specific hard and soft skills. You can use traditional job specifications to help you create new AI-focused roles.

For instance, if you are hoping to hire an AI sales manager, think about what a sales manager usually does. Then, research how they can integrate AI-powered technology into their role, such as using AI to generate new leads or automate pitches.

You might also search for typical sales manager skills, such as communication and problem-solving, alongside these AI skills, as both human and artificial intelligence complement one another.

Some of the non-technical AI skills you might hire for include:

• Prompt engineering: structuring and refining inputs for Large Language Models (LLMs) that prompt them to generate high-quality outputs. Candidates with this skill know how to input instructions, contexts and personas that lead to more accurate and relevant results.
• ‘No-code’ AI tools: these tools enable you to write programs, automate tasks and create agents without using code. Non-technical professionals with this skill can design simple automation tools for completing repetitive tasks.
• AI project management : involves using AI project management tools to automate project-related tasks, optimise workflows and manage resource allocation.
• AI ethics and compliance knowledge: strong candidates often completed data security and safety training. They are aware of any developments to UK law in response to the growing use of AI technology. You may want to train them in your business’s AI policies, but they already understand why compliance is important.
• Data analysis: non-technical AI professionals may require less advanced data analysis skills. They might have the ability to present data using charts, graphs and dashboards. Other similar skills include knowing how to use generative AI prompts to identify trends in data.

Identifying transferable skills that benefit employees in non-technical AI roles can also be useful. For example:

• Critical thinking: strong candidates are able to use their own domain knowledge, experience and judgement to review any AI-generated content. They may also be able to identify inaccuracies in data or false assumptions in generative AI outputs.
• Attention to detail: this skill helps your business maintain quality control while using AI-powered technology. Candidates with attention-to-detail skills might be good at checking content thoroughly for errors.
• Organisational: candidates with prior experience managing workflows know which tools to use for the job. They understand when to automate specific tasks and which ones to delegate to employees on their team.
• Domain knowledge: identify candidates with a strong background in the field you are hiring in. For instance, if your company is building an AI-powered healthcare app, search for candidates that have experience within the healthcare sector.
• Communication: candidates with strong communication skills can refine and structure AI-generated written content. They can ensure consistent brand voice and logical flow while correcting errors in grammar, spelling and punctuation.
• Contextual thinking: this skill involves being able to organise and interpret information according to its context or relationship to an overarching strategy. Candidates with this skill can refine generated AI content by providing further clarification, domain knowledge or more recent data to back up assertions.
• Adaptability: candidates with this skill may be quicker to adapt to using AI technology. They might be self-motivated during training sessions and are confident asking questions.
• Emotional intelligence: involves using human judgement to make sure generated content matches your brand voice. Candidates with emotional intelligence may be able to refine content so it is friendly, personable and aligns with the values of your business’s target audience. Emotionally intelligent candidates are often good at spotting bias and stereotypes in written content.
• Logical thinking: while AI-generated content can sound authoritative, it may still be inaccurate in its assumptions. Logical thinkers understand when to fact-check information, regardless of how persuasively it is presented. They are often good at identifying logical inconsistencies and incorrect citations.

Sourcing candidates

Non-technical candidates understand the different capabilities of AI without having any specialist technical knowledge. While many businesses use specialised tech boards for AI talent, you can find non-technical professionals with these few tips.

Think about:

• Posting your job online : post your non-technical AI job on Indeed to find and attract quality candidates.
• Engaging in outreach: identify and reach out to people actively engaged in online conversations about AI. They might demonstrate a general interest in AI, but don’t have advanced technical competencies. As an employer, you can build on their understanding.
• Attending AI industry conferences and startup events: at these events, network with professionals in fields including sales, marketing, content creation and product development. You can also use this as an opportunity to network with professionals with technical backgrounds.
• Identifying target profiles for your roles: for example, if you are hiring for an AI sales professional, you might prioritise a candidate who has worked on complex sales projects for a Software as a Service (SaaS). The candidate has past experience communicating product value propositions, meaning they might have the right skills to translate complex AI concepts into simpler terms.

Writing non-technical AI job descriptions

To source strong candidates for non-technical AI positions, think carefully about your job description. This provides an opportunity to discuss the kind of AI-related experience and expertise you require from candidates.

Think about the level of experience you are hiring for

If you are hiring for a role that only requires foundational knowledge of AI, consider sourcing candidates who are interested in developing AI skills on the job. In your job description, ask for skills such as flexibility, adaptability and the ability to learn new technology quickly.

Some roles might require some experience using AI, for example in prompt generation or data analysis. In your job description, you might request microcredentials or certifications that demonstrate foundational AI skills.

Making your non-technical AI job description inclusive

When writing job descriptions for non-technical AI roles, consider making them inclusive to candidates from all backgrounds. That way, you can access a much broader range of candidates, therefore attracting top talent to your business.

Ways you can make your job description more inclusive are:

• Being clear about your desirable vs essential skills. This includes whether the role needs any prior experience with AI. For example, you could say ‘experience with AI project management tools is strongly desirable’.
• Using phrasing that is free of technical jargon and focuses more on skills and cultural fit. If you are hiring for problem-solving skills, you can use a phrase like ‘confident troubleshooting novel problems’.
• Emphasising that your business is searching for candidates who are excited to learn.
• Encouraging diverse applicants who might otherwise feel excluded from AI roles. For instance, express a clear interest in non-traditional backgrounds and mention how your diversity and inclusion policies align with your AI use.

Interviewing for non-technical AI jobs

You can ask a range of questions to identify whether a candidate is highly suitable for a non-technical AI role. Ask questions to assess transferable soft skills that demonstrate that candidates:

• Adapted to technological change in the past
• Exhibit critical thinking skills in practical tests or through anecdotes
• Use communication skills to explain complex ideas
• Applied data analysis skills to past situations

Some example questions you can ask include:

1. ‘What are some applications of AI technology that you’re familiar with?’

Use this question to identify which AI tools the candidate already has knowledge of. Strong candidates are able to describe different use cases of AI and how they would apply the tools in different situations.

2. ‘What are some current AI trends you are interested in?’

This question can help you find out whether a candidate keeps up to date with developments in AI technology. Consider candidates that are interested in new use cases for AI, since they can help your business remain competitive within the field.

3. ‘How would you keep company data safe while using LLMs?’

Data privacy and compliance are central to safe AI-powered technology usage. This question helps you evaluate the candidate’s awareness of data privacy and safe technology usage.

4. ‘What tasks would you take a human approach to and when would you use AI?’

This question helps you find out whether a candidate understands how to use AI-powered tools collaboratively. Strong candidates can explain how they use critical thinking and attention to detail to correct inaccuracies in generative AI outputs.

Practical screener tasks

Use practical screener tasks to spot candidates with strong prior experience using AI tools. Prompt generation tests help you find out how candidates use them to solve a problem. They also show how they would follow up on the AI tool’s generated responses.

Non-technical roles are often in marketing, sales, legal, content creation and administrative roles. While they may not require specialist experience, candidates for these roles can benefit from transferable skills such as critical thinking, problem-solving, domain knowledge and data analysis.