Since its emergence in 2022, GenAI technology has made quick strides across domains in 2024. As a powerful and accessible form of AI technology, GenAI is capturing the attention of business leaders across multiple domains. According to a 2024 McKinsey survey, 65% of respondents were using GenAI in the early part of this year, double the number of users in 2023.
67% of respondents believe their organization will continue to invest in GenAI over the next 3 years. In the technology space, 67% of IT business leaders plan to prioritize GenAI within the next 18 months, while 33% of technology leaders are prioritizing GenAI adoption. This scale of initiative requires them to build a strong GenAI project team comprising new hires and existing workforce.
Be it through active hiring, upskilling, or partnering with GenAI solution providers, enterprises realize the importance of building an efficient team for GenAI use cases. This can be a challenging task. Let’s understand why – and how to overcome this challenge.
Why is it difficult to build the right GenAI team?
GenAI technology has emerged as a “game changer” for innovative companies exploring new business opportunities. To stay ahead in the competitive market, they need a skilled pool of GenAI resources to maximize the business potential. Besides possessing the right AI-related technical expertise, the GenAI team must have domain expertise – along with problem-solving skills.
For the best outcomes, enterprises need a “mixed” team that can combine traditional programming skills with AI and machine learning expertise. As compared to conventional software development teams, GenAI teams must have a tight coupling between application development and data science. This allows them to share their particular domain expertise to address specific business problems.
Why is it challenging for enterprises to build a GenAI team with a “mix” of relevant skills? Here are some of the challenges:
- Shortage of skilled GenAI professionals
This McKinsey study reports that 40% of business executives have highlighted the current shortage of GenAI skills, which is further expected to worsen in the years ahead. According to a 2023 Goldman Sachs report, GenAI can boost global GDP by 7% over the next decade.
This has further elevated the global demand for skilled GenAI professionals, thus worsening the current skills gap. This is driving the increased demand for professionals with LLM expertise and experience.
- Lack of understanding of GenAI capabilities
Most technology companies trying to build GenAI capabilities fail to understand business use cases that deliver the maximum benefit. IT leaders often approach GenAI as a “quick fix” to business problems without understanding the value of underlying data analysis. High-quality data is the “engine” for modern LLMs.
Additionally, without domain expertise, IT companies can fail to identify the best use cases (or applications) where GenAI can deliver success. Here are some questions they need to address:
- Which business use case can leverage GenAI for quantifiable results?
- What resources or skills are required to build this use case?
What are the skills necessary in any GenAI team?
As outlined in the previous section, a successful GenAI team requires a broader set of technical and non-technical skills. In addition to team members with domain experience, GenAI teams must possess the following technical skills:
- Data literacy
High-quality data and analytics skills are necessary for “training” any GenAI-powered model for a particular use case. Hence, enterprises need data experts proficient in relevant data collection, analysis, and visualization. Additionally, they must possess the skills to leverage AI-powered insights for business decisions – and to identify the right datasets for training AI models.
- Prompt engineers
As foundational LLMs keep improving, enterprises can deploy them immediately for use cases with a little “fine-tuning.” Hence, there’s a growing demand for AI-skilled prompt engineers, who can provide the best input (or prompt) to GenAI models – using the right words, phrases, and sentences. Besides that, prompt engineers need specific domain expertise – or knowing which AI model is best suited for a specific domain or use case.
- Natural language processing (NLP) engineers
As GenAI solutions are trained to understand natural human language, the GenAI team require an NLP engineer to analyze unstructured text. An NLP engineer can also build AI models that can extract insights from natural human language. Along with expertise in machine learning algorithms and training AI models, NLP engineers must be proficient in languages like Python and NLP libraries.
Additionally, GenAI teams must possess resources with the following non-technical skills:
- Product managers
A product manager in the GenAI team manages the entire development cycle of the GenAI product. They are responsible for aligning the GenAI product with the overall business objective or need. Product managers must possess good project management & team leadership skills. They also need the ability to interpret business needs into technical requirements, which are later embedded in the GenAI product.
- Domain experts
Domain experts are directly responsible for engaging with GenAI models through prompt engineers. Thanks to their domain experience, they can customize the GenAI model for a specific use case. For example, domain experts proficient in sales can assist developers draft an effective business email for sales professionals.
How CodingLimits can help you
At CodingLimits, we understand that enterprises need specialized talent to implement their GenAI initiatives – but often struggle to find the resources. With our specialized resources services, enterprises can easily scale up their pool of resources without any hiring and training costs.
Through contract-based or flexible staffing, we can provide human resources skilled in:
Additionally, with our managed services, we can manage your entire staffing process – from recruitment, onboarding, and training. We can help you meet all your staffing requirements. Connect with us today.
