From helping streamline daily tasks to assisting with big-picture decision-making, there are many ways AI can supercharge operations.
So, it’s no surprise that 65% of respondents to McKinsey’s latest global survey say their organisations are regularly using GenAI. It’s also driving demand for new workforce skills – last year saw a 2,000% surge in roles demanding generative AI skills(opens new window), with organisations of all stripes keen to tap into the vast potential productivity benefits.
However, even with most businesses deploying AI in some capacity, only 13% of employees have been offered AI training by their employers(opens new window).
Successfully implementing AI in the workplace is not as simple as buying a popular tool and expecting employees to adapt. To get the most value from these technologies, workforces need skills – both technical and soft.
But as it stands, there’s a significant lack of AI skills in the workplace. According to our research, almost half of leaders (45%) point to AI as their most significant skill gap.
If businesses want to leave the experimentation phase and begin to define their unique AI use cases, they’ll need employees who can use AI productively and with minimal risk.
Here are the top 10 skills we believe employees need to effectively implement artificial intelligence in the workplace:
1. Data engineering
A crucial early step in any AI implementation journey is building and maintaining robust data infrastructure. This is responsible for collecting, storing, and processing the large volumes of data AI needs to be trained on.
As such, organisations need employees with data engineering skills. They help organise and clean data, so the datasets fed to AI models are high-quality and relevant. This means the models deliver the most reliable insights, and also helps ensure data integrity, which is important for regulation compliance.
2. Data analysis and visualisation
Once you have access to clean data, it needs to be interpreted to extract meaningful insights. Data analysis skills help employees identify trends, patterns, and correlations within complex datasets so they can make data-driven business decisions.
But it’s equally important for a variety of stakeholders to be able to understand data insights. Data visualisation skills go hand-in-hand with data analysis, helping employees convert raw data into graphical representations – such as charts, graphs, and dashboards – that make it easy for others to digest at a glance.
3. Data science and programming
To go from insights to action, you need data science skills. These allow staff to develop, deploy and maintain AI systems as businesses begin building their own unique AI solutions.
Programming skills are also vital. The capacity to create efficient and scalable code in languages such as Python, C++ and Java is key when it comes to integrating AI models into existing business systems and workflows.
4. Risk management and ethics
Once a business starts implementing AI models, it needs employees capable of creating comprehensive risk management frameworks. These skills will help ensure the long-term success of AI projects by supporting employees to better identify, assess and mitigate risks, such as data breaches and algorithm biases.
However, AI initiatives will only truly be sustainable if the business continues to use it responsibly. Employees should also know how to uphold privacy and accountability, as well as minimise bias within the models they work with.
5. Planning and stakeholder management
Successful AI initiatives are connected to larger organisational objectives. This is why every business needs a plan – or several – for implementation. Training employees on how to set realistic milestones, identify potential challenges and create contingency plans is critical from idea to execution.
Alongside planning skills, stakeholder management is an important factor in the success of any AI project.
Ideally, all stakeholders should be aligned when working on AI projects, but this isn’t always the case. Skills in stakeholder management can help foster clear lines of communication between execs, employees, customers and regulators. This way, concerns can be quickly addressed and expectations managed.
6. Business analysis
One common challenge for the AI strategy leaders we speak to is ensuring that AI solutions are designed and implemented to directly solve specific business problems.
Employees with business analysis skills help ensure AI solutions are grounded in business needs and directly linked to desired outcomes, such as process optimisation or cost reduction. By assessing pain points and workflows, businesses can align AI solutions to problems and deliver the most successful AI initiatives.
7. Solution design
To gain the most value, it’s rarely a case of selecting an AI tool straight off the shelf. Custom-built solutions enable organisations to get more from AI, with use cases specific to their business needs.
Ideally, the employees using an AI solution in their everyday tasks should be involved in its design. But without training, this can be challenging to navigate.
Skills in solution design support employees to build tailored AI use cases based on their business analysis. They can seamlessly embed AI into existing workflows and identify new opportunities to scale AI initiatives, ensuring that AI solutions deliver sustained value as business needs change.
8. Machine learning
Machine learning (ML) skills help empower employees to create and implement models, analyse data, and evaluate their performance. Together, these streamline business processes and minimise the amount of tedious work for humans.
One step further is deep learning – a subset of ML – which uses multiple layers of neural networks to model complex patterns in datasets. ML skills can help businesses develop unique AI initiatives for image and speech recognition, natural language processing (NLP) and predictive analytics.
9. Cloud infrastructure
As you begin to roll out more AI initiatives, it will become increasingly important to have reliable, flexible access to the cloud’s vast computational power and storage.
Cloud infrastructure skills can help businesses better manage usage and enhance accessibility and collaboration across the entire organisation. And, as many cloud platforms have AI tools built in, employees with these skills can be instrumental in progressing a business’ AI efforts.
10. Strategic thinking and leadership
It’s not enough to only develop AI literacy among employees – business leaders should also understand AI initiatives. That way, they can strategically guide projects to make sure they are aligned with long-term goals.
By creating a compelling vision for AI and securing buy-in from stakeholders, effective leaders can foster an internal culture that embraces AI.
Employee training can accelerate AI progress
Demand for AI skills will likely continue to outpace supply in the near future. The competition for talent is fierce, but it doesn’t always need to be sourced externally.
Leveraging training opportunities to improve existing employees’ AI literacy not only removes the stress of recruitment, but also demonstrates the business is invested in the development of its current staff.
Once a workforce has the right mix of skills to get the most from AI, businesses will be able to deliver impactful change while improving or maintaining a competitive edge.
To get started on your workforce upskilling and reskilling journey, check out our AI training solutions for businesses.
