A lack of technical expertise and a shortage of staff – these are among the main reasons businesses back from adopting Generative Artificial Intelligence (GenAI) [1]. However, GenAI has the potential to revolutionise the way companies across all sectors and of all sizes operate. Drawing on our own experience, in this article we aim to provide specific recommendations for building AI capabilities within organisations.
We outline the steps organisations should take to prepare their staff to work with AI solutions whilst effectively advancing their own skills framework.
Step 1: Every team member understands the basics of artificial intelligence
Step 2: Establish development pathways for technicians and AI experts
Step 3: Strategy and innovation: the right skills for decision-makers
Anyone wishing to strengthen a company’s capacity for innovation must ensure that all staff have a solid grounding in working with AI.
Offer training courses that provide a basic understanding of AI and its potential applications. An overview of the possible applications and a general introduction to the existing AI services offered by the major providers is particularly helpful. These services can be tried out in interactive training sessions to directly engage staff. Providers usually offer ‘playgrounds’ or simple examples for this purpose [2].
Like most tools, AI tools must be used correctly to create real added value and inspire your staff. You should therefore organise workshops in which staff in all roles can learn how to interact effectively with AI tools such as ChatGPT. These workshops should teach participants how to formulate precise prompts and apply specific techniques such as the ‘chain-of-thought’ method [3].
Train staff in the secure use of AI tools, particularly with regard to data protection and the processing of sensitive data. Establish a basic understanding of the challenges faced by today’s models – such as hallucinations and bias – and what this means for their use. However, also highlight solutions to enable correct usage.
In addition to the general fundamentals, role-specific specialisations are important in order to offer technicians and AI experts tailor-made development pathways. This involves not only imparting technical knowledge and skills for integrating AI services, but also fostering creativity and innovation through practice-oriented approaches.
Technicians |
(e.g. developers, IT specialists) |
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Training on the use and integration |
For example, on (cloud) AI services, REST APIs and SDKs, and how these can be integrated into applications. |
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Pair programming & communities |
Knowledge transfer and practical experience through collaboration between software developers and AI experts. |
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Software Development with AI |
Integration of AI tools into the development process, as well as into business and requirements analysis, through training and internal knowledge-sharing. |
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AI experts |
(e.g. data scientists, machine learning engineers) |
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Specialisation |
Specialisation in specific areas such as solutions for computer vision, natural language processing or generative AI solutions. |
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Technical training courses
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Access to in-depth courses, e.g. on implementing advanced AI applications such as natural language processing or methods in the fields of data science and data engineering. |
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Community & Hackathons |
Participation in hackathons and engagement with specialist communities to discuss the latest developments and gain experience. |
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Quality assurance |
Developing expertise in quality assurance, as well as the selection and implementation of suitable frameworks and QA pipelines. |
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Security |
Dealing with threats securely (more information in the OWASP ML Security Top 10). |
Technological innovation can only emerge if the right strategic decisions are made. Decision-makers need AI skills to be able to tackle current challenges and identify future growth opportunities. Here’s how these skills can be acquired:
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Strategic understanding of AI |
Training courses and presentations on trends and potential applications of AI in corporate strategy (including the selection of suitable cloud-based AI services for various use cases). |
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Innovation management |
Workshops and presentations on fostering AI innovations and integrating them into business processes, with a focus on the planning and management of AI solutions. |
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Networking & Relationship Building |
Given the high level of complexity and rapid pace of change, professional networks are indispensable. Community events foster exchange to facilitate access to collaborative opportunities. A strong network supports innovation and collaborative development. |
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Risk Assessment & Management |
Training on technical, ethical and legal risks, as well as data protection, to promote responsible and transparent AI solutions and minimise risks. |
To ensure successful implementation, we would like to offer you a few more tips.
Training courses are a key component in developing AI skills, but should always be complemented by practical projects. This enables participants to gain a deeper understanding and to embed these skills within the company’s culture.
Radical changes often meet with resistance from staff. Start making changes in small steps to build acceptance. It is important to have a clear vision for the use of AI and to work towards it.
Current developments in the field of AI have the potential to transform businesses in the long term. To achieve this, the necessary skills must be developed, depending on the role. As different groups benefit from learning from one another, we recommend developing new products in cross-functional teams to build a shared understanding of AI deployment. It is important to make the new technologies easily accessible in order to achieve broad acceptance amongst staff at an early stage.
You can find out more about the possibilities of artificial intelligence for your business on our website.
This is the third part of our series on implementing GenAI. Be sure to check out the first two parts as well:
Part 1: Getting started with GenAI: From idea to digital product
Part 2: What businesses should bear in mind when implementing an AI strategy.
| Dr Tehseen Rug works as an AI & Data Architect at iteratec. He develops bespoke, AI-based applications. These include both tailor-made machine learning solutions and applications that put generative AI into productive use. | |
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Jens Werschmöller is a Business Analyst and Product Owner. He heads up the Generative AI division and drives the development of innovative AI solutions for various business areas. |
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| Steffen Jendrny is a software engineer at iteratec. He specialises in web development, artificial intelligence and data analysis, and is involved in projects focusing on the use of generative AI in businesses. | |
| Dr Andreas Reuschl heads the Competence Centre for Digitalisation at kobaltblau Management Consultants. His research focuses on strategy development, organisational design and digital transformation. |
Sources:
[2] https://console.groq.com/playground
[3] https://blog.research.google/2022/05/language-models-perform-reasoning-via.html