Discover your own Gen AI use cases
Want to start exploring use cases for your organisation? Below, you'll find out more about Generative AI and how we prioritise use cases.
Get started today!
What is Generative AI?
Generative AI refers to a category of artificial intelligence technologies that can generate new content, ranging from text and images to music and code, based on patterns learned from existing datasets.
It has the ability to interpret input and create brand new output.
The effectiveness of a generative AI model hinges on the richness and consistency of the patterns found in its training data. Strong, clear patterns enhance the model's ability to produce accurate and realistic outputs.
This pattern recognition extends beyond simple replication, enabling the AI to understand and innovate within the framework of its training data.
1 - General knowledge
Generative AI models have a “general knowledge” of the world.
These models are trained on trillions of words and images from many sources.
During training, the AI learns to identify and mimic complex patterns from the dataset.
Models that are trained on bigger, higher-quality datasets will ultimately have superior capabilities.
2 - Context-specific training
The introduction of context-specific data then enables the AI to generate precise and relevant outputs for specific scenarios.
This context data acts as a steering mechanism, guiding the model towards generating results that are valuable for the given circumstances. The context data can vary widely, such as product specifications, customer preferences, helpdesk records,...
Companies that possess proprietary datasets tied to their customers, industry, and operations will have a competitive edge. They can produce outputs not easily replicated by competitors lacking that contextual data.
Data builds successful products
Powered by GPT, Vesalius.ai has a very broad knowledge base.
The context-specific data we added, owned by the healthcare institutions we work with, is what makes the product successful and hard to copy by someone without this data.
Generative AI comes with challenges
Current solutions don’t think for themselves, they try to predict based on training data. This brings along some challenges:
Data privacy
Generative AI models are trained on large amounts of data, and it is important to ensure that personal and sensitive information is protected and not used for malicious purposes
Bias
Generative AI models can be trained on biased data, which can lead to discriminatory or unfair responses. It is important to be aware of this and take steps to mitigate bias during the training process.
Transparency
The decision-making process of a gen AI model can be difficult to understand, making it challenging to explain why a certain response was generated. This can raise concerns around accountability.
Reskilling
Gen AI models can automate or make certain tasks more efficient. Organisations should consider the impact of automation and plan for reskilling and workforce development.
How do you create impact?
Generative AI can supercharge many aspects of your business. For simplicity, we focus on two main areas for value creation:
- Customer experience
Generative AI enables businesses to engage with customers in a hyper-personalised, interactive way - Operations
Generative AI can augment employee capabilities, optimise workflows, extract valuable insights from data and reduce costs
Get started: Our digital innovation matrix
We use our digital innovation matrix as a framework to identify opportunities. We suggest starting in the least complex quadrant (Operational innovation).
- Define the added value
- Check feasibility
- Rank
Prioritise your use cases
Within the quadrants of the digital innovation matrix, you can start identifying opportunities and their feasibility in 3 simple steps.
Define the added value
With your team, do a brainstorm in which you think of customer journeys, internal workflows and tasks in the organisation. Examples of questions that can help you are: - What do I spend the most time on? - Which tasks do I lose the most time with? - Which customer touch points could benefit from automation?
Check feasibility
See if your current data infrastructure or tech landscape is ready for implementation of generative AI use cases or if there are any gaps that need to be addressed first.
Rank
Prioritise based on added value and feasibility from the previous steps.
Book a meeting
Discuss your idea directly with one of our experts. Pick a slot that fits your schedule!
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