Sometimes it seems to work like magic, but the reality is that generative AI doesn’t ‘just happen’. To ensure GenAI delivers meaningful value, you need to make sure that your business and infrastructure are properly aligned.
Here are seven questions to answer as your business takes its next evolutionary step.
1. How will we benefit from a range of GenAl use cases?
Spend some time talking with your colleagues across your organisation to better understand each business unit’s needs – and the GenAI infrastructure required to meet them.
2. How will we make all of our unstructured and semi-structured data available for Al?
Now you know your LLMs need retraining, you must figure out how to make all of your data available. There are many steps to consider – feature extraction, normalisation, cleaning, segmenting and more.
You will need to make data available for fine-tuning through RAG. This ensures that your role-specific GenAI delivers accurate, useful results.
You will also need to define how semi-structured data will be available for ongoing use. LLMs will apply their own vector data – and this information also needs to be stored somewhere.
3. How will we secure sensitive data for Al?
GenAI delivers the greatest benefits when it has access to all of your data. However, your business must respect and uphold data protection regulations at all times – including any artificial intelligence applications.
It is unlikely your existing compliance strategy will be directly applicable. But at the same time, your organisation must avoid creating additional bureaucracy, so adding a parallel, AI-specific compliance strategy should be avoided. Instead, you will need to develop a single compliance and access control strategy to cover all of your data operations.
4. How will teams collaborate on our GenAl strategy while keeping standards in place?
Your people want to use GenAI to be more productive and efficient at work. But how do you prevent a repeat of the shadow IT scenario that plagues so many organisations?
Creating a Centre of Excellence (CoE) allows you to consolidate best practices, tools and techniques. Users can draw inspiration from ‘AI done right’ to ensure they are working within corporate and compliance guidelines without sacrificing efficiency and innovation.
5. How can we combine the strengths of our cloud providers to maximise data availability for Al?
In the multi-cloud environment, your business has several potential AI technology choices. Unfortunately, most are confined to their technology platform – Google in Google Cloud, Copilot in Microsoft Azure etc.
Adding Oracle into your technology stack offers a way to operate databases – and AI services – across hyperscaler services. Certainly worth considering as you plan your AI infrastructure.
6. How will we procure, manage and afford the systems we need for fine-tuning and inferencing?
Training an LLM from scratch is hugely compute-intensive. And retraining is very similar. Containing costs can be a major headache.
Again, your choice of cloud services could be pivotal. Many cloud providers offer data science platforms that allow you to extend and refine existing models, reducing the cost of building a GenAI LLM tailored to the specific needs of your business.
7. Do we have executive backing for our Al plan?
Effective AI needs to cross traditional business unit divisions. Embedding artificial intelligence agents and operations will need the support of the C-suite and executives at every level of your business.
Involving stakeholders early and showing them the potential benefits of generative AI will be crucial to successful implementation.
Contact us for more guidance
To learn more about GenAI and the infrastructure challenges your business faces, please contact the WTL team.