The past year has shown just how quickly technologies can become the go-to innovation for companies globally. However, there isn’t a switch that enterprises can flick to embed generative AI within their offerings and processes. It takes planning, time, and resources.

Once an enterprise has decided to implement AI, that’s the time for software developers to shine. From identifying business use cases to determining the quality of existing data, there’s an art and a science to implementing and maintaining generative AI. 

The starting point 

Decision-makers need to be clear on the reasons for implementing generative AI. Is it to act as a stimulus for marketing activity? Is it to develop a new business product or offering? Or is it to increase business processes across the organisation? You need to ask the what, the why and the how. Without clear direction, it’s easier to stray off course. 

When it comes to using generative AI, there’s an art to what you choose to use and how you use it. Generative AI is a creative catalyst for ideas – it’s then on the human to decide what to do with them. For it to truly work as a tool, employees will need to know what it is for, how it can help them in their role, and also understand its current inaccuracies. That way, you can bring together the creativity and reality of generative AI.

Fine-tuning and testing 

Once you have a purpose for the technology, be it for your processes, product or offering, and have started to implement it into your business, it then needs to be fine-tuned and tested. How do you develop models and optimise performance? How do you evaluate this performance? Well, for both aspects, you need to take a deep dive into science. And this is where software developers come into their own.

In order to ‘know’ your AI, you need to access your data and understand your data. With a central repository system like a data lake, software developers can gather data from a range of sources, such as business applications, apps and social media, into one place. They can use this data to fine-tune your model, develop its capability and customise it based on user feedback. 

These developers can then use analytical tools to assess the model’s performance, compare datasets, and help guide decision-making to improve your offering. This is where science informs the art of building a product or service. 

Depending on the costs and ambitions of the project, you might not want to start from scratch. Generative AI libraries, for instance, offer a range of pre-trained open-source LLMs to help developers build new AI-powered capabilities into their products.

Continuous improvement

The implementation, fine-tuning and testing of generative AI is only the beginning of the process. You then have to maintain it. 

One of the flaws of generative AI is that, for the moment, it relies on past datasets. The current models have a year or two data gap from present day. Therefore, it is imperative that models are continually retrained with up-to-date data for the best results – the model is only as good as the data it is trained on. Again, a system like a data lake can allow you to import data in real time.  

Moreover, your business requirements will change, and so your model(s) needs to be retrained alongside these shifts. How do you embed it into your operations and then keep it relevant by enhancing and evolving it? Generative AI needs to drive your business processes towards the goal you set out from the starting point. 

Finally, seeing this improvement as part of an evolution is vital. A digital transformation may view reaching the end goal as job done – a digital evolution returns to the starting point and sees what can be implemented next to build continuous business improvement. 

Mastering the art and science of generative AI

Like with any technology, there’s both an art and science to using generative AI in software development. There is an art and science to implementing the technology, and knowing where and how to use it. Then, there is an art and science in fine-tuning and testing your model, knowing your data and how to use it. Both of these aspects contribute to building continuous improvement of your offering. 

Generative AI is still in its infancy, growing and learning quickly, and there is much to be explored and discovered. The businesses that can start to master the art and science of generative AI in software development now will be best placed to evolve with it.

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