From startups to enterprises, organisations of all sizes are experimenting with the technology. They want to capitalise on it and translate the momentum from betas, prototypes and demos to real-world innovations and productivity gains. But, as Adam Selipsky, CEO at AWS, said in a 2023 interview with CNBC: “We’re about 3 steps into a 10K race.”

Rather than focusing on who will win the generative AI race, the priority should be getting as many runners on the track as possible. It is not a winner-takes-all situation; there is potential for many winners in this race. Every business should have the opportunity to unlock the potential of AI.

Mario Thomas is principal in the Executive Centre of Excellence at Amazon Web Services and says the situation today is reminiscent of the early days of the internet. “Ultimately, we know where that ended up,” he says. “This generative AI period we’re now in is creating a similar – if not more pronounced – frenzy.”

With investment pouring in and more companies testing the generative AI waters, employees and other stakeholders are increasingly asking how their firm plans to use the technology – and if they’re not, they should be.

AI can transform virtually everything that we know and do in an organisation

“AI can transform virtually everything that we know and do in an organisation,” says Thomas. “It’s going to go beyond chatbots into every aspect of business, and organisations that don’t use AI will start to fall behind.”

Even today, tools like Amazon CodeWhisperer, which provides code recommendations in real time, are changing how developers work. Advanced conversational search, rapid product design and highly personalised customer communications are also within the scope of current foundation models. It’s easy to imagine a future where generative AI-powered chatbots converse with borrowers and quickly go through their mortgage applications. Or where pharmaceutical companies use generative AI to accelerate gene therapies.

Philips and AWS are already pioneering AI in healthcare. Amazon Bedrock is a fully managed service that makes leading foundation models available through an application programming interface. It is being used to accelerate the development of cloud-based generative AI applications that will provide clinical decision support, more accurate diagnoses and automate administrative tasks.

Amazon Bedrock addresses the fact that one solution, or one model, is unlikely to solve every business problem facing the C-suite. “We’ve architected Bedrock so that the foundation models, including Amazon Titan, are available for the customer to bring into their own environment in AWS,” says Thomas. “The models are pre-trained… and the customer can then apply their data from their own AWS account. The beauty of that is that none of the customer’s data is used in training the model, which is a vital security point.”

With Amazon Bedrock, customers pass their data to Bedrock exclusively through the AWS network, ensuring it is never transferred via public internet. To ensure data security and avoid competitors benefitting from model customisations, privacy should be the foundation of any enterprise generative AI technology.

Finding the right solution

With all the hype around generative AI, it’s easy to forget that AI comes in many forms – some of which may be better suited to the needs of the business.

“A generative AI narrative might not work for every customer in the same way that a machine learning or data science narrative might not work for every customer,” says Thomas. “But the core point is that change is coming with this technology, and organisations need to start to identify the use cases that will help them transform, become more efficient, grow more quickly and drive an increase in market share.”

To find these use cases they should focus on what they want to achieve with AI rather than specific foundation models or apps. “Until we talk to the customer and understand what it is they’re trying to achieve, we can’t say if Amazon CodeWhisperer or the custom chip AWS Inferentia would be the right solution for them,” says Thomas. “The first thing for me is to understand what’s driving the customer.”

The first thing for me is to understand what’s driving the customer

And that is often one of the following things: increasing revenues and growth; entering new markets or creating new products or services, delighting customers and attracting new ones; achieving operational excellence or becoming a better corporate citizen. Once the key drivers have been identified, it’s easier to figure out how generative AI could be used to achieve them.

Strong data foundations and a cloud-enabled business are crucial to achieving these goals with generative AI. And with digital transformation so high up the corporate agenda in the past few years, many organisations are starting from a good position.

“Access to large-scale compute and large-scale inference has been enabled by the cloud,” says Thomas. “You don’t need to invest in hardware and those sorts of things… so the accelerated use of generative AI is being driven primarily by this easy access to the technology.”

For those organisations that already have their digital ducks in a row, the bigger challenge is how imaginative you can be as a business.

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