You’ve done it. Your organization has used generative AI to improve the experience of your customers or users in a big way.
If you’ve played your cards right, you’ve arrived at the launch of your generative AI-powered solution, service, or experience with budget to spare. Money for marketing what you’ve made possible and continuing to make it better. You’re entering this launch feeling confident in its success and fellow stakeholders feel the same as you.
How did you get here? How do any of us get here? One way to pave your way to continued generative AI success is to use Amazon’s approach of working backwards — in this case, from generative AI-powered solution back to generative AI-friendly chip (compute matters!).
Step #4: Low-risk experimentation
To be able to bring a use case to production and feel confident it’s going to create business and customer value, chances are you’ve tested it out. Organizations can make it easier for more teams to independently explore different generative AI use cases by giving them user-friendly platforms for experimentation, or “testbeds,” that securely connect with company data sources.
At Slalom, we’ve built an accelerator — which we define as a reusable asset that combines code, tools, and processes — to help our customers quickly set up this type of testbed. “Customers can upload their documents, including ones that aren’t easily used, and then swap between different large-language models to interact with those documents in a chat-based UI,” says Stephen Nichols, director of cloud solutions at Slalom and lead creator of the accelerator. “The first thing our customers want to do is see if an LLM can make heads or tails out of these documents.”
What helps keep these documents secure is that the testbed accelerator is deployed in the customer’s own Amazon Web Services (AWS) environment. “You can see where all the buckets are, you can see the permissions on every single file, nothing’s leaving your environment,” says Nichols. Internal deployment lowers the risk of employees mistakenly sharing company data with the generative AI technology provider, which is more possible with subscription-based, externally hosted generative AI applications.
We used the testbed accelerator to help set up a generative AI platform at a major airline. The platform allows teams across the organization to experiment with different models and determine which ones work best for their unique use cases. The airline’s now moving to production with a selection of promising use cases aimed at improving customer communications.
“The key element here is that we were able to validate and confirm the value of generative AI and then develop different ways to unlock that value across the enterprise,” says Papa Ndir, a director at Slalom who helped lead our work at the airline.
Continue reading to learn three more steps toward a finished solution. Click here.