Thinking about integrating A.I. into your business?

Here is a guide to help you implement A.I. into your business without too much disruption, risk and cost.

Before we dig into specifics, let's break down where we are in the adoption cycle of A.I. so we can ensure we are all on the same page about what's ahead.

It is clear that millions of consumers and businesses are using ChatGPT as the primary interface to the power of LLMs. Although this is meaningful adoption, it is still too early to know if the “prompting” user experience will augment or replace applications. There are a lot of pieces that need to fall into place to fulfill the idea of “talking” as the new UX.

Secondly, very few businesses have implemented A.I. into their core business processes. They are either waiting for their SaaS providers to offer a solution, or they are building it internally. Either way, it's going to take time to build, train, test, and implement any type of integrated business A.I. solution.

Finally, the biggest hurdle we need to overcome for adoption to increase is the acceptance of an imperfect system. Unlike traditional software, which is programmable and predictable, applications built on an LLM are prone to making “mistakes” (much like humans). There is an expectation gap that needs to be addressed in order for businesses to push forward with mass A.I. adoption.

So in summary, the primary causes of the slowdown in A.I. adoption in businesses are:

- Adoption of “prompting” as a new UX.

- Development cycle of A.I.-powered apps.

- Expectation gap regarding A.I.’s accuracy.

Now that we understand the challenges slowing down adoption, let's break down what businesses need to consider when deploying A.I.

**Pick your LLM.** If you are looking at deploying A.I. applications across your entire business (eventually), then you will have to pick a model to integrate. Each has pros and cons—OpenAI, Google, and Meta seem to be the top three options.

**Pick a Framework.** Depending on which LLM you choose, you will want to consider using existing platforms and tools to help build, train, and test your A.I. Agents/Assistants. (We built RAIA [https://raiabot.com](https://raiabot.com) for OpenAI for this purpose.) What you choose to use will depend on how much time and money you want to spend on development.

**Pick your first use case.** We recommend starting with a “low-hanging fruit” application where it's clear that A.I. could automate tasks and where there is high potential for ROI and a quick win. The most common use cases include an internal (or external) support assistant, sales assistant, or marketing co-pilot. When scoping out your first A.I. agent, it's important to keep it simple.

**Gather Training Materials.** Once you have determined the use case, you will need to train your A.I. on performing that role. For example, if you are building a customer support use case, you will want to train your A.I. on your knowledge base, FAQs, and your website. Once you have gathered the information, it will need to be curated, cleaned, and converted into a format that you can import into your A.I. training. Typically, this information ends up in a vector database/store or is used to fine-tune a model.

**Define the Process and Goal.** Once you have your A.I. Agent/Assistant trained, you need to provide instructions on how you want it to interact with users. Typically, if it is going to be deployed as a chatbot, you would create instructions on what questions to ask and provide a clear goal for the conversation. Instructions may also include what you don't want the A.I. assistant to do. Think of instructions as one large continuous prompt that gives the A.I. a framework to follow.

**Set Up Integration.** If you are building an A.I. agent that is going to receive or send data to a third-party application (CRM, Support Desk, Database, Email, etc.), you will need to build a bridge. For simple integrations, you can use Zapier or something similar. For more complicated integrations, you may have to build custom code.

**Testing and Fine-Tuning.** Once you have your A.I. agent trained and integrated, you can begin testing. You can test simply by mimicking conversations you would assume would be common. Based on the tone, format, and content of the answers, you can fine-tune your A.I. by adjusting instructions or expanding on the training you used.

The process outlined above obviously glosses over a lot of the complexities and unique requirements each business may have, but the basics remain the same no matter what your use case is. The key elements are still training and testing.

If you are looking to deploy A.I. in your business make sure to visit https://raiabot.com and learn how our launch pad for OpenAI Assistants can help you build, train and test.

And as always follow our podcast at https://aiguyspod.com.