5 Best Practices for AI System Implementations

3 minute read

Before deploying Artificial Intelligence (AI), it’s critical that you determine your needs, your intended audience, and how often you plan to make adjustments. Read this article to find out the most important best practices for AI deployments.

Artificial Intelligence (AI) deployments require an intricate and thorough implementation process. Building, training, and perfecting the solution requires close oversight, some trial and error, and thoughtful planning. This includes determining the best way to introduce the concept of AI to your employees. Before you begin your first AI deployment, make sure that you understand why you’re implementing the solution, who should be involved in the process, and how much you’re investing in the first use case.

In this post, we’ll walk you through AI implementation best practices and what you need to know before your initial project goes live. Although these suggestions provide a rough understanding of what to consider prior to deployment, keep in mind that every AI deployment and use case will be different. Use this post as a guide, but improve upon and modify them as necessary to suit your needs.

1. Set Expectations Prior to Deployment

Prior to implementing an AI use case, calculate the anticipated cost savings. Reduced man-hours, reduced headcount, and less time dedicated to rote processes are typical examples of cost-savings associated with AI deployments. Use your cost-savings projection to help determine how much to invest initially.

Many organizations struggle with predicting the break-even point for AI deployments. By allowing cost-savings to dictate your initial AI investment, you’ll be able to confidently estimate when the break-even point should be reached. This method helps to mitigate risk and manage C-level expectations. If your deployment is wildly successful, you’ll reach your break-even threshold much sooner than expected. If your deployment needs to be reworked, you won’t have over-invested in the initial trial.

2. Determine Your AI Audience

Before designing your AI user experience, determine your intended audience. An AI system for teenage gamers should be dramatically different than one designed for insurance agents. Gamers are more inclined to want colorful, outside-the-box interfaces that feature unique fonts and animation. Business users want to be able to find information quickly and leave the environment without any diversions.

3. Determine the Language of Your AI

The language used by AI systems to interact with customers can vary between systems and projects.

An AI system for gamers could use slang, humor, and even sarcasm to provide a more enjoyable, human-like experience. On the other hand, insurance agents will want direct information delivered instantly, and without room for interpretation. An AI system speaking sarcastically could confuse an insurance customer who speaks English as a second language. It’s also important to provide information in simple and easy-to-read visual representations.

4. Build a Team of AI Super Users

As we’ve detailed in previous posts, we recommend building a small, reliable team of super users to test and revise your AI project. This should be a group of passionate and imaginative workers that will help create and build the initial AI use case. Before designing the experience, language, or roles, ask your super users how AI can make their work easier and more interesting.

If all goes well, your super users will realize that they’ll be tasked with less repetitive work thanks to an AI system, and that they also will be armed with powerful information at all times. They’ll build enthusiasm among their colleagues who will in turn also want to use the system.  This will engender positive employee opinions about AI, and foster a sense of employee ownership.  Employees won’t feel as if AI was forced upon them; they’ll feel like they helped create technology-based business process improvements.

5. Test, Innovate, and Test Again

When implementing a new AI use case, it’s important that organizations test viability in short cycles, rather than investing a large amount of resources into cross-departmental deployments. You’ll want to focus on the initial use case and master it before reproducing the experience elsewhere within your organization.

By assessing AI deployments in 60-90 day cycles, organizations will be able to determine the viability of a particular use case before committing to a large investment. If rejiggering is necessary, the initial investment will have been small enough so as not to cannibalize potential ROI. Conversely, if the initial trial is wildly successful, the organization will be able to ramp up quickly without allowing probable ROI to go unrealized.

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