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Top 3 Rules For Deploying AI Across The Enterprise

Consider how far we’ve come with artificial intelligence (AI) and machine learning (ML) in such a short time. Three years ago, AI and ML were new to everyone; data scientists were the only ones who understood the technology and could envision the applications. Plus, the only businesses that could afford to even consider it were large organizations such as the U.S. Department of Defense, NASA, and major universities.

Enter cloud computing. As adoption rates rose for the public cloud, the industry started viewing AI and ML as more mainstream, and enterprises, in turn, began to integrate this new tech. Already in the past few years, companies have made some major mistakes and enjoyed massive successes when it comes to AI projects – both of which inform deployment strategy at large. Let’s dig into three of the resulting rules for enterprise AI implementation.

 1. Gather the Right Data

 When AI projects first entered the enterprise landscape, the usual scenario looked something like this: an executive or management team would send a data scientist on a mission with a pile of data to relay a high-level objective such as, “Figure out something that will transform my business.” The particulars of the process were fuzzy, but executives were enticed by the promise of what AI could deliver. More often than not, the data wasn’t clean, it was hard to gather, or there wasn’t enough of the right data to generate a valuable algorithm. 

 And if the data scientist jumped those hurdles and managed to produce an insightful model, he or she often lacked the necessary skills to figure out how to actually deploy it and therefore unlock impressive results. The data scientist needed application developers, infrastructure team members, or DevOps resources to help. Because the process wasn’t organized from outset to deployment, the end model would often sit on a shelf. The result, or lack thereof, left executives underwhelmed. They were imagining and hearing about incredible AI applications, but the reality didn’t match.

There are a couple of major takeaways that we can gather from this initial phase of AI implementation. The first is that data is the foundation of an AI initiative and should never be an afterthought; and quantity simply does not equal quality. 

2. View the Project Holistically

 To address the issues we discussed above, the next iteration of AI emerged: “Applied AI.” This phase attempted to solve the dilemma of how to deliver end-to-end results with this new technology. It’s not just about the data science or algorithms, but the appreciation and thoughtful delivery of the other components, such as data engineering, DevOps, and application development. 

 When the industry turned the conversation to focus on the comprehensive delivery of AI, measured by real business value and operations results, it started resonating with people. That is the best practice now: to build a cross-functional, end-to-end team that includes all of the stakeholders, such as business product owners. The most valuable models are developed when business heads and data scientists are working together. It’s also important to collaborate on a regular basis because then you’ll know immediately if you’re on the right or wrong track and can boost your speed to market.

3. Consider Change Management

 As a major practice within Maven Wave’s consultancy, change management is near and dear to our hearts, and a critical component of any major IT overhaul at an enterprise. Especially with technology as relatively new, untested, and polarizing as AI, change management is critical to ensuring your whole team is on board. The entire team needs to understand the value of the new AI initiative as well as believe and trust in the tech to deliver.

A Quick Case Study

A great example of AI in action is Maven Wave’s work with a large roadside assistance company. We built an AI solution that the company now has in production and is using in operations.

 The company gets thousands of calls a day, and the team wanted to better predict the origin of calls based on historical data, weather patterns, local events, and traffic patterns. This would help keep the right amount of road crews in the right areas available at the right times to ultimately decrease response times and improve fleet management. 

Maven Wave built an AI-driven solution to predict where calls were going to come from, and then the company was able to move to the second part of the equation: where to send trucks preemptively. The algorithm provided drivers with the best locations for them to wait to cut down on lag time. Thanks to this new AI-powered model, the company is already seeing a reduction in wait time, which is a boon for customer satisfaction.  

Maven Wave works in an agile fashion to deploy AI and ML projects for our enterprise customers. We can help with wrangling the best data, setting up proper cloud infrastructure, building and implementing models into operations, and maintaining the environment on an ongoing basis. To learn more, reach out to us here.

About the Author

Kylie McKee
Kylie McKee is a Content Marketing Strategist at Maven Wave with more than eight years of tech industry experience and five years of content marketing experience. Prior to joining the Maven Wave team, Kylie worked as a Content Marketing Specialist for WebPT, Inc. and earned an Associate in Applied Science in Motion Picture, Television, and New Media Production with a CCL in Screenwriting from Scottsdale Community College.
September 5th, 2019

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