Why Enterprises Are Playing Catch-Up with AI and 5 Tips for Diving In

Artificial intelligence (AI) has completely transformed in the last five years due to the increased availability of computing resources in addition to a better understanding of techniques and customer demand. In fact, the adoption of machine learning (ML) and AI is mirrored by the general adoption of cloud strategy. A recent Gartner CIO Survey showed a rise in AI implementation of 270 percent over the last four years, VentureBeat reported.

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We’re seeing considerable AI activity across verticals, including Healthcare and Life Sciences, Manufacturing, Financial Services, and Retail. In fact, a significant number of companies are already using technologies that fall under the AI umbrella. But unfortunately, many enterprises are playing catch-up in efforts to properly leverage this powerful new technology. Despite the significant growth of AI & ML, almost every organization has to deal with many problems that are considered to be “low hanging fruit” when it comes to implementation.

Maven Wave is here to navigate enterprises through these very solvable situations. Keep reading for a few quick tips on successful AI deployment.

5 Tips for AI Implementation 

  1. Implement in Stages: When first embarking on a journey into the world of AI & ML, it’s important for a team to understand that process-forward methodology works best. There’s no silver bullet, whether it be pre-canned tools or even a standout employee, that can solve AI problems out-of-the-box. Those who are most successful at implementing AI do so in stages. They hinge their discoveries on data, prototype quickly, and put solutions into production in a way that provides a feedback loop.
  2. Determine the Problem Class: Whether it’s for data security, analytics/data visualization, HR and recruiting, sales/customer support or another situation, identify the exact use case your team is working toward. Keep in mind that AI can also be a valuable tool as assistive technology to an organization or an individual, such as with speech-to-text technology, smart eyeglasses and robotic caregivers.
  3. Communicate Between the AI and SME: There always needs to be a check and balance between the AI and the subject matter expert (SME). Maven Wave has a proven process for ensuring this communication is a two-way street and that the developed solution doesn’t miss the long-term business goals. Converting that understanding into data points becomes a core asset to the ML/AI process.
  4. Analyze Your Data: The greatest challenge in this process will either be the availability of data or the ability to understand the data. Therefore, having a platform and resources to analyze data is key. Cloud-based systems are particularly good for making data available for ML/AI.
  5. Measure Business Outcomes: Once an AI project is complete, it’s important to measure the actual business impact of the solution. More times than not, the organizations tracking impact find the value to be greater than expected in planning the automation and functionality made available by AI.

AutoML & Enterprise AI in Five Years

While there’s no silver bullet for AI deployment, enablers exist to help to automate the process and make the whole thing simpler for overbooked CIOs. One such tool is Google Cloud’s Cloud AutoML, “a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs.” Over the next five years, AutoML should allow “non-PhD” data analysts to solve some of the common problems cropping up for AI implementations. It is expected that there will be more verticalized solutions and models that solve specific industry problems and collaborative groups that share these specialized solutions among themselves.

At Maven Wave, we are using AI, ML and data science as key components of our strategy for transforming our customers’ businesses. Largely, our approach is to deliver these solutions in conjunction with cloud-enabled data. Success in this space is evident in our business results. Maven Wave is the two-time winner of Google Cloud North America Services Partner of the Year. Contact us today to learn how AI can help your enterprise.

About the Author

Brian Ray
Brian Ray
Brian Ray is a Managing Director and Data Science ML/AI Horizontal Practice Lead for Maven Wave Partners. Mr. Ray is heading the group’s mission of solving complex analytical problems for major businesses worldwide through the power of Data Science with enablement in the cloud. Prior to Maven Wave, Brian has 20 years of hands-on experience in Engineering around the Sciences. A big picture strategist, team builder, and influential top technologist, he has extensive expertise in agile delivery of Data Science — from ideation/discovery, feasibility, exploration, modeling, to engineering and architecture, to hands-on integration and deployment of best-in-class solutions.
July 9th, 2019
DATA ANALYTICS & MACHINE LEARNING

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2019-07-09T11:13:44-05:00