Overcoming the Integration Challenge in AI Adoption

Though Artificial Intelligence (AI) still has a long way to go before reaching its full potential in the enterprise, organizations are already solving everyday challenges with various impressive applications of the technology. The number of organizations that have deployed artificial intelligence (AI) has grown from 4% to 14% between 2018 and 2019, according to Gartner. In the 2019 Gartner Hype Cycle for Artificial Intelligence, the firm listed out a few AI technologies “that must be on the CIO’s radar for high and transformational business impact in the next two to five years,” including augmented intelligence, chatbots, machine learning, AI governance, and intelligent applications.

With proper planning, using AI in cloud computing enables us to walk away from monolithic systems that complicate human interactions with data. Instead, AI-powered cloud computing provides a human-centric window into that data. Quicker than ever before, we can explore insights and develop and deploy predictive models that enable people to do their jobs better. In an ideal world, that AI cloud computing ecosystem will be self-learning and improve the quality of interactions over time based on human usage.

Cloud computing and AI will provide a more intuitive, holistic, complete ecosystem with remarkable cost savings, and the technologies are already working together to reach that goal. AI will never be as easy as turning on a light switch, but it will replace the light switch altogether. No longer will we battle with man versus machine; instead, it will become man and machine. Overall, man’s day-to-day quality of life will improve thanks to AI. The only boundaries will be ones of privacy, ethics and social issues; those too may be decided or become more finite by the introduction of the mechanisms found in AI.

But right now, there’s a major challenge: integration. Initially, moving data and technologies to the cloud in the first place to allow for AI is a tricky feat. Once it becomes available and the technology is proven, the maintenance of those systems, the data, and the ability to adapt to an ever-changing environment present their own complexities. Cloud providers are inventing rapidly to lift the burden of these tasks.

Integrating AI technologies has become tricky due to a diverse combination of needs. The need for historical data raises the requirement to make that data available to train the machine learning models that drive AI. The need to host a complex set of technologies is the second hurdle. Finally, the need to enable AI to get smarter over time raises the requirements of production, retraining, and live prediction with humans in the loop.

Google is a leader in this space. In fact, in Gartner’s AI Hype Cycle blog, the firm mentioned AutoML as one of the AI vehicles with the most momentum. Google is consistently rolling out exciting updates around its AI functionality; for example, Google introduced two versions of the AutoML Translation API in November and launched new AI features in October with AutoML Vision Edge, AutoML Video, and Video Intelligence API. As a Google Premier Partner named Google Cloud North America Services Partner of the Year the past two years in a row, Maven Wave works with enterprises to develop and deploy AI projects. Check out our top three rules for deploying AI across the enterprise here.

Now’s the time to develop a business case for AI if you haven’t already. Maven Wave holds the Google Cloud Partner Specialization for Machine Learning. Contact us to learn more about how you can leverage AI for 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.
January 9th, 2020
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2020-01-09T15:27:44-06:00