As IoT grows, advanced AI models are critical
There’s a notion that modernism in the enterprise requires AI. While this is certainly true on some levels, it must be combined with an appreciation of its true purpose, potential and the outcomes one is aiming for. The real value of AI isn’t in being a status symbol; rather, it lies in delivering the insight and analytics that fuel models and allow them to solve specific business problems.
When businesses implement AI just to keep up with other organizations, they’re holding themselves back from realizing its full potential of advanced analytics and ultimately costing themselves revenue.
Not all AI models are created equal
Code-based, hand-built AI models simply are not built for the requirements of a hyper-personalized customer experience. Today’s businesses need 24/7, “lights-out” evolutionary programming that can run without human intervention. Lights-out models never stop looking for opportunities to monetize customer data. An added advantage is that they remove the data scientist from the equation and put the power of AI and algorithmic optimization squarely in the hands of marketers.
But why aren’t code-based models up to the task? First, they simply can’t keep up with today’s always-on, connected customer. When a new channel or data source emerges, data scientists need to completely reconfigure algorithms and build new models, wasting valuable time and budget.
The growth of IoT means that new data sources are emerging all the time, making code-based models more cumbersome to manage. Rather than falling further behind, businesses should be using the combined power of IoT and AI to their advantage. In fact, 80% of all enterprise IoT projects will include an AI component by the end of this year, according to Gartner.
Finally, code-based models fail to meet AI qualification standards. They’re built on predictive rules, which aren’t dynamic and become stale over time. Without an in-line optimization engine, code-based models require human intervention to refresh, allowing a dynamic customer journey to always be one step ahead. Once they go stale, humans are called back in to rebuild the models at great expense and time.
Embrace automated AI and put the customer first
Even with these drawbacks, code-based models remain popular. It seems as though businesses are reluctant to explore the full potential of automated AI as a revenue-driving engine.
This is a risky position for businesses to take, as the complexity of data and sources from which it originates will only continue to expand. As a rising number of internet-connected sensors are built into homes, cars, buildings and more, businesses are amassing vast amounts of data. It’s critical that organizations leverage always-on AI models to extract useful customer information from this data.
In addition, 37% of consumers said they will no longer do business with a company that fails to offer a personalized experience and 63% said that personalization is an expected, standard service, according to a Harris Poll survey
For this reason and others, automated AI is a business imperative. Forward-thinking and data-driven organizations have already implemented evolutionary models into business processes and have reaped the benefits of superior customer experiences. It’s time for the laggards to catch up before they miss out on additional customer touchpoints and opportunities to capture revenue.
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