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Lessons Learned From Launching An AI Product In Six Steps

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Artificial intelligence (AI) is becoming ubiquitous in our daily lives. This is exciting for businesses and consumers alike, but brands are also feeling the pressure to keep up. Oracle predicts that eight out of 10 businesses have already implemented or are planning to adopt AI as a customer service solution by 2020. As chief strategy officer of a modern conversational customer service platform company that has just launched native AI capabilities, I can attest that the pressure is real.

This massive technology shift to AI-based platforms is very similar to the web and mobile platform shifts that occurred over the last three decades. AI startups like Lemonade are creating new disruptive business models that are challenging incumbents in the insurance sector. Similarly, AI-first startups will challenge the larger and more traditional incumbents in every major vertical industry. These slower-moving companies need to avoid the mistakes they made in the past with shifts to web and mobile: ignoring and underestimating the impact of these emerging technologies and then haphazardly playing catch-up, outsourcing to third parties to speed up the implementation stage and not treating AI as a core imperative that brands need to approach very differently.

Here are some tips on building AI as a core company capability to avoid being disrupted.

Educate Your Stakeholders Early

In order to have a successful AI product, you need buy-in from the executive team all the way down to individual employees across relevant teams. The entire company needs to transform from the ground up to properly incorporate and leverage AI as a business solution. Simply taking a horizontal, top-down approach and hiring a “Chief AI Officer” is not adequate — this will lead to using AI as a fragmented tactic in different areas of the business instead of using it to transform the business as a whole.

Build An In-House Data Engineering Team To Create A Big Data Warehouse

Data isn’t just the fuel that drives algorithms to make valuable predictions, it is what makes it possible to drive the high levels of accuracy that companies require to create successful outcomes with AI. Today, large brands face the challenge of having fragmented data across operational systems and silos across business units.

Before embarking on any large scale AI effort, a brand should ensure that the data from all the operational systems are pipelined and warehoused in a big data store for consumption by the AI solutions.

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Article Credit: Forbes

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