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The Quality of AI Will Make Or Break Brands This Year - 2020 Trends

As pressure mounts to differentiate with swift and superb service via interactive channels, more and more brands are turning to machines for help delivering that service at scale. Technology researcher Gartner predicts that by 2021, 7 out of 10 companies will rely on AI to boost productivity. Consulting firm McKinsey found that 47% of companies have already implemented some form of AI; within the retail sector, 52% of companies have implemented AI to help with marketing and sales, while 23% are using machine intelligence to enhance customer service.

Thanks to big data processing capabilities and innovations in machine learning, more and more vendors are touting AI-enhanced solutions to meet the needs of merchants who are scrambling to implement intelligent solutions. But it’s been a bumpy ride so far. Not all AI tools are created equal: some feature rigid algorithms that can’t take into account individual business rules, while others fail to integrate well with other systems. Additionally, execution depends largely on the quality of data being input to “teach” algorithms how to behave. Stories of poor AI-driven experiences are commonplace; some are merely annoying, such as persistent remarketing ads for products already purchased, while others are more sinister -- such as instances of algorithms displaying racial and gender bias.  

When it comes to applying AI in meaningful ways to commerce, even the tech giants are struggling. Technology researcher Forrester tested the four major intelligent agents -- Alexa, Siri, Google Assistant, and Cortana from Microsoft -- across six commercial industries and found that just 35% of queries were answered; instead, the machines struggled with context and failed to arrive at direct responses. 

These missteps partly explain why so far, shoppers are still wary of purely automated interactions. Four in five consumers say interacting with real people will become more important -- not less -- as technology improves, according to consultancy PwC. 

As the technology improves to close the gaps, brands with quality AI solutions built on foundations of sound data will begin to realize critical gains in 2020. For others, the seams will begin to show -- and customers may defect if they lose patience with poor automated experiences. 

The takeaways: 

In order to deliver customer experiences that seamlessly blend AI-powered information and recommendations with human insight, merchants must do their utmost to ensure automated services deliver on their promises. To maximize the success of AI implementations: 

  • Avoid the “black box.” Merchants should build accountability and transparency into AI-powered offerings -- both internally and when it comes to vendors’  technology solutions. That means creating internal guidelines for data governance and ensuring AI-driven results are explainable and provable. External vendors’ toolsets should accommodate business rules, segments, and other pre-existing constraints; given that merging data with analytics is companies’ number one AI data priority, according to consultant PwC, technology providers should also provide help interpreting activity and results so that brands can build meaningful AI metrics. As business goals change, merchants should have the means to adjust algorithms and set new guardrails for AI interpretation.
  • Start with the right set of solid data. AI tools are only as good as the data the machines interpret, so merchants must collect, parse, label, and clean up information before it reaches the AI layer. Selecting the right data points is also crucial; sellers should focus on the input that’s most meaningful to the AI-enhanced task at hand. For example, in-store traffic patterns are less relevant to an AI implementation that tracks shipments of online orders for home delivery, but that same data may be crucial for reducing delays and confusion surrounding BOPIS order pickup. 
  • Be transparent about human/AI transitions. Merchants should clearly delineate the types of interactions AI-powered services can handle, and then identify and test a variety of escalating situations to ensure a seamless transition to human help. And since shoppers react negatively to machines masquerading as humans, according to SAP, chatbots and avatars should be explicitly identified as such, with handoffs to humans clearly flagged. 

You can learn more about the customer experience and how automating the shopper experience can engage your customers at every touchpoint by exploring Linc’s platform and solutions pages. Or take a look at how leading brands like Lamps Plus, Levi's, Carter's and others are using a Conversational AI platform as part of their customer experience strategies in their businesses today, on our resources page.

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