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Will You Be An Early Adopter of Enterprise AI?

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In a rapidly changing world, Data Analytics and Artificial Intelligence solutions are essential tools for growing your enterprise. Built on a foundation of statistics and probability, enterprise AI allows organizations to assess large amounts of information to suggest conclusions, support decision making, and automate various aspects of their operations.

When it comes to conversational commerce, the data generated by customers’ interactions with brands can be leveraged to maximize positive outcomes, increase conversion rates, and optimize customer experience. Interestingly enough, the most crucial component of conversational commerce is not what the live chat agent says, but how they say it. And insights derived from e-commerce companies’ unstructured data – including live chat logs – can be utilized on every level of the organization. CMOs can apply big data and machine learning insights to inform budget allocation, live chat coaches can apply insights to better coach their teams, and individual live chat agents can amend their behavior to maximize performance.

While Data Analytics and Artificial Intelligence are relative newcomers to e-commerce, these same techniques are widely used in other industries. The 2017 McKinsey report, The State of Machine Learning and AI, shows that the leading early adopters of AI are high tech, telecom, and financial services, with retail, media, and CPG (Consumer Packaged Goods) trailing behind these industries as medium adopters of AI. Furthermore, a survey of business use cases revealed that AI was only commercially deployed 12% of the time.


Enterprise AI RapportBoost.AI

Source: McKinsey Global Institute, Artificial Intelligence, The Next Frontier


Other key findings from the McKinsey report include:


There are no shortcuts for firms. A lot of companies are wondering if by not jumping on the AI bandwagon, they’re getting left behind. The short and honest answer is yes. Enterprise AI involves training machine learning algorithms on your unique data set. The more data AI algorithms have to work with and the more familiar they become with the data set, the better they are at offering smart solutions. This means that early adopters of enterprise AI have a head start on those companies that are on the fence, and the gap will only continue to grow.

The workforce needs to be reskilled to exploit AI rather than compete with it. McKinsey cites the ethical, legal and regulatory adjustments that cities, governments, and industries will need to overcome to adopt AI. But here at RapportBoost, we’re specifically concerned with using AI to manage simple human tasks, and in doing so, to help people be better at their jobs. While AI and natural language processing are quickly developing, chatbots are years away from being able to independently handle a human interaction.

Based on a compilation of five different retail case studies, AI technologies have the potential to increase online sales by 30% using dynamic pricing and personalization. We’re particularly interested in the power of personalizing interactions with customers. Our own research has found that two-thirds of the outcome of a live chat conversation is determined by live chat agent behavior. Rather than using Natural Language Processing to deploy adolescent chatbots, using AI insights to empower live chat agents holds great potential for cost savings and revenue growth.


Enterprise AI RapportBoost.AI

Source: McKinsey Global Institute, Artificial Intelligence, The Next Frontier


For e-commerce companies, lack of data isn’t holding back AI adoption – it’s a lack of knowledge about the potential benefits. McKinsey reports that 41% of AI-aware firms are uncertain about the benefits of AI. More specifically, these firms are unsure of the use case and the ROI. A recent Gartner survey substantiates this finding, showing that 43% of companies planning to invest in big data and 38% of companies that have already invested don’t know if their investment will yield positive ROI. For any company considering investing in Data Analysis and AI, it’s crucial to know the ROI of an investment in a new technology before handing over the cash.

At RapportBoost.AI, evaluating ROI is a key component of leveraging unstructured data for our clients. In fact, our initial product, RapportRoadMap™, analyzes companies’ chat data to show how live chat agent behavior correlates to Key Performance Indicators including sales, conversions, and customer satisfaction. Furthermore, the use case is specific. Although some variations of the Machine Learning technology we use are found in other enterprise applications, we’ve developed a proprietary AI platform that uses Machine Learning and Natural Language Processing specifically for the purpose of optimizing live chat.

We discourage companies from jumping on the AI bandwagon without having a good idea of the technologies available to them, and plenty of information on how they work. And to remove some of the insecurity that comes with being an e-commerce early adopter, we recommend knowing your use case, having a good idea of the ROI of your adopted technology, and partnering with an enterprise AI solution that can properly leverage your customer interactions.

Contact us today to learn more about our live chat agent training solutions

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Meredith Lackey

About Meredith Lackey

Meredith Lackey is a content producer with expertise in computer networks and systems. She received her B.A. in Philosophy from Hampshire College, and her M.F.A in Moving Image from the University of Illinois Chicago. Her output spans long form writing, B2B and B2C content, and branded video content. Meredith has worked with Tony on creative projects and at Boopsie since 2014. She loves gritty travel experiences where she can learn about diverse people and new trends.

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