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Artificial Intelligence: From Hype to Added Value for Customers

  • Roy Voggenberger

Everyone is talking about AI. According to the latest Gartner Hype Cycle for digital marketing, expectations surrounding AI for marketing have reached their peak. As per the Hype Cycle principle, we can expect the valley of tears and disillusionment to follow. This makes it the perfect time to reflect on whether using AI really does offer added value that will stand the test of time once the hype has died down.

Numerous Ways of Using Artificial Intelligence

As a digital agency, we are always on top of the latest developments in digital user interfaces. We are convinced that artificial intelligence (AI) is fundamentally changing the way companies interact with their customers. Algorithms can be used to automatically understand customer behavior based on countless data points, identify correlations and take appropriate action. Smart devices and sensors create new ways of interacting with customers, such as Smile to Pay. There are numerous ways of using artificial intelligence. It is much like salt, which improves many meals but, used incorrectly, can quickly make a dish inedible. This horizontal approach makes it difficult for companies to know where to start.

Operational Use of Artificial Intelligence

It makes the most sense to start small and identify suitable AI projects throughout the customer journey. One key way of ensuring that artificial intelligence is used successfully is by aligning it with a company’s individual strategy.

Step 1: Identifying AI initiatives

By asking two key questions, companies can identify how artificial intelligence might be used. Firstly, in which customer relationship setting do we want to use artificial intelligence? Secondly, how can we use AI to support our customers during the customer journey? The first step involves working closely with experts familiar with customer needs to outline use cases and identify the project’s business value. IT specialists and data scientists verify the business perspective to ensure it is technically feasible. 

Step 2: Prioritizing projects

When prioritizing, the focus is laid on the AI use cases that will generate the greatest value for the company due to the resource situation. Then the make-or-buy decision is made and the necessary resources are allocated to the initiatives.

Step 3: Experimenting and learning

Insights from the experimental phase are collated systematically and used to optimize AI use and develop further use cases.

Factors Critical to the Success of AI

AI use cases are created in specific processes. Experts know best what offers added value for customers and what does not. This is why the issue cannot be centralized. Instead, a centralized competence center should be established to support specialist departments in identifying and implementing use cases.

As artificial intelligence can often be disruptive, it is not always clear where to start. This is why it is crucial to provide experts with coaching, training and exchange platforms so that they can identify good places to start with artificial intelligence to increase business value. 

Data requirements for AI projects are often complex. Artificial intelligence needs larger amounts of structured and accessible data to function smoothly. This is why it makes sense to get IT and data scientists on board upstream who can then verify which systems and data can be integrated. Artificial intelligence is dynamic and requires an iterative and agile approach. There is no such thing as a perfect process. It is important to keep initiating project selection and to carry out the process iteratively.

Reflecting on lessons learned after projects have come to an end helps improve the process and increase maturity. This is the only way to embed the AI process and way of thinking within the organization. For individual initiatives to develop a momentum that drives the entire company, there needs to be ongoing dialogue that enables genuine transparency within and across projects. 

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