Is Your Business Data AI compatible? Factors To Consider For Adoption

Are IT and business leaders preparing their data for the artificial intelligence evolution?

The potential of AI to improve the customer journey is enormous, and the possibilities it opens up for businesses are exciting. But to take full advantage of the innovations people will see over the coming years, businesses need to make sure the fuel of machine learning – their data – is AI ready

Once the topic of dystopian science fiction, artificial intelligence (AI) is now an integral part of our world. From the algorithms that predict what we want to watch on TV streaming services to the fraud detection systems our banks use to identify card fraud, machine learning impacts our everyday lives. And with virtual assistants and smart homes increasing in popularity, the role of AI is continually growing.

The pace of development within AI is rapid, and investment is strong, with spending in Western Europe expected to reach $1.5 billion this year, according to a new IDC guide. The technology can process vast volumes of information – from location signals to behavioral data – and can learn from the insights the data provide to adapt its actions, however, AI is only as good as the data that powers it.

Businesses can make use of these advances in AI to more effectively engage their customers, using data to create highly personalized experiences that will help them stand out from the crowd.

They can learn individual customer preferences, using these to deliver highly relevant recommendations and provide seamless messaging, across channels, throughout the journey from ad exposure to conversion – for instance through the use of a chatbot. AI is already transforming marketing processes, with 68% of CMOs reporting their business is already using or planning to use it, and companies from outdoor product brand The North Face to Swedish retail bank Swedbank are experimenting with the technology.

Data centralization is critical

To maximize the benefits of AI technology, businesses must gain a complete view of the customer and their interactions across both online and offline channels. This requires a central data hub capable of aggregating and processing data from disparate sources, allowing access to any type of query or extraction AI technologies may require. As well as in-house systems, these data sources include customer-facing platforms that can provide valuable information but are not necessarily owned by the business, such as social networks.

When all data flows through a central hub, AI solutions can make use of this full picture to select the most effective engagement strategy for each customer. Instant data transfer facilities are also essential to make certain AI technologies can understand the customer’s immediate context and take appropriate action in real time.

Flexibility future-proofs technology

A traditional approach to systems procurement no longer works in a world where technological innovation is so fast and new possibilities open up every day. To ensure they can make the most of developments in AI, businesses must be more agile, adopting modular technologies with standard interfaces that allow individual components to be added on or replaced without restructuring the entire system.

Businesses must see technology acquisition as an on-going process rather than a project with a start and end date and must align the organization around a flexible philosophy where continuous change is not only expected but viewed as positive and beneficial.

Privacy compliance builds customer trust

Consumers are becoming ever more aware of how their data is collected and processed, with 92% of the UK’s internet users worried about their online privacy, and 39% more worried than they were a year ago. The rise of AI may be heightening these concerns, however, there is room to maneuver to allow for customer satisfaction.

To build trust and ensure customers are happy with the use of AI, businesses must make data privacy a high priority, and the upcoming EU General Data Protection Regulation (GDPR) is an opportunity to put a solid data governance programme in place. This includes appointing data governance stewards within an organization to take ownership of data management, defining a cross-department set of data policies and procedures, and executing a strategic plan to continually audit and control data flow.

The potential of AI to improve the customer journey is enormous, and the possibilities it opens up for businesses are exciting. But to take full advantage of the innovations people will see over the coming years, businesses need to make sure the fuel of machine learning – their data – is AI ready.