Under the Hood: Understanding Data as the Foundation of AI Applications

Data forms the backbone of most technology investments, including AI, and business leaders should focus their efforts on building a data infrastructure that is fit for purpose

Chris Harris, Vice president field engineering, Couchbase

June 5, 2024

4 Min Read
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Artificial Intelligence (AI) is more than just the latest must-have business technology. Given its transformative impact on areas like customer experience, AI is fast becoming business-critical as organizations try to meet the rising demand for highly personalized user journeys.

According to a McKinsey report, more than three-quarters (76%) of consumers admit to frustration when they don’t receive personalized interactions from brands. Customer loyalty is far from guaranteed. So generic experiences that no longer reflect individual preferences or buying habits could mean that a competitor secures the next sale instead.

AI is central to organizations’ efforts to stay ahead of these expectations. And since most consumer interactions are completed via apps – from customer service to mobile shopping – organizations are increasingly looking to embed AI within applications. AI-powered, adaptive applications are particularly growing in popularity as they enable organizations to deliver dynamic experiences that delight consumers. AI, transactional data and generated content are blended to create an adaptive application. These apps deliver tailored retail deals that factor in previous habits and contextual details like the weather, or demand-based event offers to users based in certain locations.

In the clamor to use AI to stay ahead of user expectations, it’s no surprise that Gartner analysts suggest spending on AI software will reach $297 billion by 2027. However, big budgets do not always deliver success. At 79%, most business leaders admit their organization does not have the necessary capabilities to handle the upcoming AI revolution.

What does AI preparation for business applications look like and where should organizations start?

Real-Time Magic

With data acting as the backbone of most technology investments, including AI, business leaders should focus their efforts on building a data infrastructure that is fit for purpose.

But just like food, gift cards and medicine, data has a shelf life. Businesses need to prevent outdated, inaccurate data from distorting AI’s output.

This rings true no matter the industry. In retail, outdated inventory data feeding a marketing push for new summer clothes could lead to missed market trends as customers are advertised out-of-stock items. Likewise, a financial services firm’s AI platform relying on old data might make flawed investment recommendations.

Such reputational and financial consequences could be severe. Therefore, the first step for organizations is ensuring that new AI applications draw on fresh, real-time data.

Without this, the output from AI applications can’t be trusted and the hope of achieving high-level personalization is dashed. Incorrect outputs will render any investment into AI platforms redundant and create more work for employees who must manually check the AI’s findings.

Defend That Data

It’s not just data accuracy that organizations must focus on but security too. Businesses regularly store sensitive information, including personally identifiable information (PII), financial records and health data. Maintaining the security of this data is paramount to protecting individuals' privacy and preventing unauthorized access or the misuse of sensitive information once AI applications are layered on top.

Without a robust data security policy, AI-powered adaptive applications could draw on PII or intellectual property owned by the business and produce an answer that shares data that shouldn’t be in the public domain. Last year, Samsung banned its staff from using AI tools after its engineers accidentally leaked internal source code. Although this example resulted from human error, the concerns over secure data being accessed by AI are clear, as organizations face possible legal and regulatory ramifications, as well as penalties and reputational damage, if data is not secured.

By ensuring data is secure – through robust authentication, encryption and embedded access control mechanisms – businesses can keep users’ data safe and make sure that AI-powered chatbots or copilots do not share sensitive information.

Modern Databases are Crucial to Data Strategy

Access to quality data is critical for modern, AI-powered adaptive applications to be successful and deliver highly personalized interactions. But to capitalize on the ability of AI to make a customer experience tailored to their specific interests and needs, the applications require access to all forms of data.

Organisations must therefore assess their data strategy to prioritise this. Modern databases are a crucial part of the strategy for organizations, providing the infrastructure necessary to manage, process and ultimately derive value from data.

Modern multipurpose cloud database platforms can handle diverse types of data, such as texts, images and videos, helping AI systems to analyze information from various sources concurrently. Such databases simplify the process of managing and analyzing data while reducing the time and costs associated with deploying separate databases.

It’s in the best interest of organizations to address their data infrastructure. A focused approach to managing data across the organization that prioritizes the freshest data and stores information securely, results in hyper-tailored, context-rich AI outputs.

Only under these circumstances can businesses achieve benefits using AI, realizing the targets set out by many business leaders to improve customer retention, engagement and sales via personalized and context-driven adaptive applications.

As AI evolves, businesses must be prepared for it and the time to act is now.

About the Author(s)

Chris Harris

Vice president field engineering, Couchbase, Couchbase

Chris is Vice president, global field engineering at Couchbase. With almost 20 years of technical field and professional services experience at early-stage, open source and growth technology companies, Chris held leadership roles at Cloudera, Hortonworks, MongoDB and others before joining Couchbase. 

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