How adaptive applications can unlock new AI-era innovation

We are on the brink of a generative AI (GenAI) revolution. About 98% of organizations have specific GenAI goals for 2024, according to new research from Couchbase, which is nearly one-third of digital modernization spending last year and 2024. occupies . Our research shows that CIOs are excited about the prospect of increased productivity, rapid prototyping, and improved customer experience (CX), among many touted benefits. But can your existing IT infrastructure support large-scale AI projects?

For many CIOs, the answer is no. To usher in a new era of adaptive applications powered by GenAI, organizations must first modernize their data management strategies to take control of the high-speed data analysis and processing that AI increasingly demands.

limits to growth

Our research shows that the average investment in digital modernization per organization in 2023 is $28 million, and is expected to grow by 27% to more than $35 million this year. However, technology, resources, and organizational buy-in remain key barriers. By our calculations, organizations waste an average of $4 million annually in failed, downsized, and delayed projects. Almost two-thirds (63%) say they are experiencing delays of three months or more due to IT modernization issues.

When it comes to GenAI, even just a few months can mean the difference between long-term business success and failure. To remain competitive, companies must increase productivity by more than a third each year. You can't afford for your project to fail or be delayed.

The challenge is that most organizations are not equipped to support the next generation of adaptive AI apps that transform the user experience through hyper-personalization and real-time updates. The necessary security and privacy guardrails are not in place. It doesn't provide the low latency needed for rapid data access, sharing, and usage. It also doesn't have a multipurpose database that helps alleviate GenAI hallucinations by creating a single pool of trusted data to interact with external models.

Place the pieces in place

If they want to drive GenAI success, CIOs need to consider change not only on a technical level but also on a cultural level. This requires getting senior leaders on board and setting realistic goals and expectations for what technology can do to give the project the best chance of success.

Speed ​​is also important. For best performance, data must be shared and accessed quickly. Otherwise, the app will provide outdated information, increasing the risk of hallucinations. Security and privacy are also important. CIOs must protect sensitive intellectual property from inadvertent disclosure. Finally, it's important to not forget the end users of your technology. Employees must be trained to use GenAI optimally and safely.

It is difficult to maintain and improve GenAI's capabilities without reducing investment in other areas. But it's possible. A good place to start is to focus on data architecture. Notably, more than half (54%) of companies admit that they don't currently have all the pieces in place to ensure a comprehensive GenAI-enabled data strategy. There are multiple things to consider.

Organizations first need to control where data is stored, who has access to it, and how it is used to ensure that it is not accessed or used inappropriately. Professional tools and procedures must also be in place to prevent sensitive data and customer information from leaking outside the organization. Developers should be provided with clear and detailed best practice advice for using data securely and effectively.

Now let's think about the data architecture itself. As mentioned earlier, it is important that any system be able to support real-time GenAI applications by ensuring that data can be accessed, shared, and used with minimal latency. With a high-performance database that can quickly manage unstructured data, there are no limitations to how GenAI can query your data. This also supports near real-time data analysis. This is another important requirement for GenAI to provide accurate answers to users. Only 18% of companies have a vector database that can efficiently store, manage, and index vector data, which also helps improve GenAI performance.

However, it's worth remembering that GenAI often requires different levels of data processing. Therefore, IT infrastructure must be able to scale to meet immediate demands without incurring unnecessary expenditures. Finally, let us consider the problem of hallucinations. Organizations need to unify their database architecture to prevent AI applications from accessing or becoming confused by multiple versions of data. Less than a third (31%) of companies have made this investment.

It's time to create

With these pieces in place, organizations can seriously consider creating adaptive applications powered by GenAI. These are apps that perform a single task but use AI to intelligently, dynamically, and automatically adapt to changing circumstances and a user's specific preferences. Booking apps are regularly updated based on real-time travel information, events and user history and may, for example, suggest travel and personalized deals.

We estimate that half (46%) of businesses will lose customers and 36% will lose employees to a rival if the applications they deliver no longer meet expectations. With these expectations constantly rising, companies cannot afford to stand still. You need to provide the highly personalized and contextual experiences these users demand. Otherwise, you risk exposing yourself to existential threats to your business.

It starts with data

Getting there won't be easy. However, database technology now exists to support these ambitions. These versatile platforms provide data storage and access control, manage structured and unstructured data at high speed, scale on demand, and support technologies such as vector-based search and real-time analytics. Masu. It also supports edge computing for high-speed data sharing and access, and enhanced security.

Rather than a traditional single-function database, it provides everything organizations need to support driving adaptive applications. The future is just around the corner.

How real-time analytics with Couchbase Vector Search and Couchbase Column at the Edge helps organizations develop a new class of AI-powered adaptive applications that engage customers in highly personalized and contextual ways. I will explain in detail.