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Realizing the Potential of AI Data Processing Applications


Far from just another tech industry buzzword, artificial intelligence (AI) is fast becoming a mainstay of data collection and analysis for many organizations. According to research by Accenture, not only do 84 percent of executives think leveraging AI is critical to meeting growth objectives, but three out of four of them believe they will risk going out of business if they don’t scale those initiatives.

That fear of being left behind is why 88 percent of companies have already invested in AI or machine learning technology or plan to do so in the near future. With some 175 zettabytes of data expected to be created in 2025, organizations without the AI data processing tools necessary to analyze and make sense of that data will struggle to develop effective business strategies and deliver a competitive customer experience.

It’s a tremendous opportunity for independent software vendors building the next-generation of applications across various industries. In order to deliver on the promise of AI, however, these software solutions also need to provide the tools that allow users to leverage their capabilities to streamline business processes. After all, a powerful AI solution isn’t of much use if it can’t be integrated with existing workflows.

Getting the Most Out of AI Data Processing

The most successful developers understand that AI data processing is only one piece of the puzzle. Their innovative AI technology is driving the car, but they still need the frame and wheels around it if the application is going to take their customers anywhere. That means building the less glamorous, but equally essential technology that helps AI data processing solve everyday tasks.

Take, for instance, document or image management. Organizations that gather data from physical forms or scanned documents need some way of extracting information so it can be converted into a format AI data processing tools can utilize. Manual data entry is both time-consuming and prone to error, so requiring users to transfer information by hand is simply not viable. By building document and image processing capabilities into their applications, developers can greatly enhance the versatility of AI data processing by automating key aspects of the collection process.

There’s also the question of what can be done with all of that data once it’s been gathered. Legal organizations, for example, often need to apply that information to contract creation, while insurance agents turn to it when assessing risk. By combining AI data processing capabilities with document assembly tools and search functionality, organizations can further automate key business processes to improve efficiency. Why painstakingly draft legal contracts or master service agreements from scratch when applications can use automation tools in conjunction with AI to assemble documents with greater speed and accuracy?

Build vs. Buy?

This often presents a challenge for software developers with limited resources. On the one hand, they need to invest as much time and energy as possible into their innovative AI data processing capabilities in order to meet the collection and analysis needs of their customers. But without also providing some way of interacting with and using that data to improve other key tasks, they will struggle to persuade potential users to adopt their innovative platform.

One solution is to build that functionality in-house. For software developers with substantial resources, this might sound like a good option. Unfortunately, the reality often proves less than ideal. Even something as basic as viewing and converting documents can quickly become a massive undertaking that draws valuable developer resources away from the AI data processing capabilities that are supposed to help the product stand out in a crowded market. 

In many cases, the company ends up having to outsource the work or push back key deadlines. Even worse, it may also end up creating more problems than it solves by relying on open source toolkits and libraries. The biggest problem has to do with security vulnerabilities. A recent study found over 2,600 bugs reported in open source projects between 2015 and early 2020. Even worse, many of these vulnerabilities were not formally reported to the National Vulnerability Database (NVD) until well after they were first exposed, giving hackers and other hostile actors time to exploit the security gaps.

The Integration Solution

Developers can avoid delays and security risks by turning to proven SDK and API integrations for their application needs. This is especially effective for complex, but essential functionality like viewing, conversion, compression, editing, and assembly. By relying on code-based integrations that are actively supported, they can ensure that users will be able to leverage their AI data processing solutions securely and effectively.

Rather than building features from the ground up and wasting valuable development resources, independent software vendors can devote more time and energy on the core competencies that will make their application more competitive. That allows them to build more powerful AI data processing capabilities and bring those features to market even faster.

Enhance Your AI Data Processing Application with Accusoft Integrations

Accusoft’s family of SDK and API integrations helps software developers realize the potential of their applications by delivering proven document and image processing functionality. Whether you need document assembly tools to get the most out of your legal AI sifters or powerful HTML5 viewing capabilities to harness the power of risk management automation, our easy-to-implement, code-based integrations can help you realize the full potential of your application’s AI data processing.

Find out more about how the Accusoft development team is incorporating machine learning into their processes or talk to one of our integration specialists today to learn how we can enhance your AI data processing application.