Does Your Company Have a Reading Problem?

With new stories every day about how Artificial Intelligence is transforming the way businesses function, it’s tempting to follow suit and attack the same problems that everybody talks about. Robots, cancer diagnostics, and predictive financial models are all exciting use cases and receive plenty of well-deserved hype. When it comes to bottom-line impact, however, there’s a huge but less obvious opportunity to apply AI – to speed up and automate the hours of time working professionals spend reading on a daily basis. If you’re worried your workforce is being underproductive, you might have a reading problem.

Companies of all sizes produce large amounts of unstructured text data. Reports, memos, documentation, specifications, contracts, and more are created constantly. To support and maintain the usefulness of all this information, a huge amount of overhead goes into organizing it in a way that makes it easy to find for co-workers who need to work with the same content. In a perfect world, people would tag, structure, and organize their own content while writing it, or directly afterwards. In reality, that is never the case – maintaining document repositories is nearly always an afterthought.

As a result, people who eventually need the information from these documents are faced with the monumental task of locating files and manually reading through them to find a needle in a haystack of data. Some companies spend months trying to implement best-practice structure and organization after realizing their inefficiency. Others balk at the costliness of the idea or never realize it in the first place, letting their people grow overworked and their data grow stale.


Reading problems require reading solutions. Here at Kyndi, we’ve built out an AI-powered platform to do just that – our platform provides a suite of tools to direct machines to read your information for you and help you find the exact things you need within it. We’ve built the platform to follow the same natural steps that people do:

  1. Gather the data to be read
  2. Read through the data
  3. Understand and process what the data means
  4. Sum it all up to inform an output

The result of all this is a system that you can task with doing the reading for you, but at a dramatically larger scale and faster speed than a real person would be able to handle. Kyndi provides data connectors and tools to assist in step 1 (data gathering), then processes and interprets the contents of documents using fully unsupervised machine learning-based techniques. Once the system is done reading, you can interact with it in the way you would with a knowledgeable person. You can ask it questions about what it read and get both answers and contextual insights.

By letting machines do the tedious work of learning what’s in your documentation, you can free up your people to focus on the higher value tasks and decisions that are informed by the automated work.

Today, you almost certainly have reading problems around your business. Diagnosing these problems is tough because they’re usually embedded deep within your employees’ day-to-day tasks. You can start by digging into the workflows of your analysts and looking for patterns such as:

  • Do they have to identify trends and business-critical status from reference documentation that changes regularly?
  • Do they have to synthesize information from large numbers of written files?
  • Do they have to answer tough questions based on past reporting or outcomes?

In any of these cases, frame the job to be done in terms of what information needs to be read, and ask yourself: how can we improve this by changing the way we read?