The document-processing workflow is an important pillar of most business operations. Streamlining processes by automating low-level tasks frees up employees to focus on more complex work.
In the field of AI, this is especially true for document analysis. Artificial intelligence (AI) technologies, including machine learning and natural language processing (NLP), can automatically analyze and extract information from documents.
AI-powered software that understands data like a human will empower employees to work smarter, which will lead to higher productivity and a better working experience. Moreover, if employees can find all the data they need in a matter of seconds, they will be able to respond immediately to customer queries.
However, if you want to get high quality product documents without spending a lot of time writing them manually, you will have to feed AI tools with intricate knowledge about your product’s behavior, the problems your product is trying to solve, and user interviews and research.
Documentation for AI can be a game changer when it comes to speeding up the production process of documents, such as user stories, PRD, technical docs, and API docs. By alleviating dev teams of menial tasks, they can focus on more complex and critical issues that will improve their application and ultimately help their customers.
In some industries, such as healthcare and energy, AI-powered intelligent document processing has been used to cut costs. For example, iEnergytics uses AI to collect data and identify ways for businesses to save money on energy use.
For others, such as mortgage lending, AI can help speed up loan processing from weeks to days, reducing the cost of issuing loans. And for procurement, AI can automate the capture of procurement data, reducing labor costs and improving efficiency.
Another way AI can reduce costs is by eliminating repetitive tasks that are tedious and time-consuming for employees. This allows workers to focus more on the jobs they were hired to do, which can also lead to increased productivity.
However, implementing an AI solution can be expensive. The technology may require a significant amount of customization for your specific business needs. It will likely cost between $1 and $2 million to build a fully-functional system.
Better Customer Service
A great customer service experience is all about helping customers find the right information they need at the right time. AI can help automate support queries by analyzing their intent, sentiment, and main topic to determine the best way to answer them.
It also can suggest articles from your knowledge base that are relevant to the issue a customer is contacting you about. This means you’re saving your agents valuable time, improving their work load, and creating an excellent customer service experience for everyone.
For example, a home warranty and home services company called Puls uses AI to respond to customers’ home-care questions. Its generative AI assistant identifies questions, provides personalized guidance, and shares the appropriate information with service technicians to reduce customer call containment and ensure faster repairs.
AI-powered sentiment analysis can also be used to analyze customer survey feedback and competitors’ reviews to uncover problems with a specific product or feature that should be prioritized. This can help you make better decisions regarding your product development and create a positive user experience.
Artificial intelligence can be a powerful tool for increasing productivity, especially when applied to low-value, rote tasks. Its ability to take over mundane processes allows employees to focus on higher-level activities, boosting productivity and improving employee satisfaction.
Documentation AI enables businesses to automate manual review processes, reducing errors and allowing users to sift through unstructured data faster. This can help companies meet a variety of business needs, such as contract processing, customer service and product management.
Although AI can save time by taking on menial tasks, it is important to remember that it is a machine and it cannot replace the human element in the creation of meaningful product documentation. It is imperative to feed it quality information about the product, users, and data. This input will allow the AI to better understand what is being written and help it produce well-structured documents.