The Rise of No-code Development

Data is king when it comes to achieving organizational excellence goals.


Companies collect significant data amounts on consumers and use those details to help make decisions on how to operate.

These details companies are gathering can include data on spending habits, location, age, and more. Plus, companies also have to process operational data such as business reports.

But, a large part of this data is unstructured and is useless for business applications. Organizations are sitting on a gold mine about modernized data management.

This guide looks at how NLP (Natural language processing) benefits the back office.

How Much of an Impact Will NLP Make?

NLP makes it easier for businesses to operate, as an NLP system can analyze the content of a report and document and help influence decisions.

An NLP system will also improve how a business can handle various operational tasks. A business can keep tabs on its assets with GPS systems, communicate with people through digital assistants and chatbots, and many other functions.

It’s a necessity for businesses to enter the NLP industry. The global NLP market will grow by $100 billion between 2021 and 2028, so getting in on the market right now is ideal.

How Will NLP Simplify Your Business?

One unique aspect of language is that it has as many rules as restrictions. Such issues make it difficult for programmers to develop relevant data processing software.

The challenge arises in producing software capable of analyzing text and voice data. Software developers struggle with these issues (phrasal verbs, proverbs, etc.). But, the product (NLP Systems) is a worthwhile investment for any organization.

It’s essential for a business that wants to achieve specific goals to use NLP. Some of the goals a business can meet through NLP include reducing response times, evaluating customer conversations, accounts, and more. Here are the key points of using NLP systems for the back office:

”One unique aspect of language is that it has as many rules as restrictions. Such issues make it difficult for programmers to develop relevant data processing software.

1. High-Quality Data Analysis

Computers find it difficult and time-consuming to process large amounts of unstructured data. The common data types include documents, emails, reports, and financial records. With NLP, any organization’s back office team can process these records.

It’s a benefit that comes up in various use case examples. For instance, NLP allows recruiters to reduce the time spent enlisting new staff members. NLP eliminates the need for performing manual resume screening procedures.

How does it do this?

An NLP program can go through resumes based on keywords or content. The NLP system will then settle on the user profiles containing specific characteristics based on whatever the employer wants to find. These points can include industry-relevant skills, portfolios, location, and age.

2. Helps to Break Down Complex Processes

Companies that handle complex tasks like accounting must manage extensive data amounts. It can include contractual documents, financial reports, legal statements, and more.

All these documents need thorough analyses and reviews. These processes help make data more meaningful in the business context.

But, going through all these documents is an exhaustive process. Plus, people such as staff are sometimes negligent when handling data-intensive tasks.

3. Improves Customer Relations and Value

NLP systems often work in tandem with artificial systems. It allows them to retrieve data and provide solutions to customer queries.

For instance, NLP-powered chatbots can give insight into various business metrics. These can include service details, shipping prices, and operating hours.

These resources also reduce customers waiting in line or communicating through email. Even so, NLP systems can provide more value outside of addressing customer queries.

Some NLP systems can process customer reviews and reports. The data they provide can be helpful suggestions for organizational improvement.

An excellent example of their applicability is in the hospitality industry. It’s a niche that relies on reviews and survey data for the customer experience. NLP systems can analyze these reviews and convey the customer’s emotions. Some of the common terms these systems can look out for include “I’m tired” or “hated.”

A trained NLP system can process these documents faster than a human. It can identify critical points and shortcomings an organization should consider when hiring people. platforms to create 70% of new applications by 2025.

4. Empowers Employee Productivity

There are various repetitive tasks that NLP systems can handle. These repetitive tasks are common causes of shadow IT issues or negligence among employees.

But with NLP systems, employees can automate these procedures. It will give the freedom to work on more engaging and demanding tasks. Thus, the productivity of employees will increase.

A good productivity use case example would be H.R.-specific NLP analysis systems. These programs often feature various progressive algorithms that support decision systems.

Thus, they provide improved precision for heads of departments about ways of improving H.R. (Human Resource) metrics. It helps address the issue of human bias when making relevant decisions relating to the H.R. team.

5. Lowers Expenses

High-efficiency levels are essential for lowering operational costs. That is because they reduce the need for manual procedures and employees. An NLP system is a perfect solution for such an initiative. It helps to reduce the need for more people to work on a specific task.

This move also helps to cut mistakes from issues like negligence. These mistakes are common in responding to client queries or data analysis.

The ability to reduce repetitive tasks and procedures frees time for other jobs. Productive employees will get the encouragement to stay longer in such settings. Thus, this helps reduce the costs of enlisting new staff members.

”Organizations will no longer have to rely on guesswork or cursory analyses. Instead, the NLP systems will ease the breakdown of relevant operational data.

6. Offers Actionable Insight for Improved Conversion

Most meaningful data from consumers, like surveys, are often unstructured. The content in this context can include reviews, comments, and reports. Such content requires high levels of analysis, which A.I. guided NLP tools can solve.

It will make it easy to convert visitors or leads to customers. The advertising department will find it easy to boost their lead conversion rates.

The customer acquisition rate is crucial for the costs an organization should spend. NLP systems are instrumental in handling data for conversion optimization goals. Chatbots, auto-complete text, and high-end search functions are examples of NLP-powered resources.

The back office is an essential aspect of any organization. Achieving organizational excellence involves ensuring such departments can leverage resources like AI-powered NLP solutions. Launching such systems takes time and financial investment, but they are practical solutions..

Apogee Suite of NLP and AI tools made by 1000ml has helped Small and Medium Businesses in several industries, large Enterprises and Government Ministries gain an understanding of the Intelligence that exists within their documents, contracts, and generally, any content.

Our toolset – Apogee, Zenith and Mensa work together to allow for:

  • Any document, contract and/or content ingested and understood
  • Document (Type) Classification
  • Content Summarization
  • Metadata (or text) Extraction
  • Table (and embedded text) Extraction
  • Conversational AI (chatbot)
    Search, Javascript SDK and API
Creating solutions specific to:
  • Document Intelligence
  • Intelligent Document Processing
  • ERP NLP Data Augmentation
  • Judicial Case Prediction Engine
  • Digital Navigation AI
  • No-configuration FAQ Bots
  • and many more

Check out our next webinar dates below to find out how 1000ml’s tool works with your organization’s systems to create opportunities for Robotic Process Automation (RPA) and automatic, self-learning data pipelines.