It’s a scene all too common around tax season — the accounting team huddled in a room, heads down, manually entering data from stacks of invoices and receipts into spreadsheets. Wouldn’t it be nice if their talent is spent on meaningful work that moves the needle? Unfortunately, it is not just isolated to accounting. A Zapier survey showed that 76% of employees spend up to 3 hours daily on data entry. 

AI data entry powered by machine learning (ML) and natural language processing (NLP) can help change that scene. Organizations have reported a 95% reduction in data entry time after switching to robust automated data entry software. Read on to learn more as we explore how AI data entry works, its key benefits, and practical tips to implement it in your organization.

What is AI data entry?

Data entry powered by AI refers to using artificial intelligence technologies to automate the process of extracting and inputting data into digital systems for further use.

Your team will no longer have to manually type information into forms, applications, or databases. And that’s only half of the story. Typically, a robust data automation solution can extract data from documents, classify, validate, and export it, or auto-populate fields in other software systems.

That means not only is manual entry significantly reduced, but the entire pipeline of data handling becomes more streamlined.

The benefits of AI data entry

A 2020 McKinsey survey found that automation reduced costs by 10-15% and cut order processing time from 2-3 days to 1-2 hours. While this includes a variety of automated systems, AI data entry is a major contributor to these kinds of efficiencies.

Let’s break down the specific benefits further:

Saves cost, time, and effort: Organizations have reported that AI data entry can automate up to 95% of repetitive data tasks. This frees up employee time for higher-value work.

For example, an accounting team could reduce the time spent on manual invoice data entry from 5 hours per week per person to just 15 minutes. With financial data errors leading to reworks, mid-size companies cost over $800,000 annually (Gartner report), it’s clear how automation pays for itself many times over.

Improves data accuracy: From extracting key financial figures for a quarterly report to capturing patient symptoms from clinical notes, accuracy matters tremendously in data entry. Even a tiny error can be costly. AI-OCR tools can provide an upwards of 99% accuracy rate.

Moreover, AI-OCR tools like Nanonets can extract data from structured and unstructured documents seamlessly. Terminologies, tax regulations, currency changes, language syntax, and more are quickly recognized and extracted accordingly.

Enables real-time data flow: Extracted and structured data from AI systems can connect directly to other platforms through APIs. This ensures real-time, synchronized data across sales, marketing, and operations — enhancing cross-departmental workflow efficiency and analytics and reporting.

For instance, customer order data from forms submitted on a website can automatically pre-populate the corresponding fields in CRMs, inventory management, and accounting systems.

Scales easily: AI data entry solutions can ingest data from thousands of documents daily without losing steam. Be it from an email inbox, a shared folder, website form submissions, or scanned copies, the ability to scale infinitely is a strong advantage.

What’s more, as these systems learn from each use. So, the more it’s used, the more refined and accurate it becomes. For industries grappling with immense data, such as healthcare and finance, it’s a game changer.

Streamlines workflows: AI data entry solutions integrate seamlessly into existing IT infrastructure through APIs. This removes friction and structural bottlenecks in data pipelines between systems. For example, invoice approval workflows can go from 5 process steps to 2 by cutting manual handoffs.

Beyond just data entry, AI solutions like Nanonets can classify documents, route them to relevant teams automatically. For example, a scanned supplier invoice received in the accounting email can automatically get classified, data extracted, posted in the ERP, and approval notification sent.

Implementing AI data entry: Applications and best practices

Let’s face it — manually moving data into spreadsheets is about as fun as watching the paint dry. It can get mind-numbing, especially when you’re asked to do it day in and day out — makes you more susceptible to errors. Studies show almost 30% of finance staff time is spent fixing data errors alone.

Thankfully, now AI is advanced enough to automate capturing, classifying, and structuring data; it can optimize workflows and unlock major efficiency improvements. Let’s explore some of the most valuable applications of AI data entry and recommendations to ensure smooth adoption.

1. Process invoices, bills, receipts

Automating the processing of documents like invoices, bills, and receipts can save countless hours for your workforce. Robust solutions can take care of the process end-to-end, with you just overseeing final accuracy and authorizing payments.

With Nanonets, all you’ve to do is select an AI model that suits your documents and then set up an auto-import option from wherever they are coming in — your email inbox, a cloud storage folder, or even a physical scanner. If you only have a handful of documents, you can upload them manually.

After that, the AI captures the information, validates it, and sends it for approval. You can then review and approve the data in a central hub. You can then export it as an Excel file or directly send it to your accounting software, ERP, or other systems.

2. Turn form submissions into actionable data

If you’re running a small designer boutique, you may collect orders via the Typeform link embedded in your Instagram bio or via a form on your Webflow website.

With Nanonets, these form submissions can straight away turn into actionable data. The Zapier integration allows you to directly extract and push everything from the customer’s name, contact information, product choice, style preferences, and delivery details into your CRM, sales or delivery management software.

Sales orders can be immediately picked up. The specifics of each order will be noted and delivered to your inventory management system. It allows you to provide a more personalized customer experience, streamlining operations and improving responsiveness.

3. Automate customer profile updates

Profile data is critical, whether it is a patient’s medical history in a hospital or a customer’s driving record in a car rental company. You can’t expect the customer or a patient to fill out long, tedious forms each time.

Utilizing Nanonets, you can scan documents they submit — new test reports and driving licenses — and immediately update the profile data without having frontline staff involved. The AI can extract data from these documents and classify and update the information directly in your CRM, EMR, or proprietary software.

This saves your customers from the frustration of constantly filling forms, and your staff from mind-numbing data entry tasks. It also ensures that data stays updated and available at all times, helping you provide better experiences.

4. Manage vendor and supply chain documentation

Supply chain and vendor management often involve dealing with large amounts of documentation: purchase orders, quotations, invoices, contracts, delivery receipts, and more. Nanonets can automate the extraction and classification of data from these complex documents.

Purchase orders can be scanned to capture line item descriptions, pricing, delivery schedules and more. Currency amounts, tax details and payment deadlines can be pulled from complex vendor invoices. Delivery receipts can reveal shipment contents and tracking IDs for logistics visibility.

It’s also capable of interpreting different document formats, currency types, languages, and legal terminologies. Also, Nanonets allows documents to be processed as soon as they arrive — ensuring timely updates even during holidays, late nights, or other non-business hours.

5. Optimize parsing and verification workflows

HR and marketing teams often grapple with processing large volumes of data from sources like resumes, feedback surveys, and campaign analytics. AI tools like Nanonets can automatically parse these unstructured datasets to extract the required details.

For HR, it could mean capturing skills and experience information from resumes or validating details from identity documents. For marketing teams, it could be capturing customer sentiment from surveys or lead capture data that could be handed over to the sales team.

The extracted data can then flow seamlessly into respective systems like ATS, CRMs, or marketing automation platforms. This eliminates tedious manual efforts, minimizes errors, and allows employees to focus on strategic analysis.

AI data capture and entry come with tremendous potential, but implementation takes forethought and strategic planning. Following best practices ensures your rollout proceeds smoothly and you fully leverage capabilities over the long term.

1. Start with smaller pilots

Before integrating AI across your entire system, start with smaller-scale pilot projects. This way, you can better understand the AI’s capabilities and rectify any issues before a full-scale launch.

For instance, you can start with expense claim reconciliation or converting customer survey responses into actionable data points. Track effectiveness and address feedback before leading into larger projects.

2. Prioritize AI implementation

Consider the areas in your organization where AI can deliver the most immediate or substantial benefits. Perhaps invoice processing is incredibly time-consuming, or missing data from customer interactions is costing your sales team. Prioritize these areas when implementing AI.

3. Clean up existing data sources

Ensure that your existing data is cleaned and optimized. Because you’ll have to train the AI models using this data. If the data is messy or incorrect, it may lead to inaccurate AI outputs. Regular data cleaning can enhance the performance and improve the decision-making ability of AI.

4. Set up and monitor KPIs

Specify KPIs like average time taken to process an invoice, order to cash cycles, customer response times, customer complaints with regards to data errors, etc. Monitor these metrics before, during, and after implementation of AI. This would help you to evaluate the impact of AI on your operations objectively.

5. Train, validate, and refine the model

First, use diverse, representative documents to initially train the algorithm. Verify early-stage outputs, define key accuracy KPIs, and enable human validation scenarios where needed. As new document templates and terminology emerge, continuously retrain the model and incrementally refine processing workflows to boost precision. Monitoring accuracy metrics and iterating on the AI will maintain relevance and trustworthiness of automation.

Choosing the right AI data entry solution

Selecting the right AI-powered data entry automation solution for your business needs careful evaluation. Here is a checklist of key criteria to consider:

☑️ Integrates with your tech stack

• Does the solution allow easy integration with your existing IT infrastructure through APIs?
• Can it export data directly into your databases, ERPs, or other platforms?

☑️ Accuracy and effectiveness

• Does it leverage advanced AI and machine learning for data extraction?
• What accuracy rates does it achieve in testing based on your sample documents?
• Does it provide transparency around confidence scores?

☑️ Customization ability

• Can you train custom models tailored to your unique document templates?
• Does it allow configuring specific data fields for capture as per your needs?

☑️ Scales up and down

• Can it handle spikes in your document volumes without losing accuracy?
• How quickly can documents be processed without latency issues?

☑️ Secures your sensitive data

• Does the vendor provide enterprise-grade data security?
• Can your sensitive business data be encrypted both in transit and at rest?

☑️ Pricing and support

• Is pricing aligned with the value derived from automation?
• Is responsive customer support available across implementation cycles?

Final thoughts

The days of manual data entry are over. Modern automation solutions present a game-changing chance to hand over the drudgery to AI. Now your team can focus on meaningful work that moves the needle.

With Nanonets, you can automate repetitive data entry tasks with accuracy. And thanks to seamless integrations, data flows directly between your systems in real-time. This means happier employees, delighted customers, and a competitive edge. Try out Nanonets today for free.

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• Source: https://nanonets.com/blog/ai-data-entry/