In this blog post, we will provide grounding in the basics of AI-powered business process automation so you can join the conversation with confidence and make decisions that are right for your business.
- What is AI-driven automation?
- How AI transforms business process automation
- Examples of AI in business process automation
- Key AI Technologies in business process automation
- Top concerns business leaders have about AI
- AI process automation trends to watch
What is AI-driven automation?
Artificial intelligence (AI) is a broad term for technology that replicates the human brain's problem-solving skills. It tackles tasks commonly linked to human intelligence. It can reason, understand meaning, generalize and improve its accuracy by incorporating new data and learning from experience.AI business process automation (BPA) makes use of machine learning (ML), artificial neural networks (ANN), natural language processing (NLP) and other AI tools to handle repetitive tasks that would usually require human effort. It can be used for processes as simple as routing an invoice to the correct person or as complex as managing the quality control paperwork manufacturers need for compliance.
Traditional automation systems stick to a fixed set of rules for repetitive tasks using structured logic based on "if this, then that" statements. In contrast, modern business process automation systems solve complex problems using reasoning that is similar to human logic.
One example of this progress is evident in the evolution of data extraction. Previously, we relied on fixed zones or templates to identify document types. Now, machine learning interprets documents, detects document types, extracts variable data layouts, classifies content and even flags exceptions. Modern BPA software can also auto-detect whether an upload is an invoice, a W-9 or contract and pull the right data even if formats differ across vendors.
How AI transforms business process automation
Enhanced efficiency and productivity
AI-based software performs tasks at greater-than-human speed and accuracy. As a result, it increases productivity. For example, intelligent document processing (IDP) systems learn and adjust over time, improving their accuracy and performance through ongoing use. In addition, AI-powered data validation minimizes errors.
From an employee perspective, when repetitive tasks are automated, staff members tend to be more motivated. Managing complex tasks becomes easier, leading to a more positive work environment that encourages collaboration. With repetitive work handled by automation, your employees can dedicate their energy to higher-value work that contributes directly to meeting business goals.
Cost savings and enhanced accuracy
Automating manual processes associated with BPA reduces labor costs, paper use, storage requirements and time spent fixing avoidable errors. For example, manufacturing AI systems can predict and prevent machine faults, reducing organizations’ downtime and maintenance costs. Digital transformation and new business models
Traditional companies typically use one-size-fits-all strategies, which restrict their ability to meet individual needs. Today's consumers expect personalized experiences. AI business models employ machine learning to understand consumer behavior, enabling highly tailored recommendations, adaptable pricing and precise marketing strategies Competitive advantages
Companies that use outdated, manual methods and neglect data privacy protection jeopardize customer loyalty. For example, if there are delays in processing insurance or mortgage applications, customers may opt for competitors that offer faster service. Using technology, such as intelligent document processing, provides correct and organized information that leads to quicker and more efficient customer interactions, resulting in higher satisfaction and increased customer retention.
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Examples of AI in business process automation
Departments
Invoice processing
- Classify invoices, cash receipts, credit notes, delivery bills and other documents by type as soon as they are received using advanced AI models Analyze and extract data from invoices from various clients in multiple formats, delivering quick and accurate results.
- Flag discrepancies, identify duplicate invoices and detect missing information.
- Enable smoother workflows so your company can handle a greater volume of work with the same or even fewer resources.
- Share essential information, including addresses, payment data, total amounts and item details, by seamless extraction and integration with ERP or accounting software.
Human resources
- Automate time-consuming tasks including recruitment, candidate screening and scheduling interviews.
- Manage employee records
- Protect Personally Identifiable Information (PII)
- Automatically enforce retention schedules that govern how long employee files must be kept.
- Simplify performance reviews
- Track the progress of each review phase.
- Send email reminders to managers and employees to keep the process moving.
- Store performance reviews securely.
Compliance
- Use powerful search capability to enable retrieval of every document needed in seconds.
- Implement access permissions to support data privacy by ensuring that only authorized personnel view confidential information.
- Enforce retention schedules to guarantee that documents are kept or destroyed according to applicable regulations.
- Meet the demands for accuracy and traceability outlined by regulations such as HIPAA, SOC 2, and industry and customer requirements with version control and audit logs that guarantee the integrity of final documents.
- Enforce regulatory inconsistencies by automatically recognizing potential risks flagging them signals and escalating them when necessary.
Customer Service
- Provide your team with integrated and instant access to customer data to enable faster issue resolution.
- Eliminate response delays, inconsistent responses and inefficient customer support ticket management.
- Analyze customer inquiries and determine appropriate support channels and escalation requirements.
- Increase customer loyalty by providing higher quality customer service.
Industries
Finance
- Automate fraud detection and loan approvals.
- Create customized financial recommendations and product options.
- Evaluate client information and perform risk assessments.
- Improve management of asset portfolios.
- Increase customer access to information to promote their educated decision-making.
Healthcare
- Analyze patient data, such as test results, history, and diagnoses, making it easier to develop customized treatment plans.
- Process healthcare claims more quickly by eliminating routine errors, identifying exceptions and flagging fraudulent claims.
- Cross check treatments and medications against digital records.
- Chatbots that can make, change or cancel appointments and answer basic medical questions.
Education
- Enact data-informed practices to shore up their fiscal operations and strategic planning to improve financial solvency.
- Assist in defining strategic goals in terms of spending, budgeting and reducing operational costs.
- Develop personalized learning platforms.
- Automate certain grading and assessment tasks such as initial evaluation assignments, streamlining the grading process and saving teachers time.
- Assist in planning course content by analyzing data to identify trends and gaps to ensure the curriculum remains relevant and aligned with learning objectives.
- Assist in administrative tasks such as scheduling, budgeting and resource allocation.
- Flag at-risk students by analyzing patterns in absenteeism, performance, or engagement.
- Aid institutions in following data protection regulations by identifying vulnerabilities and ensuring secure data management.
State and local government
- Automate the migration of legacy software to more flexible cloud-based applications.
- Implement chatbots so government employees can get answers to their basic payroll, onboarding, benefits, and other questions without placing a phone call.
- Improve service to citizens via public-facing portals, faster access to public records and providing AI tools to report issues like streetlight outages.
- Automatically pull data across agencies and departments to enable government employees to do their jobs faster and better.
Manufacturing
- Track timelines for machine maintenance.
- Make supply chains more transparent and flexible by using scenario modeling.
- Monitor ongoing production processes and adjust without human intervention.
- Optimize the supply chain with better inventory planning.
- Accelerate innovation and reduce development cycles with generative design.
- Support regulatory compliance through comprehensive documentation and audit logs.
Key AI technologies in business process automation
Natural language processing (NLP)
NLP specifies how computers understand, process and respond to human language. Its capabilities include interpreting language meaning, translating languages and recognizing language patterns. Google Translate, spam filters, smart assistants like Siri and Alexa and automated chatbots use NLP.Machine learning (ML)
ML, a subset of AI, uses algorithms trained on generic or customized data to build models that perform tasks like image categorization, data analysis and summarizing project documents. ML analyzes data and recognizes and predicts patterns so it can make decisions based on past experiences.Robotic process automation (RPA)
RPA, also known as software robotics, uses intelligent automation technologies to perform repetitive office tasks. It is most effective for managing data intensive, rules-based processes. To effectively use RPA, these processes should either be repeated on a fixed schedule or have a predefined trigger. Additionally, they should have predictable inputs and outputs.AI models
An AI model is a software program that has been trained to recognize patterns and make decisions. Generative AI models are trained on vast quantities of raw data. Then they draw on encoded patterns and relationships from this training data to understand user requests and produce relevant new content that's based on, but distinct from the original.
The models can be preconfigured based on relevant, pre-existing data or customized by using datasets that are developed to meet the needs of a particular company or deliver a specific outcome.
Generative v. Predictive v. Adaptive v. Agentic AI
Generative AI (genAI)
GenAI uses advanced algorithms to sort through large, complex data sets. It clusters information in meaningful ways and generates new content, such as text summaries, images, and audio files, in response to a query or prompt. GenAI encodes a collection of existing information into a form that maps data points based on the strength of their relationship with each other. When prompted, it finds the correct context within the existing connections between the data points.
Popular GenAI platforms, such as OpenAI's ChatGPT and DALL-E as well as Google's Gemini, can answer complex questions, summarize vast amounts of information and automate many tasks. For example, businesses use genAI to draft reports, tailor marketing efforts, develop new drugs and generate design ideas.
A Gen AI automation platform can create customized documents, such as contracts and reports, from a prompt you formulate. When you provide a prompt, these systems generate documents by gathering data from customer relationship management (CRM), enterprise resource management (ERP) and other business software, databases and medical records.
In a common use case, Gen AI can highlight information in doctor's notes that relates to treatment options. It can build patient information summaries, create transcripts of verbally recorded notes or find essential details records more quickly than a person can.
Adaptive AI
While generative AI is used for content creation using existing data, adaptive AI can modify its code based on changing situations and past experience. It is used when it’s impossible to make real-time updates manually.Self-driving cars are the best example. Adaptive AI enables the vehicle to gather real-time information about road conditions, traffic and other potential hazards. It also combines this awareness with risk scoring.
Predictive AI
Predictive AI uses statistical analysis to identify patterns and predict behaviors and future events. It makes this analysis faster and more precise by using machine learning and large amounts of data.By analyzing historical data, predictive AI can find trends and make forecasts through big data analytics and machine learning. While predictions aren't always spot-on, predictive AI helps businesses prepare for what's next and personalize the customer experience.
Agentic AI
Agentic AI is a system that operates on its own, setting goals, responding to context and adapting actions with minimal human help. It does more than respond to a prompt. Agentic AI can retain information, learn from past experiences and connect with external tools and data to manage complex workflows.It also makes content recommendations, like those offered by streaming services, and product recommendations from retailers by incorporating the most up-to-date data about a customer's preferences and circumstances.
Top concerns business leaders have about AI
Security and access control
Meeting data security and privacy concerns is a top concern. It’s critical because business systems handle sensitive information that must be protected against data breaches and unauthorized access.
AI-based software provides robust data privacy, encryption and security controls and supports adherence with compliance standards such as the Health Insurance Portability and Accountability Act (HIPAA), Sarbanes-Oxley and federal and state regulations. AI-based BPA software also uses protection algorithms that safeguard processed and stored documents against unauthorized access.
Cybersecurity is another crucial consideration. AI-driven software offers protection against ransomware and other malware. For instance, if the software has a foundation like Microsoft Azure, it already finds 99% of viruses, so any infected document is instantly detected and blocked from being saved. Even newly discovered viruses can't spread to other files in the organization because the malware can't reach the corresponding data.
Integration with existing systems
Older systems may struggle with real-time predictions or constant data flows. AI can work with your current systems like CRMs, ERPs, databases and cloud services. Lightweight APIs, middleware or batch processing bridge the gap when real-time isn't possible.Change management
Employees might be hesitant to embrace AI automation due to fears of losing their jobs or simply because they’re unfamiliar with the technology. To overcome this, businesses should invest in training and create a culture that views AI as a tool for optimizing, not replacing, human labor. But AI isn’t just another piece of softwareAI business process automation trends to watch
Current research confirms what most of us in the business community have already seen. For example, a recent McKinsey survey found:- 62% of companies surveyed are exploring or using AI agents.
- A positive impact on cost and revenue.
- A varied perspective on the size of their workforces next year with 32% expecting a decrease, 43% expecting no change and 13% expecting an increase.
Gartner analysts forecast that Gen AI is about to change the game for business applications by offering virtual assistants, new user interfaces and improved features and functionality. In addition, more complex reasoning models will be a significant breakthrough due to their ability to solve complex problems with great accuracy. The analysts emphasize the importance of aligning costs with sustainability benefits to achieve success in new AI ventures. It's clear that AI tools will become more sophisticated and have increased potential to positively impact your business.
