Business automation software, also referred to as no-code business process automation (BPA), has made digital transformation possible for many organizations. Companies looking to stay competitive in the current fluctuating marketplace turn to workflow automation for new efficiency, cost savings, security measures, system integration, and market forecasting.
In the past few years, however, businesses have amassed vast amounts of data while watching their workflows and tech stacks increase in both number and complexity.
Many have concluded that automation alone does not adequately maintain and coordinate their growing mosaic of processes, people, and software.
Fortunately, the next phase of automation has arrived: artificial intelligence.
Explore new possibilities with AI, no-code, and process automation
What is AI workflow automation?
AI in business workflow management refers to the application of artificial intelligence functions to build, analyze, and optimize processes and workflows. It augments existing systems for improved operational efficiency and newly streamlined processes.
Before the introduction of AI in BPM, for example, teams built processes manually by creating process maps by hand which required lengthy research and discussion. Now users can simply tell the AI chat prompt what process they need, which data they want to track, and other pertinent specifications. AI does the rest in seconds.
The AI attribute changing the workflow management game, however, is its rapid data analysis capability. AI boosts BPA platforms’ power, giving them the ability to search and analyze huge amounts of data in a very short time. Leaders get real-time numbers that alert them to upcoming trends and market shifts. They then leverage this data to make quick, strategic business decisions.
Traditional vs. AI-driven workflow automation: a comparison
AI-enhanced workflows transcend traditional automation by employing several additional and enhanced technologies to recognize patterns and discrepancies and make subsequent recommendations and decisions autonomously. They can be taught to not only find inconsistencies and errors in data but also correct them.
A McKinsey report states: “Traditional levers of rule-based automation are augmented with decision-making capabilities thanks to advances in deep learning and cognitive technology.”
For a function-by-function comparison of AI-enhanced workflows to traditional process automation, refer to the following table.
| Traditional workflow automation | AI-enhanced workflows |
| Designed to increase the speed and accuracy of workflows | Designed to radically improve efficiency, reduce risk, and improve customer response time |
| Decrease repetitive tasks via rules, conditionals, and pre-set automations | Decrease repetitive tasks via automation, continually search for repetitions/silos, and autonomously dissolves them |
| Increases end-to-end visibility to keep data consistent and error-free | Increases end-to-end visibility, recognizes and reveals data patterns to find errors and automatically correct them |
| Automated customer service via portals, alerts, and email templates | Bot-based, simplified interactions with humans/customers |
Key features of AI workflow automation
AI refers to the use of machine learning (ML), natural language processing (NLP), chatbots, and optical character recognition (OCR) to analyze data and make predictions. Generative AI can also learn and replicate coding languages.
Machine learning (ML)
ML is a foundational aspect of AI, integrating into each of the AI applications we observe below. Put simply, ML uses algorithms specifically programmed for data – both structured and unstructured – to mimic the ways humans think.
Natural language processing (NLP)
NLP is a category of AI that couples models of human language (also known as computational linguistics) with machine learning technology to mimic a human interpretation of text and spoken word.
NLP gives AI the ability to interpret and respond to human language, whether typed or spoken aloud. You may be among the millions who use it daily in applications such as digital assistance or GPS systems.
Chatbots
Chatbots are one of the most advanced and widely-used examples of NLP currently available. Users pose questions in a conversation with a bot, which answers them based on:
- An AI model that uses large amounts of structured and unstructured data from many sources.
- ML that rapidly searches the data for the most relevant answer.
- Common sources of information used by ChatGPT include encyclopedias, computer-coding libraries, and scraped websites.
Optical character recognition (OCR)
OCR is a technology that converts handwritten text to typed text. It’s been in use for decades, but the best traditional OCR systems are still only about 80% accurate. Applying a machine learning model to OCR, however, dramatically improves this rate with no additional human interaction.
How? First, a bot programmed to monitor an email inbox for attachments with images of handwritten text passes found attachments along to an ML model to convert the handwritten text into typed text. That text, in turn, could generate an invoice, which could then be uploaded to an Enterprise Resource Planning (ERP) system.
The role and benefits of AI in workflow automation
AI in business process management refers to the use of AI capabilities to build, analyze, automate, and optimize processes and workflows. AI helps businesses improve operational efficiency and create streamlined processes. Its role is not one of replacement; it is enhancement. AI augments existing software and legacy systems and strengthens the integrations between them.
Benefits
When savvy tech leaders learn about the countless benefits of AI-enhanced automation and their effect on the bottom line, they begin to see it as the long-term, value-adding solution it is. The most important of those benefits is better, smarter, and faster decision making.
AI bots quickly crawl data and retrieve the requested information. For example, AI-powered intelligent document processing (IDP) tools can scan information fragments containing unstructured data, including images, handwritten text, and PDF files. They interpret, transcribe, index, and format data, quickly and accurately.
How to integrate AI into your workflow
In theory, AI is a treasure trove of unlimited possibilities. In practice, assimilating the technology into daily workflows may sound daunting. The following ideas for deploying AI in operations have made the transition a positive one for organizations.
Chatbots
Chatbots can be used out of the box and assume manual duties for employees across business lines, making them the first AI tool many companies integrate into their daily operations. Their speed and accuracy in answering questions and delivering solutions cannot be overstated.
Intelligent data extraction
Bots can exceed their traditional limits in extracting data by leveraging AI techniques like ML and NLP. These intelligent bots are often used to pull data from sources like images, handwritten text, and PDF files. They also use AI to contextualize this data, reduce the noise in the source material, and improve the accuracy of data.
Adaptive process automation (APA)
APA automates digital processes by leveraging AI techniques like ML and Digital Process Automation (DPA). APA bots are programmed with self-remediating and autonomous decision-making capabilities that allow them to modify tasks to optimize processes.
They can also reason and retain information, allowing them to obtain new insights into data. In addition, APA uses AI techniques like ML and NLP to interact with humans. Other capabilities of APA include the ability to record actions, decisions, and transactions, making these solutions highly accountable and auditable.
Which teams should consider implementing AI workflow automation?
Due to its limitless potential for establishing efficiency, visibility, and productivity, AI-powered workflow automation should be a consideration for teams across all business lines. From vendor vetting to production to shipping, AI can improve KPIs in just about any process you can think of.
AI workflow automation examples
Below we’ve compiled this list of just a few tasks that businesses frequently manage with workflow automation software:
- Emails and form fills converted to service requests.
- Calendar events and invites created and sent.
- Workload distribution by volume, time, or other criteria.
- Work items assigned/routed to the appropriate person.
- Emails or alerts sent when tickets change status.
- Alerts triggered when work becomes overdue or approaches its deadline.
- Messages delivered via Slack, WhatsApp, SMS, or other platforms.
- Documents or sales contracts generated and delivered for signature.
- Notifications sent when approval or review is needed.
- Data synchronized when an email is received or a form is filled.
- Reports and dashboards created from data in the workflow.
How to choose the right AI workflow automation software
The AI automation tool you choose will, of course, depend upon several factors such as the business size, industry, and customer base you serve, however, these four steps are a great starting point when considering AI for the first time.
1. Identify pain points
Why, exactly, is your company in need of new software? Are there big, sweeping issues like low productivity or an outdated helpdesk system? This will establish your project scope and help you narrow your AI-powered automation search.
2. Establish use cases
Gather employees from across business lines and levels to brainstorm what they need to execute tasks better and fix the issues they encounter daily. This step often requires a map of every task within the organization for full visibility.
3. Rate each need and begin research
Rating use cases based on urgency will give you a good idea of where to begin with AI-powered automation, and can start you on your search for a tool. Businesses seeking to update their procurement and finances processes may find a different platform useful than those who need an advanced OCR documentation system for large-scale paperwork management.
4. Make your plan and implement
When you’ve chosen your AI-empowered automation tool, begin the implementation with any automation tools your business already uses. If this is your first encounter with automation, begin with your least complex processes to ensure a smooth transition.
AI workflow automation with Pipefy
Pipefy is a no-code automation platform that provides AI capabilities to help businesses streamline operations across departments and conserve IT resources.
Pipefy AI enables users to build their own processes even faster; they simply tell the chatbot what they need and, within seconds, they receive a ready-to-go workflow. It also enables business leaders to make data-driven decisions at any given time by providing easy access to analytics and insights about their processes.
