Nearly half of respondents to a 2023 Salesforce study reported that they have used generative AI; over one-third use it daily. Artificial intelligence (AI) has touched virtually all aspects of modern business, from big data analytics to generated content.
Few businesses make full use of AI technology, however. AI-powered tools can take organizations to new heights of productivity by optimizing their processes, which is why many businesses are using them to increase their bottom line. This article explores the optimization process and its benefits across multiple industries.
What is AI workflow optimization?
Workflow optimization is a systematic approach to analyzing and improving business processes. These improvements take the form of increased efficiency, effectiveness, and/or quality, all of which can improve an organization’s bottom line. AI process optimization employs artificial intelligence (AI) and machine learning (ML) to achieve these goals faster and with fewer errors. It generally focuses on the identification and removal of unnecessary tasks, resulting in streamlined workflows.
In 2023, a study found that the AI market was valued at 196.63B, largely due to the technology’s ability to analyze historical data very quickly. This long-term data visibility gives business leaders the ability to find market patterns and trends; they can then make data-based decisions to drive their companies ahead of competition.
AI process optimization can also automate many mundane tasks, freeing up time for users to perform nuanced, high-value, and high-priority work. Furthermore, it can analyze each stage of a production process in real time to identify bottlenecks and other process inefficiencies.
Start building more efficient workflows with The Citizen Developer’s Guide to Workflow Optimization
How AI optimizes processes
AI process optimization employs a variety of technologies, including ML models, computer vision, and natural language processing (NLP). ML statistical algorithms recognize patterns in processes, which it uses to improve them. Computer vision technology obtains information from images and videos, while NLP uses computational techniques to analyze and synthesize human languages. Together, they power AI process automation to perform the following tasks:
- Forecasting results
- Identifying bottlenecks
- Automating daily repetitive tasks
- Streamlining end-to-end processes
- Speeding up decision-making
- Redesigning inefficient processes
AI process optimization predicts customer behavior by analyzing data sources such as past behavior and market trends. It can then determine the customer’s most likely future behavior, based on their most pressing needs.
Forecast results
AI can also anticipate events before they occur through the use of predictive analytics. This capability allows the user to build models of these events, typically for the purpose of planning for worst-case scenarios. Assume, for example, that a transportation company wants to understand the effects of a natural disaster so they can determine the best routes for supply delivery and the best staff to deploy to perform the work.
This knowledge will directly affect delivery efficiency, especially during unusual events; careful planning is essential to ensure team members execute plans quickly and effectively. In addition, proper preparation would give the transportation business the ability to quickly reroute drivers in the event the primary route is unusable, while minimizing disruptions to deliveries.
Identify bottlenecks
Identifying process bottlenecks requires diligence and close attention to detail. Visual aids such as fish bone diagrams, flowcharts and time trackers are the traditional tools for this task, as they allow you to monitor business processes at each step. In addition, tracing bottlenecks requires a careful examination of both individual and departmental-level processes, depending on the scope of the project.
AI optimization software can greatly reduce the time users need to map out activities and tasks, in addition to cutting down the time needed to complete them. It can also identify processes with long queuing times – a clear indication of a bottleneck. A significant amount of idle time before the process begins is an even bigger red flag that some part of it is bottlenecked.
Automate daily repetitive tasks
Automating routine tasks at high volume is one of the most common uses of AI process optimization. AI is the most effective component for automating tasks, although other technologies can help develop complex automation strategies. In addition to saving time, automation reduces the rate of manual errors and allows workers to focus on high-level tasks.
Document processing is a common application for automation, as businesses also receive many documents like contracts and invoices in both paper and electronic format. After receiving these documents, employees have historically processed them by manually entering this information into an application. AI solutions like intelligent document processing (IDP) can convert them into usable data without the need for manual intervention.
Streamline end-to-end processes
While AI is a useful tool for streamlining individual processes, automating an entire workflow requires additional tools. Many automation technologies can augment AI, such as robotic process automation (RPA). This technology is well suited for filling in forms and other types of data entry once the data has been extracted from a document.
For example, an AI model can analyze purchase data from a supply chain in real-time to identify anomalies like a run on a particular product. The model could then alert a retail store about this issue, requiring a manager to decide if any action is required. If it is, the manager can enter the product number into an application, where upon an RPA bot connects with a supply system to order more of the product. This use of AI process optimization can save a great deal of time, especially during periods of volatile demand.
Speed up decision-making
Leaders in today’s business landscape require strong evidence to ensure they make the correct choices, and AI can assist with this process by analyzing patterns in a given data set. For example, business analysts often use predictive models to simulate the effects of price changes on profitability without the risks of implementing them in the real world. Companies can determine optimal prices without losing customers or profits.
The role AI process optimization plays in lending is another facet of its use in decision-making. Loan officers can analyze data to ensure they only grant loans to borrowers who follow sound financial practices. This strategy not only helps loan officers make better decisions, but it also reduces the time they need to make those decisions.
Redesign inefficient processes
AI process optimization can detect process inefficiencies and alert users to redundant steps. It can also identify the stages in a process that are responsible for the most delays, indicating where improvement efforts will have the most positive impact.
Benefits of optimizing processes with AI
The major benefits of AI process optimization include improved operational efficiency, fewer errors, cost savings and process standardization.
Improved Operational Efficiency
An increase in operational efficiency is the most significant benefit of AI process optimization. Businesses often use inefficient processes, typically manual processes that can be easily automated. This practice wastes time, increases employees’ workloads, and reduces profits.
AI can identify inefficiencies and redundancies in processes, reducing the time organizations spend on repetitive tasks that require a low level of skill to complete. The streamlining or complete automation of these tasks allows team members to spend more time on high-level tasks that will help grow the company.
Fewer Errors
AI effectively eliminates direct human error from processes, leading to improved outcomes. However, this benefit requires the AI solution to have access to accurate, up-to-date data. Automated data input and delivery can help improve the accuracy of input data for AI systems.
In addition to avoiding costly errors, AI process optimization helps organizations comply with government regulations and industry standards. It can also improve the performance of individual team members by relieving them from the stress of performing repetitive tasks.
Furthermore, process optimization ensures that team members always have access to the data they need to maintain a business’s success. Finally, process optimization solutions typically use a permissions system that ensures sensitive information is only accessible by personnel who need it.
Cost Savings
AI process optimization provides a fast, powerful re-assessment and organization of resources and priorities. This has a tendency to reveal persistent, long-hidden ares of waste like data errors and bottlenecks, which compromise productivity and can increase operating expenses to a surprising degree.
Process Standardization
One of the most appealing attributes of AI technology is its ability to continuously perform the same task in exactly the same way without the need for human workers. AI-based solutions therefore provide far more consistent results, and are an ideal solution for disjointed processes that need to be standardized.
AI process optimization examples
Industry leaders often pave the way for other businesses by demonstrating the possibilities of emerging technology. What follows are a few examples some of the best-known global organizations deploying AI to optimize IT, procurement, and HR business processes.
IT
Microsoft was primarily interested in improving its security and efficiency when it added AI to its IT infrastructure, including both its cloud presence and on-premise data center. AI simplified the Microsoft network by automating access controls and creating a robust, risk-based isolation of suspicious devices. In addition, Microsoft used AI to monitor and manage third-party applications, including user access.
The same technology now detects network issues and repairs them, resulting in a resilient network that doesn’t require direct human intervention. These changes increased Microsoft’s operational efficiency by employing automation to manage resources, accelerate the deployment of software installations and updates, and manage network configurations.
Procurement
Shell wanted to use AI as a means to meet its procurement needs in the volatile energy market. It initially deployed AI technology for training equipment in its exploration and drilling program, significantly cutting the costs of extraction. This application allows machinery to learn from past experience, giving operators a better understanding of the current environment. It also leads to better results during exploration and causes less wear and damage to equipment.
In addition, Shell has put AI to work improving the safety of its fuel service stations. Vision-enabled cameras are installed at every service station to detect safety hazards such as customers lighting cigarettes near fuel pumps.
HR
Because of their current ubiquity, chatbots like ChatGPT are often the first thing most people think of when they hear the term “AI-based application.” When used in human resources (HR), this type of software maintains a large amount of employee-related data.
RPG Group trained Leena AI on its internal HR data, allowing it to answer many employee questions in real time. The company then used this solution to create virtual assistants for employees that provided fast, accurate information about benefits, time off, and pay without the need for a human agent.
This system answered 92 percent of employees’ questions within minutes. It also reduced ticket resolution time from a full day to only four hours, saving the company thousands of hours in employee time per year.
Start optimizing your processes with Pipefy AI
AI process optimization is critical for businesses looking to unlock their full potential by reducing errors, minimizing risk, and maximizing productivity. Pipefy has extensive experience helping clients improve their processes with AI. Our no-code business process management (BPM) solution creates and optimizes processes by using historical data to help clients make smarter decisions.
