What is artificial intelligence (AI) automation, and how do we implement it?
AI mimics human thought and perception at accelerated speeds. With its ever-evolving algorithms, natural language processing (NLP), and machine learning (ML), AI is getting better at filling the gaps between needs and resource availability. Because of its ability to quickly analyze and interpret vast stores of data, AI can be an invaluable tool for automation and process optimization.
McKinsey Global Survey reports that 65% of polled respondents said they use some form of generative AI in their operations, nearly double the results of last year.
Automation can drive growth, cut costs, boost production, and increase customer retention, but it must be employed strategically, with an eye towards meeting clearly-defined business objectives. AI tools can add value only when they are being used to enhance sound methodologies and viable processes.
Find out what IT leaders are saying about no-code, AI, and process automation technology.
What is artificial intelligence (AI) automation?
There are three tiers of artificial intelligence in business operations:
- Assisted intelligence automates simple or repetitive tasks
- Augmented intelligence learns from human inputs or behaviors over time
- Automated intelligence offers fully machine-based decision-making
A business may choose to employ any or all of these types of AI in meeting its automation goals. Each should be used in support of improving departmental performance and meeting pre-established KPIs, not simply replacing human hands and minds.
Intelligent automation can look like a balanced trifecta of Business Process Management (BPM), Robotic Process Automation (RPA), and AI. BPM automates workflows and connects systems, while RPA relies on bots or virtual assistants to perform low-level repetitive tasks. AI extrapolates from available information and makes predictive choices accordingly. All three can work in tandem to successfully implement automation.
RPA bots are constrained by the examples and rules set by their human users, but AI can go beyond these limitations by learning over time and adjusting responses through successive iterations. Process mining for BPM is made much easier with AI’s analytical capabilities.
Is AI the same as automation?
AI acts as a virtual facilitator for many business operations, including (but not limited to) automation. It can be inserted wherever speed, consistency, and precision are needed.
AI supports automation initiatives by combing through available data, analyzing changes over time, making recommendations, building low-code/no-code user apps, generating visual interfaces, and orchestrating workflows between departments.
If we can describe automation as a car that gets us to our destination faster, AI would be the onboard computer that offers improved navigation, accurate diagnostics, and advanced maintenance recommendations.
Benefits of automating business processes with AI
According to research by PwC, Global GDP may see a boost of up to 14% in the next six years due to the involvement of AI. By 2030, it’s believed that increased AI may contribute upwards of $15.7T in increased productivity and consumption side impacts to the global economy.
AI frees humans to meet more complex needs, such as engagement, strategy, and innovation. Its integration with automation is already bringing about profound transformations in several areas of business.
Intelligent automation is especially well-suited for improving customer service. Because of its 24-hour availability, AI can handle far more tickets than a human staff, greatly reducing backlogs and customer wait times. Chatbots can answer basic questions and deal with low-level issues, escalating requests to a human agent only when needed. AI-assisted language detection and translation tools expand the global reach of real-time support.
AI can be taught to emulate human conversation, making user experiences much more intuitive and enjoyable. Working with natural language prompts widens the scope of participation across departments, as employees don’t need coding experience to begin benefitting from new apps or platforms.
AI can also be trained to reclaim valuable legacy data. Intelligent document processing (IDP) culls information from an ocean of unstructured material, such as lists, ledgers, purchase orders, handwritten notes, invoices, and other forms. Intelligent image scanning opens up other types of potential information inputs.
Cost savings
AI serves to reduce operational costs in several important ways. Increased accuracy leads to better resource management, cutting duplicate work and unnecessary revisions. Faster turnaround times mean fewer labor hours, and streamlined workflows keep projects within budget. AI-assisted quality control slashes waste by minimizing errors.
Since AI works 24/7, it removes the need for worker breaks, holidays, and multiple shifts, extending business hours across time zones and geopolitical borders. Virtual workers can handle a much higher workload in far less time.
The use of AI also minimizes the need for data entry by human hands. Bots can collate and validate information, perform simple troubleshooting, and simultaneously handle multiple requests, allowing salaried professionals to focus on higher-level tasks during business hours.
Predictive maintenance becomes much easier with AI-generated reminders and automatically scheduled repairs.
Faster decision-making
AI supports human decisions with scalable simulations and improved trend forecasting. It can swiftly crunch and categorize tremendous amounts of data, summarize it, and use prescriptive analytics to make targeted recommendations. Such analysis can identify obstacles and pinpoint areas that need reworking, dropping the need for expensive outside consultancy. Companies can use AI-generated insights to map out areas where faster decisions are needed.
Agility is key to any company’s long-term success. Teams must respond swiftly to risks and make informed moves based on up-to-the minute data. Dynamically generated reports allow departments to reprioritize tasks, adjust resource allocation, and change process configurations.
Readiness to leave a decision to AI may depend on several factors, including time limits, budgetary constraints, and the complexity of the issues at hand. Many simple and routine decisions can easily be handed over to AI, but some scenarios will require more nuance, emotional intelligence, and awareness of interdependencies. Full decision automation may be fast and consistent, but leaving humans out of the loop altogether is not advisable. Humans are likely to have a more holistic understanding of how a decision may affect other parts of a system.
The purpose of AI is to assist action and expand capacity. With the right training and continued improvements in machine learning, AI will adapt over time to more closely imitate human thought.
In Pipefy’s recent Business and IT Leader Survey, 48% of the executives polled anticipate that the application of generative AI to process and workflow automation will yield “better decision making.”
Better process efficiency and excellence
The first stage of optimization is process discovery, and AI can be an effective tool for parsing complex systems. Because AI is perfectly suited for rapid pattern discovery, it can be used to chart process frequency and complexity, and suggest possible interventions or enhancements. Turning AI towards the analysis of historical data can help identify areas where existing resources are being overextended.
Prior to investing in automation, a company first should determine which working processes are ready to be, or should be, sped up. Repetitive tasks, especially those that follow clear rules and require minimal human intervention, make especially good candidates for AI-assisted automation.
Using AI to optimize processes can result in better time management for employees, more consistent outputs, and stronger performance overall. AI can make good processes more efficient, but only purposeful application will yield excellence.
Process standardization
Reliable and sustainable procedures make it easier to establish and assess KPIs. Standardization helps companies reduce waste, eliminate redundancy, remove bottlenecks, and remain in compliance. With AI-assisted standardization, employees spend less time reconciling irregularities or improvising remedies. AI can also aid in process documentation by building flowcharts, infographics, and other visual tools.
By using AI to map out departmental workflows, automation functions can be based on metrics rather than guesswork. AI can be used to assess large volumes of data and user behavior, then make informed recommendations about where to conform or converge processes.
Impromptu workarounds and idiosyncratic approaches to problem-solving often lead to data silos and impenetrable shadow IT. In contrast, using shared processes that are supported by AI will result in increased transparency, more consistent outcomes, and faster fixes.
Challenges of adopting AI automation and how to overcome them
For all of its power and promise, automation will not fix fundamentally broken processes. Any successful improvement depends upon a stable foundation and functional workflows already in place. AI can tag those processes which should be standardized before automation.
There may be some cultural resistance to change, especially in companies that have relied for too long on legacy systems and haphazard workarounds. Users may initially feel skeptical about AI or automation. The best remedy for such friction is active participation. Employees who feel empowered to utilize these new tools and to propel them forward in their daily work will reap the greatest benefits, and executives who are thoroughly briefed on the long-term objectives of automation are more likely to sustain their support.
Lack of an overall vision will lead to wasteful spending and stalled initiatives. When turning to AI as a component of automation, it’s important to invest in the right technologies, and to thoroughly understand their intended applications prior to purchase. Preliminary research and mapping should take a high view of end-to-end processes and data flow between departments, looking towards those areas which can be enhanced by speed, precision, and consistency.
Every initiative will have its attendant costs. Companies looking to adopt AI for use in automation should first investigate where automation can realistically drive savings, and measure initial expenses against long-term benefits. Companies must compare the costs of implementation, new tech advancements, and any necessary skill hiring against projected ROI.
No revolution is without risk. How vulnerable is your business as it stands? How will you build transparency into your automation initiatives? Do you have the right talent in place, or do you need to acquire it?
Not all jobs can be automated, and general purpose tech may not be appropriate for every process.
According to a 2024 MIT study (along with The Productivity Institute, and IBM’s Institute for Business Value), only 23% of worker tasks can be automated without incurring additional costs.
The shift to automation is a continuous effort, never a “one-and-done” solution. Results should be regularly validated and refined through rigorous testing, and any contributions made by AI must be given special scrutiny.
Any digital transformation depends upon thoughtful integration of new technology with existing resources. Look to AI as a support system for problem-solving, rather than a standalone solution in itself.
Data privacy
Foundational models have been pre-trained on sets of data, and are generally self-supervising. Deep learning models can be further adapted for downstream tasks. All AI models are trained on enormous quantities of information, much of it derived from public data and web-scraping. Managing this dataset means carefully screening for bias, inaccuracy, or even toxicity. Some material may violate copyright, license restrictions, or intellectual property laws.
Disseminating sensitive information or collecting data beyond the declared scope can invite distrust among users and clients. Vigilant protection of customer transaction data may mean restricting its use in an AI environment.
Data generated by the Internet of Things is rapidly outpacing the information gathered from user experiences alone. AI can exploit this information for positive benefit, but companies have a responsibility to secure the privacy and safety of their customers.
Labor market impacts
AI has already seen explosive growth in several sectors, including healthcare, finance, automotive, logistics, retail, and manufacturing.
What role will people play in this shifting landscape? New personnel with current skills will be needed to implement and maintain AI technical support. In the coming years, we will see a skyrocketing demand for data scientists and engineers, while there will be a commensurate lessening of need for low-level data entry, call center staff, and other types of repetitive labor.
Businesses should already be conducting explorations of development in areas that can’t be replaced by AI, such as thought leadership, supervision, creativity, and ethics. Strategic hiring may become a matter of filling the spaces that exist between processes, below principles, and above workflows.
Employee acceptance and training
The hybrid workforce of the future will employ both AI and humans, working together to achieve business objectives. Employees can be encouraged to see AI training as empowering, skill-building, and an opportunity to foster a collaborative approach to problem-solving.
The use of low-code/no-code development encourages reskilling among the existing workforce, and AI is forming an increasingly consequential part of this shift. Writing a prompt is far easier than working with code, and the learning curve with AI tools is generally lower than it would be with many other types of tech adoption.
Bringing AI into automation allows a wider range of users to be involved in development, democratizing the process of workflow optimization and enabling cross-channel innovation.
AI automation examples
Automation follows rules and patterns established beforehand by human users, but its virtual workers can be further trained to form reactive solutions and predictive models of their own.
Manufacturing and freight are already seeing huge gains from the use of AI because their processes readily lend themselves to automation. Bots can be trained on supply chain data, learning how to monitor inventory spikes or predict parts starvation. Automated alerts can flag freight capacity issues and monitor weather delays or other disruptions, allowing companies to quickly locate alternative carriers.
Banks are increasingly turning to AI to assist customers, evaluate transaction data, and flag potentially fraudulent or unusual activity. AI aids in creating customized investment solutions and portfolio development, and it heavily shapes customer experiences in online transactions. In the volatile world of investments, AI’s ability to analyze historical data results in stronger predictive models and better threat detection.
The insurance industry has embraced AI automation, especially for claims processing, underwriting, regulatory compliance, risk management, and fraud prevention. It can be used to sort policyholder data, and keep up with customer preferences.
Beyond productivity gains, AI-assisted analysis has become an indispensable part of shaping customer expectations. AI has already revolutionized shopping, with more data touchpoints leading to better recommendation engines. It can chart customer demand, preferences, and purchasing habits. Online retailers are offering expanded personalized options with AI assists, and products can now be designed with customer tastes in mind. Because AI is designed to capitalize on large data sets, it can be used to segment customers by demographic and make sales projections based on past behaviors.
IT
AI automation helps IT by empowering users to build and manage their own workflows. By shifting the burden of problem solving and process optimization to other departments, IT teams are given more leeway to focus on larger issues, such as security, stability, and strategy.
AI’s use of cloud services speeds updates and machine imaging. It saves physical space in facilities by removing on-premises hardware. This is especially helpful when meeting storage-heavy needs, such as media archiving or content management. AI can help build the processes that distribute assets in the cloud, pulling IT employees out of server closets and into higher executive functions.
The performance of any AI tool is tied to the quantity and quality of available data. Before an AI program can be turned loose on banks of information, IT will need to study this material’s sources, evaluate its integrity, and determine how it will be utilized. Since AI platforms are adjustable based on dataset size, availability of computing power, and number of parameters, IT will likely be tasked with managing each of these variables.
The IT Guide to Workflow Management
Easily build and automate any workflow for any team.
Digital transformation centers on bolstering best practices with the right technology. A CIO and their IT team must pre-determine how such a tool will be integrated into the existing tech stack, who will have access to it, and how existing resources would best benefit from AI assists.
Procurement
“Spend management” is the ongoing analysis and refinement of a company’s procurement strategy. Continuous audits serve to save money, reduce risk, and extract maximum value from a company’s relationships with its suppliers.
AI can help procurement teams by classifying requisitions, sorting invoices, managing or renewing contracts, and flagging discrepancies. It can predict spending trends, and offer quick pivots in response to volatile market prices, problematic locations, or poor vendor performance.
Sourcing is a complex part of procurement, often involving a lot of form-filling and approvals. AI helps manage sourcing by fulfilling repetitive tasks, collating vendor data, and basing suggestions on reliable metrics.
Over time, the focus of AI development may shift to industry-wide applications rather than targeting needs of individual firms. Since AI creates scalable services, allowing for rapid growth at a microeconomic level, such customizable approaches may benefit small businesses who don’t have large in-house resources.
HR
While it may sound counterintuitive, human resources is a business area that benefits tremendously from automation. Though AI tools are meant to augment human efforts rather than replace employees, automation can still save HR departments a great deal of time and labor. Automation frees up staff by eliminating manual data entry and repetitive steps.
HR departments around the globe are already using AI for validating and updating records. For hiring, AI can quickly scan resumes, schedule interviews, conduct preliminary screenings, and match suitable candidates with position requirements. Employee engagement and onboarding can be simplified and standardized. AI tools can assess performance and monitor fluctuations in attendance or productivity. Customizable templates and automated document classification help HR departments keep on top of their personnel needs.
How to start using AI automation at your company
Strategic planning is essential to the maturation of any enterprise. It’s important to conduct extensive research prior to pulling the trigger on a major tech overhaul. Trying to launch too many initiatives at once may inadvertently end up creating more data silos, so choosing one process to automate at a time helps keep efforts manageable and measurable.
Process discovery (evaluating human behaviors) and process mining (evaluating data) will prove invaluable to any successful deployment. Map out the processes to be automated and seek employee feedback throughout. Study use cases, and outline the most desirable outcomes before weighing potential tech purchases.
Human oversight will become increasingly essential as we trust more of our lives to AI. Compliance is necessary in heavily regulated industries, and good governance reduces reputational risk. Keeping humans in the loop helps align intelligent automation with business objectives.
Monitor how AI is working for your company. How does it perform over time? How is its behavior changing? Look to audit trails, routine testing, and clearly defined performance metrics to help in tracking KPIs.
Here are some key steps you can take to bring the power of AI into your automation initiatives:
- Define goals ahead of time by consulting your stakeholders
- Establish use cases and identify potential value of historical data
- Determine the priorities for automation
- Weigh any potential security or safety risks
- Select the right technology for the intended purpose
- Hire the right team members
- Prepare for training and upskilling
- Look at the big data picture, including capture methods and repositories
- Try out a pilot program, and scale it over time
- Continuously monitor the results, and test for validity
- Establish firm governance policies
Leveraging AI automation with Pipefy
Pipefy is a no-code automation platform with AI capabilities that help businesses streamline operations across business lines to enhance productivity, ensure accuracy, and conserve IT resources.
Pipefy AI’s powerful ChatGPT-based technology gives business leaders a complete tool kit for making data-driven decisions at any given time by providing real-time analytics and easy access to insights about their processes – faster than ever. They simply tell the chatbot prompt what they need and, within seconds, receive a ready-made, fully customizable workflow!
Start making better decisions and identifying inefficiencies today with one AI-powered tool!
