AI automation utilises artificial intelligence technologies to automate complex tasks and processes. By leveraging these technologies, tasks that require human intelligence can be performed more accurately and consistently.
The concept of automation has fundamentally reshaped the landscape of business operations. Where historically, automation's role in business was confined to manufacturing, advances in digital technologies have allowed automation to expand beyond the assembly lines into every sector, streamlining processes and reducing human error along the way.
More recently, the advent and proliferation of effective artificial intelligence has marked a new era in this evolution. AI's ability to analyse large datasets, continuously learn from outcomes and make informed decisions is opening up unprecedented opportunities for businesses to streamline operations, drive efficiency at scale and innovate products and services tailored to customer needs.
With revolutionary capabilities demonstrated by modern AI and automation solutions, it was practically a foregone conclusion that these two technologies would be paired together to further enhance business functions. By merging traditional automation technologies with AI, organisations are not only automating routine tasks but are also building on their capability to engage in more complex, strategic business activities.
Although the terms AI and automation are often used interchangeably, they represent distinct (yet complementary) technologies. Understanding the differences between them is crucial for businesses looking to leverage these tools to achieve a competitive advantage.
Automation refers to the use of technology to perform tasks that would otherwise require excessive human intervention. Traditionally, these are repetitive, rule-based actions that can be programmed once and executed multiple times without variation. Robotic process automation (RPA) is a common form of business automation, handling basic tasks like data entry and invoice processing.
By way of comparison, AI involves creating systems that can perform tasks which typically require not only human intervention, but also human intelligence and problem-solving capabilities. AI's simulation of human intelligence is characterised by its ability to learn from data, adapt to new situations and make informed decisions based on its conclusions.
AI automation (and its related concept intelligent automation) combines the inherent advantages in both disciplines, leveraging the speed and reliability of automation with the problem-solving capabilities of AI. For example, while RPA can automate the process of entering data into a system, AI can take it a step further by analysing the data, making predictions based on it, and then generating insights to enhance human decision making.
This combination represents a major opportunity for businesses to automate many complex processes that require adaptability and learning—qualities that until recently were only found in human operators. The result is not just increased efficiency but also greater innovation, as AI-powered automation can identify patterns and insights that humans might overlook.
Just as AI is experiencing increased adoption worldwide, intelligent automation is poised to effect meaningful change across essentially every industry, allowing for unprecedented levels of productivity and innovation. As this technology permeates deeper into various sectors and disciplines, it redefines what is possible, opening the door to a new era of operational excellence.
In many sectors, this change has already begun. Consider how AI automation is improving the following business types:
Agriculture
In agriculture, AI automation facilitates more precise farming practices. From automated drones that monitor crop health to AI-driven systems that optimise irrigation and pest management, technology is optimising how food is grown. These technologies increase yield while reducing the need for chemical interventions.
Automotive
The automotive industry benefits significantly from AI automation in areas such as production and quality control. Automated machinery equipped with AI can predict equipment failures, streamline the assembly line and ensure stringent quality standards. Additionally, AI plays a prominent role in the development and application of autonomous vehicles.
Finance
AI automation in finance involves using algorithms to analyse markets in real-time to execute trades. Furthermore, the technologies can be extended to better identify patterns of fraud and handle more customer service inquiries through chatbots that can kick off automated customer service processes.
Healthcare
In healthcare, AI automation is used to analyse medical data, assist in medical processes and personalise patient care plans. Automated systems can more effectively and accurately manage patient records, while AI-driven diagnostic tools help in early detection of diseases such as cancer, enhancing the effectiveness of treatments. In the coming decades AI-powered robotic systems will handle aspects of surgery.
Insurance
AI automation reduces the need to calculate rates manually. AI algorithms can also quickly analyse data to detect fraud, assess damages and expedite claims, significantly improving customer satisfaction and operational efficiency.
Life sciences
In life sciences, AI automation accelerates drug discovery and clinical trials by analysing vast datasets to identify potential drug candidates and predict their efficacy. This technology does more than simply accelerate the time to market for new treatments, it also eliminates the risk of errors in following precise documentation for pharmaceuticals manufacturing.
Manufacturing
Within intelligent automation, manufacturing can benefit from optimised production lines, reduced downtime through predictive maintenance, and enhanced inventory management. Additionally, AI automation systems analyse production data to forecast machine failures before they occur, minimising disruption and maintaining continuous production flow.
Marketing and advertising
AI automation in marketing and advertising enables personalised customer experiences by utilising behavioural analysis to tailor advertisements and product messaging. AI automation can also predict which ads are most likely to convert with astounding accuracy, maximising the ROI on advertising budgets.
Retail
In retail, AI automation personalises shopping experiences and optimises supply chain logistics. From AI-powered recommendation engines to automated inventory management systems, these technologies help retailers ensure that they have the right supply on hand to meet consumer demands more effectively and efficiently.
Transportation
AI automation improves planning and fleet management in the transportation industry. Automated routing systems optimise delivery paths and schedules, reducing fuel consumption and improving delivery times.
AI automation blends various technologies to create systems that automate tasks as they simultaneously learn to make the processes more efficient. Each element plays an essential role in these processes, from data processing to decision-making and beyond.
The key elements that make up intelligent automation include:
Machine learning algorithms
Machine learning algorithms are fundamental to intelligent automation, enabling systems to learn from data and recognise patterns. These algorithms adapt over time, improving their accuracy and efficiency as they process more information. They are crucial across all industries, conducting tasks ranging from customer behaviour prediction to fraud detection.
Business process management/intelligent process orchestration
Business process management (BPM) and intelligent process orchestration extend beyond simple task automation by optimising and coordinating automated workflows. They help ensure that business processes are executed in the most efficient order, integrating various automated tasks into coherent, streamlined operations that align with organisational goals and strategies.
Robotic process automation
As previously addressed, RPA is used to automate repetitive, rule-based tasks that once required human intervention. By handling routine tasks, RPA frees up the AI (and humans) to focus on more complex and demanding actions.
Computer vision
Computer vision technology enables machines to interpret and make decisions based on visual data. Using image recognition and object detection techniques, computer vision is applied in various scenarios, such as quality control in manufacturing, where it can identify defects in products faster and more reliably than human eyes.
Natural language processing
Natural language processing (NLP) allows machines to understand and generate human language, making it possible for chatbots and virtual assistants to interact with users in a natural, conversational way. NLP aids in automating customer service tasks, analysing sentiment in social media posts, and extracting useful information from large volumes of unstructured text data.
Advanced analytics
Advanced analytics involves the application of sophisticated analytical techniques and tools to process and synthesise complex data sets, going beyond traditional data analysis. The discipline uses different techniques, including predictive analytics, prescriptive analytics, descriptive analytics and diagnostic analytics. Like machine learning, advanced analytics evolves with more information.
As AI automation becomes a transformative force across industries, it naturally brings with it a set of challenges that organisations need to navigate. These challenges stem from the complex interplay of technological factors, human factors and regulatory environments. Addressing them effectively is crucial for organisations to maximise the benefits of AI automation.
One of the primary challenges of AI automation is ensuring data privacy. As systems become smarter and more interconnected, they require greater access to more sensitive data. Unfortunately, this access increases the risk of data breaches and unauthorised access. Organisations can mitigate these risks by implementing comprehensive cybersecurity measures, such as data encryption, regular security audits, zero-trust security models etc. Educating employees about phishing and other cyber threats can help reinforce an organisation's cybersecurity framework.
AI automation can lead to disruptions in the labour market, as certain jobs are transformed or become obsolete. To address this, organisations can invest in retraining and upskilling programmes to help employees transition into new roles that support and complement AI technologies. At the same time, fostering a culture that values continuous learning and adaptation can ease the transition and reduce resistance from employees.
The transparency of AI decision-making processes is a paramount concern. There is a concept in complex AI systems known as 'black box', which indicates a lack of clarity about how AI decisions are made. To combat this, organisations can adopt principles of ethical AI use, which include designing systems that are explainable and auditable. Engaging stakeholders in discussions about the ethical use of AI and ensuring that AI systems are designed to be as transparent as possible will help build trust and accountability.
Integrating AI automation into existing systems poses significant technical and operational challenges. Existing systems are often complex and require careful planning and execution to ensure proper compatibility with intelligent automation solutions. Organisations can overcome these hurdles by employing modular integration approaches, where AI systems are gradually implemented in stages. Partnering with experienced IT service providers can likewise provide the necessary expertise to ensure a smooth integration.
AI is a relatively new advancement, and regulatory bodies are rushing to catch up. Organisations must stay informed about new regulations so that they may ensure compliance and avoid legal penalties. Implementing a compliance management system that is agile and adaptable to changes in the legal landscape is crucial. Regular training and updates for the legal and compliance teams can also ensure that the organisation remains ahead of evolving laws.
Challenges aside, the transformative potential of AI automation offers a massive opportunity to businesses that can successfully implement it. Successfully implemented, intelligent automation can lead to:
AI automation significantly boosts productivity by automating a range of tasks—from the simple and repetitive to the more involved and complex. This shift speeds up workflows and helps to optimise the use of human resources, as employees can redirect their efforts to contribute in more meaningful ways.
AI-driven automation systems enable businesses to provide personalised customer experiences through targeted communications, recommendations and support. AI-powered chatbots can deliver instant customer service around the clock, addressing inquiries and resolving issues much faster than traditional methods. Additionally, AI automation improves the mean time to resolution (MTTR) in customer support and accelerates the time to market for high-quality products.
AI automation promotes rapid decision-making by processing and analysing large volumes of data much faster than humanly possible. With advanced analytics and machine learning, AI can identify trends, forecast outcomes and suggest actionable insights, thereby accelerating the decision-making process and enhancing business agility.
By automating routine tasks, AI automation minimises unnecessary expenses and improves resource allocation. This amounts to significant savings, especially in labour-intensive industries.
AI systems reduce human error by consistently performing tasks to the exact standards set during their programming. This improved accuracy is particularly beneficial in fields like data processing, where precision is crucial. Improved accuracy leads to better quality control, fewer reworks and enhanced compliance with industry standards and regulations.
As AI systems are capable of handling increasing volumes of work at a consistent quality, businesses can scale their operations without the proportional increase in labour costs or other resources. This scalability is vital for companies looking to expand their market presence or wanting to adjust to seasonal demands without compromising on service quality or operational efficiency.
The processes behind AI automation involve seven key steps, each critical to the development and execution of AI-driven solutions. Below is a breakdown of each step involved in AI automation:
1. Data collection
The first task involved in AI automation is gathering relevant data from various sources. This process could include databases, sensors, social networks, customer forums or any digital interactions that provide valuable insights. The more data that is included, the more effective the subsequent training is likely to be.
2. Data preparation
The collected data needs to be prepared in a way that the AI model can use for training and analysis. Preparation involves cleaning the data by removing outliers and inconsistencies along with formatting it to ensure compatibility with AI tools.
3. Model training
With the data prepared, it is time to train the model using machine learning algorithms. These algorithms learn from the clean, annotated data to recognise patterns, relationships and potential anomalies. Technologies like NLP and deep learning are instrumental in this phase, aiding in text analysis and ensuring the model can apply learnt patterns to future data analysis.
4. Data processing
The trained model can now be applied to new datasets to perform tasks such as social listening, customer feedback analysis or detecting anomalies.
5. Continuous learning
AI systems are designed to continually learn and adapt. As the model processes new data, it updates its understanding, refining its predictions and operations based on the latest information.
6. Deployment
Once the model is trained and tested, it is ready to be deployed into the actual business environment. Deployment involves integrating the AI model into existing systems and automations, where it can start automating business processes.
7. Ongoing monitoring and maintenance
The last step in the AI automation process is continuous monitoring and maintenance. Regularly checking the system's performance and making necessary adjustments help to ensure that it continues to operate optimally.
Although there are many reasons to be optimistic regarding how AI automation will enhance jobs within various industries, the truth is that this innovative technology is likely to disrupt the future of work in many ways.
In the coming years, humans will focus most on tasks that involve major creativity and emotional intelligence, while AI automation will handle more and more of the mundane responsibilities. With many routine and repetitive tasks being automated, most jobs will change—some roles will be eliminated while others evolve to better incorporate intelligent automation. At the same time, new types of roles are likely to emerge in support of automation solutions and the new capabilities they represent.
As with any technological revolution, workers will always remain central to success. That said, many current workforces will need to prioritise continuous learning if they are to remain relevant in the job market of tomorrow.
AI automation is transforming business processes, bringing increasingly complex tasks and end-to-end processes under the ever-growing umbrella of automation. Embrace this shift and help your business grow, with ServiceNow's ServiceNow AI Platform.
The ServiceNow AI Platform integrates deep learning, natural language understanding, intelligent document processing and predictive analytics to deliver a complete end-to-end platform for intelligent automation. The platform includes generative AI, machine learning, natural language processing, AI search, robotic process automation, API integrations, analytics, process mining and more—all working together to boost the efficiency of your essential workflows.
And because intelligent automation should be available to more than just computer scientists, the ServiceNow AI Platform is designed for users at all technical levels, making powerful AI tools available without the need for extensive training.
See for yourself what the future of intelligent automation holds for your organisation; demo ServiceNow today!