AI + automation: Why AI is the next step for automation
AI automation combines artificial intelligence (AI) with automated systems to make processes more efficient. By bringing together AI technologies with automation tools like RPA, organizations can accelerate workflows, improve decision-making, and rely less on human input.
What is AI automation?
Often referred to as Intelligent Automation or hyperautomation—a term coined by Gartner—AI automation represents the integration of advanced automation capabilities with AI technologies. AI extends the reach of automation by enabling systems to learn, adapt, and make informed decisions, accelerating every stage of the automation lifecycle.
The potential of combining AI technologies with automation tools like robotic process automation (RPA) is immense, offering businesses new pathways to efficiency and innovation.
AI automation vs BPM, RPA, and other technologies.
While traditional automation and business process management (BPM) focus on predefined rules, AI-powered automation allows systems to adapt and learn. Because AI excels at making predictions and decisions, while automation tools reliably handle execution between or within systems, it can infuse automation with flexibility to respond to business needs and conditions that are constantly changing.
AI-driven automation is built on the seamless integration of AI with automation technologies including RPA and BPM, that together form the foundation of Intelligent Automation. Unlike traditional automation or BPM, which rely on rule-based approaches, AI introduces cognitive capabilities to automation, allowing systems to understand, learn, and make decisions. This cognitive layer is what defines AI-driven automation, enabling it to handle complex tasks that require adaptability and insight.
AI agents are the next big thing in this ecosystem. AI agents embody the combination of cognitive abilities for planning, predicting outcomes, and making decisions, with the ability to take action across enterprise systems, all while continuously learning from the data they process.
How AI and automation come together.
As both AI and automation technology continue to evolve, organizations and solution providers are discovering more and more opportunities to take advantage of the ways they complement each other and extend the benefits of AI to new use cases.
Human Resources: Recruiting
AI's role: AI analyzes resumes and application data to predict the best candidates for a job based on skills, experience, and fit.
Automation's role: Once AI identifies top candidates, automation systems schedule interviews, send follow-up emails, and update candidate tracking systems.
Sales: Forecasting
AI's role: AI models predict future sales trends by analyzing historical sales data, market conditions, and customer behavior to make strategy decisions.
Automation's role: Based on AI's forecasts, automation adjusts sales strategies, updates CRM systems, and triggers targeted marketing campaigns.
IT security: Threat detection
AI's role: AI systems predict and identify potential cybersecurity threats by analyzing network traffic and user behavior patterns.
Automation's role: Upon detecting a threat, automation systems execute security protocols, such as isolating affected systems, alerting IT teams, and initiating data backup processes.
Benefits of combining AI with automation.
Applying AI with automation delivers wide-ranging benefits for enterprises that ultimately drive operational transformation.
Increased efficiency tops the list of benefits, but the impact of faster, error-free, streamlined processes extends beyond efficiency gains. By enabling the automation of not only routine tasks but also complex and multi-function workflows, applying AI and automation allows businesses to allocate resources more effectively and focus on strategic initiatives that drive growth and innovation. This efficiency-driven shift can both boost productivity and reduce operating costs.
At both strategic and tactical levels, AI's ability to quickly process and analyze data at scale can redefine the speed of decision-making, enabling enterprises to respond to change in real time.
Combining AI with automation drives increased accuracy across operations, minimizes errors, and improves overall performance. Precision execution of high-volume data-driven processes is particularly valuable in industries where compliance and accuracy are critical, such as finance and healthcare.
Scalability and adaptability are additional benefits of AI-powered automation, allowing enterprises to manage mixed system environments. Because AI systems learn and adapt, they can handle a wider range of tasks, from simple data entry to complex decision-making processes, providing on-demand operational flexibility that enables organizations to quickly scale and accommodate growth and evolving business needs.
Impact of AI and automation on non-technical jobs.
The emergence of generative AI has accelerated the impact of recent changes to the nature of work, as organizations have sped up their adoption of automation technologies.
Today, the potential of AI and automation extends far beyond traditional technical roles and tasks to work not typically associated with automation, such as customer service and creative industries. In these fields, AI, and in particular generative AI, can augment human capabilities rather than replace repetitive task execution.
In customer service, for example, AI can work alongside human agents to surface expert-level guidance and execute multi-system data acrobatics to enable agents to focus on the customer interaction, enabling them to handle more complex issues that require empathy and nuanced understanding. At the same time, AI-powered automation can independently handle routine customer inquiries and provide instant responses, improving the customer experience while freeing up human resource time.
Similarly, in creative industries, AI can assist in generating ideas or content, allowing human creators to focus on refining and perfecting their work.
AI for unstructured tasks and workflows
Traditional automation systems rely on predefined rules and structured data, which means they hit a wall when facing complex and unstructured workflows. AI, however, is a perfect match for unstructured processes and environments where data is often messy, inconsistent, or incomplete.
AI excels at processing and interpreting unstructured information, such as text, images, and audio, using advanced techniques like natural language processing (NLP) and computer vision. This allows AI models to understand and extract meaningful insights from data that would otherwise be inaccessible to conventional automation systems. By processing unstructured information, AI empowers enterprises to accelerate workflows that were previously considered too complex or variable to automate.
In the insurance industry, for example, AI can analyze unstructured data from claims forms, emails, and customer communications to automate the claims processing workflow. By interpreting the context and content of these documents, AI systems can make informed recommendations about claim approvals, reducing processing time and improving accuracy.
Similarly, in customer service, AI-driven chatbots can handle inquiries that involve unstructured conversations, understanding customer intent and providing relevant responses.
In healthcare, AI can process unstructured data from medical records, lab reports, and imaging studies to support clinical decision-making and automation of administrative tasks. By extracting critical information and identifying patterns, AI systems can assist healthcare professionals in diagnosing conditions, recommending treatments, and better managing patient care.
Optimizing human-AI collaboration
By maintaining a balance between automation and human input and oversight, enterprises can harness the potential of AI while ensuring trust in the integrity of AI-driven decisions. The "human-in-the-loop" model, where human input refines and enhances automated systems, is particularly important in scenarios where AI-driven decisions need validation or adjustment based on human judgment.
Processes that leverage human-AI collaboration can increase the time available for human interaction where there is high interaction value, such as in customer experience.
In all cases, optimizing human-AI collaboration is a core factor in the success of workflows supported by AI and automation. Best practices to drive effective collaboration include embedding access to AI-driven automations directly within work applications and providing training in both the use and purpose of the technology, including understanding the AI system.
Providing training on both the effective use of the AI system and its underlying mechanics, including data sources, can greatly enhance successful human-AI collaboration. Understanding what the system does—its purpose and intended uses—enables employees to evaluate the AI performance critically and identify possible issues or biases. This awareness makes it possible for everyday users to determine if AI operates as intended and aligns with organizational goals and standards.
Optimal use of AI is further supported by robust oversight mechanisms. Effective oversight of AI systems requires a multifaceted approach that includes continuous monitoring, real-time analytics, regular audits, and impact reviews. Continuous monitoring involves keeping a constant watch over the AI system's operations to detect anomalies, performance issues, or deviations from expected behavior. Regular audits examining the system's algorithms, data usage, and decision-making processes help to maintain transparency and accountability.
At the organizational level, impact reviews that assess the broader effects of AI systems are essential for ensuring their long-term success and safe application within enterprise operations. Assessing whether the application is achieving its intended objectives and identifying any unintended consequences on employees, customers, and broader enterprise operations helps inform the overall AI and automation strategy. This process also drives adjustments or improvements to the AI system, ensuring it continues to deliver value while minimizing potential risks.
Adaptive AI - how AI learns over time
Augmenting and automating complex and creative work hinges on the ability of AI systems to adapt over time and improve on their own through continuous learning processes.
At the heart of adaptive AI are advanced learning mechanisms such as machine learning and reinforcement learning, which empower AI systems to refine their performance and decision-making capabilities without constant human intervention.
Machine learning enables AI to identify patterns and extract insights from enormous datasets, allowing it to make predictions and decisions based on historical data. And as AI systems process more data, they continue to learn and improve accuracy and efficiency.
Reinforcement learning allows AI systems to learn from interacting with their environment. That means when they receive feedback on their actions, these systems can adjust strategies to maximize desired outcomes. This age-old trial-and-error approach enables AI to adapt to new challenges and optimize its performance over time, which is particularly valuable in the context of creative and complex tasks where solutions are not always straightforward.
Examples: Companies using AI and automation.
In the enterprise business landscape, there are countless compelling examples of how AI and automation are transforming functions by working together to optimize processes and execute complex tasks. Applying the combined power of AI and automation to new enterprise use cases continues to deliver jaw-dropping results. No matter the industry, companies are seeing a profound impact on both efficiency and innovation through AI and automation.
Petrobras, a Brazilian multinational energy giant, took a comprehensive approach to integrating AI and automation into its operations, led by their CIO who championed the implementation of advanced automation and generative AI technologies. The application of AI-driven automation in the tax department alone resulted in savings of $120 million in just three weeks and a significant increase in efficiency.
A global leader in sustainable mining, Vale harnessed AI to automate process discovery. In the span of a few days, generative AI identified and documented five high-impact processes to automate, which would have otherwise taken months to surface. This rapid process discovery, which was 89% faster than traditional methods, not only saved 121,000 hours but also resulted in $5 million annual savings for the organization.
In healthcare, AI and automation can improve process efficiency and operational accuracy while simultaneously amplifying human value. A case in point is the NHS Hospital's unique application of Intelligent Automation. The hospital implemented a first-of-its-kind automation to monitor oxygen supplies 24/7. This automation significantly reduced both error risk, elevating patient care standards and resource burden, freeing up 1,500 hospital staff hours.
As examples like these illustrate, AI and automation can make a profound impact in any industry to not only optimize and accelerate but also power innovation and operational transformation.The future of AI and automation will empower teams to build automations using natural language and deploy self-healing automations that adapt to system changes in real time, reducing failures and minimizing maintenance.
