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Artificial Intelligence vs. Intelligent Automation: What’s the Difference?

Roland Alston, Appian
May 3, 2024

Intelligent automation (IA) and artificial intelligence (AI) are must-haves for any organization seeking to unlock business value at the intersection of automation and AI, from automated chatbots managing customer feedback to autonomous systems predicting customer orders. This blog explains the difference between intelligent automation and AI and highlights how IT leaders are harnessing the synergy of these technologies to automate processes, grow their businesses, and engage customers like never before.


[Learn more about the benefits of combining low-code process automation and AI.]


AI enhances process automation by leveraging intelligence for greater business impact.

AI injects “intelligence” into automation, enabling systems to execute tasks, comprehend complex data, make informed decisions, and learn from outcomes. Unlike technologies such as robotic process automation (RPA), which follow predetermined rules, AI leverages data to evaluate situations and determine the best course of action.

Now that we've explored how AI augments traditional automation tools, let's delve deeper into the realm of intelligent automation. 

Intelligent automation goes beyond mere automation by integrating AI, RPA, data fabric, and other cutting-edge technologies to revolutionize business processes. By understanding the intricacies of IA, you can better grasp how these technologies work synergistically to drive business value.

What is intelligent automation?

Intelligent automation transcends isolated automation solutions. It’s powered by a strategic blend of AI, RPA, data fabric, and other technologies to automate complex business workflows. 

For instance, AI improves tasks like automated document classification, where systems securely train private AI models to classify documents and extract information. Additionally, AI influences robotic process automation (RPA), allowing bots to auto-generate workflows and handle complex tasks.


For example, Appian process automation leverages the latest AI services with prebuilt connectors and integrations. 


How are AI and intelligent automation different?

Intelligent automation and AI serve distinct yet complementary roles. Intelligent automation primarily focuses on automating repetitive tasks and workflows, streamlining processes, and improving operational efficiency. This work may use advanced technologies, including AI, RPA, machine learning, and data fabric. 

By contrast, AI is a technology that enables machines to simulate human intelligence with the help of technologies like machine learning and large language models (LLMs), which allow automated systems to interpret complex data, make decisions, and learn from outcomes autonomously.


Intelligent automation uses a variety of technologies.

  • Artificial Intelligence (AI): AI technologies power intelligent decision-making and cognitive capabilities, allowing systems to analyze data, recognize patterns, and make predictions. AI enables automation solutions to adapt and learn from data, driving continuous improvement and optimization. 
  • Robotic Process Automation (RPA): RPA automates repetitive and rule-based tasks, freeing up human resources for more strategic initiatives. By mimicking human actions within digital systems, RPA enhances efficiency and accuracy across various business processes. 
  • Low-Code Development Platforms: AI process platforms that take a low-code approach are purpose-built to operationalize intelligent automation in end-to-end processes. By abstracting complex programming tasks, low-code platforms accelerate the development cycle and enable business users to collaborate with IT in application development. 

Can AI replace traditional automation tools?

AI doesn’t replace traditional automation tools; it enhances them. For example, AI enables systems to react to unforeseen situations without human intervention. In contrast, traditional automation tools, such as robotic process automation (RPA), operate within rigid programmed parameters. 

Traditional automation follows predetermined rules, but AI uses data to evaluate situations and adapt automatically. AI’s ability to evaluate situations, consider options, and choose the best course of action based on data distinguishes it from traditional automation tools. It’s true that AI enhances traditional automation tools, but it's crucial to recognize that AI comes in various forms, each serving unique purposes.

Discriminative and generative AI represent two fundamental approaches within the AI spectrum, each offering distinct capabilities and applications. Let's explore the differences between discriminative and generative AI to gain a deeper understanding of how these technologies complement intelligent automation.


[8 experts weighed in on enterprise AI. See their insights in the 2024 AI Outlook.]


These 3 real-world use cases highlight the benefits of intelligent automation and AI.

Use case #1: AI and automation streamline the customer experience.

In the age of hyper-personalization, innovative companies are pairing AI and intelligent automation to unlock business value. Consider, for example, the case of Leroy Merlin, a major home improvement retailer in Europe. It faced significant challenges in expediting its processes for returns, refunds, and exchanges due to manual approvals and data silos.

To overcome this challenge, the company implemented robotic process automation (RPA) to handle refund transactions and AI-powered document processing to streamline up to 90% of manual processes, enhancing efficiency and customer satisfaction. By automating refund procedures and improving data accuracy, Leroy Merlin enhanced customer satisfaction and achieved notable cost savings and operational excellence in their return management processes.

Use case #2: Global insurer expedites underwriting process with intelligent automation.

Underwriters spend up to 40% of their time on manual tasks like gathering and entering data for submissions and renewals, which could cost the industry an efficiency loss of up to $160 billion by 2027. Leading insurer CNA tackled this challenge head-on by harnessing the power of the Appian Platform to build ComPass™. This groundbreaking application delivered a 60% time savings, enabling underwriters across 164 countries to track the status of policies, potential issues, timing, claims, and payment status in real time from any location.

Use case #3: Bank accelerates governance process from days to minutes with AI-powered low-code automation.

NatWest, a leading UK bank, grappled with extensive delays in navigating multiple layers of internal policy checks and approvals. To expedite its governance processes and fortify risk controls, NatWest leveraged an AI-powered low-code automation platform—with a data fabric architecture—to unify its data, reduce front-end triage, and optimize workflows. As a result, the bank consolidated 14 separate processes into one and reduced its product governance cycle times from 4.5 days to less than 20 minutes.

[See how NatWest boosts productivity, reduces risk, and improves the customer experience here.]


Insights for success: Here are the key takeaways for IT leaders.

We've explored the capabilities of intelligent automation (IA) and artificial intelligence (AI), highlighting their pivotal roles in revolutionizing business operations. From streamlining processes to enhancing decision-making and customer experiences, IA and AI can unlock new pathways to efficiency, productivity, and customer satisfaction. For IT leaders, strategically harnessing the synergistic relationship between intelligent automation and AI accelerates the digital transformation journey and unlocks new opportunities for business impact.

[Want the playbook for end-to-end process automation? Get the Process Automation Guide: How to Achieve End-to-End Process Excellence.]