Automation and Artificial Intelligence are often used interchangeably, but they are different driving forces behind the technical revolution of diverse businesses. Although they share some common functions, they are fundamentally different in their application, benefits, and challenges.
Artificial Intelligence works best for complicated tasks that require human intelligence and intricate decision-making. On the other side, Automation performs repetitive tasks and makes decisions based on predefined rules, reducing manual work in routine workflows.
The confusion is understandable - the functionality of both technologies overlap somehow. However, making the right decision is what determines the fate of your business growth. Wrong choices can result in loss of financial resources, failed implementation with zero or no operational improvement.
Let’s dig in the differences of Automation and Artificial Intelligence, when to use each and what are the associated benefits and challenges of these technologies to maximise your business growth.
What is AI Vs Automation?
Artificial Intelligence creates systems that can mimic the human cognitive functions, such as decision-making, learning from experience, pattern recognition, and understanding natural language.
Key characteristics of AI systems includes:
Natural Language Processing to interpret and generate human language.
Machine Learning improves AI responses over time through data pattern identification and user experience.
Disorganised Data Interpretation allows AI to work with varied data inputs, such as images, text, speech, and incomplete information without following predefined formats.
Adaptability makes it suitable for data variations and exceptions. Unlike Automation, AI can adjust its approach based on the circumstances.
This versatile tool can be employed in several user cases, such as:
Chatbots & Virtual Assistants
Real-time Fraud Detection Systems
Predictive Analysis
Personalised Shopping Experience
Autonomous Vehicles
Telehealth diagnostics
Automation can perform tasks without human intervention based on pre-defined formats and structures. Automated systems reduce human error and time consumption on similar tasks. This system reduces routine workflow by handling repetitive tasks, exactly as programmed.
Key characteristics of automation includes:
Formatted Operation with pre-set rules and patterns. The whole system works based on these rules unless changed manually in the system.
Structured Input requirements make automated systems suitable for formatted and predictable data. Invoice processing automation, for instance, will generate similar invoices as formatted in the system.
Systematic Implementation allows automated systems to perform identical tasks in the similar way every time, which ensures reliability but restricts flexibility when exception occurs.
Static Model restricts adaptability in automated systems. The system can only perform programmed tasks without any modification.
From customer-related workflows to business operations, automated systems can be used in the following tasks to save time and effort:
Email Management
Customer Service Management
Staff Onboarding
Data Entry
Invoice Processing
AI Vs Automation: Understanding Key differences
Reports suggest that 73% companies employed automation for their business and reduced 25% of cost last year. Moreover, only 42% of companies used AI in their business operations. Today, businesses employ automated systems more than AI, however AI possesses more potential than automation to boost business growth.
Here’s a quick comparison between the two technologies to identify which can go well with your business needs:
Category | Automation | Artificial Intelligence |
|---|---|---|
Nature of Tasks | Pre-defined, programmed, routine workflows | Flexible, dynamic, cognitive |
Learning Capacity | Static model, require manual update for varied responses | Employs machine learning, provide varied and improved responses overtime based on user needs |
Decision-making Ability | Cannot make decision and need human input | Can make decisions by performing real-time analysis |
Applications | Scheduling, data entry, invoice processing | NLP engines, AI agents, intelligent AI platforms |
Operational Outcome | Reduces human error, improves operational services | Enhances adaptability, improves system intelligence, and process automation |
AI Vs Automation: When should businesses choose each?
Choosing between AI and Automation depends on your business domain, its complexity, and goals. Keep in mind the above key differences between these two technologies ask yourself a few relevant questions, such as
Is your data organised or disoriented
Do you need adaptable system
Does your domain require decision-making
Do you have enough budget to design AI system
Drawing a clear distinguishing line between automation and AI would help you to decide which one is better for your business challenge and will not waste your resources and time on wrong tools.
Businesses Should Choose Automation For:
Streamlined Processes: If you can structure exact steps and decision points, then automation will execute them seamlessly and cost-effectively.
Consistent Operation: Automation could be a good choice for businesses seeking customer service regulation, quality control, and compliance regulations.
Bulk Repetitive Tasks: Automation can handle large volumes of similar tasks flawlessly and efficiently. Automation aligns well with repetitive tasks to reduce routine workflow.
Cost Reduction: If your priority is to reduce cost for daily workflow, then automation is the best option to explore as it requires less budget and maintenance than AI systems.
Businesses Should Choose AI For:
Tasks involve decision-making: AI aligns perfectly with tasks involving complex decision-making, interpretation, and analysis.
Complex structured systems: AI would be a good choice for tackling varied inputs (such as format, quality, or incomplete responses) and interpreting them into reliable and consistent information.
Adaptability: Through learning capabilities, AI can change its responses over time to fit best into changing customer behaviour and market dynamics.
Building data relationships: AI works well in identifying data gaps, relationships, anomalies and trends that humans might overlook.
Conclusion
Although AI and Automation serve different purposes, they can be used together. Many large enterprises combine both technologies, AI for adaptability and intelligence and Automation for speed and accuracy. Understanding the differences between these technologies and deciding which one would give better results could be an arduous task. However, knowing the critical features to distinguish can help you build smarter and more scalable systems for your business.