Automated intelligence vs. artificial intelligence: What’s the difference?

What is the definition of automated intelligence?

Automated intelligence is a term that conflates real things (for example, automation, intelligent solutions and artificial intelligence) to indicate a vague compilation of them all; however, automation intelligence isn’t really a thing. It’s not, for example, a solution technology companies sell. Rather, it’s a popularized term that gets thrown around to cover a variety of tangentially related technologies.

In most instances, people referencing automated intelligence more likely mean artificial intelligence or automation. Although they can be compatible and complementary, they are distinct categories of technology.

In most instances, people referencing automated intelligence more likely mean artificial intelligence or automation. Although they can be compatible and complementary, they are distinct categories of technology.

What is artificial intelligence (AI)?

AI is a collection of technologies that work together to mimic human intelligence and sometimes human behaviors. To do so, the AI technologies must be fed data and then be able to intelligently process it. True AI must be able to:

1. Learn
2. Reason
3. Problem-solve and self-correct

AI solutions depend on sophisticated neuro-like pathways to connect data points, as well as on machine learning (ML), a subset of AI, to teach the AI technologies. It’s similar to how a human would learn.

A fun example of AI technologies at work is Spot, Boston Dynamics’ robot “dog” that takes in data as it experiences the world, learns how to better optimize its existence in the world to contribute to on-site business outcomes, and puts those learnings into practice without human guidance.

Using AI in business

For many organizations, AI isn’t as flashy or borderline lovable as Spot. But it can still be powerful and game-changing.

In claims processing, insurers are increasingly receiving high-value photographs and videos from customers to document claims. In an automobile accident claim, for example, AI can extract and process important information from submitted content, including:

• Confirming the vehicle make and model
• Estimating the damage and predicting repair estimates
• Identifying duplicate photos or mismatched accident images for fraud detection

Another business case for AI is intelligent document processing (IDP), which leverages AI to power document onboarding capabilities and drive new levels of automation, efficiency and accuracy. Organizations can use it to apply human-like intelligence to:

• Intelligently capture and convert text from multiple channels
• Separate content
• Classify content
• Extract data from content and validate it
• Continually learn and improve

Healthcare organizations and patients are seeing increasing uses for AI in diagnostic imaging, too. A recent healthcare- and AI-focused Gartner® report shows expanded AI use for:

• Differential diagnosis: AI provides clinical decision support, as in the case of identifying the ground-glass opacities that dominate many COVID-19 pneumonia patients.
• Abnormality detection: AI aids imaging readers by highlighting the specific area of an image that requires attention, decreasing analysis time.
• Worklist prioritization: A sort of triage application, AI can be used to prioritize time-sensitive cases by flagging images that indicate the need for urgent action.

What is automation?

Automation is a system or a project that is applied to a machine; once set up by humans, automation projects should run independently of human instruction by automatically doing the assigned processes that were once manual. An automated system:

• Integrates a machine into a manual system
• Is based on specific, identified patterns and rules
• Operates with little to no human interaction
• May be able to perform the automated tasks faster and with more accuracy than humans could t data capture and work smarter, not harder, with Hyland.

In its earliest days, automation looked like simple machines that harnessed the power of earthly elements to operate — think the sails on Medieval windmills that automatically turned with the wind.

Jump forward to the Jacquard loom, which used prepunched cards to assign specific, complex patterns for creation in woven materials, and you have what textile historian Christine Jeryan calls “the great, great, great grandfather of the computer technology we all use today.” The same punched cards concept that the loom used to automate weaving in 1804 were eventually used by IBM to load computer programs onto computers and automate the running of the machine.

Today’s modern automation technology ranges from large-scale machines, like industrial robotics and automated production lines in manufacturing, to robotic process automation (RPA), which records a human’s manual mouse clicks and replicates the process. Regardless of machine size, properly designed and executed RPA projects:

• Drive efficiency
• Improve accuracy
• Give users the opportunity to focus on higher-value work

Today’s automation horizon

The newest trend in automation is hyperautomation, which tasks organizations with identifying the best pathway forward for strategically leveraging all the automation tools at their disposal. Hyperautomation often results in organizations creating a broader, more cohesive — and more effective — automation strategy. Hyperautomation can be beneficial enterprise-wide, with especially high promise for:

• Front, middle and back offices
• Financial services
• Healthcare
• Insurance
• Order management
• Regulatory compliance
• Transportation and logistics

Automation vs. AI

Is automation artificial intelligence?

No. Automation and AI are distinct concepts, yet they can work together and complement one another.

Automation does not inherently possess artificial intelligence, but it can if ML is in place for the automation process to take in new data, learn from it and begin to make its own improved and informed decisions.

Does intelligent automation use AI?

Yes, because intelligent automation takes automation to the next level by giving it the power to learn and make decisions as a human would.

For example, a simple automated data capture technology that is directed by humans cannot learn beyond exactly what its humans programmed it for. It will:

• Recognize only the exact forms the humans prepared it for
• Process those forms (and only those forms)
• Efficiently capture the expected data from them

If the simple system is presented with a form it doesn’t recognize, it’s stymied. A simple automated data capture system doesn’t have the ability to work beyond its original scope (though it can still be a great solution).

However, intelligent data capture technology can take in new data. When an unexpected form enters its system or a field entry is misspelled or missing, it can use what it already knows about extracting data to make human-like inferences and decisions, without instruction from humans.

What is intelligent process automation (IPA)?

Intelligent process automation (IPA) is one of the tools in AI’s toolbox; it’s not a process itself.

An IPA application differs from AI by being hyperfocused on processes. IPA uses tools like capture software, low-code intelligent process automation platforms, process mining and task mining to intelligently manage business processes with little or no intervention from humans.

> Read more | Intelligent process automation best practices