Are you looking for a foothold in the confusing syntax of modern technologies?
Understandable, truly. Especially when it comes to terms that generate a lot of buzz and get thrown around by everyone from technology analysts to sci-fi fans. From automated intelligence, artificial intelligence, intelligent automation and more, it’s tough to get a trustworthy footing.
Though confusingly similar (they both break down to the AI abbreviation, after all) and with functions that can overlap, automated intelligence and artificial intelligence rarely mean — or do — exactly the same things.
Defining artificial intelligence and automation
To start with, automated intelligence isn’t really … a thing.
It’s not a solution technology companies are selling. 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.
Here’s how you can better pinpoint which technology the experts, your team or that random Reddit poster really mean.
A simple answer to the question, What is artificial intelligence?: AI is a collection of technologies that work together to mimic human intelligence and sometimes human behaviours. To do so, the AI technologies must be fed data and then be able to intelligently process it. True AI must be able to:
- 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.
For many organizations, AI can be powerful and game-changing. An AI-powered digital asset management (DAM) solution, for example, can use AI to reduce enterprise asset blindness and silos by:
- Automatically tagging content with metadata
- Empowering users with simpler asset tagging
- Surfacing usable assets to save redundant and expensive work and re-creations
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 data capture, which leverages AI to kick off an entire intelligent automation process and uses human-like intelligence for:
- Content classification
- Data extraction
- Image recognition
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.
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
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 pre-punched cards to assign specific, complex patterns for creation in woven materials, and you have what The Henry Ford 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 automation 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 effective — automation strategy. Hyperautomation can be beneficial enterprise-wide, with especially high promise for:
- Front, middle and back offices
- Financial services
- Order management
- Regulatory compliance
- Transportation and logistics
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.
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 is directed by humans to understand the exact forms it will see (and only those forms), and then the automated system efficiently captures the expected data from them. If the 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).
Read more: Examples of intelligent automation.
However, an 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.
Intelligent process automation (IPA)
IPA is one of the tools in AI’s collection of tools; it’s not a process itself.
Its application differs from artificial intelligence by being hyper focused 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.
Start your IPA project off with these IPA best practices.
Every organization needs technology to help them connect their people and their data. Oftentimes, AI and automation strategies are necessary to accelerate those processes and outcomes.
As digital transformation trends evolve and increasingly enter the marketplace and workspace, organizations can stay ahead of the game by working with the best partners.
Learn more about how you can grow your business with intelligent data capture and work smarter, not harder, with Inpute & Hyland.
This article originally appeared on Hyland.com. Inpute are proud to be a partner of Hyland.