BETA
This is a BETA experience. You may opt-out by clicking here

More From Forbes

Edit Story

Life Inside The Self-Optimizing AI Automated Company

Following
This article is more than 2 years old.

Business is becoming increasingly automated. Not just in terms of software chatbots designed to deal with customer queries online, but at a lower substrate level where the real operational mechanics of the business happen on a day-to-day basis.

As we apply an increasing amount of automation technology to business operations, it almost starts to draw the question: will human employees still be needed, eventually? Obviously, we can push the concept forward and think about some dystopian future where people interact with some monolithic  ‘deep thought’ engine that runs specific services, or perhaps entire industries. 

Thankfully, for now, in the real world, automation happens at a more defined and less pan-dimensional level.

Automation in business today

Automation applied to business operations today happens when databases are able to automatically and autonomously apply their scheduled maintenance and update processes. Automation also happens when we use software chatbots and when customers are able to perform some business interactions online without having to speak to a company representative. Automation also happens when processes can be digitized and made to run according to some defined set of business rules… and it is in this area that automation could next evolve.

With a background in Business Process Management (BPM) and Customer Relationship Management (CRM) software, digital process automation technology company Pegasystems thinks it knows where automation’s next ‘killer’ application point will be. 

The company laments the state of automation in the near recent past. From Pegasystems’ perspective, most automation solutions are too slow and rigid to meet the dynamic needs of the enterprise and even lack basic business rules capabilities. The next stage is smarter smart business... and it’s called self-optimizing AI and decision management.

Self-optimizing AI & decision management

The answer (or the Pegasystems version of the answer at least) could be the adoption of  self-optimizing AI and decision management into the way a business operates. The company says it has done this with its Pega Process AI – a new set of Pega Platform capabilities that promises to help organizations optimize their business and customer operations in real time.

Pegasystems offers this self-optimizing AI and decision management via its low-code process automation software. The company claims to be able to ‘intelligently triage’ millions of incoming customer requests, transactions and other events, all at enterprise scale. 

Take a deep breath and read that again. This is not a company with people sat at desks (socially distanced or otherwise) all dealing with ‘workplace stuff, this is a company (imagine it if you will) that runs with a digital triage system to ‘treat the wounded first’, with the wounded in this case actually being customers who aren’t necessarily dying, but who do find themselves in a such a state that they need immediate attention. Some of that customer attention could be automated and some could be carried out in person, it doesn’t matter, the point is that the decision-making process needed to decide which customer needs what (and find out what they need) is essentially automated.

This (above process) is what companies like to call ‘event resolution’, which is really just another way of saying making employee and customer experiences happen effectively. 

The engineering team at Pegasystems advises that in the case of these examples, it’s worth bearing in mind that this technology is aimed at very large enterprises with lots of customers with lots of incoming requests. Some customers tend to get lost in the process in that size of company, so Pega Process AI is all about automating the first-contact step so that customers - regardless if they are in an urgent situation or not – get what they need and expect from the brand as fast and as accurately as they can.

Part of the problem up to now in this area of technology is that although some automation offerings have been enhanced with AI, they still can’t scale enough to analyze the massive streams of data signals being emitted from customers that can help organizations make smarter decisions.

Making decisions with ‘decisioning’

Pegasystems says that its Pega Process AI product turbocharges the value of process automation by applying proven real-time AI, event stream processing, machine learning, decisioning and Natural Language Processing (NLP) to any business process. 

These capabilities analyze millions of streaming events and make intelligent decisions so each case gets quickly resolved. Using hundreds of self-learning models, Process AI also streamlines inefficient processes on the fly to optimize business outcomes. This enables enterprises to more effectively and efficiently resolve events – and even anticipate issues before they arise – so they deliver the best possible customer and employee experiences.

As a working example here, let’s imagine an insurance company who could use Process AI to triage a surge of incoming claim requests after a major storm by automatically approving claims or routing more complex incidents to the best available agent; a financial services company can scan fraud alerts in real time and automatically open a case for a relevant security expert to investigate; or a manufacturer can analyze Internet of Things (IoT)-connected device signals and (if it predicts trouble on the horizon) proactively open a service ticket and notify a dealer and end customer to the potential issue.

“Slick user interfaces quickly lose their luster with customers if the back-end processes driving the actual work are too slow and inefficient to deliver on brand promises,” said Don Schuerman, CTO and vice president of product marketing, Pegasystems. “Pega Process AI combines two of Pega’s most advanced solutions – AI and intelligent automation – to help ensure promises made at the front end are promises kept at the back end. By infusing AI into our deep expertise with case management and process automation, we help clients more efficiently and effectively serve their customers and assist their employees.”

As the system processes more events, it uses predictive analytics and machine learning to monitor case outcomes and uncover new ways to improve the process. Those learnings are applied on the fly so the process works more efficiently the next time a similar case arises, saving the organization valuable time and resources. Over time, the system continues to dynamically fine tune its processes as conditions change, resulting in even more efficiencies while adapting to new variables.

Our self-optimized automated future

On the road to our self-optimized automated future we will want to know that these systems have been built with responsible bias-free AI. Pegasystems says it has that box checked and (as with all Pega AI capabilities) Process AI adheres to the tenets of Responsible AI

This helps ensure algorithms result in fair and balanced outcomes that avoid unintended bias. Pega also gives users more control over their AI by providing transparency settings to help companies mitigate potential risks and maintain regulatory compliance. It also provides self-learning capabilities that can handle outlier events when exposed to real-world circumstances.

As we look forward to our near, immediate and future-possible self-optimized automated future, it’s unlikely that we’ll be interacting with any truly and wholly digitized beast of an organization in our lifetimes. It’s unlikely, but it’s not impossible.

Follow me on Twitter or LinkedIn