Whether you’re a procurement or accounts payable (AP) professional, or you work in the line-of-business, process digitization and automation are effectively table stakes: you need them in order to be considered a player – even a novice player. Luckily, there are innumerable applications, platforms, and solution suites at your disposal, particularly since cloud-based tools / software-as-a-solution (SaaS) have become the go-to deployment avenue for SMBs and large enterprises, alike, and they generally make modern, robust business tools more widely accessible. One cloud-based solution provider, Nintex, is doing its part to bring intelligent process automation (IPA) to the masses; and unlike other IPAs, this one probably won’t leave you with a headache the next morning.

We often hear how robotic process automation (RPA) breaks off specific sub-processes from a particular task, like data collection and collation, and automates them, and in so doing, frees end users from the tyranny of tactical, scalable, and repeatable work that still must be completed. Nintex takes RPA to the next level by applying machine learning algorithms to user and enterprise data and ultimately taking a data-driven, “intelligent” approach to process automation. I recently spoke with Ryan Duguid, Senior Vice President of Technology Strategy at Nintex, for an in-depth discussion on how Nintex goes to market with its IPA solution to help business users add value to tactical processes across the enterprise.

Democratizing Process Automation

According to Ryan, one of the ways in which Nintex distinguishes itself from the solutions market is that it provides business analysts, like those that work in procurement, with the ability to intelligently automate processes without having to be a Google, IBM, or Microsoft “super user.” The average business user doesn’t have to be able to write code in the back end of a platform to be able to automate processes and leverage the power of Big Data to do it intelligently. Nintex provides that “as a service” via a consumption-based, pay-as-you-go model. It seems to be working out for Nintex, which has eight offices worldwide supporting approximately 8,000 customers, and adds between 600 and 800 customers every year.

According to Ryan, Nintex’s cloud-based platform, which is supported by a worldwide network of 1,700 software developers, integrators, and consultants, provides users with an array of business tools that helps users operationalize a wide breadth of use cases. It begins with the workflow platform “that has a drag-and-drop designer that’s consumable by the average business user.” Think of it as a “blank canvas” on which users can build business processes and then automate using a working process library.

As Ryan said, “Any time you go down the path of user empowerment,” the user tends to “get stuck” at the starting point because they don’t know where to go from there. But Nintex provides users with a proverbial basecamp, and the map to get them started on their journey towards process automation.

The Nintex platform also features analytics capabilities; document production and automation; electronic forms, and the ability to interact with those forms via mobile applications.

Adding Intelligence to Process Automation

With more than 8,000 customers and growing, Nintex collects an enormous volume of enterprise data that it analyzes to understand not only the workflows and needs of individual customers, but also more strategic business insights. “If we start to look at the data that comes through our system,” said Ryan, “there are some really interesting things we can learn, and we can not only help our customers by looking at their own data, but we can help our entire customer base by looking at the data in aggregate.” Insights like when enterprises receive budget approval for capital resources, where they source categories and select vendors, when they onboard suppliers, when they process contracts, and when they conduct regular, periodic reviews have all been gleaned from examining enterprise data individually and collectively.

Within the past year, Nintex began “feeding these insights back” to its customers. It also recently began to build out machine learning-based data analytics models so that users can analyze the operational aspects on their workflow platforms and look deeper at human interactions and decision approvals, because, according to Ryan, company officials began to find other useful insights “hidden” within enterprise data. Users will soon be able to use machine learning to understand employee “patterns of life’ to help other users inform their business decision-making processes.

For example, the Nintex workflow platform might recommend that a business user proactively make a certain decision (e.g., to purchase surplus stock during seasonal pricing lows) based on historical analysis of whether or not a colleague or superior is likely to be on hand during a certain time block. It could help them determine that they are the only one available to seize the opportunity before them. The platform, driven by a machine-learning based algorithm, will not only automate parts of the strategic sourcing process, it will also inform their decision-making process based entirely on historical analysis of enterprise- and employee-level data.

Beyond machine learning, Nintex has partnered with Microsoft to leverage the capabilities residing on Azure, Microsoft’s cloud-based computing service that functions as a data engine and application test, evaluation, and hosting platform. The idea is to harness the inherent power of Big Data collected during process execution by automatically fusing multiple forms and sources of it to produce multidimensional analyses, like customer sentiment analysis, and automatically recommend appropriate next steps. One can imagine use cases involving the collection and analysis of multiple forms of supplier data to determine the financial health of the supplier and whether, based on its financial health and risk profile, the supplier ought to be remediated or terminated.

“What if you could take that power and put it in the hands of your average business analyst?” Ryan asked. That’s effectively Nintex’s move with the Azure platform, as they are working to offer users intelligent process automation, as well as Big Data analytics and cognitive computing capabilities through the platform.

According to Ryan, Nintex also recently built a prototype of a digital assistant. Like other digital assistants, Nintex’s incarnation appears in the bottom corners of your screen and, either through text or voice, can field questions and requests, such as booking corporate travel. For instance, if you ask how to go about booking a business trip, the assistant will say, “Okay, here’s what you need to do” and give you the steps in a conversational workflow. The more detailed the request, the more detailed and useful the results ought to be.

Final Thoughts

My conversation with Ryan was long and rather fruitful, and it ended on the subject of the recently implemented General Data Protection Requirements (GDPR) regulations, the exponential (and untenable) growth of Big Data, and how cost ineffective storing data is getting. All three factors have combined at roughly the same time and are giving Ryan and other Nintex corporate executives reason to rethink how they go to market with IPA and how (and whether they continue) to store data. “It’s been an evolving beast,” he said, “because the more data we pull, the more we have to scale and manage that.”

The swelling of data and rising storage and management costs have also forced Nintex to reevaluate their pricing models. Presently, enterprises pay for what they automate, rather than a monthly service fee. Nintex is now rethinking the cost-dynamics of it, and are working on a longer-term plan to accommodate companies that want to automate processes over a three- or six-month period.

In the meantime, we’ll keep tabs on Nintex and its IPA as we move into the heady days of AI and Big Data.

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