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BP Logix BPM tool packs AI features in latest release

With BP Logix's latest BPM platform release, predictive analytics comes to BPM without a posse of data scientists. The AI tools inside could fill BPM's machine learning gap.

Low-code BPM development tools today already help developers simplify and speed up business process application development. The next step is to make those apps smarter.

To that end, BP Logix, a business process management (BPM) company in Vista, Calif., recently introduced version 5.0 of its Process Director that adds AI features to enable predictive analysis, enhanced UIs and journals for configurable collaboration.

Rather than present complex AI features, Process Director 5.0 offers a set of basic machine learning tools that the average app developer can use, such as a point-and-click graphical interfaces that guide configuration processes and display results of analytics, with no coding required.

Embedding intelligence into business applications requires specialized knowledge and teams of data scientists, said Charles Araujo, principal analyst for Intellyx, a consulting firm in Glens Falls, N.Y. Process Director 5.0's blend of AI and low-code features brings predictive application processes to nontechnical users.

"The value Process Director 5.0 delivers is less about features, per se, and more about accessibility," Araujo said.

AI inside

Neil Ward-Dutton, research director, MWD AdvisorsNeil Ward-Dutton

The AI tools inside Process Director 5.0 enable machine learning, sentiment analysis, capture and expression of dissimilar events and conditions in a single state and configurable collaboration. The company also added UI features for iterative list search, calendar views, and inline HTML and text editing.

"AI and machine learning create prediction models that have been missing from BPM," said Neil Ward-Dutton, research director for MWD Advisors, a U.K.-based IT consulting firm. With AI, the application learns from past history, identifies trends and makes recommendations for decisions.

As an example, Ward-Dutton pointed to how AI capabilities can help with a loan request by identifying factors that make the applicant and the loan's purpose a low or high risk. Combined data mining and machine learning tools aggregate information about previous loan applications and current market conditions to help the loan officer make a decision.

AI and machine learning create prediction models that have been missing from BPM platforms.
Neil Ward-Duttonresearch director, MWD Advisors

Araujo said he sees businesses with reliable data on actions and outcomes adopt AI-enabled, predictive-type applications quickly and with good results. Developers can use that legacy data to build models that predict behavior of application users who meet certain criteria and perform specific actions. With these functions, the tool recommends a best action and prioritizes options that are presented to the user, so the application feels more intuitive or takes actions automatically.

Applying AI for nontechnical users, even with accessible tools, requires a change in traditional BPM project approaches. Araujo said project teams will have to think like a data scientist.

"Applying intelligence to applications requires imagination," he said. "Developers need to think about application usage patterns and imagine ways to use predictive capabilities to meet users' needs."

"That's not the way we've historically approached applications, particularly business-process-based ones," Araujo added.

Process Director 5.0 is generally available, with versions for both cloud and on premises. In addition to AI and low-code/no-code development tools, the platform includes traditional BPM capabilities for compliance automation, process modeling, multifactor authentication and other standard BPM features.

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