Wednesday, 05 June 2019 06:21

Pegasystems adds AI options to Customer decision hub

Pegasystems vice president of decisioning and analytics Rob Walker Pegasystems vice president of decisioning and analytics Rob Walker

Customer engagement software vendor Pegasystems has announced a new set of APIs allowing the use of third-party AI engines with Pega AI.

The initial AI services supported by Pegasystems' Real-Time AI Connectors are Google DialogFlow Text Analyzer (available immediately), Google Cloud Machine Learning Engine (scheduled for 3Q19), and Amazon SageMaker (4Q19).

While Pega Customer Decision Hub already provides machine learning, decisioning, and natural language processing, Real-Time AI Connectors provide the ability to complement Pega AI with additional capabilities including new deep learning and machine vision capabilities and alternative machine learning and NLP services.

The connectors join existing APIs for OCR and speech-to-text processing that bring other capabilities into the Pega environment.

"We want to make it easy to plug that [capability] in," said Pegasystems vice-president of decisioning and analytics Rob Walker. While Pegasystems makes the best AI engine it can, other vendors' engines are sometimes more suitable for particular purposes.

"It's such an amazing time to be part of the global AI ecosystem that’s continually testing the limits of what great customer experience looks like," he said.

"With an open approach to our architecture, Real-Time AI Connectors help bring these advancements to our clients so they can infuse the latest AI algorithms into their proven Pega AI engine."

Pegasystems director of strategy and product marketing Vince Jeffs told iTWire that in some cases customers' data scientists could have built their models using Pega AI, but for various reasons they selected other platforms and so Pegasystems needs to accommodate that choice.

But there are some techniques such as gradient boosting and deep learning that are not part of Pega AI, and therefore interoperability with other platforms is required.

For example, deep learning can determine timing-related matters, such as how quickly customer intent decays and therefore how often the company needs to interact with the customer to keep them engaged (hopefully without alienating them).

However, deep learning requires a lot of data, and even a big bank might not have sufficient data that is relevant to a particular situation.

Commonwealth Bank head of data and decisioning strategy, retail bank, Alex Burton welcomed the announcement, telling iTWire "we're trying to put as much intelligence into Pega as possible."

"Pega will always be the central brain that coordinates everything," he added.

The financial services sector accounts for around 45-50% of Pegasystems' business in the region, vice president for APAC Luke McCormack told iTWire. In Australia, all of the Big Four banks are Pegasystems customers, along with several second-tier banks including Me Bank.

Disclosure: The writer attended PegaWorld 2019 as a guest of the company.


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Stephen Withers

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Stephen Withers is one of Australia¹s most experienced IT journalists, having begun his career in the days of 8-bit 'microcomputers'. He covers the gamut from gadgets to enterprise systems. In previous lives he has been an academic, a systems programmer, an IT support manager, and an online services manager. Stephen holds an honours degree in Management Sciences and a PhD in Industrial and Business Studies.



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