The day started with my keynote on rolling your own digital automation platform using BPM and microservices, which set the stage for the two demos and the round table discussion that followed.
Business Automation as a Service, with Denis Gagne of Trisotech
Denis demoed a new product release from Trisotech, their business automation as a service platform: competing with services such as Zapier and IFTTT but with better process and decision management, and more complex service types available for integration. He showed creating a service built on a Twitter trigger, using BPMN to model the orchestration and FEEL as the scripting language in script activities, and incorporating a machine learning sentiment score and a decision service for categorizing the results, with the result displayed in the color of a flashing smart light bulb. Every service created exposes an Open API and REST API by default, and is deployed as a self-contained microservice. He showed a more complex example of marketing automation that extracts data from an input form, uses a geo-locator to find the customer location, uses a DMN decision model to assign to a sales team based on geography and other form parameters, then creates a lead in Microsoft Dynamics CRM. He finished up with an RPA task example that included the funniest execution of an “I am not a robot” CAPTCHA ever. Key point here is that Trisotech has moved from a pure modeling vendor into the execution space, integrated with any Open API service, and deployable across a number of different cloud platforms using standard protocols. Looking forward to playing around with this.
Business-Composable Services for the Mortgage Industry, with Bruce Silver of Method and Style
Bruce showed the business automation services that he’s created using Trisotech’s platform for the mortgage industry. Although he started looking at decision services around how to determine if someone should be approved for a mortgage (or how large of a mortgage), process was also required to do things like handle mapping and validation of data. Everything is driven by a standard application form and a standard set of underwriting rules used in the US mortgage industry, although this could be modified to suit other markets with different rules. The DMN rules are written in business-readable language, allowing them to be changed by non-developers. The BPMN process does the data validation and mapping before invoking the underwriting decision service. The entire process can be published as a service to be called from any environment, such as a web app used by underwriters inside a financial company or by an online prequalification review done directly by the consumer. The plan is to make these models and services available to see what the adoption is like, to help highlight the value and drive the usage of BPMN and DMN in practice.
Industry Round Table: The Coming Impact of Decision Services and Machine Learning on Business Automation
We finished the morning of day 2 with a discussion that included three of the earlier demo presenters: Denis Gagne, Bruce Silver and Scott Menter. They each gave a short talk on how decision services and machine learning are changing the automation landscape. Some ideas discussed:
- It’s still up in the air whether DMN will “cross the chasm” and become generally used (to the same degree as, for example, BPMN); this means that vendors need to fully support it, potentially as an execution as well as requirements language.
- Having machine learning algorithms expressed as DMN can improve transparency of decisions, which is essential in some jurisdictions (e.g., GDPR). There is a need for “explainable AI”.
- The population using DMN is lower than BPMN, and the skill level is higher, although still well within the capabilities of data-focused business people who are comfortable with formulas and expression languages.
- There’s a distinction between symbolic (rules-based) and sub symbolic (neural network) AI algorithms in terms of what they can do and how they perform; however, sub symbolic AI is less of a black box in terms of decision transparency.
- If we here at bpmNEXT aren’t thinking about the ethics of automation, who will? Consider the labor disruption of automation, or decisions that make a choice involving the value of life (the AI “trolley problem”), or old norms used as training data to create biased machine learning.
- We’re still in a culture of having people at a certain skill level (e.g., surgeons, pilots) make their own decisions, although they might be advised by AI. How soon before we accept automated decisions at that level?
- Individually-targeted decisions are happening now by what is presented to specific people through platforms like Google Search and Amazon. How is our behavior being controlled by the limited set of options presented to us?
- The closer that a technology gets to the end effect, the more responsibility that the creator of the technology needs to take in how it is used.
- Machine learning may be the best way to discover the best transparent decision logic from human action (unfortunately that will also include the human biases), allowing for people to understand how and why specific decisions are made.
- When AI is a black box, it needs to be understood as being a black box, so that adequate constructs can be created around it for testing and usage.
Great discussion and audience participation, and a good follow-on from the two demos that showed decision services in action.