Hi, I’m Sandy Kemsley of column2.com. I’m here for the Trisotech blog to talk about the latest Hot Topic in BPM: Artificial Intelligence.

Now, I’ve been at a couple of conferences in the last month, and I’ve had a few briefings with vendors and there’s a lot of interest in this intersection between AI and BPM. But what does that mean? What exactly is the intersection? And it’s not just one answer, because there’s several places where AI and Process Management come together.

Now, the dream, or the nightmare in some people’s opinion, is that AI just takes over processes it figures out what the process should be then it automates and executes all the steps in the process. The reality is both less and more than that. So, let’s look at the different use cases for AI in the context of BPM. Let’s start at the beginning with process Discovery and design and there’s quite a bit that AI can do in this area as an assist of Technology. Now, at this point it might not be possible to have an AI completely designed processes without human intervention, but it is possible to have AI act as sort of a co-pilot for authoring process models or finding improvements to them.

There’s a couple different scenarios for this.

First of all, you could have a person just describe the process that they want to have in broad terms, and have generative AI create a first impression of that, or a first version of that process model for them. So, then the human designer can make changes directly or add additional information, the AI could then make refinements to the process model and so on. Now, the challenge with using generative AI in this scenario is that you need to ensure that the training data is relevant to your situation. This might mean that you need to use private AI engines and data sources that are trained on your own internal data or on data that’s specific to your industry in the very least in order to ensure some reasonably good results.

Now, the second process modeling scenario is when there are logs of processes in place, like we would use for process mining, and we’ve talked about process mining in in a previous podcasts. Now, in that case, there are possibilities for having AI look at the log data and then other enterprise and domain data and using process mining using other search-based optimization suggest improvements to the process. So, for example adding parallelism at certain points, or automating certain steps or decisions, or having some activities be required for regulatory or conformance reasons. Again, there needs to be some checks and balances on the training data that’s used for the AI to ensure that you’ve included processes and regulations that pertain to your business.

Now, in both of these cases, there’s the expectation that a person who’s responsible for the overall process operation, like the process owner, might review the models that are created or revised by the AI before they’re put into production. It’s not just an automated thing where the AI creates a model or modifies a model and it’s off and running. Now, we can look at using similar types of a AI and algorithms that you would for process improvement that are based on process mining and other domain knowledge, we can also use those in the scenario where AI acts again as a co-pilot, but for the people that are doing the human activities in a process, so the knowledge workers. Now they can ask complex questions about the case that they’re working on, they can be offered suggestions on the next best action, they can have guard rails put in place so that they don’t make decisions at a particular activity that would violate regulations or policies.

Now, we already see a lot of decision management and machine learning applied in exactly this situation. So, a knowledge worker just needs a little bit of an assist to help them make complex decisions or perform more complex activities. And adding AI to the mix means that we can have even more complex automation and decision-making that can support knowledge workers as they do their job. So, the ultimate goal is to ensure that the knowledge workers are making the best possible decisions at activities within processes, even if the environment is changing maybe regulations are changing, or procedures are changing. And then also to support less skilled knowledge workers so that they can become more familiar with the procedures that are required because they have a trusted expert, namely the AI, by their side coaching them on what they should be doing next.

Now, the last scenario for AI in the context of processes, is to have a completely automated system or even just completely automated activities within a process that used to be performed by a person. So the more times that an activity is performed successfully, there’s data collected about the context the domain knowledge that all that go behind that decision, the more likely it is that AI can be trained to make decisions and do activities of the same complexity and with the same level of quality as a human operator. We also see this with AI chatbots. We’re seeing these a lot now, that where they interact with other parties processes like providing customer service information. Now, previous previously a knowledge worker might have interacted with a customer maybe on a phone or by email, we’re seeing a lot of chatbots in place now for customer service scenarios. Now, a lot of them are pretty simple they don’t really deserve to be called AI. They’re just looking for simple words and providing some stock answers but what generative AI is starting to give us in this scenario, is the ability to respond to more complex questions from a customer and leave the human operators free to handle situations that can’t be automated or rather can’t be automated yet.

Now, currently I don’t think we need to worry about AI completely taking over our business processes. There’s lots of places where AI can act as a co-pilot to assist designers and knowledge workers to do the best job possible. But it doesn’t replace their roles: it just gives them an assist. Now, a lot of Industries don’t have all the skilled people that they need in both of these areas for designers, for knowledge workers or it takes a long time to train them so letting the people who are there be more productive, is a good thing. So, using AI to make the few skilled resources we have more productive is something that’s beneficial to the industry it’s beneficial to customers. Now, as I noted earlier, the ability of AI to make these kinds of quality decisions and perform the types of actions that are currently being done by people, it’s going to be heavily reliant on the training data that’s used for the AI. So, you can’t just use the public chat, like chat GPT, for interacting with your customers. That’s not going to work out all that well. Instead, you do want to be training on some of your own internal data as well as some industry specific data.

Now, where we do start to see people being replaced is where AI, is used to fully automate specific activity, specific decisions, customer interactions within a process. However this is not a new phenomenon. Process automation has been replacing people doing repetitive activities for a long time. So, all that we’re doing by adding AI, is increasing the complexity of the type of activity that can be fully automated. The idea that we’re automating some activities is not new, this has been going on a long time. So, the bar has been creeping up: we went from simple automation to more complex decision management, machine learning and now, we have full AI in its current manifestation. So, we just need to get used to that idea that it’s another step in the spectrum of things that we’re doing by adding intelligence into our business processes.

Now, are you worried about your own job? You could go and check out willrobotstakemyjob.com or just look around at what’s happening in your industry. If you’re adding value through skills and knowledge that you have personally that’s very difficult to replicate, you’re probably going to be able to stay ahead of the curve and you’ll just get a nice new AI assistant who’s going to help you out. If you’re doing the same thing over and over again however, you should probably be planning for when AI gets smart enough to do your job as well as you do.

That’s all for today. You can find more of my writing and videos on the Trisotech blog or on my own blog at column2.com. See you next time.

Hi, I’m Sandy Kemsley of column2.com. I’m here for the Trisotech blog with a new topic now that we’ve finished my series on best practices in business automation application development. Today, I’m going to talk about future proofing your business with process automation technologies such as BPM.

Now, a little over three years ago, everything was business as usual. Organizations were focused on new competitors, new business models, new regulations, but it wasn’t a particularly disruptive time. Then the pandemic happened, and that was disruptive! So, supply chains, customers, business models, everything changed dramatically in some cases.

Now, that’s not news of course not by now, but it’s become obvious that it’s not enough to have shifted to some new way of doing things in a response to a one-time event. Companies have to become more easily adaptable to frequent disruptions, whether they’re technological, societal or environmental, or they’re just not going to exist anymore. In many cases this means modernizing the very technological infrastructure that supports businesses.

So how is your business going to adopt a change? Both the changes that have already happened and the unknown changes in the future? There’s a lot that falls under the umbrella of modernization, and you need to look at whether you’re doing just enough to survive or if you’re taking advantage of this disruption to thrive and actually outgrow your competition.

I see three ways that companies have been reacting to disruption:

more than
managing your business processes

So I mentioned BPM, but this is about more than just managing your business processes. There’s a wide variety of technologies that come into play here and that really support future proofing of your business: process and decision automation, intelligent analysis with machine learning and AI, content and capture, customer interactions with intelligent chatbots, and Cloud infrastructure for Access anywhere anytime…

So you have to look at how to bring all of those together, and just understanding how all those fit, is like an entire day’s lecture all in one, but you probably have a bunch of those that you’re using already. Let’s look at a few kind of examples of this support/improve/innovate spectrum that I’ve been talking about though and we’re dealing with instruction and then just what it means for future proofing your business. So, supporting your existing business, a matter of just doing what you can to survive, and hoping that either you can keep up or that things will go back to normal. Now basically you’re doing the same business that you always were, but with maybe a bit of new technology to support some new ways of doing things:

• Your employees might be working from home so you needed some new Cloud technology or Network Technology to help with this.
• You probably also need some new management techniques, in order to stay productive and motivated even though your your Workforce is highly distributed geographically.
• You also need to handle changing customer expectations. So you have to have some amount of digital interactions and if you’re dealing with physical goods you might be looking at new ways of handling delivery.
• Your supply chain processes need to become flexible, this is one of the things that we really saw during the pandemic is there were a lot of broken supply chains, so you want to be able to change suppliers or change other channels in the event of disruption.

But let’s just go a little bit beyond surviving disruption that you might do by sort of mandating together something to support your existing model. The next step to is to look at disruption as an opportunity to thrive. So you want to still be in the same business but embrace new technologies and new ways of doing things. So this really pushes further into looking at customer expectations: adding in self-serve options if you don’t already have them, and then coupling that with intelligent automation of processes and decisions. So, once you’ve added intelligence to your business operations to let them be done mostly without human intervention, now a customer can kick off a transaction through self-service and see a complete almost immediately by intelligent automation, same business – better way to do it, more efficient, faster, more accurate, better customer satisfaction.

Now, this is also going to be helped by having proper business metrics that are oriented towards your business goals. With more automation data is going to be captured directly,, regarding how your operation is working, and then that’s going to feed directly into the metrics. Those metrics then you can use to guide knowledge workers so that they know what they should be doing next. Also to understand how customer satisfaction is and how you can improve it.

So this lets you move past your competition, while keeping your previous business focus. So given that there’s two companies, you and your competitors, who are offering the same products or Services if one does only that survival support that I talked about previously and one does more intelligent improvements focused on customer satisfaction, who do you think is going to win?

Now, the third stage of responding to disruption and adapting to change is innovation. You’ll continue to do process and operational improvements through performance monitoring, data-driven analytics, but also move into completely new business models. So maybe you repackage your products or services and then you sell them to completely different markets, so you might move from commercial to Consumer markets or vice versa or you sell into different geography or different industries because now you have more intelligent processes you have this always-on elastic infrastructure. Here again, you’re just moving past your competition by not only improving your business but actually expanding into new markets, taking on new business models that are supported by this technology-based Innovation.

So it’s the right application of technology that lets you do more types of business and more volume without increasing your employee headcount. Without automation and flexible processes you just couldn’t do that, and without data-driven analytics you wouldn’t have any understanding of the impact that such a change would have on your business or whether you should even try it. So you need to have all of that: you need to have the the data that supports the analytics and you need to have the right type of technology that you’re applying to have more intelligent operations business operations, and this was going to allow you to move from just surviving to thriving to innovation.

Now, a lot of change here. The question that all of you need to be asking yourself now is not is this the new normal but really why weren’t we doing things this way before? There’s just a lot of better ways that we could be doing things and we’re now being pushed to take those things on.

That’s all for today. Next month I’m going to be attending the academic BPM conference in the Netherlands, and there’s always some cool new ideas that come up so watch for my reports from over there!

You can find more of my writing and videos on the Trisotech blog or on my own blog at column2.com. See you next time.

It is a valuable tool for modeling and managing decision logic in many domains, including healthcare, finance, insurance, and supply chain management. FEEL is designed specifically for decision modeling and execution and to be human-readable to business users, while still maintaining the expressive power needed for complex decision-making. Its simplicity, expressiveness, domain-agnostic functionality, strong typing, extensibility, and standardization make FEEL a valuable tool for representing and executing complex decision logic in a clear and efficient manner. Organizations using FEEL enjoy better collaboration, increased productivity, and more accurate decision-making.

What Are Expression Languages?

FEEL is a low-code expression language, but what is the difference between Expression Languages, Scripting Languages, and Programming Languages. They are all different types of languages used to write code, but they have distinct characteristics and uses.

Is FEEL Like Microsoft Power FX (Excel Formula Language)?

FEEL and Power FX are both expression languages used for data, business rules, and expressions, but in different contexts. Power FX is a low-code programming language based on Excel Formula Language, tailored for Microsoft Power Platform, with some limitations in handling complex decision logic. As soon as the business logic gets a bit tricky, Power FX expressions tend to become highly complex to read and maintain. On the other hand, FEEL is a human-readable decision modeling language, designed for business analysts and domain experts, offering a rich set of features for defining decision logic, including support for data transformations, nested decision structures, and iteration. FEEL provides clear logic and data separation, making it easier to understand and maintain complex decision models.

While Power FX has a visual development environment in the Microsoft Power Platform, FEEL is primarily used within business rules and decision management systems supporting DMN and process orchestration platforms. FEEL is a language standard across multiple BPM and decision management platforms, providing interoperability, while Power FX is tightly integrated with Microsoft Power Platform services. For further comparison, Bruce Silver’s articles FEEL versus Excel Formulas and Translating Excel Examples into DMN Logic.

Trisotech FEEL Support

Most comprehensive FEEL implementation

Trisotech provides the industry’s most comprehensive modeling and automation tools for DMN including support for the full syntax, grammar, and functions of the FEEL expression language. To learn more about basic types, logical operators, arithmetic operators, intervals, statements, extraction and filters supported by Trisotech see the FEEL Poster.

FEEL Boxed Expressions

Boxed Expressions are visual depictions of the decisions’ logic. Trisotech’s visual editor makes the creation of Boxed Expressions and FEEL expressions easy and accessible to non-programmers and professional programmers alike.

FEEL Functions

FEEL’s entire set of built-in functions are documented and menu selectable in the editor. The visual editor also offers supports for the Trisotech-provided custom FEEL functions including functions for Automation, Finance, Healthcare, and other categories.

Autocompletion

The Trisotech FEEL autocompletion feature proposes variable and function names including qualified names as you type when editing expressions thus saving time and improving accuracy.

FEEL as a Universal Expression Language

Trisotech has also expanded the availability of the international standard FEEL expression language to its Workflow (BPMN) and Case Management (CMMN) visual modelers. For example, FEEL expressions can be used for providing Gateway logic in BPMN and If Part Condition expressions on sentries in CMMN.

FEEL Validation and Debugging

Trisotech provides validation for FEEL and real-time full-featured debugging capabilities. To learn more about testing and debugging read the blog Trisotech Debuggers.

Additional Presentations and Blogs

You can also watch a presentation by Denis Gagne on using FEEL as a standards-based low-code development tool and read a blog by Bruce Silver about how using FEEL in DMN along with BPMN™ is the key to standards-based Low-Code Business Automation.