Hi, I’m Sandy Kemsley of column2.com. I’m here for the Trisotech blog to continue with last time’s discussion of Artificial Intelligence and BPM. Actually, I’m going to address the larger topic of Process Automation in general, how this impacts businesses and people, and then how AI fits into this.

We see a lot of press. Now, about the dangers of AI: it’s going to take our jobs, it’s going to take over our lives, we’re all going to become slaves to the robots… Okay that’s mostly a bit of an exaggeration but there is a lot of Doom and Gloom around the topic of AI, this isn’t a new phenomena though, it’s not just about AI. If we look at um a promo for a recent book on this topic, so there’s a book called “Blood in the machine”, it says in the promo: “the most urgent story in modern tech begins not in Silicon Valley, but 200 years ago in rural England when workers known as the Luddites rose up rather than starve at the hands of factory owners who were using automated machines to erase their livelihoods”. In short, the alarm currently being raised over AI isn’t a new phenomena.

It happens with pretty much any new technology. In most cases the technology itself is not the problem, it’s the social constructs around how it’s used and the rights of the workers and other people who are impacted by the technology. I’m not any sort of expert in those fields but I’ve always seen fears about Automation in the business process projects that I’ve been involved in. Now, I started in process automation back in the Imaging and workflow day several decades ago, and at that time there were people whose job it was to push carts full of file folders around between different desks, based on handwritten routing slips on the folders, so the routing slips and the mail carts were the workflow of that age and the jobs of the people who filled out those routing slips and pushed those carts around were definitely impacted by the projects that we implemented for Imaging and workflow it took those file folders and made them electronic and it took away most of those carts that traveled around from one desk to another.

Now, after that we had years of “business process re-engineering” which in many cases was just an excuse for companies to downsize their Workforce, but it was also to some degree enabled by business automation. So processes that were previously very manual required a lot of human decision points, were suddenly partially or even fully automated.

Now, the last several years have fine-tuned that process automation by integrating in decision management, this is a huge factor in reducing human decision- making in business processes, so the process and the decision automation just keeps getting more intelligent. Now, we’ve also integrated many other systems through direct calls between systems or robotic process automation which means that there’s a lot less people doing copy and paste or reing of information between different systems that also means that there are many fewer errors due to copy paste and rekeying data, and it takes less time, so the automation gets faster and better too.

Now, does this mean that some of the people involved in those processes have radically um different jobs now, or maybe even had to find a different job altogether? Absolutely! Does it mean that customers are seeing different levels of service and quality both improvements and failures? Of course! And does it mean that companies are succeeding or failing financially based in part on their decisions about when and how to deploy automation? Well yeah! We saw that in excruciating detail during the business disruptions of the pandemic, which I’ve talked about in previous videos.

What I’m trying to to say is that in most cases the automation technology itself isn’t inherently good or bad. It can result in job losses, it can also improve job satisfaction by reducing the boring routine work, it can help customers get what they want faster through self-service, or it can create a frustrating customer experience when something goes wrong that’s not accounted for in the automation. It can make a company more profitable and efficient, it can also backfire and create a customer satisfaction nightmare.

I think we’ve all seen examples of both the positive and negative side of of all of these for the people who work with the technology, for the customers who are impacted by it, and for the companies who bring this new technology in. So this is true for most types of business automation that we deal with today: BPM, systems decision management, process mining, RPA and yes how AI is used with all of these.

I don’t think that people on the customer side of business want to return to the pre-automation days for the most part. You remember the bad old days when a straightforward business transaction like getting car loan or processing a simple Insurance claim, could take weeks or even months. So automation is also what gives us a lot of online self-service for customers so you can now buy office supplies with a couple of clicks, or you can make a stock market trade in your pajamas at home, or you can renew your fishing license on the weekend. All of these things are possible because of automation.

Now, if you look at the business’s side of these transactions, they don’t want to return to the mountains of paper files and the manual processes. They also don’t want to return to having critical business procedures exist primarily as folklore in the heads of people within the company that may or may not stay with the company in in the long term. Now, from a purely practical standpoint, there’s no putting the automation technology to toothpaste back in the tube any more than we’re going to go back to handloom textiles from the pre-Luddite days. Organizations are going to use automation or not use it for their own reasons, and there will be both good and bad things that happen because of that. As consumers, workers, business owners, and citizens we have a say in both the positive and negative impacts of automation.

Now, as I mentioned in my last video, I believe the current doomsaying about AI is a bit over blown. AI isn’t going to completely take over all of our business processes, any more than the previous generations of technology did. AI can increase the complexity of things that can be fully automated, but that’s always being a constantly changing threshold with every new generation of Technology. The same could be said for decision management. The same could be said for business process management in general. These things always make it so that you can automate more and more complex things, the more of these technological components that you bring in.

Now, where automation technology including AI can can really help and really add value, is when it provides guidance to knowledge workers to help them do the best possible job without replacing those roles. So it’s not just about taking the repetitive low skill jobs and automating them, it’s also about letting lower skilled workers work on more complex jobs because they have some amount of a automated guidance, and they will also learn as they work without risking violating the company policies or procedures. So you can still have people in the processes, you can have some things that are automated, and you can have the people who remain in the process be guided by the technology, AI, decision management, business Process Management, to make sure that they’re doing the right thing at the right time. And given that a lot of Industries have a lack of skilled knowledge workers, letting them be more productive earlier is a good thing for everybody involved.

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 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.