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.

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.

Hi, I’m Sandy Kemsley of column2.com. I’m here for the Trisotech blog with the sixth and final episode in a series on best practices in business automation application development.

In the first episode of the series, I talked about the automation imperative that’s driving organizations, there’s a lot of new technology that lets us automate things that were never possible before, and you need to be considering it or risk losing your competitive edge. In the second episode, I talked about best practices in strategic vision, namely making business automation a strategic direction and picking the right processes that will have the maximum impact. Then, in the last three episodes, I looked at best practices in the design of business automation, covering metrics, understanding your process (understanding what to automate and what not to automate) and finishing up with a session on design anti-patterns. So you can go back and review those earlier episodes if you haven’t already watched them. Today, I’m going to be wrapping up the series with some best practices in implementation methodology for business automation.

Best Practices In Implementation Methodology For Business Automation

Now, using an agile approach for implementing software projects isn’t unique to business automation, but they are particularly well suited to each other. Today’s business automation tools are usually model driven and low-code, so that you’re able to at least get a workable prototype up and running without writing much if any code. That doesn’t mean there will be no code, since you may have parts of the system such as integrations or specialized algorithms that require traditional coding techniques.

However, agile techniques combined with model driven low-code tools means that you can quickly get a working version in the hands of a group of business users, and let them beat it up. And that speed from idea to working system enables the first best practice in implementation: get a minimum viable product rolled out into production, as soon as possible, then iterate in place. Now, I’m showing this popular graphic, created by Henrik Nieberg several years ago, illustrating how to think about MVP, and you can follow the link in this QR code to read his excellent article discussing the concepts in detail. You might find it useful if you ever need to explain the concepts of minimum viable product and iterative implementation to others in your organization.

Now, you don’t want to be in the position of taking months to perfect your system, deploy it to the business, and then have them tear to shreds on the first day, so instead, you get it to them much earlier in the development cycle. So, when they tear to shreds as they inevitably will, you haven’t invested so much effort in that first version that you’re resistant to their ideas. Then you iterate until there’s a consensus on how this system should look and behave.

That’s not something that’s going to be possible if you’re stuck in old waterfall methodologies. With waterfall, you’ll be three months just writing requirements documents, plus time to get the business to sign off on them, since you’re forcing them to decide what they want months before they’ll actually see it. Then, another six months or so writing and signing off on design documents then a lengthy implementation cycle, just to roll out a system that is almost guaranteed to not be what the business actually wants or needs. If a project is taking too long and is far over budget, take a look at the implementation methodology and you’ll probably find waterfall.

Now, waterfall methodologies work fine for specific types of implementation, such as technical integrations where you’re writing code that will connect to systems using standard protocols. Where waterfall doesn’t work all that well is whenever there are business people who are going to interact closely with the software in their day-to-day operations. Which is of course A lot of the time. Now, if there was ever a way to convince your organization to adopt agile or agile-like implementation methodologies, you want to show them the power and flexibility of model driven low-code tools in order to do that. So whip up a quick prototype in a couple of days, it doesn’t have to be operational, and show it to people, get some feedback, change it, show them another version later the same week. They’ll get the idea that implementation can be iteratives and also collaborative between business and development. And that is what will maximize the probability of success.

Now, this requires a pretty close connection between your implementation team and your business. It doesn’t mean that you can’t outsource implementation or that your developers can’t be in other locations, but it does mean that they need to work closely with the business as a team, deploy frequent iterations, and then be able to quickly integrate feedback into the implementation cycle. So, if there aren’t daily conversations between someone in the business and someone on the implementation team, you’re probably not connected as closely as you need to be. This also means that the business needs to trust the implementation team when they say that the MVP delivery is part of the process and not a final delivery.

Too many users have been burned by being stuck with version 1.0 of an implementation, while the dev team is shunted off to the next project. That’s how we end up in these lengthy waterfall cycles of requirements and design documents, and in fact, Nieberg suggests using a different term rather than MVP, such as earliest usable product, which implies that this is the earliest of many releases, rather than a final delivery.

Now, the other best practice in business automation implementation that I want to talk about, is being ready and able to pivot. This is not just a matter of changing little things on the user interface so that the business likes it better, or creating a new API to extract data from a legacy system. I’m talking about radical change in direction or functionality once the business sees that first iteration and decides that you need to go down a different path. Sometimes because it’s the business’s first real exposure to the capabilities of the business automation tools, and the flexibility of a low-code approach, they just didn’t even know that some of these things were possible. Then they look at their first iteration, they scratch their heads and then they say: “Hey, couldn’t we completely automate this part of the process?” or “Would changing this allow us to use remote workers for certain human steps?” or “Can machine learning be used to auto-adjudicate these decisions?” or “Can we integrate this functionality into our customer portal for self-service?”. You get the idea.

Now, Nieberg’s article had a great little graphic to illustrate that point. What if you were busy implementing the car by way of skateboard and bicycle as your iterative steps, but figured out they would really be better served by taking a bus? Think of it as radical re-engineering of the business process driven by the business.

Now, business goals usually align with two high-level corporate metrics: net revenue and customer satisfaction. To achieve these, the business is going to be looking for more automation, more accurate and efficient processes, appropriate levels of customer self-service and occasionally a completely different way of serving your customers, or a completely new business model in order to provide services. Now, a smart business analyst who’s well versed in the business and the automation tools should be suggesting that type of different functionality early on, but if not, then when that first version is put in front of the business, be prepared to pivot.

That’s all for today. I’ll be back next time with something completely different, now that we’ve wrapped up this series on best practices in business automation projects.

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.

Orchestration, or more specifically process orchestration, is a vital aspect of many industries, including healthcare and financial services. It involves the coordination of various people, processes, and technologies to achieve a specific goal or outcome. Multiple levels of orchestration exist, each with its own unique characteristics and requirements. On this page, we will delve into the different levels of orchestration and showcase how the Trisotech platform enables businesses in healthcare and financial services to manage and optimize their orchestration endeavors.

Pre-defined Industry standard process definitions

Accelerate your development process with drag-and-drop process definitions from Trisotech’s extended modeling support accelerators. Industry-specific templates are available for Healthcare (nearly 1,000 free pre-built evidence-based workflow and decision models), APQC (Cross-Industry, Banking, Property & Casualty Insurance, Aerospace and Defense, Healthcare Provider, etc.).

Process Orchestration Patterns and Behaviors

Trisotech Process Orchestration provides a rich set of advanced patterns and behaviors to orchestrate end-to-end processes effectively. These include:

• Sequential: Enforce end-to-end business process logic without concurrent path execution.
• Parallel: Enable concurrent path execution while maintaining end-to-end business process logic.
• Logical Branching: Control orchestration paths using exclusive, inclusive, parallel, and event-based logic.
• Event-based: Support business logic based on internal and external events, including event stream and message queue support. Interrupting and non-interrupting events are also supported.
• Temporal: Incorporate timer and escalation events to introduce temporal support to orchestrations.
• Webhook Processing: Seamlessly integrate Webhooks to simplify communication and data exchange between applications.
• Compensations: Implement special event processing logic to handle errors, failures, or changes in a business process, allowing for the undoing or reversal of previously completed actions without disrupting the rest of the process flow.

Hi, I’m Sandy Kemsley of column2.com. I’m here for the Trisotech blog with the fourth in a series on best practices in business automation application development.

In the first episode, I talked about the automation imperative that’s driving all organizations to automate. There’s a lot of new technology that lets us automate things that were never possible before, and you need to be considering it or risk losing your competitive edge. In the second episode, I examined best practices in strategic vision including making business automation a strategic direction and picking the right processes to automate that will have the maximum impact. Then in the last episode, I looked at best practices in the design of business automation starting with metrics. Metrics need to be built into automation from the start and need to be aligned with corporate goals. I also talked about how design needs to be based on an understanding of the current process, not a micro level as is analysis but an understanding of the key elements of success or failure in that process. Go back and review those earlier episodes if you haven’t already watched them.

Now in this video, I’m continuing with more best practices in the design of business automation. I want to look at the two sides of automation design that may seem to be in conflict with each other, but they’re actually the most important balancing act that you’ll have during design.

The first of these is: automate whatever possible. If there’s a technology that you can apply to make your processes more automated, you should be considering it. The second is: don’t automate out people where they add value. In other words, don’t get so caught up in the thrill of automation that you remove some essential human contributions.

Just like Goldilocks however we need to know how to apply not too little or not too much automation but just the right amount. So, let’s look a little bit more closely at this.

Starting with the idea that we want to automate everything that’s possible in order to automate effectively. This is driven by some of the reasons that we turn to automation in the first place: efficiency, speed, cycle time, quality. These can all be significantly improved with appropriate automation. Notice I say appropriate automation. And improving these, leads not only to an improved bottom line in terms of costs, but also an improved top line revenue because of customer satisfaction. This is pretty classic business automation design where you look at what you’re doing now and how to make it faster, better and cheaper. That makes things more efficient, it makes them more cost effective and often leads to some improved customer satisfaction for fairly straightforward applications.

Now, automating repetitive tasks is the easiest way to start. So, you look at those individual steps, if it’s done the same time or the same way every time, then there’s almost always some way to automate it. It might be using decision management, business process management or robotic process automation, but the basic idea is to replace a repetitive human step with a repetitive automated step. Then you can look at the more complex automation of less repetitive tasks by using things like machine learning and artificial intelligence. ML and AI can make more nuanced decisions that would normally have to be done by a person who analyzes not only the current transaction, but also understands how similar transactions and decisions were handled previously. In other words, as the automation systems get smart enough to learn from context and past activities, they gain knowledge and skills in the same way that a person does when they’re learning how to do the same task.

Now, you also want to look above the task level. So, you’re not just looking at how can we make the individual tasks automated or not automated. Any sort of broad automation should be looking at the organizational goals and then think about whether things are really being done in the way that they should be. You don’t necessarily have to do it in the way you did in the past. Understanding what you want to get out of a particular end-to-end process, lets you step back and consider alternative processes and methods that may look completely different from what you’re doing.

Now, remember old school process re-engineering where the idea was to radically redesign processes? This is a little bit like that, but with tools that can actually enable a much smarter automated process. So, think about, let’s take insurance claims as an example, think about redesigning an insurance claims so that it has fully automated data and document capture at the front end, and auto adjudication for some simpler claims adjudication. This turns what was previously a very manual claims process on its head, and through the application of a variety of technologies you can have some claims that are completely hands off from beginning to end. Nobody inside your organization has to touch them, they process faster, they have fewer data entry errors, they apply data and decisions consistently… All of this makes the customers happier because you’re getting your job done faster and more effectively and more consistently. They also eliminate a lot of manual internal tasks and this makes your operations more efficient and less expensive. This also frees up your skilled knowledge workers for handling the tough problems and customer interactions that can’t be replaced by automation.

But animation is not a Panacea. You have to understand enough about your processes and your customers to know where your knowledge workers are adding value to the process. Sometimes that’s customer interaction where they’re dealing with customers directly in order to resolve complex problems. This most often happens when something goes wrong with the normal process such as a defective product or a customer profile that doesn’t match the usual pattern, in that case you want to get a knowledge worker involved as soon as you detect that the normal process isn’t working the way it should. Now, you can wait for your customer to type or say “agent, agent” on your IVR, your chat system or you can be proactive and recognize before they even realize it that something needs to be escalated to a person for resolution.

The other main situation for involving knowledge workers is when you have a complex decision that just can’t be made using automation technologies with any degree of certainty or can’t be made yet with automation technologies. If we think back to the insurance example, there are a lot of claims that are just too complex to adjudicate automatically. These are best handled by a combination of automated tasks and knowledge workers.

That last point really brings home the message about design that I’m making in this entire segment. A lot of processes are not all automation are all manual, they’re a combination of both. As a designer you need, to understand what can be automated and what is best left in the hands of the people in the process. As Technologies get smarter, though some of the things that are best done by knowledge workers today will be able to be automated effectively. If you think about auto adjudication insurance claims, this was never possible in the past and now, it’s starting to become possible for more and more complex claims.

We’ve seen this happen in a number of different ways in just the past few years, you look at manual decisions that the what-were manual decisions are now being handled automatically with ML and AI, it really drives home that automation design is a constantly moving target. And you need to understand how new technologies impact your business operations. That means you need to keep up on the automation technologies and see how they might be added into your business operations at any point in time to help improve things without impacting your customer satisfaction. This goes back to what I talked about in the last episode, where you need to understand the key points in your business operations, so that you know what makes them successful and what can make them fail and then consider where the people part of the processes contribute to that, versus what can be automated.

That’s all for today. Next time, I’m going to finish up the design, I knew I was going to do it this time, but I’m going to cover some of the main design anti-patterns or things to look for that indicate that you may have failed in your design somewhere. And then, after that I’m going to finish up the whole series with some best practices in implementation methodologies for business automation.

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 the third in a series on best practices in business automation application development.

In the first episode, I talked about the automation imperative: all of our cool new technology lets us automate faster and better and automate things that were never possible before. However, everyone else has access to the same technology and you need to up your game in business automation or your organization won’t be able to compete.

In the second episode, I looked at two best practices in strategic vision for business automation: first make business automation a strategic direction and second pick the right processes to automate, especially at the beginning. Go back and review those two earlier episodes if you haven’t already watched them.

In this video and the next one, I’m going to delve into one of my favorite topics: best practices in the design of business automation. Design best practice that I want to talk about is related to metrics.

Metrics

Metrics are often something that aren’t thought about until after a process has already been automated, but that’s completely the wrong way around. How do you even know what to design and implement unless you understand what’s important. Metrics are supposed to be measuring what’s important to your organization, and this is not just about departmental efficiency like doing the same things faster better and cheaper. You need to take more of a business architecture view of metrics where you start with the corporate goals at the top level, and then use that to create metrics for the end-to-end processes that might span multiple departments and multiple systems but are aligned back up to those corporate goals.

Improve Customer Satisfaction

Let’s say you have a corporate goal to improve customer satisfaction, and quite frankly who doesn’t? Now, that’s a little bit fuzzy, but if you dig into the reasons for your customers dissatisfaction, you’ll probably find that this translates to operational metrics at the lowest level. Things like quality, end-to-end cycle time, process transparency. In other words, if you could process the customer’s order right the first time, get it done faster, and let them see what’s happening with the order during the process, then your customer satisfaction is going to improve.

Now, what does this translate to from a design standpoint. Well, if you want to improve the quality of your customers orders then automation is definitely your friend. Look for the places in the process where a person is doing repetitive tasks and decisions that are prone to errors. Such as re-entering data between your website and your order fulfillment system or deciding which shipping method to use. You can then design in automated connections between systems and design automated decisions where there are clear business rules. This is going to improve data accuracy and reduce decision variability. Both of which are major contributors to the entire process quality.

Improving the End-to-End Cycle Time

Now, adding this type of automation also has the effect of improving the end-to-end cycle time which is the second metric that would be related to our corporate goal of improving customer satisfaction those activities and decisions which are automated now happen instantaneously at any time of the day or night rather than waiting for a person to process them during regular business hours.

Process Transparency

Now, the third metric commonly tied to customer satisfaction is process transparency. The customer wants to know what’s happening with their order and you need to build in ways for them to get that. It might be notifications sent to their email when a Milestones reached or it could be a portal that they log into and then they can check the order status directly themselves. Once you have an end-to-end process orchestrated, even if it’s a combination of automated and manual steps across all these different systems and departments, you can start to add in these points of visibility for your customer to track their own order. And then, as an extra bonus providing ways for them to serve themselves in terms of monitoring their order, you reduce the number of calls into your call center. This reduces your costs at the same time as you’re improving customer satisfaction. Total win-win!

So that’s just an example of how you can take a corporate goal like customer satisfaction and take that down to the level of operational metrics, which you need to do. You then need to make sure you have those linkages between those operational metrics and the corporate goals so you can make sure that they’re serving the goals in the right way.

Understand what makes the process work

Now, the second design best practice that I want to raise here is that you need to understand what makes the current process work, or not work. This doesn’t mean that you do a micro level as is analysis and then just automate the same steps, rather you need to dig into your current business process to understand the key elements of success.

You might start with some sort of automated analysis like process mining, for introspecting your current process, but the key thing here is to go out and talk to the knowledge workers who currently work in the process. There is a great episode of Michael Lewis’s against the rules podcast that really highlights this Michael Lewis is the guy who wrote the book Moneyball and several other books that dig into why certain things in business work the way that they do. Now this episode is called six levels down, and it looks at a U.S Healthcare billing company and about how they became successful by gaining a deep understanding in how to apply the right billing rule at the right time. And the way that they did this was to find the people six levels down from the top of the organization. In other words, the people who actually do the work and understand how and why things work. These are the people at the front lines of the business processes, even if they’re not customer facing, they understand which parts of the process and rules are necessary and which are not.

And spending time with these frontline people while they’re doing their work has always been part of my design process. I definitely see this as part of this best practice of understanding what a process needs to do in order to be successful and I think that that’s something that you should incorporate into your design practices when you’re looking at business automation.

Now, once you’ve understood what’s needed to make the the process successful you have to look at how to design that into your new automated process. You might have some parts that still remain manual because that’s part of the secret sauce of the process, but you’ll probably find a lot will be able to be automated. You have to start however with that kernel of truth about what’s required for a successful customer transaction within that process.

That’s all we have time for today. Next time I’m going to talk about two more best practices in business automation design and a couple of failure indicators that you can watch out for. I’ll wrap up the series after that with a video on best practices in implementation methodologies.

That’s it 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.