bpmNEXT 2019 demo: intelligent BPM by @SAP plus DMN TCK working group

ML, Conversational UX, and Intelligence in BPM, with Andre Hofeditz and Seshadri Sreeniva of SAP plus DMN TCK update

We’re at the end of bpmNEXT for another year, and we have one last demo. Seshadri showed a demo of their intelligent BPM for an employee onboarding process (integrated with SuccessFactors), where the process can vary widely depending on level, location and other characteristics. This exposes the pre-defined business processes in SuccessFactors, with configuration tools for customizing the process by adding and modifying building blocks to create a process variant for a special case. Decisions involved in the processes can also be configured, as well as dashboards for viewing the processes in flight. Extension workflows can be created by selecting a standard process “recipe” from a SuccessFactors library, then configuring it for the specific use; he showed an example here for adding an equipment provisioning extension that can be added as a service task to one of the top-level process models. He demonstrated a voice-controlled chatbot interface for interacting with processes, allowing a manager to ask what’s happening for them today, and get back information on the new employee onboardings in progress, and expected delays and a link to his task inbox. Tasks can be displayed in the chat interface, and approvals accepted via voice or typed chat. The chatbot is using AI for determining the intent of the input and providing a precise and accurate response, and using ML to provide predictions on the time required to complete processes that are in flight if asked about completion times and possible delays. The chatbot can also make decision table-based recommendations such as creating an IT ticket to assign roles to the new employee and find a desk location. He showed the interface for designing and training the bot capabilities, where a designer can create a new conversational AI skill based on conditions, triggers and actions to take. This is currently a lab preview, but will be rolled out as part of their cloud platform workflow (not unique to the SuccessFactors environment) in the coming months.

Decision Model and Notation Technology Compatibility Kit update with Keith Swenson

We finished off bpmNEXT 2019 with an update on the DMN TCK, that is, the set of tools provided for free for vendors to test their implementation of DMN. The TCK provides DMN 1.2 models plus sets of input data and expected results; a runner app calls the vendor engine, compares the results and exports them as a CSV file to show compliance. In the three years since this was kicked off, there are eight vendors showing results and over 1000 test cases, with another vendor about to join the list and add another 600 test cases. The test cases are determined through manual examination of the standard specification, so represents a significant amount of work to create this robust set of compliance tests. The TCK group is not creating the standard, but testing it; however, Keith identified some opportunities for the TCK to be more proactive in defining some things such as error handling behavior that the revision task force (RTF) at OMG are unlikely to address in the near term. He also pointed out that there are many more vendors claiming DMN compatibility than have demonstrated that compatibility with the TCK.

That’s it for bpmNEXT 2019 – always feels like it’s over too soon, yet I leave with my brain stuffed full of so many good ideas. We’ve done the wrapup survey and heading off to lunch, but the results on Best in Show won’t come out until I’m already on my way to the airport.

bpmNEXT 2019 demos focused on creating smarter processes: decisions, RPA, emergent processes and machine learning with Serco, @FujitsuAmerica and @RedHat

A Well-Mixed Cocktail: Blending Decision and RPA Technologies in 1st Gen Design Patterns, with Lloyd Dugan of Serco

Lloyd showed a scenario of using decision management to determine if a step could be done by RPA or a human operator, then modeling the RPA “operator” as a role (performer) for a specific task and dynamically assigning work – this is instead of refactoring the BPMS process to include specific RPA robot service tasks. This is shown from an actual case study that uses Sapiens for decision management and Appian for case/process management, with Kapow for RPA. The focus here is on the work assignment decisioning, since the real-world scenario is managing work for thousands of heads-down users, and the redirection of work to RPA can have huge overall cost savings and efficiency improvement even for small tasks such as logging in to the multiple systems required for a user to do work. The RPA flow was created, in part, via the procedural documentation wiki that is provided to train and guide users, and if the robot can’t work a task through to completion then it is passed off to a human operator. The “demo” was actually a pre-recorded screen video, so more like a presentation with a few dynamic bits, but gave an insight into how DM and RPA can be added to an existing complex process in a BPMS to improve efficiency and intelligence. Using this method, work can gradually be carved off and performed by robots (either completely or partially) without significantly refactoring the BPMS process for specific robot tasks.

Emergent Synthetic Process, with Keith Swenson of Fujitsu

Keith’s demo is based on the premise that although business processes can appear to be simple on the surface when you look at that original clean model, the reality is considerably messier. Instead of predefining a process and forcing workers to follow that in order, he shows defining service descriptions as tasks with their required participants and predecessor tasks. From that, processes can be synthesized at any point during execution that meet the requirements of the remaining tasks; this means that any given process instance may have the tasks in a different order and still be compliant. He showed a use case of a travel authorization process from within Fujitsu, where a travel request automatically generates an initial process – all processes are a straight-through series of steps – but any changes to the parameters of the request may modify the model. This is all based on satisfying the conditions defined by the dependency graph (e.g., departmental manager requires that the manager approve before they can approve it), starting with the end point and chaining backwards through the graph to create the series of steps that have to be performed. Different divisions had different rules around their processes, specifically the Mexico group did not have departmental levels so did not have one of the levels of approval. Adding a step to a process is a matter of adding it as a prerequisite for another task; the new step will then be added to the process and the underlying dependency graph. As an instance executes, the completed tasks become fixed as history but the future tasks can change if there are changes to the tasks dependencies or participants. This methodology allows multiple stakeholders to define and change service descriptions without having a single process owner controlling the end-to-end process orchestration, and have new and in-flight processes generate the optimal path forward.

Automating Human-Centric Processes with Machine Learning, with Kris Verlaenen of Red Hat

Kris demonstrated working towards an automated process using machine learning (random forest model) in incremental small steps: first, augmenting data, then recommending the next step, and finally learning from what happened in order to potentially automate a task. The scenario was provisioning a new laptop inside an organization through their IT department, including approval, ordering and deployment to the employee. He started with the initial manual process for the first part of this – order by employee, quote provided by vendor, and approval by manager – and looked at  how ML could monitor this process over many execution instances, then start providing recommendations to the manager on whether to approve a purchase or not based on parameters such as the requester and the laptop brand. Very consistent history will result in high confidence levels of the recommendation, although more realistic history may have lower confidence levels; the manager can be presented with the confidence level and the parameters on which that was based along with the recommendation itself. In case management scenarios with dynamic task creation, the ML can also make recommendations about creating tasks at a certain stage, such as creating a new task to notify the legal department when the employee is in a certain country. Eventually, this can make recommendations about how to change the initial process/case model to encode that knowledge as new rules and activities, such as adding ad hoc tasks for the tasks that were being added manually, triggered based on new rules detected in the historical instances. Kris finished with the caveat that machine learning algorithms can be biased by the training data and may not learn the correct behavior; this is why they look at using ML to assist users before incorporating this learned behavior into the pre-defined process or case models.

bpmNEXT 2019 demos: microservices, robots and intentional processes with @Bonitasoft @Signavio and @Flowable

BPM, Serverless and Microservices: Innovative Scaling on the Cloud with Philippe Laumay and Thomas Bouffard of Bonitasoft

Turns out that my microservices talk this morning was a good lead-in to a few different presentations: Bonitasoft has moved to a serverless microservices architecture, and the pros and cons of this approach. Their key reason was scalability, especially where platform load is unpredictable. The demo showed an example of starting a new case (process instance) in a monolithic model under no load conditions, then the same with a simulated load, where the user response in the new case was significantly degraded. They then demoed the same scenario but scaling the BPM engine by deploying it multiple times in componentized “pods” in Kubernetes, where Kubernetes can automatically scale up further as load increases. This time, the user experience on the loaded system was considerably faster. This isn’t a pure microservices approach in that they are scaling a common BPM engine (hence a shared database even if there are multiple process servers), not embedding the engine within the microservices, but it does allow for easy scaling of the shared server platform. This requires cluster management for communicating between the pods and keeping state in sync. The final step of the demo was to externalize the execution completely to AWS Lambda by creating a BPM Lambda function for a serverless execution.

Performance Management for Robots, with Mark McGregor and Alessandro Manzi of Signavio

Just like human performers, robots in an RPA scenario need to have their performance monitored and managed: they need the right skills and training, and if they aren’t performing as expected, they should be replaced. Signavio does this by using their Process Intelligence (process mining) to discover potential bottleneck tasks to apply RPA and create a baseline for the pre-RPA processes. By identifying tasks that could be automated using robots, Alessandro demonstrated how they could simulate scenarios with and without robots that include cost and time. All of the simulation results can be exported as an Excel sheet for further visualization and analysis, although their dashboard tools provide a good view of the results. Once robots have been deployed, they can use process mining again to compare against the earlier analysis results as well as seeing the performance trends. In the demo, we saw that the robots at different tasks (potentially from different vendors) could have different performance results, with some requiring either replacement, upgrading or removal. He finished with a demo of their “Lights-On” view that combines process modeling and mining, where traffic lights linked to the mining performance analysis are displayed in place in the model in order to make changes more easily.

The Case of the Intentional Process, with Paul Holmes-Higgin and Micha Kiener of Flowable

The last demo of the day was Flowable showing how they combined trigger, sentry, declarative and stage concepts from CMMN with microprocesses (process fragments) to contain chatbot processes. Essentially, they’re using a CMMN case folder and stages as intelligent containers for small chatbot processes; this allows, for example, separation and coordination of multiple chatbot roles when dealing with a multi-product client such as a banking client that does both business banking and personal investments with the bank. The chat needs to switch context in order to provide the required separation of information between business and personal accounts. “Intents” as identified by the chatbot AI are handled as inbound signals to the CMMN stages, firing off the associated process fragment for the correct chatbot role. The process fragment can then drive the chatbot to walk the client through a process for the requested service, such as KYC and signing a waiver for onboarding with a new investment category, in a context-sensitive manner that is aware of the customer scenario and what has happened already. The chatbot processes can even hand the chat over to a human financial advisor or other customer support person, who would see the chat history and be able to continue the conversation in a manner that is seamless to the client. The digital assistant is still there for the advisor, and can detect their intentions and privately offer to kick off processes for them, such as preparing a proposal for the client, or prevent messages that may violate privacy or regulatory compliance. The advisor’s task list contains tasks that may be the result of conversations such as this, but will also include internally created and assigned tasks. The advisor can also provide a QR code to the client via chat that will link to a WhatsApp (or other messaging platform) version of the conversation: less capable than the full Flowable chat interface since it’s limited to text, but preferred by some clients. If the client changes context, in this case switching from private banking questions to a business banking request, the chatbot an switch seamlessly to responding to that request, although the advisor’s view would show separate private and business banking cases for regulatory reasons. Watch the video when it comes out for a great discussion at the end on using CMMN stages in combination with BPMN for reacting to events and context switching. It appears that chatbots have officially moved from “toy” to “useful”, and CMMN just got real.

bpmNEXT 2019 demos: Appian

Usually I blog about the demos in groups, but Malcolm Ross of Appian was the lone demo between the panel and lunch so he gets his own post. Smile

As a reminder, demos are a five-minute Ignite-style presentation (20 slides with an auto-advance every 15 seconds) followed by a live demo and Q&A. Malcolm had a lot to say, however, so had five minutes of slide followed by another four minutes of talk in front of a looping video before he started the actual demo.

Malcolm’s demo is on realigning BPM in the age of intelligent automation, in the context of different automation technologies (RPA, AI, BPM, integration) that are being sold as separate solutions into organizations. Not surprisingly, he positions BPM as the core technology and integration platform, but they also OEM Blue Prism’s RPA into their product suite and can integrate with many other web services to take part in the automation. He demonstrated an invoice processing application where he uploaded an invoice PDF where the data was captured using an RPA bot where BPM was used for exception handling when the bot couldn’t complete its task as well as overall monitoring of processes including the bot tasks. He walked through some of their design-time experience that is focused on integration, showing how connections to services from Blue Prism, Automation Anywhere, AWS machine learning, Google NLP and others can be used to create integration points that can then be called from their BPM processes. Good use case of using BPM and RPA together – they are much more complementary than competitive – by allowing RPA tasks to be orchestrated and monitored as part of a larger BPM process. He also had a great analogy when asked about deciding when to use RPA versus BPM: RPA is like a pain reliever that provides temporary relief, while BPM (and SOA) is like an antibiotic that cures the underlying problem.

bpmNEXT 2019 keynote: @JimSinur on technology combinations that digitally deliver

Our second keynote on the first day of bpmNEXT 2019 is with long-time presenter Jim Sinur, looking at technology combinations that digitally deliver. Unlike his usual focus on future directions, he’s driving down into what technologies work for companies that are undergoing digital transformation. This is a great lead-in to what I’ll be talking about tomorrow morning, and I fully expect to be fine-tuning my presentation before then to incorporate ideas from Jim’s presentation as well as Nathaniel Palmer’s presentation that preceded it.

IMG_3358Digital business platforms – something bigger than a BPMS – provide the real pathway to digital transformation, combining a variety of technologies. The traditional BPMS products are strong in work/process management, but they also need proactive intelligence, integration, automation, IoT enablement and business functionality. He looks at technical streams and their benefits, ranging from computational technologies to consumer delivery channels. He had a draft version of a matrix that he’s working on that shows attributes for these different technologies, from skill level required to get started with the technology to the likelihood of the vendors in this category partnering with other category vendors successfully, IMG_3360leading to a list of top productive pairs and triplets that we’re seeing in the market today: BPM and AI, for example, for processes with smart resources and actions; or architecture, low code and RPA for incremental transformation of legacy.

He finished up with how we will be leveraging the trends for marketplace collaboration between vendor products, and encouraging the vendors in the room (mostly everybody) to collaborate along the lines of his top pairs and triplets. In my opinion, this won’t necessarily being the vendors deciding to partner to offer joint solutions, but larger enterprises deciding to roll their own platforms using a combination of best-of-breed technologies that they select themselves: the vendors will need to make sure that their products can be sliced, diced and re-integrated in the way that the customers want.

Slide decks and videos of all presentations will be online within a day or two; I’ll come back and update all of the posts with links then.

Kicking off bpmNEXT 2019 with @NathanielPalmer

Except for a hiatus in 2017, I’ve been at every bpmNEXT since its inception in 2013, created and hosted by Bruce Silver and Nathaniel Palmer as a showcase for new ideas in BPM and related technologies. This is not a conference for (potential) customers, but a place for vendors, researchers and analysts to come together to exchange ideas about what’s happening in the marketplace and the technology labs. Most of the agenda is made up of 30-minute demo sessions with a few panels and keynotes sprinkled in.

Nathaniel Palmer started our first day with a look forward at the next five years of BPM by considering the five-year span from 2015 to 2020 and how his predictions are playing out from his first predictions keynote. In 2015, he talked about intelligent automation; today, we’re seeing robots and rules-based automation as an integral part of how business is done. This is pretty crucial, because the average number of systems required to present a complete view of a customer is 13.2 (!), 8 of which are external, with 80% of firms stating that they use more than 10 systems to get that a 360 degree view. He talks about the need for an intelligent automation platform that includes robotic automation, AI and machine learning, decision management, and process management, communicating with events and data via an event gateway/bus. He believes that the role of a BPMS is also to provide the framework for development and to build the user interface – an idea that I’ll be debating somewhat in my keynote tomorrow – but sees always-on, context-driven devices such as smart speakers as the future of how we interact with systems rather than traditional computers and smartphones. That means that conversational interaction will take over from worklist metaphors for common processes for consumers and employees; my interpretation of this is that the task-focused activities are those that will be automated, leaving the more fluid activities for people to deal with.

A consideration of this changing nature of automation is how to model this. Our traditional workflows have a pre-defined path, whereas intelligent automation (with more of a case management/ad hoc paradigm) has more adaptable processes driven by rules and business context. It’s more like using Waze for dynamically-adjusted driving directions rather than a pre-conceived idea of what route to follow. The danger with this – in my experience with Waze and adaptable business processes – is that you could end up on a route that is not generally followed, messes up the people who have to get involved along the route, and definitely isn’t repeatable or scalable: better for that specific instance and its participants, but possibly detrimental to others. The potential gain is, of course, that the process as a whole is more resilient because it responds to events by determining an action that will reach the goal, and you may just find a new and better way of doing something. Respond to events, definitely, but at some point take a step back and consider the impact of the new pathways that you’re carving out.

IMG_3352He spoke about problems with AI/ML and training data biases – robots are only as smart as your training data – and highlighted that BPM platforms are a great source of training data via process mining.and analysis.

Insightful as always, and it will be interesting to see these themes play out in the demos over the next three days.

2019 @Alfresco Day: RBC Capital Markets

Yesterday at the analyst day, Alfresco CEO Bernadette Nixon had a fireside chat with Jim Williams of RBC about their Alfresco journey, and today at the user conference, Williams gave us more of the details of what they’re doing. They had an aging platform (built on Pega) that wasn’t able to support their derivatives business operations adequately, having been designed for a single purpose without the ability to easily change, resulting in many manual processes.

They wanted to have a single BPM and ECM platform that would span all of their business areas for handling regulatory documentation, and they started in 2015 with their equities operations: not because it was easy, low-hanging fruit, but because it was complex and essential to get it right. They now have 14 applications built on the same framework, and 3,500+ users. Williams said that they specifically liked Alfresco because it doesn’t try to be everything but integrates with other products and services to do functions such as reporting or OCR; this is particularly interesting in the face of other vendor platforms that want to be everything to everyone, and don’t do some of the functions very well.

By 2016, they had rolled out applications in tax operations, which was essential to the changing IRS rules that required foreign banks like RBC to withhold tax on US investments unless clients could prove that they met non-resident requirements. This had to integrate with many of their other operational processes that followed. They also implemented content and process applications for HR due to some of their complex job role management in the UK, reducing dependency on spreadsheets and email for what are essentially core processes.

Like all of the very conservative Canadian financial institutions, their Alfresco implementation is all on premise rather than cloud, although they have cloud ambitions. It’s also important to note that although RBC is Canada’s largest bank, Capital Markets is a relatively small part of it; it will be interesting to see if Williams can carry the Alfresco message to other parts of the organization.

2019 @Alfresco Day: Go To Market Strategy

Jennifer Smith, Alfresco’s CMO, gave us an expansion of the GTM strategy that Bernadette Nixon spoke about earlier today.

Their platform is based on a single cloud-native platform combining content, process and governance services, on which they identify three pillars of their horizontal platform approach:

  • Modernization and migration, providing tools for migrating to Alfresco quickly and with minimal risk
  • Coexistence and integration, allowing for easy integration with third-party services and legacy systems
  • Cloud-native and AWS-first, with deep integration and support for AWS cloud platform, storage and AI/ML services

Their vertical use case approach is based on a typical land-and-expand strategy: they take an existing implementation with a customer and find other use cases within that organization to leverage the platform benefits, then work with a large enterprise or partner to develop managed vertical solutions.

We saw a demo of a citizen services scenario: to paraphrase, a government agency has old, siloed systems and bad processes, but citizens want to interact with that agency in the same way that they interact with other services such as their bank. In a modernized passport application example, the process would include document upload directly by the citizen, intelligent classification and extraction from the documents, fraud detection by integration with other data sources, natural language translation to communicate with foreign agencies, and tasks for manual review. Although the process and content bits are handled natively by Alfresco, much of the intelligence is based on Amazon services such as Comprehend and Textract — Alfresco’s partnership with Amazon and AWS-native platform make this a natural fit.

We’re off to some breakouts now then partner strategy this afternoon, so it might be quiet here until tomorrow.

2019 @Alfresco Analyst Day: use case with RBC Capital Markets

Jim Williams, Head of Operations and Shared Services Technology for RBC Capital Markets had an on-stage fireside chat with Bernadette Nixon about what they’ve been doing with Alfresco over the past five years.

The focus of their implementation is back office operations, including trade confirmations, settlement and other transactions, especially with all of the regulatory implications and changes. They started looking at this in 2015 for a specific use case (equity trade confirmations) when they had no cohesive platform and many manual processes, and now have several different applications on Alfresco technology. Their transactions tend to be more complex, not just simple financial transactions, so have specific concerns with integrating multiple sources of information, and multiple business rules regarding regulations and compliance. They were an early customer for the Application Development Framework (ADF), and it has allowed them to build apps more quickly due to shared components such as single signon. They’re now replacing some of their 10-year-old legacy processes that were initially on Pega, providing more agility in the deployed processes.

He shared some great feedback from the actual users of the applications on their experience and the benefits that they’re seeing, which included the usual operational hot buttons of simplification, cost reduction, productivity increase, reduced risk and scalability, plus innovation and transformation. He joked that they’ve reduced their organizational dependency on Excel, but that’s a very real measure: when I work with enterprise customers on improving processes, I always look for the “spreadsheet and email” processes that we need to replace.

They explored RPA technology but came to the inevitable conclusion that it was just a stopgap: it can make a bad process work a bit faster or better, but it doesn’t fundamentally make it a good process. This was an interesting comment following on a side conversation that I had with Nixon at the break about how Lean Six Sigma initiatives — still all the rage in many financial organizations — are more about incremental improvement than transformation.

Happy to see a process-centric use case taking top billing here: I may need to reassess my earlier statement that Alfresco sometimes forgets about process. 🙂

2019 @Alfresco Analyst Day: update and strategy with @bvnixon

Bernadette Nixon, who assumed the role of CEO after Alfresco’s acquisition last year, opened the analyst day with the company strategy. They seem to be taking a shot at several of their competitors by pushing the idea that they’re one platform, built from the ground up as a single integrated platform rather than being a “Frankenplatform” pieced together from acquisitions. Arguably, Activiti grew up inside Alfresco as quite a separate project from the content side and I’m not sure it’s really as integrated as the other bits, but Alfresco sometimes forgets that content isn’t everything.

Nixon walked through what’s happened in the past year, starting with some of their customer success stories — wins against mainstream competitors, fast implementations and happy customers — and how they’ve added 126 new customer logos in the past year while maintaining a high customer renewal rate. They’ve maintained a good growth rate, and moved to profitability in order to invest back into the company for customer success, developing their teams, brand refresh, engineering and more. They’ve added many of the big SIs as new partners and are obviously working with the partner channel for success, since they’ve doubled their partner win rate. They’ve added five new products, including their Application Development Framework which is the core for some of the other products as well as the cornerstone of partner and customer success for fast implementation.

They commissioned a study that showed that most organizations want to be deployed in the cloud, have better control over their processes, and be able to create applications faster (wait…they paid for that advice?); more interestingly, they found that 35% of enterprises want to switch out their BPM and ECM platforms in the next few years, providing a huge opportunity for Alfresco and other disruptive vendors.

Alfresco is addressing the basic strategy of a horizontal platform approach versus a use case vertical approach: are they a platform vendor or an application vendor? Their product strategy is betting on their Alfresco Digital Business Platform targeted at the technical buyer, but also developing a go-to-market approach that highlights use cases primarily in government and insurance for the business/operational buyer. They don’t have off-the-shelf apps — that’s for their partners or their customers to develop — but will continue to present use cases that resonate with their target market of financial services, insurance, government and manufacturing.

A good start to the day — I’ll be here all day at the analyst conference, then staying on tomorrow for the user conference.