19th Century French Philosophy, SkyNet, and the 4 realities of RPA

Any cocktail party conversation about AI in Silicon Valley inevitably comes around to the topic of SkyNet and the subsequent post-apocalyptic, dystopian future that will inevitably befall us as AI becomes more powerful. While the Terminator series does paint a bleak future for humanity, the focus on SkyNet distracts from another central theme, that AI (cleverly disguised as Arnold Schwarzenegger) working with mankind is the best hope for a prosperous future. However, having experienced multiple organizations struggle with automation, the idea of AI/human integration, while great in theory, is challenging in practice.

‘Plus ça change, plus c’est la même chose.’

Organizations on the transformation journey should take the counterintuitive approach of embracing both change and the status quo. This idea is embodied by French philosopher Jean-Baptiste Alphonse Karr’s “plus ça change, plus c’est la même chose” literally translated “The more things change, the more they stay the same.” The notion that automation must work together with humans within many of the existing constructs of an organization, lays the groundwork for a successful strategy. This strategy must integrate the four realities of RPA, which will impact the majority of your enterprise processes.

Reality #1 – Your Processes Aren’t Going Away

Anyone that has worked with current RPA tools will tell you that they can be severely lacking, and that the chance of their automating one hundred percent of a process is between slim and none. A recent study by McKinsey echoes this, finding that on average about thirty percent of a process can be automated using today’s technologies. Even as this percentage creeps higher we have to deal with the fact that the activities automated are often not continuous, human judgement must often be interjected, and exceptions happen. All of this means todays automation technologies can’t support a fundamental rewrite of your processes, yet.

Reality #2 – Your IT Systems Aren’t Going Away

One the strengths of RPA is its ability to work with legacy systems, giving organizations the ability to get more miles out of their existing technology. It’s not just the systems here that matter, though. The overall enterprise IT architecture supporting the application of identity, change management, security, integrations, and infrastructure all are going to be around for a very long time.

Reality #3 – Your People Aren’t Going Away

If your processes aren’t going away, and your systems aren’t going away, your people can’t go away. While the actual number of people executing activities may drop, the groups involved in the process execution will likely increase. IT, Security, Risk, and Compliance will be more involved in the day to day operations, now that RPA is embedded in a process.

Reality #4 – Your Organization’s Structure Isn’t Going Away

If people, process and systems aren’t going away, neither are the organizations that support them. While we can all agree that no organization is perfect, there are oftentimes decades of best practices embedded in their policies and procedures. Every component of an organization has evolved with a specific purpose that, while maybe not obvious, will most likely need to be addressed in the context of RPA.

When constructing our go-to-market strategies, we look at how can we lower the “switching costs” of our solution. A high switching cost means higher risks, higher expenses (impacting ROI), and usually a built in constituency that doesn’t look at change favorably. The same is true about an automation program. Every time the people, process, systems or org need to change, the switching costs are increasing. The key to the speed, and success of an RPA program lies in figuring out how to leverage components of the existing, while delivering change. It will at times be necessary to make large, transformational changes, but a land and expand strategy, while delivering value, presents the highest likelihood of success.

FortressIQ Exits Stealth with $16 Million in Funding to Bring Cognitive Process Analysis to the Enterprise

Its Virtual Process Analyst combines Computer Vision + Natural Language + Sequence Modeling to accelerate digital transformation projects for the Global 2000, reducing current state assessments from months to weeks, lowering costs by up to 90%

  • Innovative AI platform automates the understanding of processes, amplifying an organization’s ability to leverage technologies such as Robotic Process Automation and Conversational Agents
  • Process comprehension surpasses existing technologies by connecting disjointed processes and uncovering business rules–without any integrations, log access or manual data mapping
  • SAN FRANCISCO, December 4, 2018 – FortressIQ, creator of a cognitive automation platform that powers and accelerates digital transformation through imitation learning, today announced it has raised $12 million in Series A financing from Lightspeed Venture Partners. This new capital extends $4 million in seed funding from Boldstart Ventures, Comcast Ventures and Eniac Ventures, bringing the total funds raised to date to $16 million.

    Founded in 2017, FortressIQ pioneered a fundamentally new way for Global 2000 companies to achieve their transformation goals. Recognizing that the largest obstacle to digital transformation is the lack of detailed information on current state of operations, FortressIQ spent 18 months working with corporate transformation teams building AI to solve their most pressing needs. This powerful platform addresses Peter Drucker’s age-old maxim that “If you can’t measure it, you can’t improve it,” by delivering the quantified workforce and providing the data today’s enterprise needs to optimize their transformation initiatives.

    “FortressIQ’s technology provides the insights necessary for a global organization to integrate the digital workforce into their operations,” said Nakul Mandan, partner at Lightspeed Venture Partners. “We think the company’s strong management team, with its data-driven approach, is uniquely positioned to alter the expensive, slow and inefficient methodologies that prevent businesses from fully realizing their digital transformation initiatives today.”

    Each year, billions of dollars and millions of hours are spent with process analysts interviewing users and generating documentation. The large diversity, age and customization of enterprise applications has made traditional technical solutions to this problem impossible, forcing organizations that want a detailed understanding of their complex operations down a laborious and often error-prone path that can take months for a single business function. As PwC puts it, we should “imagine a world in which automation tools watch how we work, then use artificial intelligence (AI)-driven insights to tell us how to work better (or upgrade our work efforts for us) on the fly.” FortressIQ delivers that vision.

    How the FortressIQ Cognitive Automation Platform Works

    At the heart of the platform is the FortressIQ Virtual Process Analyst, which structurally changes how companies acquire knowledge of their operations, learning the same way that humans do, through observation. The Virtual Process Analyst transparently learns a business process and all its permutations as they occur in real-time, then assesses, analyzes and provides valuable insights to guide operational change in days.

    Leveraging computer vision instead of user interviews, APIs or transaction logs, the Virtual Process Analyst requires zero integration and can work with any application, on premise or cloud based, instantly. FortressIQ provides a time to value in weeks, and meets all the needs of compliance, IT/Infosec and operations.

    “We’re thrilled to partner with a premier enterprise investor like Lightspeed, which has deep knowledge of the Global 2000 and their digital needs,” said Pankaj Chowdhry, founder and CEO of FortressIQ. “After speaking with hundreds of companies it became clear that the dollars being wasted on process assessments and documentation are a ‘tax on innovation,’ syphoning dollars from transformation initiatives. To address this grim reality, we’re delivering on the promise of cognitive automation with the GA release of our platform, putting us in a strong position to build on the sales momentum and traction we’re already experiencing with Fortune 500 companies.”

    FortressIQ will be at the Intelligent Automation Austin conference on December 4-5. Stop by booth 35 to learn more and see a demo of FortressIQ’s imitation learning technology in action.

    About FortressIQ

    FortressIQ is the creator of a cognitive automation platform that powers and accelerates digital transformation through imitation learning. Using an innovative type of AI that combines computer vision, natural language and sequence modeling, FortressIQ learns how a business functions through live activity analysis. By radically lowering the cost, effort and time required to document business processes, Global 2000 customers can use FortressIQ’s platform to quickly gain the insights necessary to improve business operations. The FortressIQ platform requires no integration or API access, works with all applications whether proprietary or commercial, and delivers a time to value in weeks.

    Founded in 2017, FortressIQ is backed by Lightspeed Venture Partners, Boldstart Ventures, Comcast Ventures and Eniac Ventures. To learn more please visit: https://fortressiq.chesh.link/

    Media Contact: Jill Reed Sift Communications jill.reed@siftpr.com

    The Importance of Accurate Process Discovery

    Discovery, Documentation, Due Diligence, and Details: The Importance of Accurate Process Discovery

    Detailed process discovery is a necessary component for any desired process change to make a significant positive impact.

    Enterprise organizations across industries are looking to optimize their business processes, especially in non-revenue producing departments. When done successfully, this can lead to an increase in revenue, reduction in spend, and deliver better experiences for their employees and customers.

    The desired outcomes from detailed process discovery generally fall into the following categories:

    • Fix the process – often may involve employee re-training
    • Fix the system – legacy systems and old versions of systems could be slowing the process and stalling productivity
    • Outsource the process – business process outsourcing (BPO) companies may be able to save significant amounts of money, especially for common tasks
    • Automate the process — or a portion of the process, using RPA or more complex AI methods
    • Transform the process – maybe the process is moved to interact with a different system, or maybe the process is eliminated altogether if there is serious lack of efficiency

    Although different solutions exist for these scenarios, in order to be executed successfully, there is one thing needed: a detailed account of the current state operations of the processes identified.

    What is Automated Process Discovery?

    Using an AI-driven approach to discovery – instead of traditional methods – delivers a more accurate picture of your current state operations and does so in a fraction of the time. Many shifts in a business process, especially when looking at areas to automate or outsource, need to contain detailed documentation on the steps in the process, what order the tasks go in, and the length of time it takes to complete the process, etc.

    Cathy Tornbohm, an analyst at Gartner, writes about robotic process discovery in the August 2019 report Differentiate BPO Via Advanced Process Capture. FortressIQ is cited as a tool provider for robotics process discovery in the report.

    “Robotic process discovery helps discover the sequence of the different steps that are being
    undertaken. This improves the accuracy of the process document and reduces the laborious
    preparation of designing with teams of people the exact process that needs to be completed,” states Gartner.

    We see that using computer vision and other AI technology, employees executing a process, or series of tasks within a larger process are observed and an auto-process discovery tool like FortressIQ can map out all the various ways that process and/or task is currently being done.

    Why Do We Think Automated Process Discovery is Important?

    The traditional method of using people – whether you use an internal team or bring in outside help – to interview staff and manually document processes is tired.

    Gartner writes, “Much of the hard work that goes into winning a new business process outsourcing (BPO) client can have its profit margin rapidly eroded by the new client’s ignorance over its ‘as-is’ process state. Manual process capture usually extends BPO transfer of service time while building up significant costs from travel expenses (for hotels, flights, meals, visas) and labor time to complete the documentation.

    This is often a highly inefficient process; it relies on small numbers of trained individuals from the provider — with limited or no visibility on the client’s internal business and process challenges/issues — to capture the information. In addition, often when people are interviewed, they do not recall what they do 100% of the time. For example, people will forget seasonal activities and up to about 30% of their ad hoc tasks.”

    We believe using a solution to discover, document, and provide detailed information on your processes will save you countless hours in time, as well as millions in spend. And, it will provide a more accurate picture of your current state operations, helping to improve your business more quickly over time.

    Delivering Process Intelligence to Microsoft Power Automate Customers

    “We create technology so others can create more technology.”

    – Satya Nadella, Microsoft

    Process Discovery vs Process Mining and Mapping | FortressIQ

    Digital transformation can be a lot like constructing a highrise building. Starting off on a digital transformation journey can be easy, just as building the ground floor of a highrise can be easy, but the more floors you add, the more structural support you need and things quickly become complicated and can break down. Referencing a McKinsey study, a recent Forbes article, “Companies That Failed At Digital Transformation And What We Can Learn From Them,” declared that “a staggering 70% of digital transformation projects fail” because of roadblocks they encounter that cannot be overcome.

    To achieve success you need a way to eliminate many of these roadblocks from popping up when you’re far down the path of a digital transformation project. One method is to gain a deep understanding of current state business operations. Various methods exist, and some are more detailed and accurate than others. So, let’s break it down.

    There are 3 major methods used to gain a complete understanding of your current state that are in use today:

    1. Process Discovery
    2. Process Mining
    3. Process Mapping

     

    Process Mapping

    Process mapping is the human-side of establishing an ‘as-is-process.’ It’s usually performed by consultants and starts with manually measuring a business process against an organization’s larger vision to ensure that processes are aligned with a company’s core competencies, capabilities, and overarching values. Traditionally this has involved manual interviews with subject matter experts (SMEs) and is subjective based on the SME’s view of the process. Although it’s important to map out a high level process flow, at best, you capture only a couple different process iterations. And, apart from being highly subjective, it is resource intensive and expensive given the cost of consultants or business analysts to travel and perform interviews, as well as the time commitment for the SME. It can often take several weeks or months to produce results.

     

    Process Mining

    Process mining is a more modern method using technology to generate a high level view of a process in order to identify and examine bottlenecks. Mining tools also typically require a business analyst to label the data before algorithms can be applied. These solutions offer great visualizations of overall process timing and high level bottlenecks in the process, and work well in decoding the interactions within a single ERP system like SAP or Oracle. The biggest drawback with this approach is the need for access to log files. This method can be cost-prohibitive due to additional needs like building APIs to sync systems. It can also be much less accurate if the process involves applications such as Excel or email which do not produce log files; the actions performed by a user outside of what is in the logs are completely ignored, reducing overall process coverage.

     

    Process Discovery

    Taking an automated approach to process discovery is the latest generation of technology that takes a cognitive approach to learning a process. Digital process discovery uses computer vision — instead of system-generated logs — to observe and capture the process as it’s being executed by a user in real time. Using a highly scalable cloud-based platform, the data captured is translated into extremely granular time and motion studies from the processes discovered. This AI-driven approach is compatible with all systems and applications — including ERP, email, and web-based — with no integrations or APIs required. Because of the methods used in capturing the information, it delivers a 100% accurate depiction of processes and tasks. For example, you may have 40 users in a department executing the same task 35 different ways and digital process discovery can visualize these differences and calculate the length of time for each version. The insights gained from this level of detail can be used to rapidly accelerate digital transformation initiatives such as automation and RPA, process reengineering, and process documentation for compliance or auditing. 

    Overall, each method can compliment one another, and can be useful for digital transformation projects. However, digital process discovery offers the most complete solution and can add tremendous value by eliminating roadblocks that you may encounter on your digital transformation journey. For more information, check out our infographic on process discovery versus process mining.

    Enabling Automation Success: What to do Before And After Deployment

    Enterprise organizations all over the world have jumped on the RPA bandwagon. Your executive staff is asking questions about this automation technology: licensing, where it can help, and how quickly could it be up and running. Maybe you’ve deployed some bots already and are looking elsewhere for additional automation opportunities. Or maybe you’re just getting started. Where do you go from here? Below are a few key actions you must take – prior to implementation – that will enable you to deploy RPA faster and recognize better results.


    Identify a starting point.

    Just like every other major phase of your digital transformation journey, the starting point is very important. It may seem easy at first, as there seem to be myriad places that bots can be of value in any company. Refuse the urge to throw bots at a problem without details and a plan. First, make sure to get a complete and accurate picture of your current state business operations. Taking the time to get all this information will easily enable you to identify the areas that will have the most impact. And using an automated process discovery solution like FortressIQ will feed you those answers much more quickly, and with more detail so you can validate your recommendations with the team and proceed with confidence.

    Define metrics for success.

    Having a successful deployment model (or RPA roadmap) is crucial to maintain the momentum for bot utilization and maintenance. Many enterprise companies have purchased tens to hundreds of RPA licenses and have only used a fraction. Having a system to quickly identify with your team where to target automation and using auto process discovery methods to support the recommendations will keep you on track to deploy more rapidly and track along the way. We recommend creating a custom prioritization template with input from departmental subject matter experts (SMEs) and business analysts to define consistent mechanisms to track and report on progress.

    Plan on rework.

    While the implementation of bots has been made easier with more accurate documentation and easy coding, what is often unplanned for in an automation project is the fact that bots can and do break; they have to be constantly monitored and fixed. Enterprises that do not add in these steps, potential costs and overhead into an RPA project plan end up scrambling for resources when a problem occurs. Building this into your plan will ensure you have the resources to swiftly address problems and not get stuck spinning your wheels on rework.

    Continuously check and pivot strategy.

    At the end of each round of bot deployments, document how the process has changed and what the estimated ROI will be for those RPA licenses now in use. Taking this action will ensure you are checking on overall progress along the way. If you’d like to dig in a bit more on what it takes to make RPA successful, check out our infographic on questions to ask before implementing automation.

    AI in the Enterprise: A Brief Intro on the Technology Driving Digital Transformation

    Whether you’re thumbs up or down on artificial intelligence, it’s here to stay, and it’s here to change how we do business. At FortressIQ we are big advocates of using AI for what it’s good at, and alternatively, having humans focus on what they’re good at. Implementing AI effectively gives workers the time to spend on those job functions where AI cannot add value and can increase employee productivity and satisfaction. 

    Using AI technology to enable better, more effective business outcomes all sounds great but where do you start? This AI mega trend means that business executives (and other non-technical roles) are expected to evaluate and make decisions on where to implement AI in the workplace. For many employees it’s a task just to decipher the jargon, what it all means, and how it can be used to address digital transformation initiatives.

    AI technology addresses 2 key areas in the enterprise:

    • How to make sense of the mountains of data collected
    • How to make better decisions based on that data collection

    Your current systems – as well as your people – have a lot of knowledge on current processes, customers, suppliers, etc. As businesses expand, the data explosion continues. To enable better decision making through data-driven insights, a few different AI technologies can be deployed, each with a different attribute to address these challenges.

    • Computer Vision
    • Machine Learning
    • Deep Learning
    • Natural Language Processing

    Computer Vision

    Computer vision provides the ability for a computer to gain a high-level understanding of digital images and videos so that machine can then recognize and make decisions based on the set of images produced. The technology has grown to include facial recognition and the identification of objects such as traffic signals, stop signs, and pedestrians.

    Computer vision is used in the automotive industry to create anti-collision detection technology for better vehicle safety. It’s also very popular in healthcare to improve patient diagnoses through enhanced detection on MRI, X-ray and other scanned images. In finance departments, it can quickly identify and process invoices, improve cash flow, and build better relationships with vendors and suppliers.

    While machine learning focuses more on making sense of a large amount of data, computer vision and deep learning technologies are focused on training a computer to be able to understand its environment and make decisions similar to a human brain.

    Machine Learning

    Machine learning is the ability to create meaning from mountains of data. In business, this is often referred to as data mining. Machine learning technology can rapidly make inferences from a large amount of data, whereas if a human performed the same task it could take them thousands of hours. This field of computer science gives the computer the ability to learn without being explicitly programmed.

    Companies can use machine learning to accomplish anything from targeted marketing to revenue forecasting. For example, online advertising companies use aggregate user data collected from companies like Google, Twitter, and Facebook to serve up targeted ads to people identified as more likely to purchase. Credit card companies can use machine learning to quickly process thousands of applications and monitor user purchase and payment history to serve up offers such as a credit limit increase.

    Deep Learning

    Deep learning technology, a subset of machine learning, uses algorithms to learn in a supervised or unsupervised manner; the algorithm does not need to be task-specific. For example, it can be used to classify a large data set or identify and analyze patterns within that data. It can then use those patterns to predict possible outcomes. In business this is often referred to as predictive analytics. In short, deep learning replaces the traditional intuitive aspect of decision making with more data-driven decision making.

    In a supply chain scenario, deep learning can be used to reduce the number of product modeling scenarios, and laser-focus on those models that will drive the most revenue. In finance departments a scanned invoice with an abnormally high dollar amount listed will be flagged as an error and automatically sent for review.

    A system that can process data faster than a human, while simultaneously learning and applying that knowledge, can increase the overall productivity of an organization and reduce risk. And in the example above, when the task is finance related it could result in quicker revenue recognition.

    Natural Language Processing

    Natural language processing is the ability of a computer program to understand language as it is spoken. Natural language processing can be used when the text is provided. When text is produced, the computer will use algorithms designed to extract meaning associated with phrases and sentences and then collect essential data from them.

    Although very intuitive to humans, aspects of natural language processing can be difficult to implement properly and haven’t been fully resolved. Sarcasm is a good example here – most humans can identify sarcasm immediately, but a computer or chat bot has a difficult time.

    When big social media campaigns are launched, natural language processing can be used to track trends and customers’ pulse in real time, and campaign interactions can be addressed directly and be personalized, a critical element to successful brand marketing.

    Until very recently, these more sophisticated embodiments of artificial intelligence have mainly been used for academic and scholarly research. Organizational efforts to stay competitive and remain a market leader (such as the race to build the best self-driving car) has forged a quantum leap in AI technologies, making them tangible and cost effective for the enterprise.

    Even knowing the basic differences is a good starting point for researching where these technologies might be applicable for your organization. For additional information, check out our on-demand webinar “AI in Business: When and Where to use Artificial Intelligence in Your Organization.”

    Large-Scale Business Process Transformation Starts from the Top-Down

    “If You Can’t Measure It, You Can’t Improve It.”

    – Peter Drucker

    Measurement and improvement – easier said than done, especially in the enterprise. When it comes to large-scale, strategic transformation, it’s also a continuous journey. Every enterprise company is in some stage of digital transformation, and there are challenges at every stage.

    For the past decade, companies have been trying to make decisions with data collected from all lines of business in an effort to move the needle on a successful digital transformation initiative. Efforts to integrate departments such as finance, HR, supply chain, procurement, and marketing with various technology solutions have seen varied levels of success with ample opportunity for improvement. Individually it may be possible to collect data from different areas of the business, merge into a single place, and see what insights can be extracted, but without the right solution(s) in place, near impossible.

    Several companies are in the process of standing up teams to tie data together from all lines of business into one single repository to be used for analysis and decision making. When you consider that an undertaking of this magnitude requires data from several different systems to be filtered into and stored in one place, and the IT systems and processes are constantly changing in parallel, it may be time and cost-prohibitive. In addition, the surge in popularity of automation and RPA have companies implementing bots without truly understanding the overall business impact.

    Instead of shuffling data from one system to another, and trying to compile insights from disparate systems and applications, you can capture the work and tasks being executed across all applications and systems – for multiple users and with zero business interruption – using a cognitive process discovery solution. AI can be used to study, map, and deliver process information to multiple department heads so they can make data-driven decisions that improve efficiency and productivity. Additionally, automation and process improvement initiatives can move from changes to pockets of the business and expand to full departments, shared services, and centers of excellence to have the greatest effect on the company’s largest, strategic transformation initiatives. Common examples of process optimization in the enterprise often start by using AI to identify improvements to back-office systems and business processes such as finance, HR, and customer support – small changes in these areas can see huge increases in ROI and deliver quantitative results to the enterprise.

    To learn more about our automated process discovery and documentation solution, you can request a demo here.

    Process Discovery and Automation are Value Drivers for Complementary Enterprise Solutions

    Process Discovery is Essential for Automation

    Enterprise organizations who are embracing new technologies such as process discovery and automation to achieve their transformation goals understand the value that these solutions bring in addressing digital challenges. Early adopters, in particular, who have overcome initial RPA deployment setbacks, and are now looking to more intelligent automation solutions understand the importance of process discovery and mining solutions and how necessary they are to maximizing the ROI of automation tools.

    Gain a Competitive Advantage

    Companies who have adopted these solutions for internal use should also consider how their own products and solutions could add additional value to their customers if they were process discovery and automation-friendly. This is especially relevant for software and IT services companies. The same challenge of scalable implementation encountered by companies internally when they were deploying automation will be faced by their customers as they too try to implement automation at scale.

    Gartner recently published the February 2020 “Product Managers Must Use Hyperautomation to Enhance Offerings” report, which names FortressIQ as a robotic process discovery tool. According to Gartner, “within the last few years, many organizations have faced competition from digital “natives” and increasing pressure to cut costs. Automation is often key to addressing these challenges by increasing speed and efficiency while reducing costs, but the typical overly long response times from more traditional IT approaches are holding this back. Hyperautomation is about fixing these pent-up automation requirements at speed. Through excellent governance and planning by their product managers, vendors are thriving by aligning their products to this pent-up demand for quicker and more automation inside their customer organizations.” We believe that companies whose solution offerings can be configured to add increased value by complementing RPA and process tools can improve their customers’ experiences, increase ROI, and gain an advantage over competitors — a win-win.

    Understanding Process Discovery 

    In order for companies to tweak or adjust a product successfully, the product team needs to thoroughly understand the capabilities of the tools they’re trying to align with. FortressIQ is an enterprise platform that accelerates transformation with data-driven metrics on current state business operations. Using AI we discover, map, and document all processes and tasks executed by your workforce to deliver deep insights not achievable with other methods or tools. These insights enable companies to make better decisions about how to address complex initiatives such as automation.

    Not all process discovery solutions are created equal. FortressIQ brings a cognitive, intelligent approach at enterprise scale. Our hyper-scalable solution can automatically create a rich, structured view of an organization in as little as a few weeks, with no integrations or APIs needed. As a result, automation initiatives can both scale and be extremely targeted. Additionally, the extremely granular and feature-rich data collected can be used to validate and test an organization’s overall transformation strategy.

    To summarize, when companies producing enterprise software and IT services, automation vendors, and process discovery solutions all align to highlight the respective offerings, the customer wins.

    Interested in learning more about cognitive process discovery from FortressIQ? Learn about our approach, or request a demo here.

    Top-Down vs. Bottom-Up: Where to Begin Your Process Transformation Journey

    Developing a deep and detailed understanding of your business processes lets you root out inefficiencies, double down on operational excellence, and make better, more informed decisions to reach your goals. But simply mining system log files misses the details in every process, while deploying consultants to map processes takes time and disrupts operations. Instead, intelligently decoding how your people and processes really work, across systems and screens, and across your entire business, is a better approach.

    Capturing process intelligence to understand business processes has traditionally been tough to obtain because the methods have been manual. The drawback is that it results in static, incomplete process data. Today’s technologies, which evolved from these traditional methods, offer an automated and intelligent approach that’s both faster and captures more detail. 

    Here’s how process insight capture has evolved:

    • Process Mapping is the traditional, human-based route where business analysts and consultants interview and look over the shoulders of your workers. It’s slow and expensive, the sample size is limited and incomplete, and it can’t realistically cover processes across the entire organization.
    • Process Mining is a back-end, system-centric approach that captures a narrow, step-by-step workflow based on how users interact with specific systems. This method requires access to log files, which limits coverage. It also misses tasks like data collection, calculations, or other steps performed in separate applications.
    • Process Discovery is a modern alternative to mining. It tracks workflow at the UI level, no matter who performs the task or which application is used. It excels at capturing discrete sub-processes, but has trouble scaling because it ultimately requires human evaluation of the results.
    • Process Intelligence advances process discoveryby using computer vision and Artificial Intelligence (AI) to uncover actionable process insights at enterprise scale. It has the speed and coverage to capture, record, and analyze granular steps in complex use cases, plus adds intelligence to quickly identify new opportunities.

    This spectrum of process insight techniques is referred to as a top-down manual approach, versus a bottom-up intelligence-driven approach. They all help you gain a better understanding of processes, but the bottom-up approach offers more granular insights, faster and across a wider range of processes. The result is more business impact in less time.

    Choosing an Approach to Gathering Process Insights

    If you have process insight experience, you may lean towards combining multiple approaches to address specific requirements. But if you’re tackling a project for the first time, it can be difficult to determine the best approach. 

    A top-down manual approach adds the perceived expertise and guidance of a team of consultants. That can be helpful but adds more cost and time by a few orders of magnitude. On the other hand, a modern bottom-up approach offers deeper insights and faster results but puts the decisions in your hands.

    So, the question is, do you take a bottom-up or top-down approach?

    Top-Down Misses the Detail

    Top-down process mining technologies piece together a process within a single system, but since they do not capture granular user level activity, they don’t capture all the key steps of users or systems. For example, process exceptions and variations are not reliably identified because they may involve activities outside the analyzed system.

    Additionally, enterprises often run hundreds of applications. Many of those—including Excel and common email clients—don’t generate usable log files. So, any use of those tools will not be captured, and the resulting process maps won’t represent the complete business process. The missed steps then aren’t included in automation efforts, resulting in costly rework once deployed.

    Bottom-Up Provides More Insights and Speed

    In contrast, process intelligence and process discovery technologies capture detailed user activity across all systems and tools, covering every granular step, including task interdependencies and connections. There is no need to access APIs or log files to create the process maps, which speeds the entire project, and the more complete insights keep you from automating broken or inefficient processes. Analytics can also quickly compare processes and system usage across teams and tasks.

    Speed is what really separates the top-down and bottom-up approaches. You can expect to receive business value in weeks with Process Intelligence instead of months with Process Mining. 

      Bottom-Up
    Process Intelligence
    Top-Down
    Process Mining
    ACCURACY
    Completeness
    Full capture of sub-process activity and variations Limited view of end-to-end functional workflows
    SCALE
    People & Process Coverage
    Full coverage across all systems and teams Limited coverage to systems with log files
    DETAIL
    Degree of Specificity
    Level 5 step-by-step process and sub-process details Level 3 general workflows
    SPEED
    Time to Value
    Weeks to deploy to desktop sensors and collect data Months to integrate back-end systems and map data 
    EFFICIENCY
    (reduced rework)
    Granular activity data is comprehensive and actionable System-only data leaves process gaps

     

    Start at the Process Itself

    If you are exploring process insights for the first time, Process Intelligence is the logical starting point given its more comprehensive view of operations. It offers a faster time to value, takes less IT resources since you are not integrating with APIs or capturing log files, and is substantially less expensive and disruptive than unleashing a team of consultants into your operations.

    As you eventually dig deeper for process insights, Process Mining could complement Process Intelligence, however. Combining both approaches provides the most comprehensive insight on the implications of a process. Interested in how FortressIQ provides process intelligence quickly and efficiently? Read our solution brief to see how you can get started.