Together, Humans and Software Agents Drive Enterprise Automation

“What is the calculus of innovation? The calculus of innovation is really quite simple: Knowledge drives innovation, innovation drives productivity, productivity drives economic growth.”

—William Brody, Scientist

 

 

Every company – no matter what size – knows that in order to remain competitive, you have to embrace change. Massive economic shifts tend to drive change more quickly and significantly, and this has never been more true or urgent. No matter what stage of your digital transformation journey you are in, the current environment is likely accelerating your process optimization initiatives. Additionally, the traditional emphasis on front-office activities need to be reevaluated. Most transformation programs in the last decade have focused on customer experience, but today, indicators suggest the emphasis moving forward will shift toward back-office operational excellence in departments including finance and accounting, HR, and procurement.

From the New York Times to the Wall Street Journal, much has been written about the potential permanent job loss from the drive for increased automation in the enterprise. Experts predict up to 800 million jobs worldwide could be lost to automation by 2035. Enterprise veterans know, however, that the human element will never go away – it will merely change. While these headlines spark a lot of article views, they fail to address the positive opportunities and changes we will see in the workforce as a result of innovative automation.

Improved Employee Experiences

When done properly, automation should support and complement human activity. It removes the low-value, manual and tedious tasks that consume a majority of our work hours, letting employees focus on higher-value activities. Identifying areas where employees can add value by applying their skills to more strategic work not only increases productivity, but also overall employee happiness. The shift away from manual tasks has been going on since the Industrial Revolution, and HR departments are hyper aware that retaining employees is more cost-effective. A recent study by Employee Benefits News states the average cost of losing an employee is equal to 33% of their annual salary.

Increased Need for a Human-Centric Approach

When first approaching automation projects it’s important to validate the tasks and processes identified to make sure they are good candidates for RPA. Those who have been involved in enterprise workflow programs know the golden rule that there is nothing worse than automating a bad process; it simply magnifies the inefficiency. On the flip side, automating a good process has the advantage of magnifying the efficiency. Without the use of modern discovery techniques to accurately document the current state and identify process variations, it’s very difficult to distinguish a good process from a bad one. Automated discovery tools like FortressIQ surface the insights of tasks and processes at a detailed level previously unattainable with more traditional methods. Once this data is gathered, however, it ultimately needs a human element for success. Whether internal or external, process optimization experts, business analysts, automation SMEs, developers, project managers, and others will play a crucial part in successful planning and execution.

Enabling the Citizen Developer

The future of automation will see a shift from a more traditional top-down “you must automate” approach led by management to a bottom-up “how can I be more efficient” employee-driven trend. Bots will be packaged up with standard office apps and services to increase usage across organizations. Coupled with no-code offerings this will allow anyone – no matter what department or role they have at the company – to easily automate a tedious task or portion of their job without engaging IT. Imagine if sales executives could spend less time updating CRM systems, sales operations could auto-generate pipeline reports, product managers could consolidate and group customer feature requests from various channels, the finance and accounting department could eliminate copying and pasting of PO numbers into multiple systems, and if call center and support employees could auto-fill customer ticket information, among countless other examples to make life easier for employees. Putting the power of this technology into the hands of your workforce allows them to make data-driven decisions more quickly, increasing overall corporate performance.

The future of automation will be a seamless blend of the next-generation workforce and software agents, including bots and assistive technology. Enterprises who determine how to successfully incorporate these capabilities will remain competitive and relevant. It will improve their organizations and accelerate the pace of innovation. For a jump start on your RPA efforts, check out our handy guide, “Should my Enterprise Automate That?” available here.

Announcing our $30 Million Series B to Continue the Mission of Decoding Work

“We weren’t here to code the hosts, we were here to decode the guests…”

— Bernard Lowe, Westworld S2E7

And with that line, the audience learns the true purpose of the sprawling investment in Westworld, it’s not about building the best androids, it’s about understanding humanity.  Three years ago we set out on a similar mission, to build AI that can decode work. Today we’re excited to announce a new B round investment of 30 Million dollars with new investors, Microsoft’s M12 and Tiger Global, joining our existing investors Boldstart, Eniac, Comcast and Lightspeed, to help us deliver on that mission.

Since day 1 we’ve held the belief that the key to the future of work was locked in our inability to understand its nature. Opening that lock would start us on a multi-year journey pushing the envelope on computer vision, natural language processing, and sequence modeling. Along the way we’ve assembled an incredible team that shares a similar dedication to understanding how to best integrate AI into the global workforce. We’re excited to add to that team, and welcome Tamara Steffens, from M12 to our Board of Directors. I’m sure her decades of experience working closely with enterprise customers will prove invaluable as we scale FortressIQ.

First Contact

Early last year customers got their first glimpse at what we were building, and we got to see firsthand how they could leverage the process insights that our platform could uncover. It was eye opening to say the least. Our first conversations usually revolved around automation and cost savings, but as customers became more comfortable with our data, the conversations evolved. This new category of data we could provide fueled their ability to innovate, enabling them (for the first time) to make truly data-driven decisions to guide their transformation strategies. Since then we’ve had the honor of working with some of the worlds largest companies, helping banks, retailers, insurers, and CPG companies to better understand their organizational DNA through interpreting their processes.

We were extremely surprised to find out how starved most organizations were for this type of data. While they had incredible analytics on digital journeys from their web properties, they had a much more rudimentary understanding of their internal processes. Another interesting byproduct we observed was how much simpler the decision making process was when good data was available. For me this was one of the most exciting revelations. I believe people in general want to do the right thing, it’s just sometimes very hard in the face of organization inertia. Often decisions devolve to simply appeasing whoever is yelling the loudest, the proverbial squeaky wheel getting the oil. It’s also well documented that unrepresented communities don’t advocate for themselves as forcefully in these types of situations. With data, we can minimize the impact of the loudest voice and amplify the focus on the best actions. This is a firsthand, tangible way in which we can leverage AI to reduce bias, reinforcing our mission here at FortressIQ around the responsible and ethical use of AI in the enterprise.

The Road Ahead

With this new round of capital we’ll be doubling down on our efforts to build the system of record for work. Our experience over the last year as customers have leveraged our platform has guided us to deliver new ways for them to leverage process data to improve every aspect of the employee experience, which translates to a better customer experience, and eventually the bottom line. The largest area of focus for us will center around helping our customers to achieve results from our platform even faster. With that in mind, we’ll be building specific industry, application, and workflow accelerators that can deliver instant insights from process data. We’ll also be looking to expand our partnering platform, working with domain experts across the globe to deliver externally targeted solutions based on data that would be unattainable without a process intelligence platform like ours.

I’m excited to see how customers can leverage our platform to power their transformation journeys, and grateful for the support of my team and our investors as we endeavor to decode work.

Now back to building!

Changing the Game with FortressIQ & Microsoft Power Automate

Today I’m excited to kick off the next phase of our Microsoft partnership. FortressIQ + Power Automate, generally available starting today, make it possible to grow your business productivity by automating repetitive, time-consuming tasks through digital and robotic process automation. With the integration of FortressIQ and Power Automate, we provide organizations with an end-to-end solution for intelligent automation–from cognitive process intelligence to AI-enabled workflows and business insight.

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.

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.

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.

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.”

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.

Delivering Process Intelligence to Microsoft Power Automate Customers

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

– Satya Nadella, Microsoft

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.