Beyond Hyperautomation: What’s Next in Automation?

When I explore trends, I avoid looking at the obvious advancements we will experience in the next 12-24 months. Those are too easy to predict. But I also try to avoid the science fiction range of looking 10+ years into the future. While the concepts may be fun to consider, they are meaningless to most organizations today because their potential value is not yet tangible and their likelihood of commercialization is limited. The sweet spot for impactful trend spotting is really in the 3-6 year range. You have enough time to plan and course correct, plus you can assess the realistic impact on the business with some certainty.

We’re now at a point where automation should be returning huge dividends to those that deploy it. But why it’s falling short isn’t due to a lack of trying or a lack of deployment speed. Instead, accelerated approaches, like hyperautomation, and even regular old automation itself, won’t reach their potential until organizations have the right ingredients in their approach to automation. 

Unsafe at Any Speed

Hyperautomation burst into the technology lexicon when Gartner named it the Top Strategic Technology Trend for 2020. But, there is a lot of nuance to the meaning of hyperautomation. Gartner defines it as “the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans.” It complements intelligent automation but provides higher-level functioning, which enables more predictive analytics and adaptive decision-making. 

Hyperautomation sought to address many of the challenges companies faced when scaling their robotic process automation (RPA) programs and re-igniting excitement in the technology. Despite strong market growth, there were cracks appearing in the foundation. HFS Research’s Phil Fersht, ironically the analyst who introduced RPA to the world in 2012, declared the technology dead in his famous 2019 blog post. At the time, only 4% of companies had deployed more than 50 bots, and a Deloitte survey of 400 global firms found that 63 percent of surveyed organizations did not meet delivery deadlines for RPA projects. Regardless of how long it took, an EY study found 30-50% of initial RPA projects fail. 

Hyperautomation was introduced to address all of these shortcomings in the market with its suggestion of speed. Unfortunately, as we all know, speed can kill.

More than a decade before the concept of RPA was even introduced, Microsoft’s Bill Gates taught us in 1996 that, “The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.” 

This lesson still largely holds true today, and unless enterprises deeply understand their processes first, any efforts at automation will be less than successful. Hyperautomation just accelerates the path to magnified inefficiencies.

The Future is Better…and Faster

So what’s in store for automation once industry and practitioners get over their thirst for speed? First, they need to focus on solving the business issues that RPA is not addressing today. Only then can they make transformational progress on automation and get past the abysmal deployment and failure rates. Here’s how I see this situation resolving itself.

In my sweet spot near future timeframe of 3-6 years, I predict three major ideas will influence the next generation of automation technologies.

  1. The first is CONTEXT. To help address the issue of blindly automating an inefficient or incorrect process, you need better context. Having observability into the current state of operations will drive better decisions and better outcomes. You have to be able to understand today to improve tomorrow. Through a mix of AI-enabled technology systems, organizations will have increased awareness of their operations and will be able to make more informed, data-driven decisions. Automation activity will no longer just be fast, it will be fast and correct
  2. The second is CONTINUOUS. All the current process technologies, with a few minor exceptions, are timeboxed. They are episodic. In today’s world, any core data system you are using to run your business has to be real-time and continuous. You do not run your security software for two weeks and then turn it off. Or you do not turn your web analytics on and off based on the data you are looking to explore. We’ll see this shift in automation as well. Organizations, applications, and processes are evolving constantly. And, if we are always just looking at a snapshot to drive automation, we are undoubtedly missing something.
  3. The third idea is COORDINATION. While this aligns with the letter C theme, it is really more about orchestration and how the systems, applications, and workflows integrate together to create better outcomes. They can self-adjust based on the changing conditions and requirements to work together more efficiently. Orchestration includes human-agent partnerships and managing the new digital workforce. This will really move us towards a next-generation operating model and a more autonomous enterprise.

Automation success requires a deep understanding of business operations, which are continually updated to account for change and adaptation, and which are utilized to orchestrate every aspect of the workflow and business. I hope they’re not termed  “hypersonic automation”, but regardless of how the marketers decide to frame the next generation of automation technology, it will be more data-driven to better support key business initiatives across the enterprise.

RPA Is Just One Piece of the Automation Puzzle

With any technology experiencing massive adoption, there is no shortage of unwarranted hype. RPA is no exception as it has been the fastest-growing enterprise software category for the last three years. But, beyond the hype, RPA does deliver significant value and can radically transform how your enterprise works. You just need to realize what to expect of RPA before it’s unleashed.

Hype Leads to RPA’s Perceived Failure

The biggest fallacy fueling RPA’s hype machine is the term itself. It is not robotic process automation. More accurately, it is robotic task automation. The misnomer is responsible for some of the challenges the technology has faced in terms of scaling across the enterprise. The vast majority of companies that have deployed RPA still can’t get more than 10 bots into production.

Another major misperception for RPA is that automation will reduce operating costs by eliminating headcount. There is no doubt RPA does enable a team to do more with less, but promises of fewer resource requirements too often fail to materialize. Companies need to think more about the impact on employee experience and the value those employees deliver. RPA does a great job of automating low-value work so employees can focus on high-value activities. 

Finally, despite its reputation as a panacea for many enterprise ills, RPA often adds to an organization’s technical debt, which will eventually be a problem someone has to correct. There is no doubt RPA is fast, efficient, and less expensive than other methods. But, it’s more of a band-aid than a cure. 

For example, with analytics workloads, in particular, data is often generated in legacy mainframes and RPA is used in place of costly API integrations and other fixes. It provides a temporary fix to get a few more years out of a legacy system. Yes, it avoids a costly, time-consuming, and error-prone system upgrade. But it ignores the less obvious costs and inefficiencies associated with maintaining the legacy application, like performance, compliance, and security. These issues would typically be addressed by an upgrade but are too often pushed off by deploying RPA as an interim solution. 

Focus on Automation’s Value

The grand promise of RPA so frequently falls short due to over planning, the inability to identify key automation opportunities, and a disproportionate focus on short-term objectives to show wins even if they’re not aligned with corporate objectives. This is why many organizations spend 12-18 months before getting a single bot into production. 

Today, automation is the new transformation. After a decade or more of mediocre success rates and an overemphasis on customer experience initiatives, automation provided needed relief to address the transformation fatigue that many companies faced. Fortunately, RPA is not the only automation technology available to drive business value. 

Automation technologies are quite flexible and are being implemented across a wide variety of settings, from structured transactional activities to end-to-end processes. Use cases across the enterprise can be addressed using a variety of technologies. In addition to general user interface (UI) layer tools like RPA, there are domain-specific tools available to support various functions, like marketing automation. Other tools focus on data extraction to capture unstructured data and make it more usable. Technologists also have digital process automation platforms available to address deeper and wider workflows, and more traditional APIs can be used to integrate enterprise systems.

4 Automation Prerequisites

Regardless of the automation technology deployed, there are four vital actions to take before your company or department invests time, energy, and budget into a project. 

  1. Assess the overall process complexity. Insights on the applications involved and the amount of human expertise required will help guide technology selection and determine the feasibility of success. 
  2. Decide if the process is mission-critical or if you are automating just for the sake of automating. 
  3. Identify key metrics that will define a winning result. Whether it is speed or scale or accuracy or efficiency, you want to establish the goal in advance so you know when you can celebrate. 
  4. Proceed only with fully detailed process documentation. To improve tomorrow, you need to understand where you are today. It’s common sense. No one says they are going to lose 20 pounds but do not know how much they currently weigh. Unfortunately, too many organizations try — and fail — to automate a process that they do not fully understand how they complete today.

It all makes sense. Before you begin, you need to know where you’re starting from, where you’re going, and how you’re going to get there. Automation is no different. And, if you truly want to succeed in transforming how your business operates, you must realize RPA is just one piece of the automation puzzle.

The Low-Code Movement Will Spur Process Intelligence

Interest in low-code development is skyrocketing. Annual market growth is predicted to exceed 25%, growing from $13B in 2020 to $65B in 2027, according to research from Brandessence Market Research. For organizations looking to win in the digital-first agile world, low-code is quickly becoming a critical component of a modern enterprise technology stack. If you’ve been around technology, you have probably heard the phrase, “Faster, better, cheaper — pick two.” But low-code gives you all three and adds “flexible” to the mix.

With their roots in the Rapid Application Development (RAD) tools of the 1990s, low-code platforms are an application development environment that use graphical user interfaces and configuration instead of traditional hand-coded computer programming. Formal software engineering skills are not required to create applications since a visual user interface in combination with model-driven logic is used. This opens the door to a wider range of people who can build apps for the business. With a little training, employees can rapidly create and deploy secure scalable software. One note to remember – low-code is not interchangeable with no-code which is a subset of technologies aimed at business users primarily working on enhancing their individual productivity.

A New Player in the Enterprise Technology Stack 

It’s impossible to argue against the need and demand for low-code in the enterprise. According to Gartner, more than 65% of application development in 2024 will be performed by low code platforms. That’s a remarkable shift for a software category that did not exist a decade ago. The number of digital applications and services being built is exploding as well. Between 2018 and 2023, more than 500 million apps will be created according to IDC. To put that massive number into perspective, that’s more than the previous 40 years combined. With low-code, companies can rapidly produce applications within a shorter time span and at a fraction of the cost. Schneider Electric launched 60 apps in 20 months, delivering most in just 10 weeks, and Ricoh replaced critical legacy systems 3X faster with a positive ROI in 7 months using Outsystems. Some skeptics may point out the lack of available IT resources, but business users can learn low-code development methodologies quickly, typically in less than one month. Clearly the old way of building apps cannot keep pace with today’s digital marketplace. 

Process Intelligence Jump Starts Low-Code Journey 

To help accelerate utilization of low-code and scale it across the enterprise, process intelligence is a key enabler. You cannot improve how you operate tomorrow if you don’t fully understand how you work today. And most companies truly don’t understand how they operate on a daily basis, especially at a granular user activity level required to automate a process or streamline a workflow. They have limited process understanding. They don’t know how their applications and data interact, and they don’t really understand what their customers expect.

The impact of this gap in process data is well documented. The 70% failure rate of transformation programs is widely reported. McKinsey pegs the cost at nearly $1 trillion annually and noted on 14% of companies have seen a sustained and material improvement in their business. Another study from Gartner noted only 1% of companies have sufficient understanding of their processes to take full advantage of the technology solutions. 

Before embarking on a major initiative, a company must map its processes, its systems and its experiences. Today, that necessary level of operational intelligence just does not generally exist in most companies and on top of that it is very difficult to obtain without process intelligence.

Low-Code and Process Intelligence – Better Together Than RPA

Process intelligence was a similar catalyst for Robotic Process Automation (RPA), but the opportunity with Low-Code is even greater. RPA programs enjoyed massive early uptake, but the challenge was how to scale the initiatives. Companies still struggle getting more than 50 bots deployed. Once any obvious low-hanging fruit is automated, it becomes difficult to identify what to tackle next and how to tackle it. Process intelligence answers those questions to help scale RPA. 

With Low-Code, Process Intelligence accomplishes that and more. The combination will overtake RPA as the gateway for AI and automation. Low-Code is more efficient and scalable than traditional RPA development because it does not operate at the user interface (UI) layer, making the applications more resilient. Additionally, where RPA is traditionally limited to task activities, Low-Code is much more capable of handling sub-process and process level activities making it much more valuable to the enterprise. Coupling Process Intelligence with Low-Code helps steer an enterprise toward a next-generation operation model that is faster, cheaper, better and flexible. It may also fully realize the promise of Citizen Developers that RPA struggles to achieve.

VentureBeat: Why Process Mining is Seeing Triple-Digit Growth

ISO 9000 is a set of standards for quality management developed in the late 80s. It was based on procurement standards used by the U.S. Department of Defense. But, in a long-ago training session, the entire premise was summed up by the instructor as, “Document what you do, then do what you’ve documented.”

The reasons those types of programs exist is because the documented process is rarely the actual process. But today, given the massive scale and breadth of change over the past year, it’s likely many of your processes have been altered in one way or another. In fact, a survey by McKinsey found that organizations have accelerated their digitization efforts by three to four years during the pandemic. So, before enterprises can change, improve, digitize, or automate a process, it’s imperative they first understand what’s really going on. 

A recent article in VentureBeat, “Why process mining is seeing triple-digit growth”, points to the past year’s massive change, along with many other factors, as what’s driving explosive demand for process mining tools and technologies. Gartner estimates that the market for these solutions has already tripled since 2018, and there’s more to come. Here’s a quick overview of why, according to VentureBeat.

RPA Isn’t Living Up to Expectations

Robotic process automation (RPA) initiatives and their expected ROI are based on the underlying process. Saving an hour per day for a documented process completed by your expensive procurement team could generate a nice return, for example. But, if Procurement is, in reality, already working around the systems and tasks that slowed them down, the return could be considerably lower, or it could even cost you money. Or, maybe that entire process is done differently in other regions or isn’t really valuable for Procurement, so you’ve spent money automating a bad process you’ll eventually need to spend more money to rework.

Companies are now finding that, in their haste to automate, they neither understood the actual process nor developed an optimized, scalable process built for their current needs. As the article states, “Many enterprises are finding it difficult to scale beyond a few software robots or bots because they are automating a bad process that cannot scale.”

Process intelligence, however, is giving enterprises detailed information on why their RPA isn’t driving more value. It’s also giving visibility to the true as-is processes, how they vary across the business, and where optimizations might make those processes better well before RPA is considered. 

Download our ebook: 6 Strategies to Drive Successful Automation

Benefits Beyond Simple Process Mining

Process mining, which “involves mining data logs from applications like ERP and CRM to assemble an accurate model of how a business process, like order to cash (OTC), works,” is inherently limited. Many tools in modern enterprises, such as Microsoft Office, virtual desktops, and email, don’t produce data logs. Process intelligence, or what VentureBeat refers to as “task mining,” uses computer vision, artificial intelligence (AI), and machine learning (ML) to directly record how a worker accomplishes any given task.

See our related post: What is Process Intelligence?

Since process intelligence doesn’t rely on data logs, it can capture more data from more applications, but also how today’s workers jump between applications. For example, maybe the OTC process requires a clerk to copy order data from an ERP system and paste it into an invoicing system, which then generates a PDF invoice that’s manually emailed to a vendor. Process mining would likely capture the ERP and invoicing logs, but process intelligence would provide insights across the end-to-end process and all applications, including the manually created email. 

Those cross-application actions are more indicative of how today’s workers work, and that’s the level of data enterprises are now demanding. Process intelligence adds AI and ML to uncover the nuance of processes and surface opportunities to optimize before you might apply RPA to automate suboptimal subtasks. 

New Applications Across the Enterprise

The inclusion of advanced AI and ML, along with the benefits of the cloud, enables enterprises of any size to quickly capture and evaluate data on enterprise-scale processes. But it’s not just limited to processes, per se, since other insights can be gleaned from knowing how your enterprise operates today and at a detailed level. VentureBeat calls it “a new sensory system for organizations.”

“In the ecology of companies battling for market share, a company with even a primitive capability of seeing invisible workflows better than the competition has a huge advantage over companies that cannot see. As Erasmus stated in 1500 AD: ‘In the land of the blind, the one-eyed man is king.’”

Some examples of these in-demand capabilities of process intelligence include:

  • Understanding and mitigating complex cybersecurity threats
  • Optimizing the physical logistics of warehousing
  • Improving manufacturing processes
  • Breaking down organizational silos
  • Reducing training time
  • Identifying the root cause of quality issues

It’s also being used to enhance how humans work, such as increasing safety and automatically altering workers when steps are missed by mistake. So while the word “automation” may sound like jobs are at risk, one of the most human benefits of process intelligence is in making humans better at how they work. 

“Rather than just looking at process mining as something to impose on workers, companies may see the biggest gains by finding ways to include and reward employees as part of the adoption. After all, thousands of eyes in the field may see some opportunities that a few experts in the office might miss.”

You can read the complete VentureBeat article here.

Your Checklist to Answer, “Should We Automate That?”

Every enterprise is under pressure to accelerate growth, find efficiencies, increase productivity, and do it all while working remotely. Customers are also demanding more speed and greater value at less cost, while suppliers want streamlined invoicing, delivery, quality control, and other processes. It’s pushing your digital transformation efforts to their limits, especially as more of your internal stakeholders assume automation is as easy as flipping a switch. 

Process automation is a highly-efficient, high-ROI, and proven tactic for digital transformation. But you can’t just assume every process is ripe for automation. Before selecting a process for automation, you must understand that process in great detail. What are the inputs and desired outputs? What are the expected steps? What are the actual steps taken by workers? Which systems and data are involved? What would an ideal process look like? And that’s just scratching the surface. 

To help you answer questions and guide your process automation efforts, we’ve developed a new checklist: Should My Enterprise Automate That?

This checklist helps you create an enterprise-wide strategy for successful process automation. It’s built on a foundation of first discovering candidates for enterprise process automation in a fast and efficient manner. Then, it explains how deep, granular process data is needed to decode and document the intricacies behind how your business actually works. Finally, it provides a guide to determine which processes are ideal candidates for automation and how you can track, measure, and ensure the success of every process automation effort. 

Should My Enterprise Automate That? is an automation-centric checklist to align your process automation goals with the realities of your workplace operations. The checklist covers four key areas of enterprise process automation exploration:

  1. Process complexity
  2. Mission criticality of target processes
  3. Key success metrics
  4. Importance of detailed process documentation

Download this new infographic-style checklist today and get started on your path to successful enterprise process automation. 

Don’t Create a Monster, Create a Beast

Halloween is right around the corner, and nothing is scarier than transforming your enterprise into a Frankenstein of incompatible tools and techniques. 

You started with the best intentions. You thought you chose the right tools, most attractive processes, and the best minds. 

But you soon found the amalgamation birthed a hideous creature of stitched-together processes, disconnected guidance, and opposing insights. It’s turned your transformation into a nightmare of rotting results, festering budgets, and decaying productivity. Instead of electrifying your enterprise with new life, you’ve created a monster.

Download the full infographic

Consultant’s Brain

Consultants are the experts, and you would expect them to be the brains behind your transformation. But they’re working with limited resources and time, trying to capture process details and then extrapolating anecdotes across your organization. They’re bound to miss things, which leaves your project wandering between options and never really making transformational impacts.

Miner’s Arm

Process mining and discovery techniques are slow, expensive, and highly manual. It’s an outdated approach that only captures a slice of a process based on how users interact with one system. It limits your coverage, misses steps performed in separate applications, and could pull your transformation in the wrong direction.

Software’s Arm

Digging for process details via APIs requires even more connections with IT, developers, and application vendors. It adds time and expense to your transformation project, and even more opportunities for messages to get lost and connections to break. Plus, each application requires a separate API connection, delivers data in different formats, and forces you to manually reconnect and reanimate the insights, adding even more delays.

Data Guts

Most companies truly don’t understand how they operate on a granular user level, and this lack of current state understanding is a major roadblock to transformation success. Process Intelligence helps eliminate nightmares by efficiently creating a dataset of user activity not previously available to kickstart strategic initiatives. Convert your process problems into big data solutions.

Bot’s Leg

RPA is great, but every organization struggles with scaling programs. Process assessment and prioritization delays development. Vendors have promoted screen recorders for task discovery, but they fail to scale and actually create more rework than traditional methods. Scary indeed. The only real answer to scaling your RPA program is adopting real process intelligence. Deploying FortressIQ means rapid assessments, and finding automation opportunities at scale

App’s Leg

Know what’s really scary?Most enterprise processes–especially the valuable ones–require workers to hop between software, move data across screens, or use multiple apps simultaneously. Trying to combine those steps into a coherent process using just log files and APIs is a recipe for disaster. Process Intelligence can give you the insights to start correcting the issue so you can understand today to improve tomorrow.

Stop the Madness! Turn Your Enterprise into a Beast

FortressIQ goes way beyond all of these scary, cobbled-together options to capture every step of every process, without expensive and disconnected tools pulling you in different directions. It brings your transformation to life with real-time, end-to-end process insights captured with DNA-level analytics across all applications, departments, and processes.

Don’t let your transformation turn into a monster. Use FortressIQ to make data-driven decisions that successfully propel your enterprise into tomorrow.

Modernizing Corporate Compliance Programs with Data Analytics Tools

Incorporating data-driven capabilities into corporate compliance programs speeds response, extends coverage, and eliminates bias. But despite the value added by operational data, the pace of adoption has been glacial. That may be changing, however, courtesy of an unexpected stakeholder: the government. The result is that, as companies are scrambling to meet these new compliance expectations, they are finding surprising benefits.

Nudging Compliance to be Data Driven

This past summer, the U.S. Department of Justice instructed its prosecutors to ask companies under investigation whether their compliance teams have access to data. If so, they’re further asked if it is being used to test policies and monitor for risks. The authorities have even shown a willingness to cut penalties for companies that have implemented data analytics or monitoring tools into their compliance programs. This bias towards leniency is a major incentive for compliance officers to explore solutions that can provide access to financial and operational data.

While other parts of the businesses, most notably Sales and Finance, have long used data to drive decision-making, the adoption of analytics tools in Compliance has been slow. Budgets are often a constraint, but there is also the cultural issue of not wanting to uncover unknown issues.

Companies should be embracing the shift, however, and not only because data-driven companies tend to outperform their peers. According to Forrester Consulting, businesses that rely on data management tools to make decisions are 58% more likely to beat their revenue goals than non-data driven companies, and they see an 8% boost in customer trust. The benefits are clear and compelling.

Compliance-Centric Analytics Tools

One challenge for compliance officers in adopting data analytics tools is the lack of purpose-built solutions to support robust compliance programs. Some companies have begun the journey by simply hiring data scientists to support ad hoc exploration, or by tapping into a nascent market of third-party vendors with limited capabilities. A bespoke data analytics application may be appropriate, but it is a timely and costly proposition most companies are unwilling to tackle, instead preferring to delay implementation until solutions mature.

To help jumpstart compliance analytics programs, FortressIQ has adapted components of our next-generation cognitive process intelligence platform to serve customers today. The process archiving solution runs on-premise or in a virtual private cloud (VPC), and it can be up and running in less than 48 hours without any integrations. FortressIQ virtual agents are also non-intrusive, meaning they will not cause process disruptions or slowdowns due to network access or VPNs because they use very little bandwidth.

With FortressIQ, compliance officers can capture all the work happening right now and review it for insights when time or resources permit. They can also integrate FortressIQ insights into existing business intelligence platforms for broader and deeper analysis.

Automating Compliance in Our Current Reality

Today’s unprecedented business environment has forced organizations to adopt new distributed and remote work patterns at a breakneck pace, but that comes at the expense of existing compliance activities. Every organization has had to rewrite their playbook to deliver value in a new, uncertain world of broken supply chains, changing customer expectations, and evolving communication patterns. In addition, traditional compliance and process discovery methods in the form of on-site consultants or business analysts, which have typically been used to address strategic challenges, are currently not an option. That is especially true for those with a global, remote workforce.

Through trial-by-fire tactics, companies are triaging and re-engineering processes just to address the basic needs of their employees and customers. Countless hours, combined with trial and error, has enabled distributed teams to build tribal knowledge on new and innovative ways of delivery. But the need to overcome pandemic-induced obstacles has forced many companies to pause traditional compliance programs. That is unsustainable.

Anticipating that variations of today’s disrupted business environment will likely occur again, it is imperative that this business knowledge be converted into institutional comprehension. Compliance initiatives are an ideal starting point. And not just because of potential judicial leniency.

Guaranteeing Privacy in a Time of Unprecedented Uncertainty

Three years ago, we founded FortressIQ with the idea that by leveraging computer vision, we could radically transform the way organizations understand their processes, thus accelerating their transformation. A little over a year later, we released our process intelligence platform, which has been deployed across Global 2000 companies in diverse industry segments. At the same time, the pace at which computer vision has been advancing is nothing short of breathtaking, but with great power (AI) comes greater responsibility. At FortressIQ, we believe that if we invest tens of thousands of hours in improving computer vision, we also have an ethical and moral responsibility to address use cases that can abuse those same advances. With that in mind, today we’re releasing Privacy Enhanced Gateway (PEG), the world’s first adaptive and learning computer vision-based firewall. And to stand unambiguous in the belief that privacy is a human right and that organizations who create technology that has the potential for abuse have an obligation to provide solutions to that abuse, we are making Privacy Enhanced Gateway a standard part of our platform at no additional cost.

With the adoption of FortressIQ across major global enterprises collectively employing over 5 million people, there’s proof in the industry that computer vision-based process intelligence can deliver unprecedented levels of information, creating an entirely new category of data and insights. The need for process intelligence has only become more critical with current workforce upheaval caused by work from home orders, global supply chain disruptions, and ongoing uncertainty. Part of our mission is to expand the usage of process intelligence to improve outcomes across the enterprise and to mitigate external forces at play. But, to attain the level of ubiquity that we’re seeking, we can’t ignore the security and privacy concerns associated with large-scale deployment of computer vision-based applications.

The scale of a challenge created by AI can only be solved with AI. This is why we took our expertise in computer vision (object detection, scene text detection, and recognition), natural language understanding, and data mining and created PEG, the world’s first learning and adaptive computer vision-based firewall.

A core component of our Process Intelligence Platform is the software-based sensors that are deployed on an end user’s desktop or laptop. These sensors tap into the video card of the machine and create a low bandwidth video of all screen activity, which streams to our cloud, where we convert the unstructured video into a structured log file. While we are HIPPA, SOC II Type 2, and ISO 27001 compliant, the idea of streaming a user’s activity (with the associated data) to our cloud, proves to be challenging.

Privacy Enhanced Gateway essentially takes some of the core AI from our platform and allows it to run at the edge under customer control. It is packaged as a virtual appliance that can be deployed on-premise or within a customer’s VPC on the cloud provider of their choice. Any sensors deployed then connect to their private gateway, which redacts all customer information from the video stream before sending the data onto us.

The core of this technology is a joint AI model which has learned the individual types of objects which compose a software screen, and the meanings of their labels and values. We can determine every control, like a drop-down list box or radio button, the label for that control, along with the data within that control. We then can redact the data leaving only the empty software screen remaining, thus alleviating the challenge of protecting sensitive data.

Using PEG, our customers get the best of both worlds. They can leverage the scale, cost savings, and rapid advancement of our cloud-based platform with the security and privacy of an on-premise deployment.

PEG takes us one step closer to unlocking the limitless potential of the global workforce by accelerating the responsible and ethical use of AI in the enterprise. We look forward to partnering with our customers to provide an unprecedented level of insights while at the same time proactively protecting the security of their data and the privacy of their employees. We’ve shown how computer vision-based AI can co-exist with privacy, and we invite other process intelligence vendors to join us in delivering responsible AI-based solutions.

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.

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.