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.

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.

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.

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.

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.

Ensuring Growth and Transformation in Uncertain Times and Beyond

As the pandemic continues to challenge business and society worldwide, the natural tendency of organizations is to retreat toward familiar business models and put innovations programs on hold. It is an understandable and justifiable reaction in times of uncertainty. But, for companies that want to be prepared to thrive when we are on the other side of this crisis, it is a time for continued investments in digital transformation.

In contrast to recent disruptions that we’ve experienced, such as 9/11 and the 2008 financial crisis, the global nature of the pandemic is putting significantly more strain and attention on the impact to supply chains. This will accelerate the shift in transformation programs from an emphasis on front-office activities to back-office digital operations.

Across industries, the majority of digital transformation programs in the last decade sought to address the customer experience (CX) paradigm shift as technologies put more information and control in the hands of end-users. Despite the fact that shifts in operational excellence (OX) would drive more value to the enterprise, the focus has been on addressing CX issues.

Forward-thinking leaders will examine their digital operations today to explore how they can be leveraged to enable their enterprise to be more competitive and resilient. The critical first step in achieving greater operational excellence is understanding the current state of your activities. Without situational awareness of how your company really operates, it is nearly impossible to undertake a successful transformation program.

Gaining process intelligence enables the systematic collection of data to analyze the individual steps within a business process or operational workflow. It helps an organization to identify bottlenecks and improve operational efficiency. FortressIQ’s cognitive process intelligence platform automates the discovery and analysis of workflows using computer vision, natural language processing, and machine learning. By automating previously manual activities, we capture more activity data and deliver a more comprehensive picture of operations at less cost and faster than previously possible.

Here are three areas where you can start leveraging process intelligence to accelerate your digital operations transformation and prepare your company for continued growth: 

  1. Scale Your RPA Program – While companies have committed to RPA technology, a majority are failing to scale their programs beyond a handful of bots. It turns out the challenge is not programming the automation—it’s prioritizing and planning the automation. Process intelligence is ideally suited to quickly and cost-effectively address this scaling issue and help transition companies from problem solving to strategic transformation.
  2. Conduct True End-to-End Process Assessment – Enterprise application sprawl is significant and expanding with Netskope reporting the average large company today uses more than 1,000 SaaS applications. With this level of complexity, it is challenging to achieve full end-to-end process understanding of core functions. Today’s environment makes it critical for organizations to accurately understand their entire supply chain—from the procurement of raw materials through acquisition by the actual customer.
  3. Gain Unprecedented Operational Insight – Every company is striving to be more data focused and implement more data-driven decision making capacity across the enterprise. FortressIQ’s cognitive process intelligence solution delivers the capacity to ultimately map the enterprise, providing the depth and breadth of data not previously available to assess current operations and anticipate future requirements to meet corporate objectives.

While it seems easier to play defense in uncertain times—from FedEx and General Electric to Facebook and Salesforce—many massive enterprises have successfully launched in challenging financial times. Companies that continue to innovate and transform will emerge from the current crisis in a stronger and more competitive position than their industry peers. 

If you’re interested in how FortressIQ is providing immediate benefits to organizations as they navigate remote work and new and changing business processes, please click here to learn more.

A Virtual Summer With FortressIQ

When we first started planning our summer internship program, there was no pandemic and thus, no social distancing requirements. Just after we kicked off the recruiting process, it was clear to us that the impact of COVID would extend into the summer and likely longer. Businesses started rescinding and canceling their internship programs, and we were seeing reports that close to 75% of summer programs were impacted, including planned remote workers. For a program like this, there are benefits for the students themselves as well as the company, especially given our size. Fortunately, we were agile enough to quickly revamp our program and deliver an entirely remote experience for our summer interns.

“All of my friend’s internships were canceled this summer, except for mine.”
— Thomas Reznik, Undergraduate Computer Science, University of Redlands

In total, we were able to bring on seven students for our premier virtual paid internship program. Because of the shift to remote work, we even have students participating from out of state. And our goals remain the same – to provide students the opportunity to learn how AI and technology applies in the real world while experiencing life at a San Francisco startup.

“At first I thought it might be a little different to work remotely, but it was no problem whatsoever after the first week of the internship. The experience is the same as working on site.”
— Mihir Patel, Graduate Software Engineering, San Jose State University

Recruiting & Onboarding
The process of recruiting students started and ended entirely online. All candidates applied digitally, and managers reviewed resumes and profiles to narrow down the list just as we do for recruiting full-time positions. Since so many companies had canceled their programs, we had a much higher volume and interest, with some candidates applying to more than one position.

“The interview process consisted of a preliminary call from recruiting, followed by a 1.5 hour zoom interview in which I spent 30 minutes talking to 3 different individuals from the customer success team. I felt super welcome throughout the interview, and I left feeling like I really wanted to join the FortressIQ team.”
— Binat Gousinov, Undergraduate Business Economics, University of California, Los Angeles 

Just as we do with our regular recruiting efforts, candidates that are selected for first-round interviews complete a phone screen with the recruiting team first and then passed along to the hiring manager for an additional phone interview. A second-round interview is completed over Zoom with the hiring manager and select team members. In total, we placed students in several departments covering customer success, data science, DevOps, security, quality assurance, and marketing.

FortressIQ welcome package for summer interns

“As I progressed through my interviews it became clear that the data science team was evaluating me on my approach to solving problems, not on what tools I knew how to use. The interview process taught me that the goal of a technical interview should be to showcase the way I think and the different techniques I use to solve problems, not necessarily whether or not I know how to use a specific tool or solve a specific problem.”
— Sam Truax, Undergraduate Applied Mathematics, University of California, Berkeley

Delivering an Online Experience
Summer internship programs offer an opportunity to learn and apply practical skills that would otherwise require the previous professional and technical experience. We wanted to make sure that even while working from home, our interns were able to get direct exposure to advanced software and programs used at FortressIQ. Working with team leaders and management, they were able to expand their knowledge and get their technical feet wet in multiple areas, including automation, functional testing, text detection, and machine learning.

“For the past three weeks I have been working on a project involving NLP [natural language processing], OCR [optical character recognition] text, and predictions. I have been working closely with my mentor and a couple of others on the data science team.”
— Seaver Dahlgreen, Undergraduate Electrical Engineering, Carnegie Mellon University

This experience has granted interns the opportunity to approach problems where the solution is unknown, which is a critical skill in academics and the tech industry. In addition to hard skills, the interns have also sat in on customer and team meetings, set up informational interviews with FortressIQ staff outside of their specific departments, and gained insight into FortressIQ’s mission and goals.

“While I would prefer to be working side-by-side with the rest of the data science team in the office, the remote internship experience has been excellent. My team has made it clear what I should be working on, and I can reach out over Slack whenever I need help. The positive atmosphere at FortressIQ has also allowed me to connect with other people outside the data science team.”
— Sam Truax, Undergraduate Applied Mathematics, University of California, Berkeley

For several students, this was their first time participating in a summer internship program. Although we wanted to give them a chance to work in the heart of downtown San Francisco, adjustments were made to figure out how to provide the best remote experience possible. The managers were incredibly supportive, holding daily check-ins, weekly meetings, and encouraging participation in our intern buddy program. The buddy program was designed to give extra support in the form of a department mentor, which provided more opportunity for formal and casual virtual facetime.

“From my perspective, FortressIQ is a great place to work remotely. The communication has been smooth and well thought out.”
— Hunter VanDyke, Undergraduate Computer Science, University of Tennessee, Chattanooga

Preparing for a Digital Future
This experience is unprecedented for everyone. As businesses across the world prepare for the unknown, I’m thankful that FortressIQ was still able to offer students an opportunity to learn and grow this summer. We successfully transitioned our strong and supportive culture to a virtual platform while enabling our interns to foster relationships with the FortressIQ staff.

“Everyone I’ve spoken with is always eager to talk to me, whether it be in technical advice, life at FortressIQ, or career advice. It’s also been fun to attend some online social gatherings, including a virtual movie night as well as the weekly Zoom water cooler socials.”
— Seaver Dahlgreen, Undergraduate Electrical Engineering, Carnegie Mellon University

Not only did we want to give everyone real-world experience in the B2B enterprise space, we also wanted to make it a fun and enjoyable summer for everyone. Our caring staff designed events to encourage more casual engagement, as well as created exclusive events for interns and their managers.

“Although I never met anyone face-to-face, I was able to build strong relationships and create a meaningful impact during this experience. Thank you FortressIQ!”
— Elizabeth Mitelman, Undergraduate Business and Marketing, University of San Francisco 

Over the last three months, our interns have been immersed in the exploding AI industry. Exposure to AI, tech, and startup culture has provided them with unique insights on what to expect after academia. I’m pleased to see that they’ve made such a positive impact in this short time. Their contributions to the company are known and visible, but I’m more excited to see such personal and professional growth in each of the interns. By creating such a unique environment to explore challenges and become engaged in helping us solve problems for the world’s largest companies, we were able to deliver something special with our first summer internship program.
Are you interested in career opportunities at FortressIQ? Click here to see our open positions.