New advancements in Intelligent Capture and RPA

With RPA set to supercharge productivity and efficiency across a range of businesses, interest in this field is developing almost as fast as the technology itself. Indeed, one study reported by Forrester predicted that RPA will represent a $2.9 billion industry by 2021.

Within such a dynamic environment it can be hard to keep on top of the latest developments. To make that simpler, we’ve summarized some of the most interesting trends in Intelligent Capture and RPA in recent months.

#1: Developing next-generation RPA

Early RPA was fairly limited in terms of its capabilities. Tasks like basic data entry, copying and pasting, and other processes that resembled simple macros were the mainstay of what was available.

Although perhaps not revolutionary, RPA of this type was nonetheless stunningly effective, allowing companies to achieve an output up to 15 times greater than what was possible with human workers alone, according to Deloitte.

RPA, however, is rapidly becoming an even more complex and useful technology. Although challenges remain in applying available machine learning and natural language processing to business needs, many companies are already adapting their processes and making ready for the “next step” towards a complete digital transformation. This may involve advanced robots capable of handling contracts, invoices and other complex documents, as well as end-to-end automation.

As cognitive processing develops, the role of RPA in business is only likely to grow.

#2: Intelligent Capture becoming prominent

Many businesses have their sights set on intelligent capture as the next technology that will streamline their business practice. It is undoubtedly a crucial part of task automation and one that will allow businesses to complete tasks in-house that previously would have required expensive outsourcing.

CapturePoint is just one example of a simple technology that automates a process that might otherwise require a significant amount of human time and attention.

Business process management is set to change dramatically in coming years. While previously this field was focused on the allocation of tasks among human resources, in the future it will likely look significantly different, with the deployment of intelligent machine resources taking prominence.

#3: Agility vs. cost-cutting

Intelligent data capture and RPA will, of course, reduce costs for many companies. Indeed, the National Association of Software and Services Companies (NASSCOM) predicted a reduction in costs of 35-65 percent for onshore operations in companies that successfully implement RPA.

And that’s not to mention the other benefits inherent in automated data capture.

It’s interesting to note that the focus of many businesses looking to employ these technologies is efficiency rather than simple cost-cutting. The higher process efficiency afforded by the use of RPA and intelligent capture will make businesses in a range of spheres more agile, with shorter development cycles and more agility once on the market.


With these developments in RPA and intelligent capture, it’s no wonder more businesses than ever before are looking to implement these new technologies.

Doing so, of course, is not without potential pitfalls, but with an experienced team of experts on board, almost any business stands to reap the rewards of well-implemented RPA processes, from increased efficiency to a lower error rate to the freeing up valuable human resources.

Preparing your business for machine learning and big data

Companies are buried in mountains of big data.

Big data keeps growing bigger every day. Between the IoT (Internet of Things), social media, mobile devices and cloud computing, the mountain grows higher and higher. Machine learning offers companies a way to transform the ever-growing mountains of data surrounding them into actionable insights.

But scaling that mountain is easier said than done.

A recent report from Gartner looks at the tricky situation facing businesses who want to conquer their data using machine learning. The report lays out the challenges of machine learning and the need to take proactive steps to prepare for it.

Let’s start at the beginning.

What is machine learning?

Part of the field of artificial intelligence, machine learning uses statistical techniques that give computer systems the ability to progressively improve on a specific task. Or, said another way, these computers can “learn.”

Although machine learning has technically been around since 1959, its importance has increased as businesses find better ways to understand big data.

What’s driving the adoption of machine learning now?

Big data is a tantalizing ideal, with 74 percent of companies saying they’re trying to be more “data-driven.” But the dream of big data has fallen far short of reality.

Many companies are simply overwhelmed by the structural and staffing challenges of managing big data.

The primary challenge is simple. How do you sift through huge amounts of data to get actionable insights? Currently, only 29 percent of companies able to connect data to action. Machine learning offers a key to unlock the insights buried in data.

It’s all about analyzing data to make accurate predictions.

What are the challenges of using machine learning with big data?

The Gartner report calls attention to a specific issue facing IT analysts. When analytic architectures and end-to-end data aren’t set up to work together, the disconnect creates an underlying problem.

In order to conquer that challenge, it’s ideal to have a data scientist on your team. At the very least, you need access to someone who deeply understands machine learning.

But learning the algorithms is difficult without a specific mathematical background. The way businesses integrate data also becomes more complex. In addition, infrastructure must be addressed.

How should SMBs prepare for machine learning and big data?

The challenges are different for small and midsize business than at the enterprise level, where there are more resources. According to the Gartner report, there are many ways to prepare for machine learning. But the leadership needs to come from the IT department

The people who understand the technical challenges of the issues simply must be involved.

Being proactive is the key to deriving the benefits of machine learning. Here are some of the specific recommended steps:

  • Examine the computer clusters and storage infrastructure needed to support machine learning
  • Select a machine learning platform that works with different machine learning frameworks when planning to work with off-the-shelf algorithms
  • Make sure the data organization is updated in an end-to-end analytics architecture in order to support machine learning algorithms
  • If you’re planning to build custom machine learning algorithms, put a development life cycle in place capable of supporting learning models
  • Take note of new frameworks being packaged with AI and machine learning solutions, which provide seamless solutions
  • Go to the cloud if you don’t have appropriate engineering infrastructure and staff to support machine learning, as it’s flexible enough to scale

The next big thing in big data

Machine learning is what’s next for dealing with big data. Companies should be building for the needs of machine learning by understanding the problems they’re trying to solve.

That means going to the IT support experts who uniquely understand machine learning issues. There’s simply no other way to be prepared for what’s next.

4 reasons RPA implementations fail and how to avoid them.

A surprising 30 to 50% of RPA projects initially fail. Business owners are often left wondering what went wrong. To help you avoid common pitfalls, we created a list of 4 reasons RPA efforts fail and how to avoid them.

“57 percent of organizations surveyed look to increase process quality through innovation.” ―Deloitte

#1: Not selecting the right processes to automate.

If you’re a first-time RPA implementer, you’re likely excited to improve your workflow. And that’s great. You just need to make sure that you’re automating processes that will have the greatest impact on productivity.

Focus on automating simple tasks that employees do on a daily basis or that take a long time to complete. Things like data entry, social media posts, and payroll are great places to start. Automating these tedious tasks frees up your team’s time. That way they can focus on tackling more critical projects.

“Implementation challenges can be serious, but proactive planning up front can reduce or eliminate them.” ―Deloitte

#2: Attempting deployment too fast.

It’s easy to get hyper-focused on using RPA to increase ROI. Sure, you want to increase your profitability. But you also have think about your current resources and capabilities. Can your IT department take on a project right now? Is your network equipped to handle an implementation? How will you measure an RPA project’s success?

Rushing into deployment is a risky move. One that will likely lead to frustration and wasted resources. Creating an implementation plan will help you achieve success. Get your business strategy team and IT department involved in its creation. And be sure to maintain an open line of communication throughout the entire project.

#3: Neglecting your RPA software after deployment.

Even after an RPA implementation, there’s still work to do. RPA software requires ongoing maintenance so that it continues to run smoothly.

Robots never deviate from their configured algorithms. But software interfaces, data formats, and company processes change. To make sure your RPA software continues to work in your environment, you need to make sure your business strategy team maintains it.

#4: Relying only on RPA.

While RPA is a helpful tool, it’s not the only tool. There are lots of productivity-enhancing solutions on the market. You should use RPA software as a part of your overall process optimization strategy. Not the entirety of it.

Take the time to analyze your business processes and determine where automation will make the most impact. You can also enlist the help of a provider skilled in RPA implementations. They’ll be able to provide guidance how to maximize your ROI using RPA software.

“It’s important to recognize and mitigate these [common issues] in order to facilitate the success of the organization’s RPA program.” —Ernst & Young

Deploying RPA in your business.

You need to consider every aspect of an RPA implementation before you undertake one. Evaluate the risks, rewards, and what resources you’ll need to maintain your RPA software. And remember, if at first you don’t succeed in your implementations, try again. There’s always a lesson to learn and apply to your future digital transformation efforts.

Related Blog: 3 areas of business that robotic process automation is optimizing.


Digital Transformation Trends to Watch in 2018

As technology continues to advance, so does the rate of digital transformation among growing businesses.

Companies that leverage technology to automate their workflows gain a significant edge over their competitors. They are able to increase their productivity and drive down operational costs, maximizing their ROI.

In short, digital transformation makes rapid growth more attainable.

Many business owners are looking to the future, wondering what digital transformation trends will emerge. We’ve created a list of the ones that will likely continue to gain momentum in 2018.

Artificial Intelligence (AI)

AI is the technology behind advanced automation. It encompasses things like machine learning, cognitive computing, and more. It essentially allows computer systems to learn from data batches and past mistakes to adapt and fine-tune their operations automatically. With it, optimization opportunities are endless and innovation is inevitable.

“The aim of artificial intelligence is to have machines learn and deduct mass amounts of individual and collective data –and process through it quicker than a human brain would– to give the user a truly personalized response.” – Forbes

Arguably the most common example of AI in action is IBM’s Watson. This tool uses AI to help users to build customer service chatbots analyze content, translate natural language, and predict personality characteristics via text.

The Internet of Things (IoT)

Since its inception, the IoT has revolutionized the business world. It enables companies to automate, monitor, and improve their processes. And it simplifies the transfer and analysis of data.

For example, IoT automation enables manufacturing companies to reduce the number of employees they need to maintain asset production.

“The industrial IoT market is estimated to reach $123.89 billion by 2021.” – IndustryARC

Another major advantage of IoT is that it allows businesses to more easily provide customer support. For instance, Tesla recently began implementing “over the air” auto upgrades, allowing the luxury electric car owners to easily keep their firmware up-to-date.

Expect IoT to expand as different industries find innovative ways to take advantage of its capabilities.

Robotic Process Automation (RPA)

Data collection and analysis is the key to process optimization, and the latest approach is leveraging robotic process automation (RPA). In data analytics, RPA is a software configuration that quickly captures and interpret information.

Using this technology, business owners can easily identify bottlenecks and make fact-based decisions on how to improve processes and develop innovative solutions.

“47% of businesses are currently using big data tools to predict customer behavior.” – IDG

Uber currently uses RPA to more accurately calculate surge pricing and identify unsatisfactory drivers. Manufacturing firms are also leveraging it to receive supply chain analytics in real time and easily identify changes in product demand.

Adopting digital transformation trends.

Digital transformation is going to continue innovating the way business is done. As technology continues to advance, the number of ways to leverage AI, IoT, and RPA will increase. And the number of organizations harnessing the technology to increase productivity and decrease their human workforce will grow.

If you’re interested in learning more about how you can digitally transform your business, reach out. With so many applications available to improve results, our goal is to help you find the ones that work best for your organization.

3 Technology Trends in 2018: Our Predictions

Technology continues to advance and evolve at breakneck speed. 2017 saw the growth of tech like self-driving cars, facial recognition, gene therapy, and machine learning, among others. Most new things are trendy. Fashion, ideas, slang. At first, each innovation is the cool, hot thing. If it’s particularly interesting or meaningful, it sticks and becomes integrated into society.

Essentially, in any field, there will always be trends and IT is no different. The question is always, what will these technology trends be? As we gear up for 2018, we put together a list of our predictions for the New Year. Without further ado…

Artificial Intelligence

Artificial Intelligence (AI) deals with the simulation of human intelligence in computers. Behaviors and capabilities like visual perception, decision-making, language translation, and more all fall under this area of science.

We predict that developments in AI aren’t going to slow down anytime soon. In 2018, it’s likely that businesses in a variety of industries are going to increase productivity by automating their workflows with artificial intelligence technology. In fact, a recent Gartner survey showed that 59% of organizations are still gathering information to build their AI strategies, while the remainder have already made progress in adopting AI-driven solutions. More applications and equipment are likely to begin incorporating AI into their functionality, helping businesses augment the day-to-day tasks their employees already perform.


Also known as the tech behind Bitcoin, blockchain is essentially a continuously growing database of time-stamped records (known as blocks) that are linked together using cryptography. It allows various parties to exchange transactions securely, with no one single point of failure. This means, there’s not just one person in charge of the entire chain. It’s a self-managed, peer-to-peer ledger of records.

About 50% of business leaders predict blockchain will be mainstream by 2020. Currently, the financial sector is primarily using it with about 90% of major European and North American banks exploring blockchain solutions. The challenge will be diversifying its applications so that businesses in different industries (healthcare, government, content distribution, supply chain, etc.) will be able to take advantage of the technology.

3D Printing

In traditional manufacturing, objects are formed of different materials using fabrication tools. However, in 3D printing, things are created through a technique called additive manufacturing, when materials are layered on top of one another to form an object. A major benefit of 3D printing is that far more complex shapes can be created than in traditional manufacturing, allowing for greater customization.

Our prediction is that 3D printing will continue to rise in popularity this coming year. With the advent of machines priced (and sized) for the average customer, more and more people are starting to use them in their homes and small businesses.

And there you have it! 3 technology trends to keep an eye on in 2018. We look forward to seeing what new tech developments will emerge during the next year.

If you ever have IT-related questions or want to learn more about how your business can harness technology improve their processes and increase network security, please reach out. It’s our resolution to continue serving our customers and offering an educational resource for business leaders looking to improve the way they approach IT.

4 Ways Digital Transformation Accelerates Your Processes

Time is money, there’s no way around it. If your employees are spending valuable time performing manual labor-intensive projects, your business is wasting money and time waiting for results.

Enter digital transformation.

Digital transformation is essentially the integration of digital technology into business processes to improve work and business outcomes. The idea behind it is to enhance how your company does business so you can boost productivity, save resources, and eliminate inefficiencies and flaws associated with human error.

Here are four ways to use digital transformation to  take your business to the next level:

Data Capture

Data capture is the automatic identification and collection of an object’s data. This data gathered through optical or audio technology is converted to a digital format and entered into a computer system, sans human involvement. A great example of data capture in action is the conversion of a paper document into an editable, digital formats like PDF or Microsoft Word or Excel. Manual data entry is often error-prone and time-consuming. Automating this activity eliminates both of these issues and allows you to add data gathered to your digital workflow, boosting productivity, increasing efficiency and saving valuable resources at the same time.

The Cloud

As 90% of organizations use the cloud in some way, you’ve probably heard of it before. The cloud is a massive, online server that allows employees to securely access company documents anywhere and anytime. Thus, it maximizes collaboration, productivity, and efficiency in a business setting. Cloud technology also helps control access to documents and data belonging to your company and your customers. It’s no wonder that 50% of businesses using the cloud cite it as their preferred digital transformation solution.

Artificial Intelligence

AI technologies can help your business automate processes and reduce the amount of time it takes to perform a variety of key functions. Depending on your industry and operational requirements, there are a few examples that are accessible through a range of business software tools and platforms:

  • Machine Learning: A branch of AI that uses algorithms to find patterns in data in order to predict outcomes and classify data. A more in-depth write-up on how machine learning can add value to your business can be found here.
  • Natural Language Generation: Technology that produces natural-sounding language and text from computer data to automate content production. It’s especially useful in customer service, operational reporting, and business performance analysis.
  • Robotic Process Automation: Artificial intelligence that uses scripts and other forms of communication to automate human support. For instance, when you receive a generated reply in regards to your email inquiry or when a robot directs your call.

There are a variety of other AI approaches that can help your business embrace digital transformation.


It’s both cumbersome and risky a pain to send multiple versions of sensitive documents back and forth between colleagues. There’s a large margin for human error, and it takes time to make sure you’re updating the right document(s). Blockchain is here to eliminate these headaches. Essentially, blockchain is a continuously growing database of records (aka, blocks) that are linked together using cryptography. Each block is timestamped, making it easy to track edits.

50% of financial organizations see mainstream adoption of blockchain by 2020.” That’s fast growth, but not surprising as the new technology is already revolutionizing the way financial organizations are recording transaction histories.

Digitally Transform Your Business

With so many options out there, it can be hard to know where to start when it comes to selecting the right digital transformation tactics for your business. We specialize in helping businesses determine processes that they can implement and update in order to boost productivity and save resources. If you’re interested in learning more about how digital transformation can benefit your business, reach out today.

The State of Machine Learning and AI

At Process Fusion, we’re committed to passing along relevant industry information whenever we find it. In that vein, we’d like to make you aware of a significant study recently published by the McKinsey Global Institute.

The study examines the state of AI research and development. Forbes published an excellent article summarizing some of the more pertinent stats included in the study. The article also include links to both the original study and McKinsey’s own summarizing article.

We encourage you to read Forbes’ summary by clicking here.