Sustainable automation is elusive, but it doesn’t have to be.

Globally, the automation industry has seen a sharp uptick in the last few years. Between 2018 and 2019, it grew by a whopping 63.1%1, making it the fastest-growing category in enterprise software! If that wasn’t enough, Gartner expects RPA to be a $7 billion market by 20222. So, the (literally) billion-dollar question that needs to be asked is this: What percentage of this investment will bring a sustainable competitive advantage?

Beginners vs. mature organizations: what leaders do differently

Companies frequently rush into implementation, motivated by quick wins – but this is not a sustainable strategy. While initial investments might be driven by cost savings (which in itself can be valuable), a mature enterprise, or automation “leader,” will take a mid- to longer-term view, with a different set of objectives and priorities. For example, just 1% of automation leaders prioritize cost savings, compared to 8% of beginners. 

In our experience, companies that answer key questions around extensibility, cost-efficient maintenance, visibility without black-boxing, and sustainable investments will be on the right track towards using automation as a driver of business transformation – making automation the gift that keeps on giving. So, how can the design and implementation of automations tick all of these essential boxes? 

Making efficiency and scale intersect for a sustainable roadmap

It all begins with re-envisioning the automation lifecycle for economic maintenance, going beyond the simple design-build-deploy structure. We find a more holistic, 7-step approach to be the most effective, covering exhaustive testing, post-deployment monitoring, management of new opportunities, and culture realignment. 

Companies must also ensure with clear accountability and ownership to support business transformation. Nearly half of automation leaders (46%) have a dedicated team/committee tasked with identifying and approving automation projects. For beginners, this number is an underwhelming 7%. Aligning automations to newly conceived processes calls for centralized governance, so you might want to consider a dedicated center of excellence (COE) to manage planned and ongoing automations. 

Finally, you need a three-pronged strategy for sustainable investment

  1. Plan early – Start off on the right foot. Take the projected gains from automation to plan long-term and super long-term automation deployments, building a continuous cycle of returns and investments. 
  1. Implement efficiently – Leverage an app-based approach for implementation. An automation platform powered by HyperApps will give you better visibility into your IT footprint, business processes, and the needs of various stakeholders, without any “black-boxing”. It also makes the implementation more democratic by applying a good information layer to ease comprehension, testing, and validation, by technical and business users alike. 
  1. Re-direct into growth- Position your automations as the bedrock for business innovation. Regularly test new automation ideas using HyperApps, in line with your evolving business model and new processes, so that returns do not stagnate. 

Gartner suggests that automation could save you around 25,000 hours of work annually3, and that’s just in finance. Sustainable and ROI-focused automation isn’t just possible – it is an actioned reality for our many customers out there. To know more about JIFFY.ai’s pathway to sustainable automation using HyperApps, with a detailed 7-step life cycle, download the e-book here.


Gartner, Market Share Analysis: Robotic Process Automation, Worldwide, 2018

JIFFY.ai Wins Gold in Software Reviews’ 2020 Robotic Process Automation Data Quadrant Awards

We are very proud to be named a gold medalist and to claim the #2 provider spot overall in the SoftwareReviews 2020 Robotic Process Automation Data Quadrant Report. The report studies over 40+ parameters across three main dimensions, including product features, vendor capabilities, and the relationship clients have with their software vendor.

A division of Info-Tech Research Group, SoftwareReviews recognizes outstanding vendors in the technology marketplace as evaluated by their users annually. The report is unique because it studies not just internal and operation parameters but also client sentiment around the product. JIFFY.ai ranks consistently high on customer satisfaction, product support, and more.

We are incredibly grateful to our customers for sharing their experiences and reviews, which helped us rank #1 in the Emotional Footprint scoring where we achieved a Positive Net Emotional Footprint of 97%.

“Selecting top software vendors is becoming an increasingly transparent and data led process” says David Piazza, President of SoftwareReviews. “SoftwareReviews Data Quadrant provides a total view of the performance of a software vendor, from core features and capabilities to the important client-vendor relationship, what we call the Emotional Footprint. Vendors who have the strongest Net Emotional Footprint in the category demonstrate they have been successful at building strong relationships with their customers.”

Client sentiment is fundamental to us and we thank everyone who has invested time and effort in providing their feedback.

At JIFFY.ai, we believe automation accelerates innovation and we have the singular vision of putting the power of automation in business users’ hands. Our product strategy and roadmap are driven by this philosophy, which has helped to drive our first-place ranking in categories such as Native AI and Automation Apps and our second place ranking in business-level parameters such as Product Strategy and Rate of Improvement.

I would also like to highlight the Automation Apps Differentiating Feature category of the report where JIFFY.ai outranks all competition. Our HyperApps are focused on end-to-end automation of complex business processes and on making these automations quick to implement, resilient, and easy to maintain. We uniquely combine business process management, cognitive automation, and low code/no code development into a single platform to achieve lower total cost of ownership for our customers. To be ranked first on this parameter is exciting and reinforces our strategic leadership in this area of enterprise automation.

With much gratitude and appreciation to our clients and to the JIFFY.ai team, we look forward to continuing the work of helping our clients innovate through automation. Please find the full copy of the SoftwareReviews report here. https://jiffy.ai/resources/reports/rpa-softwarereviews/

Procurement in the Post-pandemic World

How to build efficiency using intelligent automation

When we think of Robotic Process Automation (RPA) in procurement, we know that adoption is already on the rise. Many businesses are using RPA in their value chain, and for those that aren’t yet, it is a factor of ‘when’ and not ‘if’ they will use RPA at some point in time.

In a domain as complex as procurement, RPA ensures that most tasks and processes are automated at a fraction of the cost of adding headcount/resources or deploying new teams. The benefit in addition to using a computational system is also being able to work around-the-clock (thanks to RPA), which  significantly reduces dependencies on human resources.

The true value of RPA is being able to repeat complex tasks and follow decision trees effectively. But with machine learning, cognitive processing, and natural language processing gaining traction and advancing at an accelerated pace, it is only natural to integrate this with RPA to deliver a more effective solution across the value chain.

Enter intelligent automation.

Now let’s dive deeper into why machine learning, cognitive processing, natural language processing, analytics and RPA must go hand-in-hand, and how learning algorithms coupled with RPA’s execution capabilities are the future of full automation.

What is cognitive procurement?

In the field of supply chain automation, cognitive procurement refers to the process of using automation with machine learning, analytics, and other cutting-edge technologies to help automate further, faster, and more efficiently.

Procurement as a process is characterized by large amounts of unstructured data, which may be impossible to process using traditional systems.

Apart from solving the problem of unstructured data handling, cognitive procurement also helps:

  • Transform all existing purchase and transfer order systems, sometimes entirely
  • Transform supplier onboarding and the associated processes with automation
  • Forecast prices and inventory needs, create reports with usable data and power better decision-making
  • Conduct risk assessment to prepare for known threats to the value chain

The best part? A cognitive procurement solution can also connect to external sources of data and tie these parameters into the recommendations it makes. RPA alone may not be able to do so, but when supported with the right data and learning systems, the possibilities are nearly endless in the space of procurement.

Intelligent RPA and its role in cognitive procurement

Cognitive procurement is often referred to as the final frontier in the procurement process. However, wisdom and experience show us that most of the quantum of human knowledge is actually ahead of us. In the era of information, we need a system that can handle three aspects of any complex task:

  • Research and data processing: This is where analytics come into the picture.
  • Learning from past data to make accurate predictions for the future: Machine Learning works on the principle that when an artificially intelligent system is given enough data to work with, it can make decisions that are as good as, or better than, their human counterpart.
  • Execution: Any plan is only as good as its implementation, and the sheer volume of work and number of branches in the process. Post-machine learning interventions need RPA to help in seamless execution.

As a final product, businesses with a vast and demanding procurement function can expect to achieve efficiency in analyzing their data, manage their supply risk, procure and pause material based on real-time needs, plan logistics for better efficiency and optimized costs, evaluate their suppliers based on their monthly, quarterly or annual performance across as many parameters as needed, and provide 24X7 support throughout.

Why should you implement an intelligent RPA solution in procurement?

According to a KPMG research report, “Delivering value in procurement with Robotic Process Automation,” implementing intelligent RPA can deliver over 50% savings in procurement costs, increase Return on Investment (ROI) by 5X, and reduce the number of strategic suppliers needed by nearly 50%.

How should businesses decide where and how to implement RPA in their procurement process?

Start by reviewing existing procurement processes to identify areas where the scope for automation is high. These tasks often represent repetitive actions that offer less value per extra time unit spent.

However, for an RPA system to work, the process needs to have a clear workflow and lead to non-ambiguous outcomes. Technical specifications include processes that run in relatively stable environments, and cases where manual intervention to solve for an impasse can be kept low.

Next, identify these processes based on how much business impact using automation could create, and how much effort might be necessary to implement RPA in this process. With these features in mind, the tasks can be classified into low-impact, low-effort-to-implement processes which make for good early adoption and trial cases, and high-impact, high-effort-to-implement processes which can effectively transform the business.

As a process laden with numbers and data, procurement presents the best use-case for implementing RPA in tandem with data analytics and machine learning. Companies that have already done so report unprecedented results across crucial parameters. One of the barriers for RPA implementation is worry around the cost-to-benefit ratio, which these numbers quickly disprove. The next barrier is a fear of ‘machines taking over the world’, which in cases as complex as a global supply chain – may be a good thing, as the pandemic’s disruption to this key process has shown.

The human capital that has been freed from the clutches of repetitive tasks and handling data too complex to process, can now be used in functions needing more human intervention and creativity. This leaves the machines to do what they do best – repeat every process error-free, follow the rules and use data effectively.

Advantages of web-based RPA

Almost all business today is being conducted on web-based systems. Collection and collation of data are done on the cloud, form filling, data extractions, website testing and a large number of other repetitive tasks have all become web-based. Most businesses have started moving virtually all their operations on to the cloud in search of greater efficiency at a lower cost.

Organizations are increasingly finding that most infrastructure, software, and processes are now only being offered on web-based platforms. Now, with the emergence of Robotic Process Automation (RPA) as one of the key drivers of efficiency, the cloud and RPA have become closely linked.

Technologies like the cloud and the Internet of Things are helping businesses and individuals become more interconnected. Being able to send, analyze, and interpret data on the go is one of the key drivers of IoT, and the cloud helps facilitate this. With RPA, all of these tasks can be more efficient.

Web-based RPA has changed the way people think of robotics. The strategies used have to be changed to adapt to the cloud. By using a web-based RPA, customers can see quicker results from using robotic automation.

RPA on Cloud– Quicker, more convenient solutions

Developing on-site RPA solutions for businesses can be cumbersome and time-consuming. This is why automation companies are making a move towards pre-built, customizable cloud-based RPA solutions, where a network segment has already been built into the cloud. This way, a client can start using the bots as soon as they start using the cloud.

Web-based RPA also allows for customizable solutions, where customers can choose specific processes to focus on so that bottlenecks can be addressed easily. By maintaining a library of ready-made RPA components, web-based systems can combine a number of automated processes to efficiently automate new processes. This provides a higher degree of personalization without installing an unwieldy system-based RPA solution.

Scalability

As businesses grow over time, there is often a need for new bots to increase efficiency. By adopting a web-based RPA solution, it is possible to put new bots into service when they need it. Businesses often will not need many bots when they start out, and their needs will increase slowly, but surely with time. It is also possible that certain bots will become obsolete and will need to be phased out. For example, if a certain department shuts down, or service is stopped.

Since the process of upgrading and updating cloud-based systems is a lot easier than system-based bots, a web-based RPA system can be a lot more flexible. It also takes away the need to buy a higher capacity than what is needed when you first install an RPA system.

Lowering costs

Easy customization and flexibility mean that companies can pick and choose what they want to automate. This means lower costs of licensing, and operations. By taking some simple steps to understand and identify what they want to automate, where the technology can be used, and the potential savings, a web-based RPA system can help companies save a lot of money. In a system-based RPA, companies will often have to purchase a lot more licenses than necessary to avoid high installation costs, and increased downtime, the cloud-based system can provide extremely cost-efficient.

Safety

The cloud has become ubiquitous. Almost all companies rely heavily on cloud-based solutions for more efficiency. By using robots instead of humans, they can also increase the safety of their operations. The robot’s access rights and scope of work can be clearly defined, so as to minimize any potential problems and minimize human errors.

The ability to scale and replicate cloud services improves the competitiveness of businesses.

For a detailed discussion, write to marketing@jiffy.ai

Is partial automation hindering your success?

Have you ever said, “Let’s start small and then build it up based on how it goes,”? You sure have. So have most of us. In our world, this is typically how all automation begins.

During the initial days of robotic process automation (RPA), organizations were mostly skeptical. They saw potential but were unsure of real impact.

So, they tried it out for small non-critical functions — they wanted to minimize risks. Understandably. Say, the finance department would automate one task in the Accounts Payable first such as reading data from a file and transferring that to the ERP system. However, other aspects of the Accounts Payable process would continue to remain manual. Also, understandable.

This is what is called partial automation — quite literally, automating just a part of something much bigger.

But why would anyone do that?

In fact, there are plenty of reasons for handling automation this way.

For one, the earliest automation systems could only automate basic screen capture – in other words, anything that couldn’t be seen on a screen would break the process and need manual intervention.

Some of them are financial — end-to-end automation is more expensive and incurs higher opportunity costs to run business-as-usual in the interim because every sub-task would need investment in a bot. Partial automation, on the other hand, was cheaper. Organizations could pick a few bots for shorter processes and pay-as-they-go. This also helped them understand the effectiveness of automating and calculate ROI in the longer term.

Some industries worried about security. A bank would use RPA tools to move data from a front-end system to a legacy back-end system but wouldn’t let bots analyze their customer data. Even to this day, security remains an important reason companies choose partial automation. Why risk exposing critical data while their mandate – bolstered by regulatory requirements – is to protect it and keep it confidential?

Some others just weren’t ready for end-to-end RPA — automating a process end-to-end would necessitate standardization of formats, fields and rights, and that requires an investment of finances, as well as time and energy from their internal teams.

It also didn’t help that monitoring each automated process or bot was not easy. So, there was greater risk of broken automation if the scope was end-to end.

The initial RPA landscape had its limitations, lacking seamless integration with the human input when the time came for decision-making and without a human-in-the loop concept.

Most also feared that they might not have the people trained and equipped to intervene and improve the end-to-end RPA, making it a bigger risk. Partial automation is less demanding.

To be clear, in all these cases organizations certainly understood the value of RPA, invested in partial automation and derived value from it. Most of them are “somewhat happy” with the results their RPA systems are delivering.

Partial automation only provides partial success. Why?

Process measurement issues: Partial automation meant that a major part of the processes still had to be done manually, so there was no way to measure the ROI per process or per team/department. In other words, there was no way to make a strong case for automation because the results couldn’t be measured objectively.

Efficiency deficit: The improvement in overall process efficiency, while automating only a part of it, can often be so minimal it doesn’t seem worth the effort.

Savings deficit: As efficiency is only marginally improved, cost savings also end up being marginal.

Stagnation: Partial automation can be a dead investment without the bot’s ability to learn, adapt or grow with the needs of the organization. Likewise, it can be a dead investment if the organization doesn’t have the ability to see and manage how automation is being applied across the enterprise.

Resource blocking: Without the ability to improve intelligently, partial automation still needs people to fill its gaps. This means that people continue to work on mundane tasks, leading to low productivity, fatigue and dissatisfaction.

Right, so is Intelligent Automation a possible end-to-end solution?

Intelligent or Cognitive Automation in its simplest form, is an intelligent version of RPA — one that can learn from the data and apply it to present needs. Automation can become limiting when not supported by the learning capabilities of AI, which is where intelligent automation comes into the picture. It is flexible enough to understand and adapt to non-templatized data inputs. It can process structured, semi-structured and unstructured information with ease.

Take JIFFY.ai’s cognitive automation tool, for instance. It is able to read and extract non-templatized information. Even in cases where JIFFY.ai doesn’t understand or cannot read certain parts of the document, it will extract all the other parts and reduce manual intervention to a bare minimum. This way, with cognitive RPA, you can automate the entire process, not just a part of it.

With its ability to learn, cognitive RPA is also scalable. As a business becomes more complex and processes more intricate, cognitive RPA can learn and grow along, making the ROI significant in the long term. For instance, intelligent automation systems that trigger alerts to floor supervisors in a manufacturing unit can learn to spot newer anomalies over time, making all aspects of productivity, quality and capacity predictable. Enterprises are addressing their requirement for end-to end automation using a combination of RPA tools (for repetitive tasks), BPM tools (for process management), OCR , IDP tools (for document extraction), Data platforms for data streaming and beyond.

Instead, a platform that makes all of these features available in a single stack can help save costs and time, and also translate to easily calculable returns over a period of time. This way, they can adopt cognitive RPA for all processes, interconnect them and enable them to work in tandem.

Cognitive RPA also comes with basic skills. Pre-built RPA systems, customized for industries and functions, are now available with the ability to hit the ground running immediately. Once installed, they are in auto-pilot mode needing very little help from people, even for setup, training or maintenance.

With prior knowledge, pre-built cognitive RPA solutions can automate end-to-end with a more meaningful understanding of the process landscape.

With cognitive RPA, the solution is no longer piecemeal. Unlike partial automation, cognitive automation impacts the entire value chain.

Today’s context

The global situation businesses face today is a reason for organizations to take seriously how end-to-end automation can help them to be more resilient in the face of crisis.

As an example, a large automaker based out of Europe has worked with JIFFY.ai in automating their financial processes. This truly helped them recently when there was no business shutdown in their country, and they continued to send in their documentation to JIFFY.ai’s offices where physical offices were shut down. Thanks to automation, backend support continued seamlessly while production continued as planned.

It is completely understandable if you have a partially automated system now. It made sense in its day. But today, to see the real value of automation, end-to-end cognitive automation is the way to go. With a clear view of the entire system, end-to-end RPA will be able to bring together various processes into a smoother journey, be it for your customers, vendors or employees. It will also future-proof you as the system understands your existing processes and can expand to accommodate newer ones.

If you have adopted partial automation and aren’t fully realizing its potential, speak to one of our consultants to explore newer avenues. We understand where you are and we’re happy to help.