Why JIFFY.ai is a Leader in the Zinnov Zones “Breakout Zone”

JIFFY.ai—a company that officially launched less than a year ago—is already a top-scoring disruptor in the Hyper Intelligent Automation (HIA) Breakout Zone, according to global management consulting and strategic advisory firm, Zinnov.

Zinnov regularly performs a comprehensive assessment of Hyper Intelligent Automation platforms as part of Zinnov Zones, an industry-leading annual rating of global technology service providers of cutting-edge technologies. For its 2021 report, Zinnov evaluated over 70 companies, including JIFFY.ai.

Zinnov closely examined JIFFY.ai technology for its technical prowess and scalability across multiple categories, including HIA, Use Case Discovery, Intelligent Document Processing, IT Automation, Intelligent Virtual Agent, F&A Automation, Customer Success Automation, and Talent Management Automation.

JIFFY.ai came out top-ranked in the Breakout Zone because our AI and ML-based platforms bring high-performance automation solutions to companies worldwide.

JIFFY.ai has come far in a short amount of time because we were founded to do things differently. We’re a newcomer in the marketplace, driven to disrupt the terrain of business automation with new ideas.

How HyperApps Leapfrog Other Automation Tech

The JIFFY.ai HyperApp approach leapfrogs past automation solutions such as RPA and SaaS-based point solutions for Business Process Management. (For a detailed rundown on how HyperApps excel in areas where RPA, SaaS-based point solutions, and hyperautomation don’t—read our post, From RPA to Hyperautomation to HyperApps: Level Up Automation Deployments in 2021.)

Zinnov looked at us closely and put JIFFY.ai at the top of its Breakout Zone because our unique approach allows businesses to combine the simplicity of low code with the power of intelligent automation, and the cost convenience of SaaS. Our HyperApps encapsulate all the various capabilities required to achieve successful business process automation—including designing, building, deploying, monitoring, and analyzing.

Our Invoice Processing HyperApp, for example, eliminates the roadblocks to maintaining frictionless cash flow by minimizing the risks and costs associated with inefficient processes. This HyperApp meets the complex technical and business requirements for seamless invoice processing—and then puts it in an easy-to-use, self-service application for business users, with no development team required.

Because our platform makes automation app development easier, we help businesses to avoid a common pitfall: With only technical users and data science professionals involved in automation development and deployment, there is a risk that real business requirements will get overlooked. HyperApps help to demystify the automation of complex business processes, simplifying deployment for business and technical users alike.

Most businesses silo their employees, which allows them to use and develop their specific expertise for the business’s success. This division is especially evident in software and technology areas: One employee may conceive a new idea but must rely on yet another employee to implement it due to a lack of specialized expertise.

While there’s nothing inherently wrong with this breakdown, it can lead to frustration for the idea’s creator. Creators of new ideas must spend a great deal of time and effort translating their thoughts to another group. Some things may get lost in translation, and often, this uphill climb means new concepts don’t come to fruition because the idea is too hard to enact, or too expensive, or too time-consuming.

With JIFFY.ai technology, it’s different: Ideas can be put into development more efficiently. Innovative power is easier to enact. Ideas come to fruition, and innovation moves forward.

When businesses use HyperApps developed on a single platform—rather than stitching together multiple technologies and vendors on their own—they’re able to structure their automation with reusable building blocks. These extensible and scalable building blocks allow them to stay on track through inevitable changes, such as workforce restructuring or application and process changes.

JIFFY.ai is proud to be a leader in the Zinnov Zones Breakout Zone. But we’re just getting started. Society is heading into the Great Reset, and process automation and reliance on digital channels will be a crucial ingredient to our recovery. JIFFY.ai HyperApps technologies will enable predictable wins in the short term, low effort overheads and greater democratization in the mid-term, and radical advantages in the long term. It’s time to disrupt business automation with HyperApps.

From RPA to Hyperautomation to HyperApps: Level Up Automation Deployments in 2021

In the early days of automation, robotic process automation or RPA brought the promise of radical transformation and improvement. Organizations could automate mundane, repetitive tasks, potentially giving back thousands of work hours to the business and reducing FTE efforts. 

However, these automations were not integrated or even necessarily connected to automate end-to-end tasks or processes – leading to fragmentation. A decentralized approach and a focus on “a bot per user” has increased technical debt for enterprises, putting true digital transformation out of reach. Over time, enterprises cobbled together disparate automation technologies to protect their original investments in RPA and were forced to assume the risks involved in integrating them. Gartner coined the term “hyperautomation” to define this integration of technologies, encompassing RPA, machine learning, artificial intelligence, and these technologies’ growing sophistication. Despite RPA’s massive market share, it was fast becoming apparent that RPA alone could not keep pace with today’s digital transformation requirements, necessitating hyperautomation – but this had its own share of issues. 

Organizations choosing to automate via RPA as well as those venturing into hyperautomation report a significant trade-off in terms of growing complexity, mounting technical debt, and a snowballing total cost of ownership (TCO) – which does not make sense in the long-term. 

In 2021, as we enter a new era in digital transformation, it is time to revisit our automation approaches and level-up. 

Traditional RPA is more a white elephant for enterprise automation

During COVID-19, we saw several years’ worth of digital transformation (3-7 years, according to McKinsey) take place in a matter of months. As we enter the next phase marked by consolidation, maturity, and long-term sustainability, organizations should rethink one of the core tenets of digital transformation – automating business processes. 

Robotic process automation (RPA) is entirely task-based, where you define precise rules to guide workflows in business process automation. Let’s say you are setting up an RPA software for invoice automation. At the invoice registration step, you can configure RPA to read from a file/folder, but every new source has to be manually configured. As you receive invoice submissions from multiple sources like cloud-drives, email, etc., the RPA script has to be updated and managed accordingly. 

Over time, this leads to RPA becoming more of a white elephant than a genuine value generator, as you will be spending outsized efforts on updating, cleaning, and maintaining your automation scripts as your enterprise grows into diverse functions/areas. 

A survey found that over 4 in 10 enterprises are having to spend more time and resources to maintain RPA than originally expected. 

Another issue is deployment timelines. Enterprise leaders start with the best of intentions but adapting RPA to a typical enterprise’s scale, and process complexity takes time – often up to three years. More than two-thirds of deployments take anywhere between 1 and 3 years, delaying your time-to-value. And once RPA is in place, just 4% are able to scale, mainly due to the complexity of projects (57%).

This leaves you with mounting technical debt and sunk costs, further increasing your TCO. 

Improving on this approach, Gartner introduced hyperautomation as the next phase of maturity, which would take advantage of AI/ML to cut down some of the inefficiencies of traditional RPA. 

The rise of hyperautomation, the no. 1 strategic technology trend from 2020

Gartner calls hyperautomation “the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans,” with the ultimate goal of enabling AI-driven decision making. 

It was the no.1 technology trend from 2020, poised to simplify several of the complex scenarios that would stymie traditional RPA.

Here’s a simple AP automation example: If you are using automation to extract invoices, RPA would require you to pre-train the engine and create separate templates for each supplier. Hyperautomation improves this through ML so that the data extraction isn’t template dependent. Similarly, when it comes to validating invoices, hyperautomation can crosscheck via intelligent OCR, in contrast to RPA, which only reads specific ERP fields or structured information.

But even hyperautomation does not match up to the promise of true digital transformation. Breaking down the above scenario, you will find frequent human involvement (often at preventable intervention points). For example, hyperautomation-based invoice extraction still lacks continuous learning capabilities. ML models are mostly a “black box” that cannot be adapted to business user behavior. For invoice validation, you still have to write complex scripts – only now, it is compatible with both structured and unstructured information. 

For this reason, hyperautomation remains confined to the “promising trend” segment, with limited real-world usability. Research names only Amazon and Google as key players, owing to their rich AI/ML capabilities. 

Does this mean enterprises who need immediate and effective outcomes from automation are left in the lurch unless they are willing to spend on a 5-year-long ROI generation roadmap? 

This is where HyperApps come in. 

Progressing to HyperApps – a pragmatic model with human-in-the-loop

HyperApps combine the functional principles of RPA, the intelligence/cognitive capabilities of hyperautomation, and the self-service convenience of SaaS apps to enable automations that show value in months and last for decades. 

Continuing with the scenario of invoice automation, here is how a HyperApp would do it: 

  • Invoice registration – Business users can integrate their preferred invoice source through a simple, point-and-click UI.
  • Invoice extraction – Any exception not covered by existing formats is routed to the business user. The user’s behavior is taken as a learning point, and the ML will adapt its future actions accordingly. 
  • Invoice validation – All validation rules are pre-configured; business users can toggle a rule on/off for a specific supplier when validating. 
  • True cloud native – Pushing new configurations to existing automation implementations is easy, allowing for constant upgrades of the HyperApp’s business process automation capability. 

HyperApps introduce a few important changes to the RPA-to-hyperautomation maturity curve. 

First, HyperApps rely on self-service, empowering business users to set up automated workflows and configurable business rules. What the HyperApp eliminates is the dependency on technical resources to make business configuration enhancements and changes. HyperApp designers can also add new functionality to the app and business users can turn them on based on their needs. 

Second, HyperApps are modular, with their components reusable as you grow, by applying the same components to multiple scenarios. This brings down the total cost of ownership and generates cost savings, while also shrinking time to value because of its turnkey nature. 

Finally, the human-in-the-loop user interface can replace the bulk training ML approach in cases where it is not possible to create a pre-trained ML model. This business user-led approach allows enterprises to build or enhance ML capabilities with their own business data.

As you can see, HyperApps address the key impediments to traditional RPA and hyperautomation. They ensure fast deployment and low maintenance, adapting to complex processes during business growth. They also keep a human in the loop to power continuous learning, reducing your efforts for manually configuring AI/ML models. Importantly, HyperApps are already in action at several enterprises, enabling long-term digital transformation without having to wait for technology or infrastructure maturity. 

Learn from the frontlines and level up today

Demonstrating a remarkable improvement over RPA alone, one of the world’s largest automobile manufacturers was able to achieve 85% straight-through processing (STP) for invoicing processes in just a 12-week period. The company first tried RPA in their AP automation journey to replace manual execution. But it was too rigid and rules-based, unable to handle frequent changes in invoice templates as the manufacturer added new vendors, new invoice formats, new types of suppliers, etc., as part of its growth journey. 

RPA solutions couldn’t keep pace with the company’s 5000-strong supplier network, processing 150,000 invoices per month. 

An Invoice Processing HyperApp successfully addressed this by learning from 12 months’ worth of historical invoices and continually updating itself whenever it encountered an exception. Using a HyperApp, the manufacturer can process one invoice in three minutes vs. the pre-automation 24-hour turnaround. And unlike most implementations, it saw measurable ROI in six months. 

At JIFFY.ai, we help organizations around the world with their digital transformation roadmaps by making it possible to level up their automation projects. This pragmatic progress from RPA to hyperautomation and finally, to HyperApps has proven to bring about battle-tested outcomes. 

To learn more or discuss any automation bottlenecks you might be facing, please email us at AcceleratingAutomation@jiffy.ai. 

Is the HyperApp Your Answer to These Seven Invoice Processing Challenges?

In an ideal world, invoice processing would look like this: 

But this is rarely the case. Straight-through-processing or STP of invoices remains out of reach for most businesses, despite advancements in automation over the last decade. Legacy processes, complex workflows, and a chronic lack of agility are commonplace for Account Processing (AP) teams, leading to seven challenges: 

  1. High manual dependencyResearch reveals that 51% of companies use manual efforts for something as simple as data entry. You could be losing out on thousands of dollars in efficiency gains, not to mention added efforts in correcting the 3.6% error rate.
  1. Convoluted routes for invoice approval – As 37% of companies still route their invoices manually, unexpected delays prevent timely payments to vendors. In drastic scenarios, the invoice could hit a brick wall and require a fresh billing cycle from scratch. 
  1. Mounting liabilities – In the face of delayed approvals and manual errors, invoices could sit unactioned for months. This is a challenge for 27% of companies, leading to accumulated liabilities over time, mounting pressure at EOM/EOQ, and the risk of non-compliance. 
  1. Difficulties in handling exceptions – The cause for an exception could range from incorrect price, quantity, or volume, to missing taxation details, PO number, or other information. They derail invoices from a straightforward path, requiring even more manual interventions. 
  1. Failure to gain from timebound discounts – A business might negotiate more favorable terms and discounted rates if invoices are processed on time. Unfortunately, nearly 1 in 5 companies cannot realize these benefits due to delayed vendor payments
  1. Lost invoices and effort duplication – As the saying goes, “too many cooks spoil the broth” – and this is certainly true for AP. In 33% of companies, manual dependencies, ineffective exception handling, approval complexities, and decentralization cause invoices to get lost
  1. Decentralized AP – With invoices pouring in from multiple business units, and no consistent or cohesive workflow, AP teams’ work can be fragmented. This hinders centralized visibility and governance, which becomes a problem when it is time for the business to scale. 

Automation has long been touted as a silver bullet to these challenges, helping companies achieve 100% STP. Research from Ardent Partners suggests that top-performing companies have 2.5 times higher STP rate than their laggard counterparts – clearly, there is a yawning gap to fill. Most companies cite the cost of ownership, a high degree of technical involvement, and a lack of cognitive capabilities as reasons to put off automation. As a result, they fall to the bottom of the pack, lagging far behind industry leaders. 

This is where a HyperApp can help. 

Instead of a rigid, sweeping automation landscape, a HyperApp offers near-surgical precision when it comes to handling complex processes. A self-contained, ready-to-use, and integration-friendly HyperApp can transform invoice processing in as little as four weeks. Its architecture is designed from the ground up to give business users the ability to configure workflows to their unique needs without any support required from IT. 

This can lead to massive effort savings in the long-term, while also making businesses more agile for emerging invoicing needs and handling, or changes to business processes. 

For example, a company with HyperApp-led invoice processing automation will find it significantly easier to adapt to the touchless needs of the ongoing COVID-19 pandemic, automatically “learning” new template structures through ML.

To learn more about our HyperApp and how it answers the most pressing challenges in invoice processing today, download our e-book here.

You can also contact us at AcceleratingAutomation@jiffy.ai to see a demo of our HyperApp solution.


Understanding Total Cost of Ownership in Invoice Processing Automation

Whether you’ve already automated invoice processing in your enterprise or you’ve not yet implemented a solution, it’s important to understand exactly what you’re buying – and what you’re NOT buying – when you choose a vendor. 

There are quite a few automated invoice processing solutions in the market that with varying levels of automation sophistication can facilitate the elimination of human errors, increase efficiency, and reduce costs. Choosing the right vendor is critical to your company’s ability to manage cash flow and properly process invoices and payments.

But when choosing an Accounts Payable automation solution, the question is not whether these vendors can do what they claim. The real questions to ask are: How much does the comprehensive solution cost and, once you sign on, are all the necessary elements bundled in a single, transparent price tag? Will the solution be flexible and efficient in the long run? And will it require many external resources to manage it well into the future?

“Real” TCO in Invoicing Processing for Accounts Payable Automation

We’ve put together some guidelines that can help you to select the right innovative automation solution while delivering a low Total Cost of Ownership (TCO). Here are some things to consider before selecting an Accounts Payable automation solution for invoice processing:

  • Does the solution provide a unified platform that supports your end-to-end invoicing process at every step?
  • Can the solution be easily implemented and adapt to your existing technology infrastructure?
  • Is the solution scalable with built-in functionality for expansion of your invoice management process?
  • Does the solution provide analytics on real-time data to help you see the current status of your cash flow and invoices, as well as provide visibility into improvements that can be made to the process itself?
  • When your business processes change in response to market demand, will you need an army of consultants or additional resources to implement and manage the changes to the automation solution?
  • Does the solution have built-in cognitive capabilities to handle complex invoices as well as automatically create templates for new invoices?

Costs can also be hidden. For instance, some solutions will still require an Optical Character Recognition (OCR) engine for the conversion of images to text. Transparency around these additional costs is crucial and, typically, such additional requirements aren’t identified until you’re in the middle of implementation.

JIFFY.ai’s Automated Invoice Processing HyperApp for Reduced TCO

The JIFFY.ai Automated Invoice Processing HyperApp combines the power of Artificial Intelligence with the efficiency of RPA to give you an all-in-one, ready-to-install Accounts Payable automation solution. We help your organization to maintain high-quality invoice data with improved processing times, zero errors and absolutely no hidden costs.

Here’s what our HyperApp offers:

  • Supports Cloud-based Saas or on-premise/private cloud solutions
  • Pricing based on volume of invoices processed per year – not a licensing fee
  • Zero additional or hidden technology/implementation costs
  • Best-in-class guarantee of cost for invoices processed
  • Scalable solution for the long run
  • Easy to configure workflows for adding new suppliers, processing rules, or adjusting the process overall
  • Integrated cognitive workflow with Machine Learning, making automated cognitive decision-making a reality 

Our customers have reported reduced costs, errors, and time spent on the invoicing process. Higher levels of automation with the cognitive learning ability in JIFFY.ai’s HyperApp has helped reduce transaction and implementation costs, as well as TCO.

Bring Home Lower TCO

Automated invoice processing solutions are available to help companies optimize and expedite the invoicing process. However, hidden costs in implementation can derail the savings and improvements quickly.

Therefore, it is essential to have clarity regarding optimizing the “real” TCO of the Accounts Payable automation solution. It should be one that doesn’t have any hidden costs and pricing based on the number of invoices processed per year, not on the number of procured licenses. JIFFY.ai’s Invoice Processing HyperApp can optimize your invoicing process with advanced intelligence and lowered TCO for your organization. Request your demo today.

Losing More than Money: The Cost of Payment Errors

Does your enterprise deal with an army of suppliers regularly? Is significant team effort spent on reviewing and sorting a multitude of invoices, but you still end up with late or erroneous payments?

Time to take a look at how JIFFY.ai’s Automated Invoice Processing HyperApp can help you save more by making the right payments at the right time.

The Hassles of Manual Invoice Processing

Any manual process is by its nature more tedious and error-prone than an automated solution. The incremental issues that arise when invoice payments are delayed or inaccurate can lead to severe issues in partner relationships and even break the supply chain.

Manual processing is costly and drawn-out and often fraught with a lot of duplicate and erroneous entries. The enterprise is left dealing with collateral damage in many ways:

  • Strain in supplier relationships and a threat to company reputation
  • Loss of purchase discounts in the invoice payment process
  • Inclusion of late penalties, increasing the costs
  • Rework to correct the errors adds to the processing time and costs more to the company
  • Erroneous invoices may take weeks to straighten out with the suppliers and hamper the end-of-month closure of accounts

Need Of The Hour – An Intelligent Invoice Processing System implemented through Accounts Payable Automation

According to AQPC’s Open Standards Benchmarking Accounts Payable 2020 survey, on average top-performing companies report that nearly 0.8% of their annual disbursements are duplicate or erroneous. On the other hand, bottom performers report more than twice the amount, at 2% of total annual payments. Just look at this in light of your yearly invoice payment numbers, and it will be staggering enough to take a real relook at the process. 

An automated invoice processing system helps in cultivating positive vendor and supplier relations. It enables users to maintain accurate records and respond to invoices in a timely fashion while ensuring prompt payments and precise records of supplier relationships. 

RPA-Enabled Vs JIFFY.ai’s Intelligent HyperApp Invoice Automation

RPA can quickly automate repetitive tasks in the invoice process and works on a rule-based approach. You can specify rules to flag exceptions when certain conditions are met and raise a request to the human agent to resolve the issue before the payment is approved. An intelligent Accounts Payable automation system applies NLP and Machine Learning (ML) on top of RPA extending RPA’s ability to provide substantial augmentation. 

Our HyperApp Automation solution builds on RPA’s capabilities to ensure the invoice is accurate before being sent for payment.

  • ML examines data captured in different fields and tries to establish a mapping between fields that hold the same pattern of data values
  • Robust in handling the cognitive 3-way match between the Purchase Order, report of goods received, and the invoice
  • Helps to check if the invoice is accurate, is not a duplicate and the invoice corresponds to the goods requested and received
  • Can learn as fast and accurately as experienced humans in identifying and interacting with suppliers, automatically performing input intake, coding, processing and routing invoice workflows, denoting payment deadlines, approval workflows and approvers. It requires human interaction only at critical checkpoints.
  • Makes it easy for supplier business users to interact directly with the invoice process through easy to use interfaces
  • Provides the analytic ability to detect payment schedules well in advance, to reduce errors, and to save costs by incorporating purchase discounts in the invoice process
  • Improves cash flow transparency with the validation engine confirming the accuracy of invoices before pushing them into the ERP system and reducing the need for downstream updates

Invoice Right on Time with JIFFY.ai’s HyperApp

JIFFY.ai’s HyperApp’s unique features for accurate invoice processing are:

  • Intelligent Invoice Extraction – with built-in cognitive capabilities to handle complex invoices like line items, tables, etc. and the cognitive capability can automatically create templates for new invoices
  • Customizable Supplier Portal – exercises full control over portal customizations to ease supplier onboarding and ongoing invoice management
  • Configurable Workflows – allows faster implementation cycles, additions of new suppliers or new product lines, configurable for a multitude of suppliers and invoice types
  • Powerful Validation Engine – ensures all validation is handled ahead of ERP to avoid downstream updates in the ERP system
  • Powerful Data Analytics – the underlying data layer allows us to provide analytics around the process itself and also around business intelligence
  • Pluggable ERP connectors – can plug directly into your existing infrastructure

Impactful ‘App’lications

Many organizations have reported reduced errors and improved overall process efficiency after implementing the JIFFY.ai HyperApp solution for their invoice process. 


Error reduction – 90%+
Efficiency Improvement – 85%+

Intelligent Accounts Payable automation of the complex invoice process with a more collaborative and connected approach and smarter, dynamic data-driven decision-making ability is required in today’s hyper-competitive, fast-paced business landscape. The JIFFY.ai HyperApp solution is the right choice for you to transform your invoice process, increase your revenue and maintain trust with your suppliers.

Hands Off: Invoice Processing Should Be Touchless

Is your business treating invoice processing as an unavoidable cost of doing business (pun intended!)?

When seemingly mundane activities are not optimized using intelligent automation, you could be leaking significant dollars without even realizing it. Interestingly, it is such tasks that can be transformed to be not only effortless but also provide deep insights into your fund management.

Any opportunity to improve business processes and create bandwidth for other critical activities – especially during the current economic conditions – is valuable and worth pursuing. Manual effort spent on repetitive administrative tasks like managing invoices results in a significant waste of time, effort and money. Studies show that Accounts Payable automation can reduce invoice processing costs by 90 percent.

It is common for large enterprises to interact with thousands of vendors and pay thousands of invoices per year. Most organizations still require a great deal of manual intervention to manage various aspects of invoice processing even after implementing RPA to automate the process.

How Do Businesses Currently Capture Invoice Data?

Large enterprises receive thousands of invoices every month from numerous vendors, each in a different format. Many suppliers still present their invoices via paper or as a PDF attachment in an email. These delivery methods require a great deal of manual processing, including scanning, data verification, and exception management.

An invoice goes through several stages of scrutiny before it gets paid by the account payables department. The dedicated teams that handle invoice processing manually feed the data into enterprise workflow tools like SAP and similar ERP applications to ease the orchestration of invoice processing, including routing the data for multiple scrutiny checks, approval and validation for payment.

Potential Concerns in Today’s Scenario

A majority of the invoices today are electronically processed where a vendor uploads the invoice on a supplier portal of the organization. Internal systems like SAP have integrated automated invoice processing solutions to manage the invoice data electronically. However, many vendors don’t adhere to the submission process through a supplier portal and end up sending the invoices directly via email. Even invoices submitted electronically are not standard and the same vendor may regularly change formats and fields.

Larger enterprises have teams to manually sort all the invoices, create the invoices in the system, validate the details across the ERP and GRN (Goods Receipt Number) systems and transfer the entries into the SAP system for further processing and approval.

The to-and-fro communication between the business and the finance team for validation and the overhead efforts to perform a match between a number of invoices and associated business unit included in the delivery of goods makes it difficult in many cases to validate a one-one match between the purchase order to a goods receipt.

For delivery of services, there is no concept of a GRN. In such cases, the invoice process goes through additional approval. It requires human intervention to route the invoices for the required approval, which further increases manual input.

Manual processing can result in high processing costs, increased risk, process and accounting errors, duplicate and late payments. According to AQPCs 2018 survey of 1,485 organizations reporting data on the cost to process an invoice, the bottom 25% are spending $10 or more per invoice processed. The median cost to process an invoice was $5.83.

The Challenges of using RPA for Invoice Processing in Accounts Payable Automation

  • With the advent of RPA, automation of invoice processing was done based on fixed invoice templates or rules. With the number of vendors increasing, it was difficult to train the automation tasks to handle frequent changes in invoice templates.
  • As organizations added new vendors and invoice formats, the RPA system failed to handle the process due to a lack of cognitive ability or flexibility to understand different formats, and an inability to adapt to new features.
  • RPA solutions were also weak in ensuring the accuracy of data validation owing to the inability to perform 3-way match in the invoice validation and business rule enforcement throughout the invoice process.
  • RPA solutions were not able to handle exception cues or provide many analytic insights of invoice status, improving cashflow or reducing processing costs.

Every touch to an invoice increases costs associated with processing the invoice and accuracy in handling and payments.

Our Automated Invoice Processing HyperApp with integrated Artificial Intelligence and Machine Learning capabilities can exercise full control over Supplier Portal customizations to ease supplier onboarding and invoice management. It has the built-in cognitive ability to handle complex invoice formats and create templates for new invoices. Equipped with a powerful validation engine, the validation process is dealt with ahead of ERP processes to avoid downstream updates in the ERP system minimizing errors. The platform allows faster implementation of configurable workflows for new suppliers, product lines or invoices. The potential to provide powerful data analytic features gives the competitive edge for Accounts Payable automation.

JIFFY.ai’s Automated Invoice Processing HyperApp is truly touchless in every sense. Our customers reported significant improvement in straight-through invoice processing, 90% improvement in invoice processing turnaround times and 85%+ efficiency improvement after implementation.

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.