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.

Improve Design and Development Team Efficiency: Document React Components using Storybook, Typescript, and CI/CD Pipeline

One common problem every new developer to a team experiences is the difficulty in knowing whether components already exist or if new ones are needed. Creating reusable components that are well documented with a clear API not only helps avoid duplicating code across the application but has several other benefits. Your reusable components represent your palette of ready-to-use pieces that you can share instantly with your team.

Creating simple and easy components that accept clear props and are decoupled from the data is the best way to share a library of generic and reusable components across your team of developers and designers.

We’re sharing some best practices our team adopted to make our applications robust and effective.

Creating a React Storybook – A Powerful and Effective Tool to Share Your Components

A style guide is a solution for all your design woes. It is the visual collection of every single component that will be used across an app. And it is an integral, useful tool that lets you easily exchange information with team members who have differential skills, while keeping the style consistent as the number of components increases.

The advantages of using a React Storybook:

  • React makes it simpler to create reusable components but tools like React Storybook help build a visual library from the code of the components themselves. 
  • React Storybook isolates single components so that you can render them without running the entire app, perfect for both development and testing. 
  • It also lets you write stories to represent possible states of the components. For instance, if you are creating a To-Do list, you can write a story for a checked item and another for an unchecked item. 
  • It is an excellent tool for sharing components across the team and with developers to improve collaboration. A new team member can look at existing stories and find out whether there is a need to create a new component or use an existing one as a solution to a particular problem.

Step-by-Step: Solution Approach

Step 1 – Our UX team identified all common components used across our platforms and developed design guidelines to manage how best to use them.

Step 2 – We developed an independent common-component library that will be used across our different projects.

Step 3 – We built a CI/CD pipeline to publish the components in our internal NPM registry (Verdaccio) with semantic versioning for managing versions.

Step 4 – We documented stories for our components using Storybook.

Improving How We Work

Documentation is essential, so creating, maintaining, and sharing needs to be easy. With Storybook, we can work with components in an automated sandbox environment. It handles the build steps as well, so we can simply write a story for the components and immediately see the result. It lets us experiment with small pieces of code without having to set up and maintain a test environment. And our developers can easily pick up the stories from the library and start working right away. Ultimately, Storybook has become an integral part of how our team works, stays productive, and keeps projects moving.

And Then There Were HyperApps: Next Leap in Your Automation Journey and Why Point Solutions Don’t Cut it

There are now increasingly mixed feelings about business process automation, and rightly so. While initially benefits lived up to initial hype (implementations achieve 30% to 200% ROI in the short term, reports McKinsey), mature projects are more disillusioned and typically run into a slew of challenges, particularly scaling. Traditional approaches to automation like RPA or points solutions for Business Process Management run into roadblocks around scalability, adaptability, and ease of use as automation comes of age. The number of companies scaling RPA is growing at snail’s pace, found Deloitte, with just 4% of companies successfully moving into implementations involving 50+ bots. According to another report by IDG and Appian, automation was only “somewhat effective” (at best) for 65% of business users.

As your company gears up for a speedy recovery post-COVID-19 – taking advantage of a bullish market – can you afford to be held back by stumbling automation projects?

What Are Your Options

Over the last few years, RPA has emerged as almost an industry default for automation. 

Nearly 1 in 3 companies use robotic process automation (RPA) technology, despite its numerous shortfalls. RPA-based process automations are mostly inflexible, with additional configurations needed for any change or extension to the system. You have to put in a lot of development efforts, and even when using low code platforms, there is significant effort duplication. 

For example, if an RPA-automated invoicing process runs into an exception, it has to be manually configured into the script or even require individual processing into your ERP. 

As an enhancement to this, enterprises can choose hyperautomation that uses intelligent, cognitive technologies like AI-based process mining, machine learning algorithms, optical character recognition, etc., to make automations more intuitive and, well, more automated. Gartner named hyperautomation among the top ten strategic technology trends for last year, anticipating its widespread potential. 

But hyperautomation is far from reaching maturity. Unless you are a massive organization with a dedicated RPA budget to throw at promising experiments, hyperautomation remains out of reach, barring a few one-off projects. 

A much more common approach to automating business processes is through SaaS-based point solutions. 

Point solutions introduce a significant degree of automation without most business leaders even realizing it – for instance, a simple scheduling feature on email, automated “nudges” for communication follow-ups, or a copywriting tool automatically checking documents against a style guide. In the wake of COVID-19, point solutions have exploded in popularity as employees/individual business units choose their favorite automation aids without always facing IT intervention. 

But for the organization, this means mounting shadow IT, the risk of fragmentation, and growing dependency on external providers to support dynamic business processes. 

What Point Solutions Get Right (and What They Do Not)

There is definitely an argument to be made for SaaS-based point solutions. They are turnkey, easy to use, and – on the surface – involve minimal investments. It was only a matter of time before the “app-ification” of digital activity in the consumer world percolated into business processes, helped by a massive boom in B2B SaaS solutions. 

However, point solutions’ biggest USP is their ready-to-use nature, which inherently makes them inflexible. As they target the widest possible user segment (without cognizance of the specific business use case), it is impossible to configure their automation capabilities as per your precise requirements. Or, if deeper configurations are available, you need an in-house expert with knowledge of that particular point solution. 

As your business – and process map – evolves, you will find yourself reaching out to SaaS providers repeatedly to introduce the necessary features. In the long-term, this is an unsustainable model. 

HyperApps vs. Point Solutions for Business Processes

In addition to the three commonly discussed options (RPA, hyperautomation, and point solutions), companies can also consider the HyperApps approach when automating business processes. HyperApps combines the simplicity of low code with the power of intelligent automation and the cost convenience of SaaS to provide an automation solution that truly empowers your business users. 

Here’s a simple example: As part of a new regulatory requirement, you must report all invoices in a specific currency and upload them into a e-invoicing portal. In the point solution scenario, the enterprise will have to rely on the SaaS vendor to enable this change and, many times, will charge an extra fee for that feature. But with a HyperApp framework, the enterprise can configure that change themselves on the platform and make it available not just for a function, but roll it out across the enterprise.   

Unlike point solutions, you can scale HyperApps to any volume of invoices (as per our example – it is applicable to virtually any business process) and integrate with new/existing workflows. 

Further, HyperApps bring in the flexibility you need in a dynamic business environment. Adapting your automations to new process requirements is made simple via a point and click interface, while integrations are available natively for use by business stakeholders, with little or no intervention from IT. 

This could be a game-changer for companies as they enter a new era of digital transformation post-COVID-19. 

Strengthening Your Ramparts on the Road to Recovery 

As companies gear up for what could be the world’s steepest recovery period to date, digitalization could either cripple growth or push it to new heights. 

It is estimated that process automation and an even greater reliance on digital channels will be vital. For example, the number of public sector organizations citing automation as a top 3 priority grew from 23% pre-COVID to 35% in the post-COVID period. HyperApps enable predictable wins in the short term, low effort overheads and greater democratization in the mid-term, and radical advantages in the long term – addressing the challenges of using point solutions for automating business processes. 

There’s something to be said for doing the right thing, the right way. The benefits of process automation are undeniable, especially in a new contactless and low-touch world. HyperApps help companies strike the right balance, achieving immediate targets and paving the way for more opportunities.

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. 

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

Celebrating the big and little things that make JIFFY.ai a great workplace

JIFFY.ai named Great Place to Work (mid-size comapny)

Great people make great workplaces, and great workplaces make amazing products.

December is typically a reflective time of year for most people and this year is – by far – no exception. Indeed, the pandemic and our response to it gives us further reasons to reflect, offer thanks, and look forward with hope.

This year, JIFFY.ai announced our Series A funding, received incredibly positive client reviews, and most importantly, managed to create real success stories for several clients reeling from the pandemic’s impact. 

As I write this article, we’re also very excited to be finishing off the year on such a positive note because our workplace has been voted as one of the best, most trustworthy workplaces in the Great Place to Work Survey 2020

Every year, more than 10,000 organizations from over 60 countries partner with Great Place to Work® Institute for assessment, benchmarking and planning actions to strengthen their workplace culture. Great Place to Work® Institute’s methodology is recognized as rigorous and objective and is considered as the gold standard for defining great workplaces across business, academia and government organizations.

Specifically, the metrics around the pride we take in what we do and the camaraderie we share are truly heartening. The free-flowing conversations, the honesty, the friendships we build at work – whether online or in person – these are the things that truly matter to us. While it is often hard to quantify great culture, we’re pleased to report that we’re building a true family here at JIFFY.ai.

From the days of our company’s inception, our leadership team has made building a people-driven culture a key tenet of what drives us. And in the year of the pandemic that forced us to connect remotely and often figure things out over a video call, a culture of putting people first has only gotten stronger. 

Being nimble on our virtual toes has been an unprecedented challenge, but when it comes to service, every team member has found it in themselves to rise to the occasion and do what would have once seemed impossible. These attributes are reflected in our high ranking in the Trust Index score, and we couldn’t be happier!

To many more years of changing the world of intelligent automation and remembering to do it with a smile! The JIFFY.ai team wishes you a safe and happy festive season ahead.

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.


Gratitude for Momentum at JIFFY.ai

While 2020 has been challenging on so many fronts, I wanted to take a pause to express gratitude and excitement for some of the great successes JIFFY.ai has achieved. Our company has had a transformative year. We proudly announced our Series A funding and launched our few brand this summer. And we are aggressively working on the next spectacular chapter of our growth story.

Since our funding announcement in June, we have been investing in our growth exactly as we outlined. One of the critical investments is in our leadership team. Over the last couple of months, we have added two savvy leaders who will strengthen our ability to build trust in the marketplace and accelerate innovation for our clients across the globe.

Algernon Callier (Al) – Head of Business Development 

Al is an experienced leader, communicator, and builder across multiple business segments, with a focus on Digital Transformation, Innovation & Technology Strategy, and Brand Development. He is a founding partner of Reaction Global, a new Venture Capital organization founded by Stanford executive alumni and a named co-inventor on patents achieved for Facial Recognition technology. Al has been recognized as a winning honoree of the Digital Edge 50 Award from IDG’s CIO organization, among other towering achievements.

Mahesh Vaidya – Head of Strategic Development and Partnerships

Mahesh is a Wharton alumnus and a seasoned executive, having worked with innovative companies across sectors and investing successfully across multiple geographies and through economic cycles. He is a former General Partner of Epsilon Venture Partners, a VC and growth equity fund focused on technology opportunities in digital infrastructure across South and SE Asia. He had led investments in and served on the Board of companies such as policybazaar.com and Imanis Data. His stint with Intel Capital was successful in making it a top-quartile VC in India.

I am so pleased to have these exceptional leaders join us as our company continues to grow. The future is bright for a new era of intelligent automation, and JIFFY.ai is proud to be at the forefront.

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.