Category: blog

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.

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.

There are now increasingly mixed feelings about business process automation, and rightly so. While initially benefits lived up to the early 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. As automation comes of age, traditional approaches like robotic process automation (RPA) or point solutions software for Business Process Management run into roadblocks around scalability, adaptability, and ease of use. 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 ¹. According to another report by IDG and Appian, automation was only “somewhat effective” (at best) for 65% of business users.²

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 is RPA?

Robotic process automation (RPA) uses technology governed by business logic and structured inputs to perform high-volume repetitive tasks in enterprise productivity applications. Using RPA tools, you can configure software, or a “bot” (robot), to process a transaction, manipulate data, trigger responses and communicate with other digital systems. By combining APIs and user interface (UI) interactions, RPA bots can emulate human processes and complete autonomous execution of various business activities.

How to Move Beyond RPA Technology: Is Hyper Automation the Answer?

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

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

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

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

But hyper automation is far from reaching maturity. Unless you are a massive organization with a dedicated RPA budget to throw at promising experiments, hyper automation 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 software.

Point solutions introduces 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 Software Get Right (and What They Do Not)

There is an argument to be made for SaaS-based point solutions software. 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, the biggest USP of point solutions 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 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.

How HyperApps Help to Automate Enterprise Business Processes End-to-end

In addition to the three commonly discussed options (RPA, hyper automation, and point solutions), companies can also consider the HyperApp approach when automating business processes. JIFFY.ai’s HyperApps can combine the simplicity of low code with the power of intelligent automation and the cost convenience of SaaS to provide a comprehensive solution that truly empowers your business users.

Here’s a simple example from probably one of the most critical areas of your business, accounts payable processing in enterprise accounting: Let’s suppose as part of a new regulatory requirement, your accounts payable team must report all invoices in a specific currency and upload them into an e-invoicing portal. In the point solution scenario, your team will have to rely on the SaaS vendor to enable this change, who will charge an extra fee for that feature. However, with a HyperApp framework, your invoice processing group can configure that change themselves on the automation platform and make it available not just for the enterprise accounting function, but roll it out across the organization.

Unlike point solutions used for accounts payable automation, you can scale HyperApps to process 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 enterprise automation solutions to new business process requirements is made simple with 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 through end-to-end enterprise automation post-COVID-19.

Road to Recovery: HyperApps Can be the Pivot for Meaningful Digital Transformation

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 business process automation and an even greater reliance on digital channels will be vital in the emerging future. For example, the number of public sector organizations citing automation as their 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 in the right way. The benefits of process automation beyond robotic process automation or point solutions software are undeniable, especially in our new contactless and low-touch world. HyperApps help companies strike the right balance, enabling them to achieve immediate growth targets and paving the way for more opportunities in the future.

Accelerate your automation journey with JIFFY.ai’s low-code platform.

Achieve end-to-end business process automation. Accurately. Easily. Quickly.
Email us at marketing@jiffy.ai

References

¹https://www2.deloitte.com/content/dam/Deloitte/bg/Documents/technology-media-telecommunications/Deloitte-us-cons-global-rpa-survey.pdf

²https://assets.appian.com/uploads/2020/05/Business-Automation-Technologies-and-the-Customer-Experience_May-2020.pdf

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. Hyper automation will eventually transform traditional automation capabilities into impactful automated processes.

The original types of automations were not integrated or even necessarily connected to automate end-to-end tasks or processes – leading to fragmentation. A decentralized approach and focus on “a bot per user” have 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.

What is Hyper Automation?

Gartner coined the term “hyper automation” 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 hyper automation – but this had its own share of issues.

Organizations choosing to automate via RPA as well as those venturing into hyper automation 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.

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

Traditional RPA is more of a white elephant for enterprise automation.

RPA vs Hyper 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.

The Hyper Automation Journey

Improving on this approach, Gartner introduced hyper automation 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 hyper automation, the no. 1 strategic technology trend from 2020

Gartner calls hyper automation “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. Hyper automation improves this through ML so that the data extraction isn’t template dependent. Similarly, when it comes to validating invoices, hyper intelligent automation can crosscheck via intelligent OCR, in contrast to RPA, which only reads specific ERP fields or structured information.

But even hyper automation 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, hyper automation-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, hyper automation 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 hyper automation, 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 hyper automation. 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 with Hyper Automation

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 hyper automation and finally, to HyperApps has proven to bring about battle-tested outcomes.

Accelerate your automation journey with JIFFY.ai’s low-code platform.

REQUEST A DEMO

Achieve end-to-end business process automation. Accurately. Easily. Quickly.
Email us at marketing@jiffy.ai

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.

Companies that recognize their employees as their greatest asset and invest in people first go longer distances. Human Resources in many companies has become a key strategic partner to the C-suite, helping to spearhead digital transformation across the organization to not only drive value but also to support the attraction, retention, and engagement of a happy workforce. Focused transformation within the HR practice itself can also drive efficiency and improve employee attraction and retention. More HR leaders than ever before are looking at themselves and their teams as a critical starting point for employee experience excellence, digital transformation and innovation within the business.

Cases in point (and there are many) are Google and Microsoft, which are both highly people-oriented in terms of the opportunities they provide to their employees and how seriously they value engagement as a metric.

Technology has helped the HR function immensely, both to achieve efficiency and to make processes smoother. Particularly for growing organizations, HR technology can ensure all stakeholders, as well as potential new talent, receive a consistent, excellent experience.

Creating Capacity For The Real People Work

HR leaders today have a tough job, indeed. Massive job losses and pay cuts are being reported across the board and managing the people function is twice as challenging as ever. In the post COVID-19 reality, employee experience is going to be extremely important, and significant. Not only is it important to protect and engage internal stakeholders, potential talent will closely watch to see how a company’s people are treated before making a decision.

The strategic and thoughtful use of technology and tools can free up time for professionals to focus on tasks that need true hands-on attention. One of the key points of resistance to technology adoption is that it might replace the need for people. Yet, the reality is that technology helps take over routine, repetitive, cumbersome tasks and allows people to focus on areas that need a more well-rounded approach to problem-solving.

The same goes for using technology in HR.

What was once an unending stack of paperwork or an unending stream of digital documents to move along through a process can now be automated. Some of the areas with the highest need for automation in HR include employee onboarding, data management, employee offboarding, payroll management, and talent acquisition.

For one of our clients, Jiffy.ai has been able to manage the regular churn and process over 600 contractors per cycle in a fully automated manner. The entire employee onboarding and offboarding process can be automated using intelligent automation, too, ensuring at each step that the process is not only seamless but also human. In the context of employee data management, automation can help accomplish what would ordinarily take at least two people working for 1 hour per employee. For a luxury real estate firm, Jiffy.ai has created tremendous value by automating processes for a 400% reduction in manual effort.

This is time best spent in quality people interactions. The New Now will see a rise in the need for managing not just the work people are doing but having a greater pulse on their wellness and emotional state. HR managers will be called upon to bring teams together by showing each individual the path to their growth and the synergies with the big picture of the organization. All this will need human time and energy.  

HR And Automation In The Context Of Remote Work

If there’s one thing the pandemic has done for the New Now, it is to accelerate the process of technology adoption. While employees now work from locations as diverse as the size of the team itself, management teams have had to adjust to a completely new way of reconciling performance and rewards and getting their bearings on what is actually going on.

Indeed, many companies are reeling under the pressure of circumstances that change every week, maybe sooner, and the C-suite is reacting as strategically and as swiftly as possible to keep their businesses open.

In this context, delegating not just performance management but also the entire onboarding process to a technology tool is one great way to free up time and the much-needed headspace to work on complex decision-making and have meaningful interactions. Jiffy.ai has successfully implemented automation in HR for a leading global audit and consulting firm. Here’s what we have been able to do for them:

• Moving contractor interactions to a web interface
• Making communication smoother and more efficient with Exchange mailboxes
• Automating the onboarding process entirely

As a result, the firm has been able to achieve these significant outcomes:

• An increase in the onboarding capacity to 100 contractors per month
• A sharp decrease in onboarding time from 3 hours (manually) to 17 minutes (when using RPA)
• 16 of the 20 core onboarding processes are now fully automated
• An onboarding process that runs 24X7 without the need for, or with minimal, manual intervention

Here’s a simple example of what a manual HR process would look like when automated, and the savings on time that can be expected as a result.

The role of HR has never been more critical than it is now. New tools and applications can aid HR leaders immensely in making this shift for their organizations. By relegating processes to automation and shifting its focus to its people, HR has an opportunity to help their entire enterprise not just to survive change but to be able to thrive in it.