Procurement in the post-pandemic world

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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.

Understanding Total Cost of Ownership in Invoice Processing Automation

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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 a 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 Process 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 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 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 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

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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 Automated Invoice Processing System

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 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. 

Statistics:

Error reduction – 90%+

Efficiency Improvement – 85%+

Intelligent 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

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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 automating accounts payable can reduce invoice processing costs by 90 per cent.

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 Automated Invoice Processing

  • 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.

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 implemention.


The Evolution of Automation: From Mundane tasks to Enhancing Human Innovation

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With COVID-19 causing risks to human health and disruption to our way of life in general, especially the way we work, companies have been forced to pursue alternate ways of making progress. Many of them are striving to use intelligent automation and AI to innovate their way forward while working safely from home.

This shift does not mean that we do not value human work, or the role people play in the enterprise. Think about the agricultural revolution. In the 18th century, people transitioned from hard laboring stationary farming to original inventions that altered the farming process. The new patterns of crop rotation and livestock utilization paved the way for better crop yields and the ability to support more animals. It was an opportunity to produce more, not a judgment of the reduced value of human work.

These agricultural changes impacted societies as there was a decline in both the intensity of the work and the number of farming laborers needed. Nevertheless, the positive effects of this disruption gave life to new technologies and opportunities as people migrated to the city to work in industrial jobs. As humans, it’s in our nature to innovate and create new solutions that become paramount to organizations and the people that work within them. We believe that as intelligent automation, Artificial Intelligence, and Machine Learning continue to evolve, we have an opportunity to harness this energy of innovation in a whole new way.

Our mission is to enable organizations to cross the human machine divide that has existed since the introduction of machines and enable them to co-exist seamlessly. We aim to reduce the friction between the two in a natural, human-friendly way. Eliminating the need for expensive translation mechanisms in the form of data entry, data synchronization and mundane activities allows organizations to become extremely efficient and resilient. Enabling innovation within the enterprise using natural language instructions, we bring out the innovator in the everyday business user. By letting the machines understand human language to achieve automation we drive speed in business transformation previously not possible. This is the core of our perspective on automation.

For too long, enterprises have placed contradictory expectations on their most talented thought leaders and employees. We have expected people to be innovative while also weighing them down with administrative tasks. Research shows “task switching” disrupts flow of thought and creativity. Ultimately, we launched JIFFY.ai to reduce this phenomenon and to allow creativity to flourish and innovation to be unleashed in its most uninterrupted form. Our relentless commitment is to see a change in how organizations redesign their work, supporting them through the power that automation and AI offers to maximize strength, resiliency and scale.

Historically, automation was seen as a point solution for mundane actions. You gave it a specific function or set of functions, and it performed. Now, technology allows us to elevate and redefine the process and achieve progress through automation. This change is necessary in the ever-evolving landscape in which we live.

How Businesses Can Shift into Life Beyond COVID19

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As we navigate a time different from the one we would have liked or wanted, we’re bringing to you a series of blogs, writeups, and LinkedIn resources that we call New Now. In today’s New Now blog, we talk about how businesses can begin to recover and mitigate some of the significant disruption with help from automation.

One McKinsey & Company report suggested, well before the pandemic, that over 30% of manual jobs currently could be automated by 2030. The same report also says that this change could actually bring in more jobs into the economy and that people would need to skill themselves accordingly.

But automation isn’t just a good-to-have feature anymore. For their own well-being and those of the people around them, companies and individuals alike will actively look to automate as many processes as possible, thus reducing the need for manual intervention and the close calls that might involve.

We also need to bear in mind that as all-consuming as it seems right now, the pandemic in its current state will change and subside. What this radical shift really does for businesses is it helps them see what costs they can cut, and where they can better utilize their workforces.

For the fiscal quarters that follow, many industries will continue to focus only on the costs they can cut. Three main solutions can help, and the use of technology and automation can make these processes faster and easier.

1.Analytics for real-time information: Everyone in the retail industry is feeling the heat, but the fashion and apparel industry is feeling it the most. In the business of clothing manufacturing, what is essentially a nice-to-have product in a thriving economy likely will not be purchased in current circumstances. The industry is looking at steep reductions in demand, as well as a restricted ability to predict future trends.

The only fallback, then, is real-time analysis. Fashion and apparel retailers have a tremendous opportunity here – to use real-time analytics and data to predict what they should stock up on, and how much. This could be a big welcome breath for brands that continue to struggle with too much inventory and a severely fragmented supply chain.

Another example is the airline industry. Using real-time data on infection rates and noticing which sectors open up first, they can plan their flight rosters and figure out which staff they need to bring back, and in what time frame. By linking powerful analytics with automated flight rosterscomplex, data-driven decisions that now need to be made can be made that much faster.

2.Preserving brand value and customer satisfaction: Much has been said about marketing in the current scenario. For some time now, most businesses have been riding the wave of a thriving economy. For about a year, though, we have heard predictions of a possible recession but certainly not on the scale we see now.

Companies around the globe have to rethink not just what they say, but how well they can walk the talk. Consumers of both B2B and B2C brands are navigating some very sensitive times and simply do not take kindly to an undelivered or under-delivered promise.

The next logical step for brands is ensuring that they can deliver on all the promises they make. We are heading into a territory where every individual is trying to find a new job or hold onto the one they have, and to save money while they can. Something as simple as getting a timely refund can put everyone at ease.

Serving multiple stakeholders in a shorter time window can be achieved using intelligent automation. For instance, Jiffy.ai has been able to help clients in the airline space accomplish improved turnaround times of 300% on ticket cancellations and refunds, while also significantly reducing errors.

Suggested Reading: How Jiffy.ai helped a leading airline company save 2000+person hours and process 90,000 transactions in a week

3.Preparing for the future: Companies today face a twofold challenge – delivering on an authentic customer experience and managing their cash flows to ride out the storm, and regroup for the next phase.

Generating demand is crucial for the cash registers to start ringing again. Unfortunately, demand forecasting will be a real struggle for many industries in the post-pandemic world. Consumer habits have changed, in some cases forcibly, and  wallets have tightened in a tough economy.

AI solutions can problem-solve in real-time when demand forecasting may seem like a mirage. Inventory management can be integrated with AI, helping retailers sync demand and inventory better. AI can also hook new and returning customers with a personalized experience along with identifying gaps in the offerings. This would certainly help businesses bounce back faster.

Looking ahead and planning for the future

Here are some other ways to optimize costs while also maintaining efficiency:

  • Adapt to the new virtual culture such that all non-operations staff continue to work remotely, ensuring their continued safety and well-being
  • Use automated cleaning and QC tools for spaces where people are needed physically
  • Automate complex processes using intelligent automation to help cut costs and improve efficiency

The fact remains that people need to buy things and consume services. The growth will first be visible across essential and semi-essential commodities. Several businesses will have to display tremendous resilience as the demand curve slowly rises. Investing in intelligent automation now can create a path to more efficient processes being run at lower costs, setting the tone for overcoming the current challenges and a viable recovery.

Advantages of web-based RPA

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

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

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

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

RPA on Cloud– Quicker, more convenient solutions

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

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

Scalability

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

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

Lowering costs

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

Safety

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

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

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

The Strategic Use of Technology to Boost the People Function

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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.

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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.

Is partial automation hindering your success?

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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.