Even with all your enterprise-level digital adoption, accounts payable can still be one of the most paper-intensive departments in your organization. The team’s primary function, invoice processing, costs the company resources due to time-consuming and repetitive tasks, slow processing cycles and human-introduced errors. The longer you ignore the cost of manually processing invoices, the deeper the dents it tends to cause in your organization’s bottom line. Learn how the benefits of accounts payable automation can reverse that trend.
The True Cost of Your Invoice Processing Flow
The U.S. Institute of Finance & Management (IOFM) suggests that the cost of processing a single invoice can be anywhere between $1 and $21. Putting this into perspective, think of a mid-sized company that has approximately 1,000 invoices to process per month. They would lose significant money through the gaps caused by process inefficiency. AP automation benefits can help to solve those inefficiencies and reduce your invoice processing costs.
What Makes Invoice Processing Expensive?
Wondering how best to calculate the expense of processing invoices in your organization? The simplest way is to equate it with the costs of associated human effort. Typically, a member of your accounts payable team would take at least 30 minutes to process a single invoice. Considering the average salary of an accounts payable clerk in the U.S. is $43,917 (approximately $21 per hour), processing one invoice could cost $10.50. For the mid-sized company mentioned earlier, this would add up to more than $10,000 every month.
And that’s not all! At this point, we’ve only discussed the base costs involved. But there’s more to it, such as:
Cost of fixing manual errors: Invoice processing is highly susceptible to errors due to daily variances, volume-based pressure or sometimes even sheer human fatigue. To fix such errors on a paper invoice, you might have to spend a significant $53.50 to create a new document, communicate with different stakeholders and redact payments already made.
Lost opportunity costs, such as discounts: Most vendors offer discounts for early payments, which can be as much as 2% to 5%. Manual invoice processing can create delays, causing the payment to miss the discount window.
Strained vendor relationships: The inefficiencies related to manual invoice processing, such as delayed payments, payment redactions and multiple requests for the same information, can seriously damage your brand’s reputation in today’s vendor and supplier landscape. More severe mistakes could even harm long-term relationships, adding to your overall invoice processing costs.
Physical costs, like storage and paper: Manual invoice processing goes together with paper-based processes, involving costs for physical file storage, stationery, etc. Unstructured hybrid systems can be even more expensive as the accounts payable team might have to switch between digital and paper formats, spawning duplication.
Cost of efforts diverted from core functions: Finally, complex approval processes coupled with frequent exceptions call for measures by personnel outside the invoice processing team. Business leaders might have to intervene in invoice processing, and their valuable person-hours (which would otherwise be spent on higher-value functions) must be factored in.
What is Invoice Workflow Automation & STP?
Invoice automation and the benefits of accounts payable automation go beyond barebones e-invoicing, which only recreates paper processes in a digital format and replicates its inefficiencies. The intelligent automation of invoice processing leverages technology in a meaningful way to remove the bottlenecks in your accounts payable workflow, bringing human intervention down to near-zero. This enables straight-through processing, or STP, where automated technology manages the end-to-end invoice lifecycle, and the average handling time by humans is dramatically reduced.
6 AP Automation Benefits That Achieve STP and Help Reduce Invoice Processing Costs
1. Extract Invoice Data Using Artificial Intelligence and Machine Learning
AI/ML-based technology such as object recognition and optical character recognition (OCR) can extract data from scanned images, PDF snapshots, etc. and automatically populate the fields in your accounts payable system. Intelligent invoice extraction is compatible with country-specific EDI formats, XML/JSON files, scanned images and even mailbox attachments.
2. Set up A Custom Supplier Portal
The worst long-term issue caused by inefficient invoice processing is probably the erosion of trust in vendor relationships. The smart UX of an automated solution allows you to set up a digital portal where vendors and suppliers can choose their relevant forms, make data entries and enjoy seamless interactions with your invoice processing team.
3. Configure Workflows to Handle Exceptions
Among the many benefits of accounts payable automation, automated exception handling lets your accounts payable staff tackle complex invoice scenarios without claiming the time of multiple business stakeholders. For example, they can set up workflows to handle exceptions such as potential signs of fraud, invalid vendor data, invalid file formats and specific PO detail mismatches. Configurable rules like these for invoice validation reduce an agent’s time to manually process an invoice by 80%.
4.Integrate with Your ERP
An AP automation workflow can connect with your existing systems like SAP, Oracle, Pegasus, Microsoft Dynamics, Salesforce, Infor, Sage or homegrown applications to enable bi-directional data flow. Your ERP can act as the reference for validating extracted invoice data (which otherwise needs to be performed by an AP staff member) and document the workflow information.
5. Gain from Analytics and Data Insights
Over and above AP automation benefits like lower invoice processing costs, automation becomes a true value generator here. First, it uses validation rules to assign a risk assessment score to every invoice. It also prioritizes tasks automatically based on load, productivity or your unique segmentation rules. Next, it uncovers vital data from your invoice processes to highlight productivity trends, KPIs and improvement areas, creating real-time visibility into invoices pending approval.
6. Consider Hosting on the Cloud
Cloud-based workflow automation software significantly lowers your upfront costs and ongoing maintenance overhead, while reducing your overall TCO. On-premises partly managed hosting is also an option in areas where there are critical regulatory requirements.
Save More as You Grow. Make Accounts Payable a Profit Center.
While traditional invoice processing methods become more expensive with scale (as volume and costs are directly related), intelligent automation and STP allow you to reduce costs as you grow. As the solution architecture is inherently scalable, your automation partner can offer volume-based efficiencies — for example, incrementally reduced pricing for volume tiers above 5,000 invoices per month.
JIFFY.ai delivers invoice processing and accounts payable automation benefits for small businesses, large finance and accounting teams and every organization in between. We can help them achieve 80% STP and reduce the human efforts needed to process invoices from a new supplier to 0%. Sophisticated AI and ML-based workflows allow you to look beyond just replicating age-old manual processes in a digital wireframe. Leveraging our intelligent and scalable automation HyperApps, we are committed to helping future-oriented enterprises derive business value across critical functions like accounts payable.
Get the Benefits of Accounts Payable Automation with JIFFY.ai
If you want to iron out bottlenecks or inefficiencies in your business processes through sustainable, intelligent invoice processing automation, please email us at marketing@jiffy.ai. Our HyperApps experts will be happy to help you accelerate!
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2020 was a landmark year for digital transformation. In the face of an unprecedented global crisis, technology emerged as the key enabler for resilience for all types of businesses —small teams or multinational enterprises, consumer-oriented or B2B vendors alike. Now, as we work through the road to recovery, many face the slightly uncomfortable question: How much of this change is scalable and operationally sustainable for the long term, including in terms of ROI?
The question becomes more pertinent as we look at these facts from McKinsey & Company’s recent report on the sheer pace of digital transformation last year:
The digitalization of customer interactions accelerated by 3 years globally.
Companies started to digitally reimagine their products/services 7 years ahead of schedule.
Over 6 in 10 executives believe customer expectations have changed forever.
Yet, most companies are not positioned to sustain their digital transformation projects for the long haul. Only half of them believe that the rapid surge in adoption of technology for their operations will continue after the pandemic – which could potentially stall progress or set the other half back by several years and tens of thousands of dollars of hurried sunk costs. Of course, there is always this risk when the shift is due to a black-swan moment and not organic, evolutionary change.
Moving beyond the pandemic, organizations will have to take hard-won learnings about the need for flexibility and responsiveness of AI digital transformation initiatives and determine ways to build real and lasting resilience that can ensure growth and the capability to weather future storms. A prime example of this is the accelerating trend of using intelligent automation digital transformation roadmaps, and automating more complex end-to-end business processes in a scalable, more sustainable way.
How the Pandemic Revealed Crucial Gaps in Digital Transformation plans
Many businesses learned vital lessons through the last year — involving crucial gaps that otherwise would have gone unnoticed — when they tried to push the pedal to the metal on their digital transformation plans.
Technology sprawl: In a bid to speed up digital adoption, business units and individual users were ready to adopt their favorite applications and enablers ‘under the radar’. This contributed to the ever-sprawling shadow IT landscape without organizational visibility, coordination or leverage. Only an integrated enterprise automation solution can put the power of technology enablement back in the hands of business users while keeping it within the realm of the organization’s overarching digital transformation framework and technology strategy.
Higher propensity for technical debt: If shadow IT was one side of the coin, the other was retro-fitted automation often requiring many code changes that aren’t always reconciled. In the pre-pandemic world, organizations were already struggling with technical debt as a multitude of non-integrated automation technologies were applied to legacy systems. In the emerging normal, they will look for ways to implement technology capabilities faster, without dramatic overhauls and inflexibility that results in technical debt. Adopting a HyperApps approach is a more viable way to achieve scalable, sustainable automation.
Culture woes: Cultural dissonance blocks digital transformation like no other variable. If business users, leaders, and decision makers aren’t on the same page, both strategically and in terms of execution, the dissonance will bring digital transformation to a standstill sooner than later. To prevent this, technology enablers must intersect larger strategic goals with short-term business outcomes and democratized use, and adopt platforms that enable this.
The first step to overcoming these challenges is to place sustainable automation at the core — of course, leaning into the best practical way of doing it — and to leverage the convergence of all its powerful capabilities. These include AI, Machine Learning, cognitive capabilities, cloud computing, no-code/low-code application development, and end-to-end integration, combined with a human-in-the-loop approach, where enterprises can achieve maximum advantages.
Can Digital Transformation Happen Without Automation?
Automation in many forms, including robotic process automation (RPA), has quickly become one of the integral pillars for digital transformation. An RPA digital transformation brings about incremental improvements in process efficiency, and step-level improvements in enterprise capability when applied strategically. It builds resilience to crises, like a pandemic or economic slowdown, where labor shortage and market volatility would disrupt traditional production systems. Automation also makes room and provides for further innovation by freeing up budgets and resources and ensuring that digital transformation works in an ongoing, iterative cycle. Digital transformation without automation is like a car with a limited engine: it simply cannot go the distance with the power to transform and accelerate, and can consume more effort to maintain than the value it generates.
So, it shouldn’t come as a surprise that 81% of IT organizations will automate more tasks to allow team members to focus on innovation over the next 12 months to 18 months, which according to Salesforce, will drive true digital transformation.
What should come as a surprise is that problems such as integration, script maintenance, control, compliance and scalability continue to plague traditional RPA deployments. This is a worrying fact, given that 84% of decision-makers plan to increase their investments in automation – without the assurance of sustained ROI or an uncomplicated implementation roadmap.
While traditional RPA relies on rule-based engines and technical configurations to automate workflows at the task level, HyperApps take a more democratized, business user-oriented approach for achieving end-to-end business process automation, and optimizing machine-human collaboration. They have a GUI platform where business users, data scientists, and IT professionals – stakeholders across the digital spectrum – can participate in setting up and operating meaningful business process automation.
This approach enables teams across the organization to implement, iterate, scale and grow, and addresses many of the challenges organizations currently face when preparing transformation projects for long-term sustainability. It also facilitates optimum teamwork and a culture of innovation.
HyperApps can automate business processes across the board, shrinking the technology sprawl.
The no-code interface empowers teams, reduces maintenance needs and technical debt.
HyperApp components are reusable and scalable, which lowers total cost of ownership (TCO), and accelerates time to value and ROI.
Compliance is baked-in, without requiring further investment.
Ultimately, automation will be central to long-term digital transformation as organizations look beyond short-term responses and adopt SaaS strategically to address new omnichannel opportunities. By opting for HyperApps, they can address the foundational challenges of their RPA investments and take enterprise automation to the next level.
Making Digital Transformation Scalable, Sustainable and Future-oriented
The pandemic brought about a rapid global paradigm shift, wherein businesses focused more than ever on delivering against digital-first customer expectations. It heralded a new normal of empowered employees who seek more autonomy, and of organizational resilience without taking success/market leadership for granted. For this emerging future, your investments in digital transformation shouldn’t be only considered as ‘crisis response’, but must hold their own and continue to evolve with the same urgency going forward, generating value beyond the pandemic.
Contact Us Today to Learn More About Intelligent Automation Digital Transformation
If you are looking to iron out bottlenecks or inefficiencies in your business processes through sustainable, intelligent automation, please contact us and our HyperApps experts will be happy to help you accelerate!
Written by
Payeli Ghosh, Chief People, Marketing and Operations Officer |
Updated on August 4, 2023
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+ bots1. 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
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.
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?
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.
Achieve end-to-end business process automation. Accurately. Easily. Quickly. Email us at marketing@jiffy.ai
Unlock the potential of AI-powered transformation. Talk to one of our experts today.
