Tag: RPA

We cannot emphasize enough the impact that artificial intelligence (AI) and automation (specifically Robotic Process Automation) has had, and will continue to have, on our industry. In fact, most advisors have grown so accustomed to their benefits, that they may not even realize the daily impact to their practices and clients. Consider your custodial platform and the way a client’s information seamlessly prepopulates in the system if he/she/they already has an account there. Or think about quarterly performance reporting; with the push a few buttons, you can generate reports for every single household in just a few minutes. AI and automation help reduce costs, increase accuracy in processes (like account opening) and ultimately create capacity for advisors, conceivably leading to increased revenue and profits.

There are challenges, however, that individual advisors face as it relates to embracing this new age of technology and fully automating their operations. First of all, some advisors are not yet reaping the full benefits; advisors within larger institutions are oftentimes limited to the systems and tools available at their home firm, many of which are siloed, fragmented, and outdated. 

Advisors who CAN build their own tech stack often struggle with “technology overload” and feel paralyzed by the many options and customizations.  In addition, training staff can be challenging. Even with access to the best operational tools, many advisory teams still use excel spreadsheets to manage and analyze data about their practices.  Finally, redeploying human capital towards revenue-generating activities is no easy feat. Work expands to fill the time we have to complete it, so ironically, just because capacity has been created, does not necessarily mean the time will be filled with productive work.

While digital transformation has recently become a key priority for the financial services sector, last year was a landmark period in this journey. Nearly half (49%) of firms have accelerated their initiatives, finds Deloitte, and the financial services sector has far outpaced other industries in their digital investments (arguably,  because our industry was so far behind others).

Over the years I’ve been part of several C-suite management teams and corporate boards for leading organizations at the intersection of financial services and tech. One first-hand observation I have made is that automation not only achieves short-term benefits like headcount of expense reduction, but it also creates a ripple effect that permeates across the entire company’s ecosystem. Strategic automation at the backend allows advisors to be more efficient in their work, and in turn, drives a far superior experience for the end customer. To achieve such holistic impacts, it is vital that we relook at our understanding of automation in financial services and the future roadmap.

Financial Services institutions have been primarily focused on building out an integrated infrastructure with interoperability between systems that enable a unified rendering of data for advisors. But this is only step one: It is when effective automation of front, mid, and back-office processes meets 360-degree business objectives, only then, advisors are free to build more capacity in their day and devote their time to more meaningful work, and eventually drive long-term business growth through lasting relationships with customers.

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.

1. 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.

2. 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.

3. 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.

The HyperApps approach: Evolve beyond your existing RPA implementations

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!

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Globally, the automation industry has seen a sharp uptick in the last few years. Between 2018 and 2019, it grew by a whopping 63.1%¹, making it the fastest-growing category in enterprise software! If that wasn’t enough, Gartner expects RPA to be a $7 billion market by 2022². So, the (literally) billion-dollar question that needs to be asked is this: What percentage of this investment will achieve sustainable automation and the competitive advantage it produces?

Sustainable automation in beginner vs. mature organizations: what leaders do differently

Companies frequently rush into implementation, motivated by quick wins – but this is not a sustainable automation strategy. While initial investments might be driven by cost savings (which in itself can be valuable), a mature enterprise, or automation “leader,” will take a mid- to longer-term view, with a different set of objectives and priorities. For example, just 1% of automation leaders prioritize cost savings, compared to 8% of beginners. 

In our experience, companies that answer key questions around extensibility, cost-efficient maintenance, visibility without black-boxing, and sustainable investments will be on the right track towards using automation as a driver of business transformation – making automation the gift that keeps on giving. So, how sustainable is automation when design and implementation tick all of these essential boxes?

Making efficiency and scale intersect for a sustainable roadmap

It all begins with re-envisioning the automation lifecycle for economic maintenance, going beyond the simple design-build-deploy structure. We find a more holistic, 7-step approach to attaining sustainable automation, covering exhaustive testing, post-deployment monitoring, management of new opportunities, and culture realignment.

Companies must also ensure with clear accountability and ownership to support business transformation. Nearly half of automation leaders (46%) have a dedicated team/committee tasked with identifying and approving automation projects. For beginners, this number is an underwhelming 7%. Aligning automations to newly conceived processes calls for centralized governance, so you might want to consider a dedicated center of excellence (COE) to manage planned and ongoing automations. 

Finally, you need a three-pronged strategy to reap the benefits of sustainable automation:

1. Plan early – Start off on the right foot. Take the projected gains from automation to plan long-term and super long-term, sustainable automation deployments, building a continuous cycle of returns and investments.

2. Implement efficiently – Leverage an app-based approach for implementation. An automation platform powered by HyperApps will give you better visibility into your IT footprint, business processes, and the needs of various stakeholders, without any “black-boxing”. It also makes the implementation more democratic by applying a good information layer to ease comprehension, testing, and validation, by technical and business users alike. 

3. Re-direct into growth- Position your automations as the bedrock for business innovation. Regularly testing new automation ideas using HyperApps, in line with your evolving business model and new processes, will help maintain the benefits of sustainable automation.

Gartner suggests that automation could save you around 25,000 hours of work annually³, and that’s just in finance. Sustainable automation isn’t just possible – it is an actioned reality for our many customers out there. To know more about JIFFY.ai’s pathway to sustainable automation using HyperApps, with a detailed 7-step life cycle, download the e-book here.

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Gartner, Market Share Analysis: Robotic Process Automation, Worldwide, 2018

Implementing and managing an intelligent automation platform, which includes Machine Learning and Robotic Process Automation (RPA) powered by Artificial Intelligence (AI), can be a complex endeavor, depending on where an organization is in their automation journey. Customers prefer vendors that offer strong service and support enabled by help desk AI technology, especially early in the process. It can make a big difference in the outcomes of a complete, end-to-end business process automation project. Here, JIFFY.ai AUTOMATE users on IT Central Station discuss the importance of support, as well as their use cases and the benefits they’ve seen with the technology.

Process automation use cases

IT Central Station members are finding a variety of uses for JIFFY.ai AUTOMATE in process automation. For example, John G., a VP Engineering at a computer software company, uses the tool in production for driving a Windows application. It extracts content from the Windows app, using JIFFY.ai’s OCR features in the process, and submits images into the application. The actual images are being extracted and returned to them as a document.

For Johnson M., a National Professional Officer at an international affairs institute with more than 5,000 employees, JIFFY.ai AUTOMATE is primarily used for processing documents in procurement and payroll. In one case, the tool creates an attestation letter for Human Resources (HR) for people who are leaving the organization. An IT Manager at a smaller tech services company has two use cases: Bank conflation and a Wage Protection System (WPS), which is a way to calculate and manage salaries per person.

Benefits of process automation

“The system in production completely eliminated the need for human intervention,” explained John G. “From time to time, we need to check the user interface and results, but that is very rare. It can be done once a week, or even less frequently.” A Managing Director, Business Transformation at an airline with over 10,000 employees similarly noted, “We’ve seen really good productivity gains. For the areas where we’ve chosen to automate, it’s not that we have freed up 30 heads in one area with one project. It’s more an aggregation of 20 heads across all of the different bots that have been developed. But we’ve seen tremendous value, especially in the pandemic.”

This user provided additional context, sharing, “As an airline, we have had to cut costs and we were able to go through some pretty strong voluntary separation efforts and redistribute resources and cover things because of the automation work that we’ve been doing.” For the tech services IT Manager, “[JIFFY.ai AUTOMATE] makes the developer’s work easy. With the customization option, we can write custom expressions using its compatibility with Python or other programming languages. Their web automation is good.”

Johnson M acknowledged the tool’s accuracy. He said, “For staff, it is common when doing repetitive work that there tend to be mistakes. There will be a missing digit or letter here and there, but JIFFY.ai AUTOMATE never does that. It is accurate to a ‘t’. It encompasses errors only if the input is poorly done, so it does not commit errors when it does this process. JIFFY.ai AUTOMATE has reduced manual processing for the 25 percent of the processes that it has automated.”

How great service fits into the user experience

Process automation can get complicated, so it’s useful to be able to draw on healthy vendor support. The airline’s Managing Director provided an example, saying, “Our cybersecurity positioning and stance on what we expect and what we allow and don’t allow, are pretty advanced. It was a pretty tall order to meet a lot of our cybersecurity constraints, so that’s an area where we had to do quite a bit of work. The very first bot we put into production fell into the PCI realm. We actually have two environments, one that has to be PCI-compliant, and a regular environment, and JIFFY.ai has been fantastic from a partnership perspective.”

They further noted, “A lot of the challenges that we uncover are really internal to our environment, as opposed to the platform. There’s a little bit of both, and that’s where it really comes back to the partnership with JIFFY.ai. They are always super-responsive in addressing any challenges with the product or the platform and supporting us as we work through how to integrate or automate a certain homegrown application of ours that is probably an outdated legacy application.”

“They have good support,” observed the tech services IT Manager. “There is a team who is ready to build whatever we ask. That is why we are still using this solution. When we are stuck on issues, there should be a team to back us up. We needed somebody like JIFFY.ai because we found we could go into the code level and make changes. Their team was there for support. They never complained whenever we threw non-standard practices at them; they never tried to correct us.”

To learn more about what IT Central Station members think about process automation and JIFFY.ai AUTOMATE, visit JIFFY.ai on IT Central Station.

How to build efficiency using intelligent automation and cognitive procurement

When we think of robotic process automation (RPA) in procurement, we know that adoption is rising. Many businesses already use RPA in their value chain. For those businesses that aren’t, it is a matter of “when” and not “if” they will use RPA.

In a domain as complex as procurement, robotic process automation ensures that most tasks and processes are automated at a fraction of the cost of adding headcount/resources or deploying new teams. Another benefit of procurement automation is the ability to work around the clock, which significantly reduces dependence on human resources.

The true value of RPA is the ability to repeat complex tasks and follow decision trees effectively. As machine learning, cognitive processing and natural language processing gain traction and advance at an accelerated pace, it is only natural to integrate these systems with RPA to deliver a more effective solution across the value chain.

Let’s dive deeper into why machine learning, cognitive processing, natural language processing, analytics and RPA must go hand-in-hand. We’ll also discuss how learning algorithms coupled with RPA’s execution capabilities are the future of full automation — especially after the pandemic.

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
• Transform supplier onboarding and the associated processes
• Forecast prices and inventory needs
• Create reports with usable data
• 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 data sources and tie these parameters into the recommendations it makes. RPA alone may not be enough, but when it’s supported with the right data and learning systems the procurement possibilities are nearly endless.

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 that there is still much to learn. 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, it can make decisions as good as or better than those made by a person.
• Execution: Any plan is only as good as its implementation, the sheer volume of work and the number of branches in the process. Post-machine learning interventions need RPA to help in seamless execution.

As a final product of automated procurement, 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
• Provide 24/7 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 cut procurement costs more than 50%, increase return on investment (ROI) by five times and reduce the number of strategic suppliers by nearly 50%. These numbers should eliminate the concern over RPA’s cost-to-benefit ratio, which is a frequent barrier to implementation.

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 an impasse can be kept low.

Next, identify the processes based on how much business impact automation could create and how much effort might be necessary to implement RPA. With these conditions in mind, classify the tasks 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.

A fear of “machines taking over the world” is one barrier to implementing robotic process automation in procurement. But machine control 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 is 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.

Contact JIFFY.ai for the Help You Need

Now that you know more about the role of robotic process automation in procurement, you’re ready to discover how to transform your business with AUTOMATE. Our easy-to-use intelligent automation platform empowers your teams to innovate faster. Contact us to learn more.

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

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.