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are oftentimes limited to the systems and tools available at their home firm, many of which are siloed, fragmented, and outdated.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.
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:
- 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.
- 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.
- 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.
Download E-book
Gartner, Market Share Analysis: Robotic Process Automation, Worldwide, 2018
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.
