Low code automation streamlining internal apps development

More and more companies today are adopting low-code approaches, and exploring and creating low-code apps to fast-track their application development process with limited or no coding knowledge. This attempt utilizes the visual modeling process in a graphical interface to configure and assemble applications, thus allowing developers to bypass time-exhausting infrastructure tasks and reenact the patterns to foster business efficiency. In line with this, low-code automation platforms can confront challenges by minimizing the reliance on IT teams and facilitating business users to design applications easily.

Hence, it is evident that these results indicate that in today’s era of rapid change, low-code automation platforms will continue to increase as they will be used to offer quick, creative, and competent visual cloud environments for both organizations and systems analysts with low-code knowledge.

What is low-code automation in app development, and how does it work?

The highlight of the low-code platform is that it uses drag-and-drop tools to design applications without or with little understanding of coding language. Thus, low-code automation, an automated software development process, is easy to make your business more effective and create automated workflows without writing any programming code language. Thus, business managers can use these visual tools to automate model-driven application designs.

Low-code automation is an emerging technology that digitizes and optimizes business-critical operations within minutes, thus allowing employees to focus on other high-value tasks. With low code, users can use pre-built templates and quick-start tools or add customizations or changes to existing functionality, to help define key app components – constantly providing workable app versions in real-time as they are ready.

With low-code workflow automation, business owners use a straightforward decision-making structure (similar to a flowchart) to create automated workflows used across multiple departments in companies.

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How does LCNC help enterprises scale automation without coding experience?

Low-code/no-code solutions do not provide any detailed customization of the applications. However, it is not a significant shortcoming. So, this brings us to a tricky scenario where there is an element of doubt over low-code/no-code significance without the intervention of IT teams in a company. Despite this limitation, applications such as conversation bots, workflow management systems, and virtual assistants can still be easily developed with LCNC automation.

Still, there is a role of the IT professionals and the developer’s assistance for scaling, maintenance, integration, and control of the applications, especially with enterprise-range structures that have a crucial role in service delivery.

Low-code automation software is apt for small and medium businesses (SMBs) that are reusable to design process flows across various divisions in the same company. As these can be deployed quickly, apps designed using low-code or no-code technology usually cut down an administrator’s workload.

For instance, businesses implement low-code automation platforms to develop mobile and websites. Website design and hosting services companies provide low-code process automation to manage SEO, analytics, digital marketing, and web personalization, among many others.

Why do businesses require low-code automation?

Low-code automation tools assist small enterprises in improving their business workflows. The key reason is that low-code automation empowers SMBs to design tools without much knowledge of the programming language. Consequently, these tools can be used by anybody, regardless of skill or experience. It helps people who may not know how to program and access the software. Having more users means there will be more customers and revenue for businesses.

Some elementary low-code no-code (LCNC) applications are leveraged to design small apps. These LCNC tools help companies to quickly develop apps and execute the ‘human-bot’ communicative exchanges as it does not need any custom web application whenever a human is involved in the development process.

Key benefits of low-code automation:

Low-code automation simplifies the app development process

Low-code application development platforms such as AssistEdge Engage help business processes to become more reactive and satisfy corporate business requirements containing process automation, quicker business processes, better customer journeys, and policy and regulatory compliance.

Therefore, most leading companies globally can leverage the power of AssistEdge Engage to enhance customer experience and ease compliance and management of global threats. Low-code automation solutions can thus considerably accelerate the development of advanced applications that can automate the business workflow, integrate with existing information systems, and offer a seamless user experience.

What are the challenges of conventional supply chain forecasting?

As the traditional supply chain comes up with certain constraints, it requires urgent strategic revamping to accomplish the latest consumer demand trends and effectively manage the ever-increasing supply chain challenges. These concerns within the supply chain network are primarily driven by a sequence of consumer expectations, international convolutions, abundant routes to the market, and other miscellaneous factors. Hence, supply chain administrators need to create smart business strategies to nullify the challenges to keep everything flowing smoothly. Given the ongoing crisis, tech-enabled supply chain forecasting and demand sensing platforms are the best bet for organizations across all industries and sectors.

Supply chain challenges will likely persist in the coming days. Usually, these challenges are propelled by higher consumer spending, increased awareness of the convenience of online purchases, and catch-up needed from last year’s blockages. It has now become imperative for organizations to implement efficient supply chain forecasting tools for connected and seamless operation of the demand and supply business networks.

Significance of supply chain forecasting

Supply chain forecasting predicts a business’s customers’ demand, supply, and expenses. It primarily comprises exploring the competition, accumulating, compiling supplier data, and ultimately evaluating past patterns to predict the future of an industry. Hence, this justifies that forecasting is a significant skill for a supply chain supervisor as it embodies numerous skills one should attain and develop.

Enterprises aim at making the best decision about their future demand and supply. Whether it is stock inventory, cargo booking, budget planning, or expanding to new markets, a proper pre-defined plan can ensure the smooth outflow of goods and cash inflow. Needless to say, forecasting is the starting point. But AI-powered supply chain forecasting tools determine future events based on market statistics, where individual opinion and bias have no say, as is the case with traditional methods. Hence, there is little room for errors.

Also, market disruptions open new doors for growth. A move towards online retail, a rise in consumer confidence, curbed demand, and accumulated savings can all spring immense opportunities for online brands. In order to stay ahead of changing times, predictive analytics with technology plays a defining role in forecasting changes before they occur. Hence, businesses gain an edge over the competition and stay ahead in the rat race within the e-commerce fraternity. Sadly, conventional methods fall short of doing just that – as proven by 43% unused inventory compared to the total product sold in a year.

This emphasizes the need for AI-powered harmonized data to handle multiple types of internal and external data sets to enhance forecast accuracy.

Challenges of conventional supply chain forecasting

Siloed data: One of the most significant challenges for supply chain forecasting in recent times has been the siloed data systems with inconsistent collaboration and visibility for others, leading to issues such as:

Additionally, globalization has increased the data integration problems in modern-day supply chains. Latest business simulations with outsourced manufacturing, partnerships and acquisitions have blurred the divisional boundaries. Consequently, with data playing an important role in such a landscape, silos data puts the business at a bigger risk. As a result, it becomes difficult to give demand forecasts that can lead to inaccurate supply chain forecasting.

Fragmented demand forecasting: The pandemic has made the supply chain more complex by smashing the currently followed demand forecasting approach of many suppliers and retailers, thus putting them in a situation where they are not sure of the inventory stock or manufacture at a given time. Hence, companies are developing strategies to improve customer demand forecasting by using a new data-driven in real-time for more accuracy.

Shifting consumer attitudes and changing demand patterns: Consumer attitudes and behaviors have been shifting in some big ways during the pandemic, like lowering the threshold for delivery times and raising the requirements for a positive customer experience. For this to happen, a quick responsive supply chain can utilize the power of some of the best supply chain forecasting tools to optimize delivery execution and handle augmented demand comfortably.

Key benefits of supply chain forecasting:

Conclusion

It cannot be denied that amid the pandemic crisis and supply-demand volatilities, companies are better prepared now against future shocks. Thus, they are implementing the latest predictive analysis and supply chain management (SCM) optimization.

Enterprises need agility to stay afloat in uncertain times. And agility is a trait that can be acquired with real-time insights. Intelligent software solutions for supply chain forecasting capture granular data from the network and help businesses to stay agile and prepared with decision intelligence. Hence, it is increasingly imperative for modern-day supply chains to embrace a holistic perspective with artificial intelligence and analytics.

What is order management, and how it enhances supply chain efficiency?

With the supply chain becoming increasingly complicated with each passing day, it is now crucial for enterprises to craft perfect sourcing and effective order management strategies in every stage of a product’s delivery lifecycle.

Hence, it will be apt to acknowledge that laying down an active blueprint in lifecycle management design and decision-making offers an opportunity for an organization’s marketing, operational and financial performance. Also, adequately affiliated supplier selection and order cycle time management assists in addressing and managing the risks involved in the supply chain. This is where an efficient Order Management System (OMS) turns out to be the perfect solution for a disparaged supply chain.

The most significant KPI for e-commerce or supply-chain management operation is the total order fulfillment cycle time. But why is it vital, and how can a business operation minimize its total cycle time to make the overall business more profitable and competitive?

Order management and its importance

Whenever a customer places an order in an online portal or a mortar and brick store, there are many processes functioning in the back-office settings until the consumer collects the wished product. This complete process is referred to as order management, primarily keeping track of the orders and managing all the stages involved, thus accomplishing them. Hence, the method comprises various steps starting from accepting the orders, picking, packing, and shipping the items mentioned in the order; and finally tracking them until they get delivered.

In simple terms, order management is the sequential activities aligned with the business processes, orders, and staff required to meet those orders, followed by handling the order’s consumer data.

As order management software is synchronized with several distinct networks for effective communication, it assists a firm in tracking orders so that the orders can be supplied quickly and efficiently.

Additionally, order lifecycle management can help the involved parties forecast the demand level, as predicting the demand helps avoid understocking and overstocking. Hence, this will keep the companies delivering the products swiftly without any mistakes. Moreover, accurate forecasting through OMS aids processes to an already decided budget and structures the inventory, thus conserving money and time.

Benefits of accurate order management

Avoid under-stocking and over-stocking issues: Over-stocking and understocking are the two major concerns in the traditional supply chain. Over-stocking leads to surplus inventory that becomes outdated after some time, besides occupying the storehouse space, thus incurring additional costs. On the other hand, understocking leads to the loss of prospective sales or backorders in which the consumers have to wait for their wished products. Therefore, by implementing an end-to-end order management system such as TradeEdge, businesses can obtain the necessary sales metrics and determine inventory levels.

Reducing order fulfillment errors: If a business is undergoing high order volumes coming from multiple sales channels, there is every chance that the staff can choose the wrong product or place an incorrect address on the shipping label. However, companies can guarantee that suitable goods are correctly sent out to the customers by applying some of the best order management software.

Reliable data metrics: An efficient order management system will allow the supply chain networks to have consistent data that they can utilize to generate informed decisions, whenever essential, quickly.

Boost productivity and maintain time limits: With an accurate order management system, businesses will consume lesser time finding solutions to the problems of the order fulfillment process, thus enhancing productivity and saving time.

How can companies overcome the challenges in order lifecycle management?

Boosting supplier cooperation and management: Due to several sourcing points in an order lifecycle, timely deliveries become a challenging task. As a result, companies must integrate the sourcing, inventory, and transportation decisions to track the shipping from the supplier and team up with partners for cost-effective solutions. In line with this, an order management system provides a centralized view of the effects on orders due to disruptions.

Organizing the demand levels: Demand management tactics facilitate companies to maximize profits for products with short lifecycles. In addition, these strategies, along with OMS, allow coordination of activities within the supply chain, improving the visibility and helping the companies to supply ample products in the right place at the right time.

Strengthening order and product margins: Appropriate supervision and disposition of product returns through intelligent order management can substantially boost product margins. Thus, the users will be able to see the detailed specifics of returned products, thus augmenting the value of the reusable stock with minimal wastage.

Order management system – A perfect solution for the future

Considering the benefits of an efficient order management system, it is now apparent that by using OMS, supply chain management companies easily identify loopholes in every stage of product delivery. It enhances visibility and, consequently, helps companies to track the accurate position of product delivery. Furthermore, implementing a solution such as TradeEdge Order Management is time-efficient and cost-effective for the company.

Evaluating the role of supply chain network optimization for enterprises

A basic supply chain for an enterprise comprises simple sequential and linear process networks connecting various vendors for resources needed to produce and distribute traded goods. Given the complexity of the demand market and the variability of consumer preferences, supply chains have recently evolved into highly dynamic processes. A network usually supports a two-way interconnection between various links. When a simple problem in a single linear supply trail can disrupt the whole chain, the same damage can become more significant in a network of supply chains. Supply chain network optimization software finds an optimal solution by combining different factories and distribution centers in the same value chain. Surprisingly, only 22% of companies are working with a proactive supply chain network in 20221.

Proper network optimization is necessary for modern supply chain enterprises as it helps curb operating costs and deliver stellar customer experiences. Since a network includes an intricate web of resources, technologies, and facilities, optimization of the same raises the urgency for advanced analytical solutions. Supply chain network optimization tools leverage the power of Big Data, AI, and IoT for optimization and scalability.

Simply put, network optimization creates a crystal view of the organization’s supply chain to unearth inefficiencies and bottlenecks. Such data analytics are used to define ways to optimize existing processes for curbing various operating costs and ensuring each node in the network performs seamlessly.

Why enterprises need to optimize their supply chain network?

Traditional linear value chains are not designed to meet the increasing complexities of the new-age economy. Complex hierarchies, rigid supply chains, and information silos have limited partner reach, persistent out-of-stocks, higher costs, latency, and lost opportunities. More importantly, conventional value chains are territorial in nature; hence, they are inherently rigid. It creates supply chain inefficiencies and limits any possibility for growth. And as mentioned, information silos impact supply chain planning, which results in delayed execution of critical actions.

Proper network optimization connecting critical parties in a non-hierarchical manner eliminates information silos. Furthermore, by leveraging new-age technologies like Artificial Intelligence in shared data, networks are transformed to become more cognitive and responsive to sudden disruptions or change.

For instance, AI-enabled supply chain network optimization software solutions like TradeEdge Network create opportunities for swift actions. It connects businesses in a peer-to-peer network accommodating prompt responses to products, services, and information needs. In addition, their multi-tenant architecture allows many-to-many connectivity, fostering a multi-enterprise network with a global reach.

Supply chain network optimization enables companies to gain an edge over the competition, discover new partners and products at scale, transform complex data into meaningful demand signals, and do much more. The network supports a 60-70% improvement in partner network visibility, a 12-15% reduction in out-of-stock scenarios, and many such enhancements.2

For example, one of EdgeVerve’s clients, a leading beverage manufacturer, could meet market demands quickly and grow sales by 2% with the help of the network-based approach. They leveraged the Business Network platform, TradeEdge Network, to connect with their partner retailers and distributors for enhanced visibility. As a result, they could move inventory quickly in the presence of restrictions by reaching out to another vendor. A similar connection with their suppliers allowed them to continue manufacturing without facing supply shortages

Key requisites of a supply chain network optimization software

A purposeful network design is needed for a unified cognitive business network to fulfil its objectives effectively. The network should:

Four network optimization constraints for enterprises

In network optimization business constraints can range from a limited number of facilities to fixed costs for each process. And the same constraints act as datasets for AI-powered supply chain network optimization software solutions to decide the most feasible strategy to create a more seamless channel of the demand-supply network. These network constraints could be:

However, every business is unique, and so are the requirements of its supply chains. Hence, in the absence of predetermined values, custom constraints are created for the baseline to further the comparative analysis and identify solutions for network optimization.

Specific supply chain network optimization tools have custom constraints, allowing users to create the best optimization solution for driving maximum profit.

Benefits of supply chain network optimization

Usually, supply chain complexities arise from changing dynamics that the industry is subjected to. Hence, the requirements of network optimization vary, as mentioned before. Some consider tech implementation a requisite for a growth strategy, while others think improving business processes is the key to success. Regardless of the requirements, supply chain network optimization brings manifold benefits to enterprises.

Reduce waste: Resource wastage is probably the most significant challenge enterprises face; some are born from poor demand forecasting and inventory planning. Supply chain network optimization tools help businesses discover areas where waste is prevalent and define strategies to curb them.

Reduce unnecessary costs: Some supply chain areas can raise unnecessary expenses if the network is left unoptimized. Proper network optimization pinpoints those areas so enterprises can identify ways to prevent excessive spending.

Optimize transportation: Higher transportation costs impact the product’s final price, which could have been less if the distribution network had been efficient and optimized. Supply chain network optimization aid enterprises find the best shipping methods or cost-effective distribution channels. This leads to reasonable product prices and higher customer satisfaction.

Enhance quality control: Quality control is harder in the later stages of the supply chain, soiling the brand’s reputation in the market. Proper optimization of supply chain networks provides granular insights about the performance of various suppliers so companies can choose the best one that upholds the quality requirement of their brand.

Mitigate risk and planning: Data analysis supported by supply chain network optimization software provides an eagle-eye view of the changing business dynamics. Enterprises can harness those insights to plan accordingly and stay future ready.

Improve visibility and flexibility: Supply chain network optimization tools enhance visibility by capturing data from different parts of the network and helping enterprises to take steps to remediate challenges. Further, optimization improves process flexibility, allowing businesses to meet unexpected changes.

Supply chain network optimization software leverages modern technologies to help enterprises understand how the demand-supply value chain works. Such insights are essential for addressing various challenges and curbing unnecessary costs, with a focus on enhancing customer experiences.

Transforming into a connected global supply chain to unlock unlimited possibilities

The COVID-19 pandemic has unleashed catastrophe and disrupted businesses across sectors to such an extent that the traditional supply chain, unable to bear the shockwaves, has come to a crossroads. Thus, the conventional approach within the global supply chain is gradually becoming a strategy of the past. However, the primary reasons for the conventional model waning away are its linear progression approach and the consequent disjointed supply chain networks.

Adding to the woes, companies rely more on the ‘plan and react’ tactic followed by the traditional supply chain. As a result of this antiquated model, companies are finding it tough to apply plans to resolve the global supply chain crisis. This is where a connected global supply chain enters the scene as the need of the hour to bridge the gap between the patchy networks.

Why disconnected systems block the path to a resilient supply chain?

Supply chains currently depend on various kinds of disconnected technologies that lead to a lack of visibility and valuable data in the supply chain operations. Moreover, the involved parties often rely on legacy ERP solutions that are generally very slow and expensive.

In such situations, these structures get optimized for efficiency rather than agility; hence, they do not meet either target. Also, to fight the recent disruptions, many businesses have been compelled to make decisions that have dented their profits as they were not ready with substitute suppliers and agile systems.

Connected global supply chain – the next best solution for enhanced visibility

Transforming the supply chain is daunting as one cannot just replace the existing technology investments. However, the companies do not require to start from the grassroots level and can improve the fundamental technologies they already have. The first and foremost thing to do is to begin mapping and identifying the business processes to improve visibility to optimize the processes.

Hence, for the global supply chain to become intelligent and automated, it must first be documented in a process prototype that will guide the makeover attempts by underlining the processes that need to be automated. After that, these process models can be utilized to simulate various settings to build up an automatic process application. This agile application can then deliver performance data suggesting the location of bottlenecks, thus enabling the parties to transform the operation.

How to build a connected network of global supply chains?

Despite being aware of the benefits of an integrated supply chain, the transformation toward supply chain visibility is sluggish, with many companies commencing the same, only recently with pilot projects.

There are two things to consider when planning a connected supply chain. The first one is to guarantee that the firm leverages digital technologies that can connect the organization with partners and consumers. The second one is to realize that the supply chain will not be able to reach deep connectivity in a single step. Therefore, it is safer and better to take up the phased approach.

Key advantages of a connected global supply chain

The connected supply chain has become a primary strategic driver and domain of competitive differentiation for business processes. However, supply chain data siloes and bottlenecks can only be nullified if all parts of the integrated machine work in coherence.

A company looks to attain three goals through the connected supply chain, which are:

Demand-led supply chain: A connected supply chain provides data for real-time operations and delivers the required insights to entirely identify what the customers want. Subsequently, companies can move from a supply-focused to a demand-driven business. Hence, the firms can find out demand spikes and utilize the trends to predict the inventory they require at every stage.

Visibility of supply chain: Every part of the global supply chain can be traced and scrutinized by linking all the partners and facilitating the protected and efficient flow of information. Therefore, with visibility and clarity of issues, it becomes simpler to boost efficiency and respond quickly to supply chain shock waves, thereby improving customer experience.

Supply chain optimization: Once the visibility increases, the companies can receive the supply chain data that enables a process of continuous improvement and optimization. The manufacturers can raise business agility, boost efficiency, and decrease costs. The connected supply chain facilitates more effective supplier performance management by mitigating risks through improved operability at all stages, including sustainability and ethical performance metrics.

What does a connected global supply chain mean for businesses?

It primarily connects everyone engaged in a supply chain, pulling together the partner networks and dissimilar systems to ensure that information can make an end-to-end and seamless flow across the supply chain. The core objective is to combat the global supply chain disruptions and enhance visibility, that too in real-time. This enables the involved parties to establish a unified system across the company between individual networks such as manufacturers, suppliers, logistics partners, dealers, and consumers.

A connected supply chain, a journey towards digital transformation, is one efficiently defined system as the involved networks can effectively communicate with each other by sharing critical insights. This responsiveness enables organizations to forecast and respond to market volatility via augmented visibility.

Connected supply chain – The final frontier

Major disruptions like the COVID-19 pandemic, market volatilities, and other insecurities have compelled companies to lift and shift their supply chains quickly. Organizations have realized that a traditional or linear approach to a supply chain operation will no longer work, especially with the tattered functioning of the logistical processes. Hence, the key to a success story lies in the renovation to a more connected and self-orchestrating supply chain landscape where firms can quickly foresee opportunities and deal with the challenges and risks of global supply chain management before they pile up.

Task mining: Key features and business use cases

A typical business process comprises myriad tasks broken into small components that are mostly executed manually. For automation to take over entirely, businesses need to identify the granular nuances in small process components and sub-tasks. Process mining proves incompetent in identifying empirical variations existing within each process. This is where task mining can genuinely make a difference.

When a combination of process mining and task mining capabilities are used, businesses can fully understand the end-to-end process, analyze handovers and resource utilization, and identify automation potentials.

What is task mining, and how does it work?

Task mining tools leverage AI capabilities like Machine Learning, Optical Character Recognition, and Natural Language Processing to understand and evaluate employees’ actions when interacting with computers or software. It identifies business outcomes patterns, allowing owners to optimize and replicate those actions at scale.

Task mining solutions help businesses to understand user interactions with software systems, analyze how tasks are executed, and identify ways to make them better.

There are specific steps that sum up the primary function of task mining.

Recording user activities: Task mining records user activities to understand employees’ core responsibilities. Installed on user systems, task mining tools work silently in the background, monitoring every single action like clicks, scrolls, time stamps, and screenshots.

Recognizing context: Task mining leverages OCR technology while recording user activities to understand the task’s context. The tools collect words, numbers, and additional text from the recordings and the screenshots captured during workflows.

Grouping similar tasks: With the help of NLP capability, task mining can better understand the context of tasks and group similar activities together. This allows its tools to capture particular actions employees implement while performing a specific task.

Matching user activity groups: The step after grouping involves matching user activities with specific business tasks. This step assesses performance metrics with the recording.

Evaluating performance: Lastly, the tools consider records against business data and KPIs. This allows owners to see how employees perform each task and how well they perform those tasks. Evaluation of each task execution can improve the performance metrics of each process.

Slack found that companies that have embraced AI are 90% more likely to report increased productivity levels than those that have not.

Task mining: Features and benefits

A typical task mining solution includes the following features:

Efficient task mining software solutions yield many benefits for organizations scaling automation growth across end-to-end processes. A few such benefits are mentioned below:

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Five use cases of task mining

Discovering automation opportunities: According to Global Market Review1, “37% of respondents said increasing process efficiency was the biggest driver for intelligent automation in their organization.” In order to achieve that, enterprises needed data to discover automation opportunities in shared services. The current applications and use cases of Task mining are lined up perfectly to match the exact requirement. The real-time user data collected reflects how much time each employee spends on software and compares that to other users. This information benchmarks their productivity using specific software and further discovers automation opportunities in existing workflows.

Improve task efficiency: The same report showed that nearly 27% of companies listed said increasing workforce productivity is another driver for intelligent automation. And workforce productivity can be directly linked with task efficiencies. Enterprises should be able to track employees’ process efficiency as they enter a new operating model. Task mining provides data about process efficiency in real-time so owners can consider transitioning into automation to address any gaps during seamless task execution.

Removing redundant actions: Task mining helps businesses identify which activities consume the most employee time. Specific workflows are redundant and recurring but are integral to the process. However, the same tasks can be time-consuming and monotonous. Task mining track those applications to identify the total execution time and compare them against the ones that add value. Task mining software solutions not only help businesses remove redundant actions; it also assists the latter in rationalizing application implementation.

Improve user and customer experience: Task mining is adept at analyzing tasks and identifying system bottlenecks or other inefficiencies. That empowers the end user to improve the software or process, perform automated actions based on pre-defined process KPIs and improve overall process performance.

Task mining thus has long-term business value. It should not be considered a tool that businesses can use only to understand their business processes. Instead, these tools should be utilized for continuous process transformation, workforce productivity, and ensuring data privacy for employees and users. When used alongside process mining, both can work together in perfect sync to drive optimal benefits for the company. Task mining and Process mining are equally vital in helping organizations scale automation growth and achieve end-to-end process transformation.

How can task mining boost employee productivity and process optimization?

The onset of the pandemic forced organizations to shift to remote working, thus transforming the dynamics of employees’ productivity monitoring techniques. Hence, companies had to reorganize priorities, rethink the way employees desire to engage, and embrace hybrid working with some sections of the staff likely to never return to usual office settings.

How can organizations enhance employee productivity in today’s remote work environment while continuing to drive efficiencies in business processes? This is where task mining, a form of Process Discovery, comes in.

Task mining and its requirement

Task mining allows the business to capture a company’s resources’ actions as they perform them, so the steps can be analyzed and provide you with loads of data to design the business process. It captures employees’ screen recordings, mouse clicks, keystrokes, and data records –desktop-level event data. It applies NLP and optical character recognition (OCR) tools to infer the data and generate actionable and profound insights.

These understandings can, in turn, help detect incompetence, improve service, and improve the employee experience. Thus, data mining tasks use AI/ML patterns to identify and contextualize the captured data to produce process maps involving last-mile variations and provide process and workforce-related insights.

Most companies lack the resources to commence their automation journey; therefore, they need sources to evaluate the process’s present status and their inclination toward automation. Process and task mining can give that very insight. Additionally, firms’ post-pandemic efforts to scale automation can get slowed down due to inefficient processes. But then, here is the task mining solution to build data from a single task that offers information to identify the holdups of their business processes.

Hence, task mining is an efficient solution to these modern problems as the granular data task mining compiles from users and facilitates businesses to know their processes and how employees work as they move into a new working style.

Benefits of task mining

Productivity: Task mining encapsulates and scrutinizes the way people interact with machines through records and snapshots, thus helping companies distinguish and have a deeper understanding of employees’ productivity user-data such as clicks, data entry, and keystrokes. The data is then transformed into user and task insights to oversee employee productivity and, consequently, to guide automation efforts. So, task mining basically enhances user-data visibility by mitigating the roadblocks in process execution.

Find automation prospects: Task mining utilizes performance, productivity, and frequency insights to identify significant automation opportunities and helps automate standard processes and inefficient, repetitive tasks.

Task mining discovers automation prospects by assembling employees’ desktop data that includes logs of user actions (mouse clicks, keystrokes, etc.) and screenshots. Following this, it operates an ML model to evaluate this data and recommend a list of processes with high-level automation potential.

It enables the clients to record some particular users’ actions, examine them and then put down the outcomes in dashboards to:

Ensure privacy for users: One of the most significant benefits of task mining is that it guarantees the utmost privacy for users. As mature products assure absolute authority over the data capture process and the apprehended business data. As a result, the processes can determine the data to be captured and the ones that can be exempted apart from the data that should be distilled and visualized. Thus, this total control over the data capture process gives businesses a clear idea of the data that can be transformed into actionable insights.

Reduce process gaps: Task mining develops tactics to diminish the most vital process gaps by focusing on the root causes. It provides businesses with a way to identify bottlenecks within their processes and scrutinizes the smallest of the gaps in a business process by identifying the task that checks the smooth progress of a network. Hence, this step assists the involved companies in finding out the systems that are decreasing the efficiency of a process, thus exploring the bottlenecks involved.

Discovery, compliance, and performance: It combines process mining (business data) and task mining (user interaction data) when conducting business process analysis to provide automated insights, like:

Performance analysis: Task mining, a complementary solution to process mining, has substantial benefits as it allows companies to discover the inadequacies of manual human labor outside of their transactional systems and to evaluate and optimize their employees’ productivity.

Thus, the ability to connect the fragmented dots between the user task data, system logs, and business data allow the companies to lower the business process friction, enhance customer service, and accelerate digital transformation by augmenting the employees’ productivity and minimizing the manual labor burden, thus reducing the time consumption of a task. This will subsequently lead to a reduction in the operational expenses of an organization.

Provides alerts on process violation: The assembled data from the selected users using the task mining automation unlocks the breaches in the actual functional process if any. These processes include sequential task steps to be implemented, time of events, and whether any step is getting repeated or missed.

Task mining – A must-have solution in the future

Task mining plays a significant role in formulating business processes for optimized automation; still, several companies have not yet incorporated this handy solution. Less than 30% of shared service organizations (SSOs) have capitalized on this technology, says Intelligent Automation 2022 Benchmarking Survey.

Task mining can aid firms in realizing how they handle tasks by accumulating and evaluating user interactions rather than business metrics and log files for scrutinizing processes. To sum up, it helps deliver a complete view of processes that enhance business productivity by connecting individuals, data, and processes and mining user interactions.

Evaluating the role of low-code, no-code applications in end-to-end process automation

Digital transformation is a need of the hour, and automation is the key. Many reports suggest that nearly 37% of enterprises surveyed are stepping up efforts to bring automation into their existing workflows1. Yet, total digital transformation requiring end-to-end business process automation continues to remain a distant dream for many. Hence, data silos deter seamless transformations. Automation is intricate and requires relevant skills; not anymore. With low-code-no-code automation, businesses can still build and automate existing workflows without coding skills or experience.

Low-code-no-code platforms are leveraged to create app-lets or little applications to accommodate human-machine interactions without needing a custom web app. And connecting humans and machines can make automation far more capable.

What are low-code-no-code platforms?

Low-code automation is a software solution that enables businesses to build and automate existing processes and workflows by minimizing the use of codes for automation. A visual interface is used to access features and make changes. It speeds up automation development cycles.

Contrarily, no-code is more of a no-code automation approach where a prior understanding of coding is not needed to efficiently and quickly build applications.

Both are leveraged to scale the enterprise automation journey.

Benefits of low-code-no-code automation:

Low-code-no-code automation is increasingly important in the current digital landscape. They counteract the present shortage of IT specialists in automating existing processes effectively. Each department can easily take up the responsibility without relying too much on expert coders.

There are other benefits attached to such applications, such as:

How does a low-code-no-code application support end-to-end process automation?

Low-code-no-code allows enterprises to build automation quickly and gradually scale their growth across more complex areas and workflows.

It offers citizen developer-friendly tools, empowering every employee with contextual guidance to democratize automation. Hence, this enables automation footprint by over 70%2. Employee Personal Assistance can help empower employees to easily automate everyday tasks with contextual guidance, manual efforts are reduced by 20%, and employee satisfaction improves by 3X or more3.

Various low-code platforms offer infinite customizations of workflows and UI/UX personalization. It is easier to create one LCNC connecting processes across the enterprise and effectively optimizes the UI/UX for specific use cases without affecting the workflow. They work in perfect sync while assuring compliance requirements are aptly met.

However, processes constantly evolve or shrink to accommodate changes to get jobs done. Such regulatory updates can limit the reuse of your codes. Hence, end-to-end LCNC applications are powered by AI and ML capabilities to allow cross-channel applications, process improvements, and scale automation growth from a single platform. Such platforms can easily integrate all types of data sets, sources, and core systems.

For example, one of AssistEdge’s clients, one of the oldest communications companies, accelerated its plans for scale after implementing AssistEdge Engage.

The market leader in communications focused on providing exceptional customer experience struggled to increase efficiency to scale its business operations. Having operation spread across multiple locations, the company suffered from inefficient service delivery. This became a hindrance when the client sought to scale operations on the back of legacy architecture. Executives had to multitask at speed while searching for information on multiple apps during a customer conversation over chat or the phone. This resulted in inaccuracy and, consequently, a poor customer experience. The system relied on experienced agents, which eventually impacted productivity and quality.

With the help of EdgeVerve’s AssistEdge Engage, a low-code orchestrator, the client could reduce agent dependency on subject matter experts. Further, process bottlenecks were successfully eliminated that impaired productivity. And the client built on a stellar legacy of customer success and innovation with the help of EdgeVerve’s end-to-end low-code solution.

Low-code-no-code platforms foster end-to-end process automation

LCNC platforms are emerging as essential catalysts for enterprises looking to expand their digital capabilities effectively. However, enterprises need to ensure that LCNC platforms align with their core branding strategy to fast-track success and create customer value. And, it remains to be seen how LCNC will adapt to further changes in the near future while democratizing process automation and its capabilities.

Process mining: Definition and five business use cases

Digital transformation and automation of specific workflows are a few measures to improve operational efficiency. However, the lack of process insights in real-time hinders the smooth transition and prevents the optimal utilization of automation to its full potential. In fact, the opacity of current business processes arising out of existing silos can prove detrimental to a company’s health and performance. Hence, process mining is leveraged using advanced algorithms to create transparency in current business processes and aid organizations in streamlining and improving them effectively with the help of automation.

What is process mining?

Process mining extracts knowledge from event logs in the information system and analyzes and monitors them to improve fundamental business processes. It identifies inefficiencies and nuances existing within the process and enhances visibility into actual performance. This helps in making the processes more transparent. Process mining tools automatically generate process maps and track event logs more efficiently when compared to manual methods.

Benefits of process mining

Process mining captures system events rather than user and activity tasks; hence, a substantial manual effort is needed to interpret the data generated. There are also other limitations, but the benefits of using process mining software solutions are undeniable.

Generate fact-based insights: Process mining extracts empirical data to provide objective, fact-based insights that support auditing, analyzing, and improving existing business processes.

Faster and cost-effective: Unlike lengthy and subjective mapping processes, it is a cost-effective approach garnering accurate results faster than manual process mapping.

Visibility of as-is processes: It enhances process and workflow visibility and provides a solid understanding of how they work, the problem areas, and the existing variations.

Reduce operational costs: It reveals existing inefficiencies and bottlenecks in business processes, which can be improved with automation and help curb operating costs.

Improve performance management: Process mining software solutions track event logs and evaluate key performance indicators to continually monitor processes in real time for improving performance.

Improve customer experiences: It helps organizations to quickly identify the root causes of issues and react fast with better customer service.

Improve compliances: Process mining analyzes data faster and cost-effectively compared to system audits. It can identify compliance issues in real-time.

Mitigate risks: By increasing transparency of processes, it can effectively identify potential trouble spots and help companies to mitigate risks in real-time.

Five use cases of process mining

Financial shared services: Financial shared services are critical for any organization and should be handled with care and expertise. Collaboration between departments and stakeholders ensures seamless execution of workflows. And financial shared services are one of the critical use cases for process mining. With the help of this solution, stakeholders can gain complete visibility of the real-life execution of each workflow. Errors, variations, and non-compliances are quickly identified, allowing process automation to take over.

Procurement: The purchase-to-pay process, also known as procurement, is another use case for process mining. This department takes care of multiple tasks, such as requisitioning, purchasing, confirming, receiving, paying for, and accounting for goods and services. There are heavy transactional flows and comprise complex steps for approvals. There is enough room for errors. By leveraging process mining tools, owners gain end-to-end visibility of each workflow and gain insights into root causes for system bottlenecks, and uncompliant processes.

Order management: The order management system oversees the entire cycle of workflows, from receiving customer orders to delivering them on time and everything in between. These critical business processes witness high volumes with many variations. Process mining tools help owners visualize how each process is executed and what the delivery blocks are, and the compliance issues. The data extracted are analyzed to offer clear insights, garnering further monitoring of the end-to-end process.

Digital transformation: Digital transformation is necessary for companies wishing to thrive amidst market competition. To digitize existing processes, one needs to understand how each works. Process mining identifies areas of improvement and fosters end-to-end visibility, enabling the identification of suitable process candidates for transformation.

Regardless of its limitations, process mining helps map key business processes accurately and initiates the following stages for seamless digital process transformation.

How can AI accelerate the legal contract analysis process?

Advanced technologies such as Artificial Intelligence (AI) and Natural Language Processing (NLP) are transforming how companies operate. In fact, AI firms are exploring ways to develop solutions that can oversee labor-intensive tasks such as manual contract analysis – a keystone for modern-day business transactions – across industries for more accuracy and better speed.

With industries going the AI way, legal businesses are also embracing this automation technology adoption to resolve the challenges of managing over-loaded legal contractual agreements through AI-powered contract analysis.

According to a survey of in-house professionals within Fortune 1000 companies by Corporate Counsel Custom Solutions conducted on behalf of EdgeVerve, 69% of respondents have witnessed a rise in their contract review assignments. And 56% of respondents expect spending for contract reviewers to increase over the next year. Hence, firms have generated a growing interest in employing AI in legal contracts, gradually changing the domain dynamics and minimizing the effort of legal researchers, paralegals, and litigators.

However, with the legal departments increasing the implementation of AI to enhance efficiency, it will be very apt to acknowledge that AI can play the finishing role in automated contract analysis.

The difficulty of managing a large volume of legal and contractual agreements with the organizations is overloaded with enormous contract data points to juggle. Unfortunately, this leads to an inefficient contract analysis process.

This is where artificial intelligence (AI) plays the role of a redeemer by modifying how contract analysis functions are carried out. Thus, AI-based contract management can aid the best legal teams by relieving them of relentless manual work and freeing them from repetitive tasks to invest quality time evaluating and nullifying the risk, finding opportunities, and providing critical insights to their organizations.

Should legal experts leverage AI for contract analysis, and if so, why?

Artificial Intelligence can be of immense use to law firms. EdgeVerve survey suggests that only 33% actively apply AI or automation technology in contract analysis, while 24% have not even considered using the solution yet. Automating legal requirements like due diligence through AI can significantly enhance the efficiency and productivity of legal teams. Hence, this is a very significant step as lawyers depend on these due diligence reports to unlock the material challenges of contract analysis.

Thus, AI-based contract analysis can be applied throughout the contract lifecycle management to assess and track the required information by integrating AI technology into the business processes.

Automated contract analysis enables legal and business users to create, store, review, index, retrieve, analyze, negotiate, and approve agreements. It speeds up the contract assembly activity for sell-side contracts, accelerates the buy-side contract reviews, and delivers profound insights into the business performances across the company.

AI significantly impacts the practice of law with the technology used to review contracts, identify the needed data through the discovery process and conduct legal research. Additionally, it is being implemented for drafting agreements, forecasting legal outcomes, and recommending judicial decisions regarding bail or sentencing. AI-powered contract management can significantly improve legal teams’ productivity and eliminate the possibility of error.

Enhancing legal process performance with AI

Legal experts can effortlessly pitch in unique AI technology along with deep learning competencies to automate contract indexing, streamline, and simplify their tasks to resolve vital pain points in a contract management lifecycle.

What AI application will eventually do is it will allow the legal departments to:

Is AI helpful in efficiently managing contract cycle time?

Below are the two core areas where AI makes a company’s contract analysis lifecycle more efficient.

Building contracts: Businesses employ attorneys to accomplish contractual tasks. However, the human workforce will not be able to create volumes of contractual drafts quickly. This is where AI-powered contract analysis comes into the picture, as it helps to set up contracts from clauses and templates.

Simplifying legal processes: Managing contractual obligations efficiently is perhaps one of the most challenging tasks. Sometimes, it is difficult to determine whether the business is operating according to the set contractual terms or if there is any breach of contract. In such a scenario, companies can incur heavy losses due to the inaccessibility of contracts. Thus AI-powered contract management provides access to information and analytics solutions, allowing the parties involved to work on an economy of scale for enhanced cooperation and operational business management.

Other key benefits include:

With the legal units embracing technology adoption, it is possible that Artificial Intelligence will augment law practice in the future. AI is shifting traditional legal workflows by facilitating specialists to better realize the contract language and obligations during negotiations. Therefore, sensing the countless advantages, legal firms have been applying AI-powered contract analysis to identify and extract terms and clauses such as limitation of liability, SLA penalties, termination policies, and many more.  These data will enable the companies to track the risks involved accurately and thus find ways to eliminate such vulnerabilities.

Hence, it will be fair to conclude that organizations investing resources into AI contracts will be able to accomplish the benefit of AI in contract analysis in the times ahead.