Extending possibilities of Spend Analytics with AI

In today’s era of digital transformation, procurement leaders across the world have been pulled into various discussions on how to align procurement strategies with the enterprise as a whole. Never in the history of procurement has it been so important to harness the power of data and transform it into insightful intelligence resulting in performance excellence. As procurement moves from being a mere support function to playing a strategic role, business leaders across verticals have begun to acknowledge the role of Artificial intelligence in driving procurement performance.

As per the 2017 Deloitte Global CPO survey — which polled 480 procurement leaders from 36 countries — the leaders believed that the impact of robotics and automation will increase from 50 percent to 88 percent by 2020, and up to 93 percent by 2025.

However, industry experts say that procurement has still got a long way to go. They believe embracing intelligent technologies can help procurement teams generate more business value. With procurement teams always under tremendous pressure, it only makes sense to harness the power of AI to reduce some of their workload. Towards achieving this goal, procurement leaders are constantly looking for AI-powered solutions to streamline processes and improve decision making. This has provided wider acceptance for software such as spend analytics and contract analytics, which enable procurement teams to identify areas where cost can be saved. Most enterprises today have deployed Spend Analytics Software, to give their procurement teams more visibility and control in managing contracts.

Use of AI in procurement can be seen as a major paradigm shift in the way procurement functions are performed. They provide a clear-cut advantage to enterprises vis-a-vis traditional methods, helping procurement organizations gain new insights and shape new strategies, which weren’t possible before with standard spend analytics approaches.

Data is power when it comes to taking strategic and tactical decisions and machine learning is an AI capability that helps transform data into insights facilitating more accurate and informed decision-making.

Leveraging AI for procurement functions can actually give a greater control over vendor and spend management. However, currently most of these technologies are restricted to processes of collecting, classifying, reviewing and analyzing spend data. Though they help identify areas where savings could be made, it is still at a very basic level and not leveraging the ultimate power of AI. When leveraged to its peak, organizations can streamline their supply chain, which involves data from inventory, brand perception, risk mitigation, advertising, vendor management among others.

Impact of AI on Spend Analysis

Spend analysis is an important step in establishing an effective procurement organization. Providing a recurring spend visibility could be the driving force behind a long-term cost optimization strategy. However, with limited time and resources, it also becomes a major challenge for organizations. In spite of significant investments in technology solutions, procurement functions often lack the quality, caliber and analytics required to function strategically.

This can be addressed with AI, particularly machine learning, which helps increase the speed to spend analysis results, ensuring accuracy with limited manual effort.

Such enterprise-level spend visibility helps drive strategic planning, refine operational focus and improve business results.

Strategic Sourcing: Strategic sourcing teams around the world are required to provide detailed spend analytics that inspires procurement insights. The advent of automation and upcoming cognitive technologies help predict and guide analyzing unstructured data. Also, many organizations have deployed Cognitive Procurement Advisors, Virtual Personal Assistants, which use Natural Language Processing and Natural Language Generation to provide insights from the available data aiding strategic sourcing.

Spend Classification: Traditionally, spend is classified manually or with the help of rule-based software with supplier names and keywords. Not only are these processes labor-intensive and time consuming, they are also inaccurate, which limits sub-categorization due to higher dependence on supplier names than product descriptions. Machine learning helps automate at a granular level of categorization from varied fields including product, supplier or line-item. This way, ML helps enterprises improve their spend visibility up to 95% by capturing data in real time and making complete sense of it.

Supply Management: Negotiating with a supplier can be tricky with incomplete knowledge about the supplier and market trends. For procurement teams worldwide, striking a deal with a supplier can be an elating situation. However, once sourced, as the supplier joins the list of various others, it becomes difficult to monitor or keep a track of their performance to ensure they bring value to the enterprise. The basic Spend Analysis helps determine the spend factors such as what, with whom, how much etc. But the AI-powered version extends analytics beyond the confines of traditional approach and provides insights into operations and logistics as well. This is possible with enhanced Lachine learning analytical services which ingest semi-structured data and others quickly and accurately. For instance, apart from purchase orders and invoices, ML capabilities can run reports on inventory turnover and warehouse utilization, helping procurement teams determine inventory overhead costs and predict stockouts.

Risk Management: Not only do AI-powered analytics provide accurately classified data in less time, they also enrich this data with external content and help arrive at an insight. This can help integrate risk scores, sustainability and other scores related to risk. This information can then be used by procurement to analyze the spends with a particular supplier and reduce the risk of falling into a jeopardy due to the supplier’s misdoing. With this data and more, Machine learning can help discover trends, reducing risk and carry predictive analytics to determine price and negotiate with suppliers.


Though AI and Cognitive procurement are relatively new, spend analysis is definitely going through an accelerated transformation with advancements in ML and NLP. Exploring these technologies will only further help spend analytics, by interpreting structured and unstructured data, understanding descriptive and predictive outcome.

In short, use of AI in procurement can result in enhanced delivery commitment, as it reduces manual intervention. It optimizes processes and enhances productivity, thereby accelerating processes. All of this together provide quick insights through dynamic dashboards, helping procurement leaders make informed and strategic decisions.

Enterprises using AI for procurement are enabled to buy smarter and manage their suppliers better. AI also helps businesses automate their everyday operations, backend ERPs, purchasing and accounting systems. All of this in turn enable employees to provide customers a faster and better experience while ensuring better control and visibility over spends for the business. Procurement leaders are empowered to make better informed purchasing decisions in collaboration with complete spend visibility, automated processes, and data driven insights.

Marketplace: Using Intelligent Automation through Pay Per Use services

Robotic Process Automation has been rapidly making its presence felt in a myriad of industries and domains. One of the most versatile automation solutions available in the market, it’s heavily domain agnostic.

Robotic Process Automation is a game changer for business leaders. From increasing efficiency at a reduced cost to helping CIOs comply with changing regulations, RPA streamlines processes for a mid-level manager.

RPA is as domain agnostic a solution as you can get to solve your day-to-day enterprise problems because it focuses primarily on UI automation.

While all of this sounds hunky-dory in theory, the reality is a tad bit more complicated. If you juxtapose this from an implementation perspective, it is almost impossible for an RPA solution to build in-house features which will cater the needs to all use cases across domains. That leads to RPA solutions relying on substantial programing and customizations. Though the customized solution packages are different, there is bound to be usability overlaps between industries and domains. This led to the inception of the marketplace by mid-2018 in the RPA industry.

AssistEdge, one of the top products in the RPA industry is launching its own marketplace. The marketplace will act as an aggregator portal for business processes and use cases which might be relevant to their customers across verticals.

To understand how a marketplace works, we can take a look at how technology giants like Amazon and Facebook transformed from their core offerings to a marketplace. No matter how diverse their user base is, there was a scope of collaboration that they envisioned gradually during the growth phase.

Currently, both Amazon and Facebook have a marketplace offering where users can buy and sell products/services and collaborate on a global scale. Similarly, AssistEdge has heavily focused on creating an ecosystem for both the enterprise and the developer to collaborate on use cases which have similar functionalities as well as underlying applications.

RPA marketplace offerings can be broadly classified into three categories:

The broad level of offerings/categories of an RPA marketplace described above are all critical from an implementation perspective, but the Intelligent Automation/Cognitive Services aspect takes prime importance primarily because of the complexity of implementation of a similar in-built intelligent solution. An easier option for enterprises would be to avail services of an RPA marketplace and choose cognitive solution packages which make the solution smarter.

A classic example where a cognitive solution becomes crucial would be automating invoice processing. Invoices might come in different formats from different vendors, and it might create a furor of confusion for the RPA software to process the desired output. This is where a Machine Learning model can help where they can train the software to identify invoices of different formats and extract all crucial data like vendor name, payment method, account number, etc. which are required to process the particular invoices.

The real-life enterprise use cases for Intelligent Automation would span across domains. Apart from the classic Invoice Processing example cited above, there are various other use cases which require diligent training of the data model starting from Fraudulent Claim Detection to Intelligent Credit Assessor to Sentiment Analysis. Implementing processes with such level of intricacies would require an immense amount of time, money and resources should an enterprise choose to build it from scratch. It would be easier to visit an RPA marketplace and simply download such cognitive process packages. A little tweak here and there with the input parameters and voila the process is ready for deployment, thereby reducing significant time and cost. Moreover, enterprises can crowdsource expertise level from the entire ecosystem of RPA developers, and cherry-pick their solution from a plethora of options.

The marketplace promises a healthy ecosystem of service providers and consumers. The providers have the opportunity of building customized solution packages for a large consumer base with IP protection while ensuring full authority over the pricing model of the same. On the other hand, consumers of these solution packages have the assurance of quality from providers, a comparative view of different processes uploaded by different providers with a consolidated bill for all the services availed from the marketplace. Marketplace will redefine how enterprises consume automation as a service and elevate the experience for the entire ecosystem as a whole and its imperative that RPA practitioners keep an eye out on the recent developments by AssistEdge and the industry as a whole.

Seven roadblocks you may face in your automation journey!!

Digital workforce has evolved as an integral part of modern-day enterprise in the last few years. The Robotic Process Automation (RPA) market has already grown by 63.1% in 2018 to $846mn1 and is estimated to be worth $2.4bn by 20222. Digital workforce mimics human actions on the user interface layer of applications, which makes it more user friendly and secure, as compared to traditional API based automation.

Let’s understand this with the help of an example. Consider a scenario where an HR executive has to add the details of a new employee received through E-Mail into the ERP system. The HR executive performs the following steps to complete the job:

A digital worker would mimic each of these steps with far better speed and accuracy than the HR executive. Furthermore, digital worker keeps going 24*7 without any breaks.

While digital workforce has become an important part of modern-day enterprises considering the advantages and value it has to offer, there are certain ground level challenges, which have to be addressed. For example, it is a very common scenario that an enterprise application is down for maintenance during a certain period. The human worker understands that and tries to complete the job either by accessing a different application or by attempting the job post the maintenance period, whereas a digital worker throws an exception stating that application is not available. In order to ensure the continuity of business-critical processes being executed by digital workers in a fragile application and infrastructure ecosystem, the key challenges have to be identified and handled in run time.

Although the road has been paved for organizations to start their automation journey and reach the destination, there are bumps on the road. These bumps need to be identified and called out loudly so that a strategy to drive through these can be devised. Following are a few challenges that organizations may face in their automation journey:

Application unresponsiveness

Digital workers access specific applications in order to complete the assigned job. In continuation with above example, a digital worker needs to access an ERP application to add or update employee records. While it attempts to access the ERP application, the application may be unresponsive due to various reasons cited below:

In each of the above scenarios, the digital worker is unable to update the employee records and thus is not able to complete the assigned job.

Change in application credentials

Digital workers need login credentials in order to access applications that require sign-in.

As per the standard organization policies, the application passwords or the SSO passwords expire after a certain defined period and have to be reset. Once the credentials are reset as per the organization’s policy, digital workers are not able to access the application with the earlier configured credentials, thus are unable to complete the assigned job.

Accidental access to digital-worker-run applications

Similar to human workers, digital workers execute the assigned jobs by working on specific application on a given machine. But unlike human workers, multiple digital workers share the same machine to execute the assigned jobs in order to optimize the usage of the machine.

Like any other software, digital workers too encounter exceptions occasionally. The administrator and the monitoring teams thus access the digital workers’ machine in order to understand why the exception occurred and collect the required information to fix the exception & to make sure that it doesn’t happen again. Since multiple digital workers are working on the same machine, it may happen that while the monitoring team is analyzing the exception encountered by one of the digital workers, they may accidentally access applications being accessed by other digital workers.

Consider a banking scenario where money transfer in a specific currency is involved. Even a small accidental change in currency can cause huge financial irregularities for the organization.

Change in application user interface

As digital workers are configured to perform actions on specific applications in order to complete the assigned job, they identify the area on the application UI, to act upon, on the basis of various properties such as ID, CSS3 selector, class, coordinates, image etc. In case, these application properties change, digital workers won’t able to identify the right areas to act upon and encounter exceptions.

As an example, consider Mr. Jack ordered a pizza from a website, which has his address details saved as House No. 67, but pretty recently he moved to house no. 520 in the same locality. The delivery boy who has to deliver the parcel to Mr. Jack is attempting to reach him at House No. 67 and is not able to deliver the pizza. In a similar way, if the address of the UI elements on an application UI changes, the digital worker won’t be able to find the right address in run time.

Not so intelligent digital workers

Modern day digital workforce works closely with cognitive services and models to intelligently understand data and make decisions. Cognitive services such as OCR and VISION enhance the capability of digital workers to identify and understand the relevant data whereas Domain specific cognitive models enable the digital workers to make business decisions in run time.

For example, the digital worker has to make a decision regarding whether or not to approve the claim filed by an employee using the submitted travel bills, claims history and other employee related data. It performs the following steps:

But there are times when digital workers are not really confident about their understanding of data they have extracted and the business decisions they have recommended. In such scenarios, digital workers are not able to complete the assigned job.

Business exceptions

Digital workers while performing their assigned jobs encounter expected or unexpected business exceptions. Consider an example where invoice details have to be stored in an excel sheet. Digital worker performs the following steps:

Now if the invoice number is incorrect or the details have still not been updated in the Invoice Management System, while attempting to search for the corresponding invoice, the digital worker would get an error regarding invoice’s non-existence.

In such cases, digital workers won’t be able to complete the assigned job unless specific measures are taken to correct the source data.

Limited bandwidth of digital workers

Just like human workers, digital workers also have limited bandwidth to complete the assigned job. In every organization, there are processes, which are high priority & business critical and then there are others, which are relatively of lower priority and can be completed a little later.

Initially, a specific number of digital workers are assigned for high priority process depending upon the available trends. More often than not, there are times, when there is unexpectedly high workload and existing bandwidth of the digital workers is not sufficient to cater to the same. Although the digital workers complete the assigned job successfully, they miss the defined SLAs.

Organizations may face one or more of the above challenges while in their automation journey. The strategy to manage these challenges has to be in place for a successful automation journey. Although the RPA market is growing rapidly, these challenges can hold back or hamper the success of their automation journey. In a recent study by EY, it was found that 30-50% of initial RPA projects fail 3. It is not as simple as it looks. In my upcoming blogs, we’ll talk a lot more about how to cater to these challenges so that organizations can drive over the bumps without any jerk.


1 https://www.gartner.com/en/documents/3923903/market-share-analysis-robotic-process-automation-worldwi
2 https://www.marketsandmarkets.com/Market-Reports/robotic-process-automation-market-238229646.html
3 https://www.forbes.com/sites/cognitiveworld/2018/12/02/the-big-rpa-bubble

Maximizing your contact center investments through intelligent automation

We live in an exciting time for business. The proliferation of, and ease of access to, technology has meant that new and innovative companies can challenge established conglomerates previously inaccessible as even benchmarks, much less as competition. Innovation in both service design and service delivery is essential to actualize the advantage, but the changing customer mindset is the most crucial component of this shift. Customers today are more powerful than they have ever been in human history. They can choose from a host of products and services on the go, interact with companies on channels of their preference, and broadcast complaints on social media. The good news is that their search for convenience means that they are also open to new products and services that facilitate ease of use and seamless issue resolution.

What does that mean for companies? Well, speed and service quality are no longer differentiators but prerequisites. In an environment where economies of scale are commoditized, and brand loyalty is a thing of the past, customer experience is perhaps the most significant downstream advantage. Today’s customers have high expectations and are always hard pressed for time, especially when waiting to have a query or issue resolved. They expect their customer service experience to be simple, interactive, and contextual. As companies and customers across sectors will testify, this ease of use is the exception and not the norm. If you run a contact center of any scale, you understand that delivering best-in-class customer experiences can be a mammoth undertaking. It can be challenging to identify, recruit, and engage great customer service talent and, even if these aren’t concerns, retention is a challenge. Factor in the high operation costs alongside shrinking budgets, legacy systems, and an under par agent experience, and it becomes easy to see why delivering great experiences can seem like an uphill task. With the smart use of technology, it doesn’t have to be that way.

The ability to drive efficiency and unlock value across the delivery chain, automation is fast being seen as the first step to digital transformation. By automating repetitive tasks, companies can now direct their resources to value-creation activities and also enable employees to focus on functions driving business growth. A credible solution provider can help companies identify the best candidates for automation and help them build a system primed for scale and incremental results. The real difference, however, arises from intelligent automation i.e., the efficiency and speed gains for automation combined with the power of AI. In their initial stage of evolution, AI and automation have traversed their path but, here at EdgeVerve, it has become clear that they complement each other exceptionally well. That’s why we built AssistEdge Engage – an intelligent automation platform that combines best-in-class AI with industry-leading automation capabilities. Think of RPA as the heart of the platform and AI the brain that offers sensory capabilities for intelligent decision making. With over 250 active customers across the world, AssistEdge Engage can power your contact center operations through a combination of assisted and unassisted bots to address a wide variety of use cases.

Before we get to discussing ways to measure the effectiveness of this technology, there is merit in addressing a few points about implementation mindset. First, companies must realize that they do not need to choose between prioritizing agent or customer experience. Both are crucial to business success. In fact, an added focus on agent performance, in addition to the apparent impact on customer experience, can prove to be a valuable investment in the medium-long term. Most enterprises today have complicated systems that have seen several iterations, creating complexity and inefficiency for contact center teams. Agents are often juggling multiple applications for a variety of routine functions from retrieving customer data to updating records and finding process guidelines. The resultant confusion can affect the quality and accuracy of service. On the flipside, an empowered and motivated agent can drastically improve the customer experience and brand image, consequently impacting customer retention and lifetime value. Furthermore, building intelligence into your CX model will improve your compliance, achievement of SLAs, and generate actionable business insights through robust analytics.

In addition to these benefits, CX automation can spike your contact center performance. By ensuring that agents are better equipped, AssistEdge Engage implementations have driven up to 40% reductions in AHT, increased FCR rates by 15% and reduced repeat calls by 30%, representing substantial savings of time, effort, and resources. Efficiency is but one part of the payoff, and customer loyalty is where AssistEdge Engage can make a real difference. We have also recorded increases in client NPS numbers, up to 8%, attributed to contact center automation. Another key advantage of the tool is that it plugs into practically any existing system, so you don’t have to worry about making a sizable investment to replace legacy systems. As a thin, powerful, and effective intelligence layer, AssistEdge Engage sits on top of your existing IT framework while transforming your contact center operation.

We wholeheartedly believe that the platform will disrupt the way contact centers operate and human-led digital workers are the future of how companies will interact with their customers. Our vision is for the contact center to become a revenue center and we are bullish about the fact that advanced capabilities will only make them more agile, more intelligent, and allow them to influence the top line.

To find out more about how AssistEdge Engage can revitalize and accelerate the success of your contact center, click here.

Accessing the bits and bytes of processes with Process Discovery

Can you imagine driving your car blindfolded? You may even succeed with good guesswork and a solid exposure of surroundings! But is it really reliable, and more importantly is it worth trying?

Business processes are a similar case. It needs special acumen to see what exactly is happening inside, where are the bottlenecks, should it get automated, what needs to be done to maximize efficiency and amplify value. It must not be based just on guesswork or SME’s biased view! Organizations, for decades have been striving hard to gain insights and transparency to understand the as-is processes to identify gridlocks in the flow and create a baseline for improvement.

Digital advancements across industries also assuage enterprises’ efforts to model virtual view of the processes. There are products which consume logs generated by the disparate systems, capture actions performed by individual users, and analyze them using AI algorithms to create as-is process maps. Crafted on the foundation of empirical data, these process models paint the end-to-end process flow comprising of multiple sub-processes and tasks executed by various actors with associated lead times.

The depth and granularity of these process models vary based on the business need, and hence, the way to capture such information will vary accordingly. It ushers in a brand new opportunity of process discovery which in contrast to process mining, captures more granular control level actions at each step of the task.

Process mining products heavily rely on analyzing event logs captured from enterprise systems. It needs to build a data warehouse, integrating the logs from these applications. Process discovery products record users’ click-stream across systems and with the help of AI, they reconstruct the virtual map of what users do. Since it captures human actions on screen, process discovery products may miss seizing the non-systemic data (like decisions based on visual inputs) overlooking potentially relevant steps.

Process mining, for example will give an overview of a procure-to-pay process showing the execution time of individual tasks such as purchase requisition, vendor selection, purchase order creation, invoice reconciliation etc., the lead time between these tasks, and different ways in which users are traversing to execute this end-to-end process.

On the other hand, process discovery will help create business requirement documents for a robotic process automation project to automate a particular task (say purchase requisition) providing as many details as possible focusing on:

The RPA tools available in the market today operate at automating step level activities of individual tasks. Most often, a single actor executes all the steps of these tasks.

Balancing the right level of granularity of process knowledge is crucial to amplify the process value. And if you want to accelerate your automation journey, automated process discovery will certainly be your first step.

So, would you prefer driving the car blindfolded or enjoy an intelligent driverless automatic car instead?

Just sit back and relax!

SAP certified RPA product – The right choice for your enterprise!

The backbone of operations across most of the large and very large organizations is the enterprise application software. SAP is the market leader in enterprise application software, helping companies of all sizes across all industries run at their best. It is estimated that almost 77% of the world’s transaction revenue touches SAP system1! This high-volume transaction processing consists of tasks, which have varying complexities and belong to different business domains and functions. Despite these differences, there are a few common characteristics of these processes:

These characteristics, when aggregated, point to one direction – all such processes being typical Robotic Process Automation (RPA) candidates! This is a vast ocean of RPA-ready processes using SAP at one stage or the other across enterprises. To automate these processes, it is advisable to deploy an RPA tool, which has been tried, tested and vouched by several SAP customers. But what considerably adds value to an RPA tool’s credibility is a validation by SAP itself.

SAP’s Integration and Certification Center (SAP ICC) administers an open certification program to boost customers’ and prospects’ confidence in partner products2. The certification program evaluates and awards certification to RPA products. Let us look at the various aspects of the RPA tools being evaluated as part of the SAP certification.

SAP does not liberally award the certification to any product or solution. The above evaluation ensures that the RPA product undergoes a stringent qualification procedure to achieve the designation of ‘SAP certified solution’. For an enterprise, which is looking to invest in an RPA product and has processes running on SAP, this certification is a yardstick against which it can measure prospective RPA vendors. The SAP certification goes a long way in cementing the enterprise’s confidence in their RPA product selection and investment decision.

Let us look at the benefits that an enterprise can garner by choosing an SAP certified solution:

AssistEdge recently became the first and only leading RPA solution to be SAP certified. AssistEdge has automated multiple business functions that involve SAP across multiple global enterprise clients. Using AssistEdge RPA, the clients have realized business benefits like operational cost reduction, improved customer experience, accuracy improvement, increased automatic posting and higher compliance. With AssistEdge now being SAP certified, you are further assured that it is the best choice for process automation.


Employee Engagement in the Age of Automation

Not since the Industrial Revolution has technology been as pervasive as it is today. With its promise of efficiency, intelligence, and insight, the power of AI is transforming the world of work faster than anyone imagined. Businesses that use AI-powered innovation like automation intelligently can now experience greater success while using fewer resources all while uncovering new opportunities for growth.

As the buzz word goes, the change has substantial ramifications for human workers across the world. While that debate is extensive, as is the ‘technology vs human beings’ argument, that’s not what this article is looking to solve. In a capitalist system, companies will inevitably strive for efficiency to drive bottom-line results. There are, however, several intangibles that have an equally significant role to play in growth, profitability and longevity. Employee motivation easily tops this list. So, how does a business that is trimming the fat communicate with empathy and clarity? What can it say to its employees to confirm that automation is not a threat, but an opportunity? Here are some aspects of the transition that employers can communicate and employees should consider as automation plays a more substantial part in their organization.


Easily the most significant benefit of automation at the base level is time. Most companies spend an excessive amount of time performing repetitive tasks to a moderate level of accuracy. If an employee leaves a company, the additional strain on the team means the instinct is to look for a replacement, but what if you didn’t need to. What if you could focus on deploying a software-based bot instead and use that hiring budget for a more strategic role? It’s a win-win situation – for the company and the team.

The big one – Work-life balance

Work-life balance is one of the most debated topics in corporate culture. The challenge is that the idea means different things to different people. For instance, the needs of a single hire may be very different from an employee with a family. The long hours can take a toll on their personal life and adversely affect performance. What if you struck a balance at work? Instead of automating the role entirely because of the manual intervention required to ensure compliance, you optimize the process. With tools like AssistEdge, specifically AssistEdge Engage, you can create a process where the tool performs all the actions until the stage manual signoff is required before resuming once the review is complete. These hybrid process models could redefine process delivery and provide work-life balance much to the delight of employees also alleviating their concerns about automation taking away their jobs.

Quality and Accuracy

Automation has a direct impact on both the quality of the work product and the quality of the talent within a company. For instance, a US retail giant had a substantial budget allocated for the refiling of sales tax owing to manual errors. Deploying robots meant that the quality and accuracy of the work product improved significantly. Given the sheer volume of the workload, the blame for this inaccuracy cannot rest entirely with the analysts. By completing even complex repetitive tasks, an automation solution can inject vigour and growth into any organization. Furthermore, by reducing the hiring need for such tasks, teams can now focus on creating value-driven roles. Organizations can assess employees on their ability to generate value and deploy intrinsically human skills like people management, creativity, and strategy.

So, there you have it. Irrespective of innovation, transformation, or evolution, great organizations will always be built by inspired people. To motivate your teams, it is crucial that you have a culture of clarity, transparency, and learning. After all, the issue is no longer whether you will automate, when you will automate, or how you will automate. Each of those is a given. The real question to ask is – when you do make the change, will it be in a way that rallies your people? The speed of your answer could determine your future. Remember, Rome was not built in a day.

Slow and steady, the robots are ready.

Magic or tragic – what is your contact center saying about your business?

Do you see your contact center operation as a cost, revenue or value center? If you are anything like the majority of businesses worldwide, you may say a cost center with plans afoot for its transformation into a value-based function. Customers are changing, and your company is changing with it. The issue, however, is that while your services and products evolve to keep pace with users, your contact center operation, the face of your company, may be lagging. Don’t worry. You’re not alone.

Today, many unprecedented changes have blindsided businesses across the globe. Customers have more choice than ever before. The market is the most competitive it ever has been, and, most importantly, it’s never been more comfortable for your clients to take their business elsewhere. That’s why the ability to deliver exceptional customer experiences is crucial to the success of your organization. Customer experience will soon supersede product features and even price as the reason people choose your company. Customers have incredibly high expectations of CX, especially true if you’re a large company with a loyal user base. However, while investments in operations and hiring can help you run an always-on customer support operation, they can’t help make your systems, people, and processes smarter overnight.

On the one hand, companies must deal with disconnected legacy systems and processes that make for poor customer experience. On the other, the gap between consumer expectations and company platforms means that agents juggle multiple applications and screens, all while managing increasingly difficult expectations for AHT and first call resolution. Combined with the fact that technology and processes are usually resistant to chance, a substantial challenge emerges. Even if businesses can find the budgets to carry out this capital-intensive transformation, they have to manage a complex change management exercise and the potential consequences such as increased attrition and reduced customer satisfaction, at least in the medium term.

The time to change is now. Companies that don’t transform their customer experience framework soon will struggle to survive, much less thrive. First, businesses must move their contact center thinking to a quality-driven approach from a cost-driven approach. Then, they must work out if they measure the right metrics for success. Once that process is complete, it is essential to assess the factors affecting performance before running a thorough top-down evaluation against best-in-class benchmarks. It is pertinent to note at this stage, that organizations shouldn’t just think about how they can move a step or two forward, but be prepared to redesign the way they do things. What if a whole operation could be automated? In some instances, would it be possible to offer a better customer experience by eliminating the need to phone or chat with an agent? With technology today capable of servicing practically every requirement, why aren’t companies harnessing it better?

It is widely accepted that knowledgeable and proactive staff and pre-emptive resolution are the characteristics that determine truly exceptional customer experiences. In our experience, while these qualities still stand, you would be surprised at the number of firms that fall short. Don’t look too far beyond for examples. Just think about your own recent experiences dealing with contact centers. Was the agent aware of your problem before you explained it in detail? Did they manage to address your query on a single call? Were you given contextual and relevant advice that served your purpose and strengthened your relationship with the company? Before you go ahead and answer those questions, consider the agent’s perspective. Was the agent empowered with the right tools and training that offered a comprehensive view of the customer? Do they feel like their job is to add value and not just rush through to meet an increasingly challenging AHT metric? The fact of the matter is that customers don’t think much of the state of current customer service and think it could get even worse. That’s why we decided to fix it.

Who does a great customer experience genuinely serve – Is it the business, contact center agents, or your customers? The only right answer is all three and that’s the purpose on which AssistEdge Engage was built, our automation and AI-powered solution designed to inject efficiency into your contact center operations. When we designed AssistEdge Engage, we set out to create a comprehensive solution that could help businesses reimagine and automate processes for effectiveness and quality, enable agents with the insights, skills, and tools to excel at their work, all of which is directed towards offering customers an exceptional experience. The platform does all that and more. Here is a glimpse of the tool’s powerful benefits:

For Companies

AssistEdge Engage for Agents

AssistEdge Engage for Consumers

A standout feature of AssistEdge Engage is that it can be implemented straight out of the box since it integrates easily with your existing enterprise applications, with zero changes to your current IT landscape. The time and investment saved will reduce your time to market, time to value, the total cost of ownership, and increase your profitability. By improving the agent experience, the solution also has a direct effect on your attrition and employee satisfaction. The most crucial advantage is for your consumers. Through an intelligent results-driven framework, AssistEdge Engage’s frictionless performance substantially improves customer satisfaction, priming your business for a phase of unprecedented growth. You could even call it magic.

Click here to discover how AssistEdge Engage can help you create customer experiences befitting your organization’s reputation and goals.