A Map as Big as the Territory

“A map as big as the territory” is a line inspired from a Spanish fiction story by Jorge Luis Borges. Fiction stories allow authors to create their own world of possibilities. In many cases, such vivid imagination has been the precursor to reality. A Boston Consulting Group (BCG) article on digital disruption maps this notion of Borges’s map to emerging business architecture. This architecture highlights the importance of data as infrastructure.

Interestingly, we are in a world where a map as big as the territory is becoming a reality. Today, we have a detailed digital mapping of any territory that is of interest to us. The cost of capturing, consuming and keeping this information up-to-date is decreasing. Telematics and connected cars will take this a notch further.

It is predicted that by 2020, the cost of human genome sequencing will fall much below what it costs to get a chest x-ray today. This has been achieved after more than a decade of painstaking work. Another territory conquered!

The US government has funded 100 million USD to start mapping the brain. Private funding is also pouring into this research area, which is primarily driven by the implications of deep learning. Suddenly a 3D image of a brain as visualized in Iron Man 3 (Aldrich Killian giving a virtual tour of his brain to Pepper Potts) doesn’t seem just futuristic. Borges’s world is no more fictional. We are keen to create a highly granular digital print of the physical world.

Let’s see if we can extend this further to our dynamic world. Is there a need for this? Do we have the instrumentation to enable this?

Let’s solidify this with a specific example. We are heading towards massive urbanization. Energy, water, waste, transport, healthcare, education, law and other amenities expected by every citizen needs to be made available in a socially equitable way. Also, pollution levels need to be minimized for all. A push towards renewable energy will lead to a large population living off the grid. India alone has a published target of 40 GW of solar energy, using roof-top panels, by 2022. We definitely need to map the demand, consumption and supply patterns in real-time at a granular level.

Will citizens support in sharing such information? Globally, social behavior is gravitating towards notions of shared future and collective upliftment. Privacy fears aside, today we are more open towards self-disclosure of lifestyle choices and our surroundings if it serves a greater purpose and the effort involved is minimal. So this is not a problem and furthermore, sharing tends to have a strong network and information effect where people follow each other.

Do we have the instrumentation to enable granular measurement? Yes! Smart meters are becoming the norm across Europe (electric, gas, water, heat). Smart meter PoCs are already being done in India. IoT enablement of consumer devices is even enabling granular measurement and ease of sharing. Measurement devices for water pollutants, indoor and outdoor air pollution, are available and prices seem to be falling much faster, driven by economies of scale and technology advancement. Telematics is enabling the measurement of transportation. Building automation technology is enabling measurement of energy and pollution for large structures. Availability of IoT in healthcare-including home and geriatric care -is well documented.

So, all we need is a framework to collect and aggregate the data provided by citizens. The information available with local municipalities, counties, and governments for residential, industrial and commercial properties, transportation and related citizen information provides such a framework for aggregation. Imagine the possibilities, where you as a citizen know the level of information that is available with the government and can benchmark it against the best. This real-time map, as granular as our society itself, can open a whole new realm of possibilities in self-governance.

What do you think?

Evolving from Robotic Process Automation to Intelligent Automation

No strategic planning exercise is complete nowadays without prioritizing digital transformation initiatives to improve cost efficiency and unlock new value. Leaders in growth-focused organizations strive to integrate digital technology into mundane operations to streamline them and deliver more value to customers. This way, employees also get to focus on higher value-added tasks and decision making, to a large extent.

Robotic Process Automation (RPA)

RPA is enabled by rule-based deterministic automation tools that delegate repeatable, predictable tasks to bots. The worldwide RPA software market grew 63% in 2018 and is forecast to reach $1.3 billion in 2019, according to a Gartner study, with banks, telecom firms, insurance companies, and utilities alike embracing this change.

Just look around and you can find plenty of examples in action.

Has RPA hit real-world hurdles?

RPA adoption is growing, but you must have noticed several real challenges that have emerged over the recent years.

Most RPA solutions work efficiently in a linear way. They, however, depend on human intervention to correct complex issues that are outside their scope of functioning. They cannot judge how to use specific information contextually, work with exceptions or variants, or work with unstructured data. And in general, 60-80% of organizational data happens to be unstructured. Also, while RPA may automate tasks efficiently, the time taken for it to integrate into overall processes is often underestimated – limiting its success. Most importantly, it doesn’t lead you to real process transformation by redesigning already existing inefficiencies in end-to-end processes.

Recent industry research suggests that RPA-optimized companies with conventional enterprise ops centers only achieve about 30% of their targeted automation goals. You should, in fact, only see RPA as a step towards intelligent automation that enables robots to automate processes with increased context-awareness.

The promise of Intelligent Process Automation (IPA)

This is where IPA or Cognitive Automation, comes into the picture.

It refers to the combined applications of RPA, Artificial Intelligence (AI), and other advanced technologies such as Computer Vision Technology (CVT), Optical Character Recognition (OCR), and Machine Learning (ML). It uses human-like bots creatively to mimic human activities, think and learn on their own, providing reliable support to the workforce using rich data and insights.

Here, you can augment rule-based automation with decision-making capabilities through an intelligent platform capable of processing structured data to generate deep insights. It can support employees by removing routine, repetitive tasks, and can transform customer journeys by making interactions simpler, smoother and faster.

This intelligent automation will enhance your bots with as many human characteristics as possible, including the capabilities to see, sense, learn, communicate, collaborate, empathize, and self-heal.

Recent advances in CVT, OCR, and ICR enable bots to See – read documents, identify images, and recognize objects. Your machines can easily download and analyze complex information contained in scanned images, including text, numbers, logos, and even other objects. Imagine a seeing bot that can scan thousands of invoices, purchase orders, or KYC documents per minute and also deciphers powerful information.

Bots enhanced with AI can capture key attributes of a use case, identify trends and then take the required action. Statistical models like Artificial Neural Networks, Bayesian Networks, and Support Vector Networks can provide the sensing capabilities for applications specific to your processes. Some common examples of Sense and Learn can be seen in the dynamic load balancing of process volumes and bots, business exception management, and triggering of automation based on specified event patterns.

As you know, human-human interaction is required for optimized customer service and employee engagement processes. Speech recognition software, Natural Language Processing (NLP), and Semantic Analysis now let bots understand spoken and written language, and the successful automation of such processes. For instance, chatbots connected to RPA-enhanced bots can now Communicate effectively. They can interpret, understand, and respond to your client or employee queries while also executing transitions on their behalf.

Collaboration between your machines is possible nowadays through Advanced Knowledge Management Systems (KMS), which collate, tag, store, and process data through powerful search engines. A live example of this is your chatbots that answer queries and navigate the customer or employee through a series of preprogrammed solutions.

Empathy is a key factor in customer-facing processes and in processes where customer interactions and customer satisfaction levels are the key performance indicators. Experts now predict that AI will be able to Empathize by helping bots assess human emotions. For instance, sentiment analysis algorithms can detect your customers’ emotions based on their speech, text or handwriting. Once the sentiment is determined, the human worker can be directed to the proper solution much quicker, or the chatbot can adjust its responses and solutions accordingly.

How to approach IPA adoption in your organization

In order for your IPA program to be successful, you will need a strategic approach avoiding random installations of discreet applications. Your C-level executives must definitely be involved throughout the adoption process.

To start with, your entire organization needs to be clear about the potential benefits of IPA, and how best to maximize the related data analytics and decision-making capabilities. There must be clarity on what the goals of the program are, and where it fits within your existing operations.

Next, prepare a wish list of tasks and problems to address using IPA. Prioritize these tasks based on potential value and feasibility and define realistic ROIs for each.

It is now important to identify the strengths and limitations of your IT team and establish a well-defined governance structure. Seek professional consultation and set up a pilot program to set the ball rolling. Also set up a smart in-house team for data clean-up and integration.

Remember to start small, collect feedback, learn and adjust all along the way, and expand slowly. Plan additional storage requirements as your organization expands its IPA scope. Also, set up a feedback mechanism encouraging your employees to provide feedback.

As your IPA program builds up over time, you will have to address quality and transparency concerns. Pay close attention to protocols like bandwidth requirements, storage capabilities, safety, and security.

Combining AI with your human workforce is a critical component of IPA adoption especially during the first steps of automating complex processes. You will need to find innovative ways to audit the decisions made by these machines. In many cases, only humans will be able to recognize and analyze complex patterns and explain them. In the initial stages, you may even allow your employees to override AI-generated decisions until they gather enough data to validate the results.

The way forward with IPA

IPA is in its infancy stage of adoption, and there are few success stories to substantiate it yet. At the moment, it is only on the draft board of many organizations, but its arrival is near and imminent.

As an innovative organization, you must have already automated a majority of your routine processes and are learning from the resulting data sets and outcomes. Now is the time to identify the right partners to strategically embed AI and ML into your systems, software and hardware infrastructure.

When configured and managed properly, IPA will integrate with predictive analysis technologies, automate even your complex processes, and fundamentally redesign your business processes. Ultimately, with the resulting employee productivity improvements, and enhanced value provided to customers, you stand to gain a significant competitive edge over the others.

From OCR to Computer Vision — The journey

It’s a typical day for Samantha at work; checking, verifying and classifying a multitude of sales invoices which is time-consuming and tedious. How she wishes for an automation software that could extract the information from the images, allowing her to focus on more strategic tasks!

Are you also having trouble converting bank statements, sales invoices, and computerized receipts into digital format? Do you want to digitize your bank statements? Optical Character Recognition or OCR is the answer.

In this blog, we will delve deeper into what OCR and Computer Vision is and why enterprises should make the shift from OCR to Computer Vision.

What is Optical Character Recognition (OCR)? — The basic concept

OCR refers to the process of converting different types of data including PDF files, printed documents or images into editable, accessible and searchable formats for computers. The power of OCR is limitless — can read documents in multiple languages and formats and convert documents into text-searchable data, thereby maximizing accuracy, eliminating manual efforts, driving analytics, and enabling enterprises to adapt to the ever-evolving business needs.

The history of OCR and its related technologies

Did you know that the history of OCR dates back to 1914? In 1914, Emanuel Goldberg developed a machine that read characters and converted them into standard telegraph code. During the late 1920s, Emanuel developed a statistical machine used for searching microfilm archives. The optical code recognition system he developed was acquired by IBM, with many inventions to his name. In the 2000s, OCR was widely available “as-a-service”, and its use grew significantly.

Another example is the CAPTCHA program that was developed to avoid bots and spammers. From HPE’s Haven OnDemand to OpenCV, OCR is a field in artificial intelligence, pattern recognition and computer vision that enterprises continue to explore.

Whether you’re looking to convert handwritten scans to machine-encoded text or automate data entry tasks, OCR has got you covered. Optical Character Recognition is the most common means of extracting business-critical data, translating data into digital forms.

Limitations of OCR and the need for enterprises to adopt Computer Vision

Indisputably, OCR helps enterprises save time and effort in scanning, processing and editing documents of all forms. With OCR, you can extract information from a printed contract or image without the need to retype or scan the image.

As Intelligent Automation has evolved significantly, there is a need for greater inherent understanding of where data is located on a document, and the various forms the same data may take across different types of documents. OCR generally depends on templates and rules that define document layout.

Is OCR 100% accurate? Does it work with all types of documents? Can OCR differentiate between characters? These are some of the perplexing questions that enterprises are trying to answer. How can Computer Vision solve these challenges?

What exactly is Computer Vision?

Picture this — Google Lens, Face Recognition, Snapchat filers, and Google maps aerial imaging.

They all have one thing in common — Deep Learning-based computer vision algorithms.

According to the SSON report on Computer Vision and Cognitive Automation, Computer Vision (CV) refers to the ability to see, read and recognize specific objects or data within an unstructured format. It falls under the broad area of Artificial Intelligence.

Computer Vision works by digesting massive quantities of data on related images to recognize specific characteristics and patterns. It helps understand the concept of digital images, extracting information from images/pixels. The primary role Computer Vision plays is to identify those ‘areas’ or ‘regions’ of interest in a given document, and pass this information on to an OCR engine, where the information will be converted into a structured format.

In the 70s, David Marr, a neuroscientist at MIT, set up the building blocks for the modern Computer Vision and thus is known as the father of the modern Computer Vision.

Take your document/image conversion to the next level with CV’s capabilities such as Deep Learning. From translating text into several languages to tagging friends in photos, Computer Vision provides superior performance, surpassing human-level accuracy.

As per a report, the Computer Vision market is expected to reach 25.32 billion U.S. dollars by 2023, at a CAGR of 47.54%.

With such staggering growth, it’s no wonder that Computer Vision with AI-enhanced capabilities is applied across a plethora of industry sectors — consumer, healthcare, automotive, sports and entertainment among others. AI in Computer Vision is not a pie-in-the-sky goal anymore, rather an emerging technology that is driving business growth, strategic partnerships, collaborations, and an increase in revenue.

With data growing at a mammoth pace, there is a huge opportunity for enterprises to leverage new technologies such as Computer Vision to find patterns and make sense of the available data.

Why should enterprises make the shift from OCR to Computer Vision?

Adopting OCR in business processes signals a new wave of modern enterprises, where ensuring customer satisfaction and improving user experience is critical. Since the technology involves reading text from images and extracting value, it increases data access to customers, eliminates traditional systems, thereby reducing manual errors and improving cost efficiency, productivity, speed, and accuracy.

In this age of digitization, it’s not surprising that enterprises are gearing up for a future that is conducive for human-digital collaboration — A future where the humans and bots work alongside each other, taking automation and AI capabilities to the next level.

That’s where breakthrough revolutionary technologies such as Computer Vision, ICR or Intelligent Character Recognition, coupled with analytics, are expanding the scope of process automation across the enterprise.

Computer Vision is an area of artificial intelligence that can be used to simplify paper-driven processes across the enterprise including financial services (loan applications, vendor onboarding, receipt processing), manufacturing (accounts payable, sales order purchasing), insurance (claims handling), healthcare (billings and claims management), and government (passport applications).

Using deep learning models, computers will be able to accurately collect, analyze, and classify data. It’s time enterprises transition from tried-and-tested techniques to ML-backed technologies that will help beef up your processes and systems.

Don’t be left behind! Make the start to data-driven solutions with computer vision software today.

For further insights on Computer Vision and Cognitive Automation, download the SSON report — Enabling Intelligent Automation using Computer Vision now.

References:

https://en.wikipedia.org/wiki/Optical_character_recognition

https://towardsdatascience.com/computer-vision-an-introduction-bbc81743a2f7

https://www.marketsandmarkets.com/Market-Reports/ai-in-computer-vision-market-141658064.html

https://medium.com/@hdinhofer/optical-character-recognition-ocr-a-branch-of-computer-vision-76887e1d6ab0

NCMC – One Nation, One Card

A logical next step for the banks; giant leap for the digital economy

As of March 2019, Indian banking ecosystem has over 920 Million debit cards in circulation. That is almost 20 times as many active credit cards in the country. In spite of the great number of cards in circulation, our actual-usage of it is far lesser than desired.

Sadly, each debit card gets used, on an average of 1.4 times a month. Worse still, 7 out of 10 times, the Debit Card is used at an ATM. These statistics from the Reserve Bank of India (RBI) paint a very clear picture.

We Indians, use our debit cards very sparingly, and even when we do, it is mostly to withdraw cash.

ATM & Card Statistics – India:

If our national aspirations of being a digital economy must be realized, this trend of conservative-usage of Debit Cards must be overcome. There is a strong need to articulate new use-cases and roll-out proactive initiatives that promote card based transactions. One such framework/initiative is the National Common Mobility Card (NCMC).

The Infrastructure and applications

Also known as ‘One Nation One Card’, it is regular debit card with the additional capability to make contactless-payments (tap and go) as well. NCMC has the potential to catalyze rapid adoption secure and fast digital payments. The underlying reason, is that it operates on the ‘EMV Open Loop Card with stored value’ model and supports dual interface (contact & contactless).

Let’s understand what it really means (in layman’s terms)

The NCMC framework combines the benefits of both the contact and contactless payments into a single card. Going forward, the RuPay cards built on NCMC framework can be availed from any of the 25+ large banks as either debit, credit or prepaid cards.

Clearly, the intention behind NCMC is to use the same card for several purposes,

Context, Challenges and Impact

In India, 90% of credit card and almost all debit card transactions are domestic in nature. The Two-Factor Authentication (2FA) has been largely successful in averting misuse of cards; especially among the low-literacy customer base. While the 2FA lends the much needed layer of security, it may often create an element of friction that could be done away with.

In the low-internet connectivity areas, insisting on 2FA could be impractical. Ex: Highway Toll-Plazas, underground Metro stations, cafeterias etc. Even in areas with strong internet-connectivity, the 2FA might cause delay and holdup for small transactions. Ex: Cafeteria, Bus, Tolls etc.

The NCMC has potential to overcome these challenges and deliver value in three ways,

The RBI has waived-off the need for 2FA for contactless cards as well as for online card not present (CNP) payments less than Rs. 2,000.

These transactions can be done without keying in any additional PIN or OTP.

Of the 920 Million debit-cards in the country today, only 15 Million are estimated to be NFC (contactless) enabled. Also, of the 3.7 Million POS terminals, about 900,000 can support NFC payments. This shows the gap and also the opportunity to accelerate digital payments.

The road to implementation of NCMC will have its fair share of challenges. The success of this ambitious initiative depends on several factors, including,

Conclusion

Transport for London (TfL), the integrated transport authority for London has enabled contactless payments on their buses and trams. They found that the once the contactless cards were introduced, the adoption of digital payments more than doubled. Today, TfL supports Apple Pay, Barclaycard Contactless Mobile, bPay, Fitbit Pay, Garmin Pay, Google Pay and Samsung Pay.

In future, NCMC cards could also be made available as wearable-accessories. Considering the Indian urban commuter, there is a potential to at least triple the number of card-transactions in the near future. After the success of UPI and RuPay platforms, the NCMC framework may just be the next big blockbuster in the Indian payments ecosystem.

Automation Singularity —The Robots Are Here to Stay. But the Humans Aren’t Leaving.

All of us who have focused our attention on the technology evolution in business in the last few decades can think back to when companies had a clear delineation between the ‘business’ and ‘IT’. Traditional business lines ran aspects of the company and leveraged technology provided and supported by their counterparts in the IT department. When engaging with a new company on something new, the common question that always sprang up was, “Is this being driven by the business, or IT”?

Of course, today lines of responsibility have blurred. Companies realize that their fundamental strategies and go-to-market plans are highly depending on their available technology. Technology decisions are rarely made without involvement from the business. Most companies have employees in roles that jointly represent one aspect of the business and the IT department.

There’s a similar phenomenon happening with Automation. As Robotic Process Automation (RPA) continues its rapid-paced transformation of business, the strict delineation of responsibilities between human workers and robots is becoming blurred. Particularly with the advancement of ‘Intelligent Automation’ and smart robots, the role of the digital worker and the human worker are becoming more intertwined.

This interconnectedness between the human and digital worker is known as ‘Automation Singularity’.

What is Automation Singularity?
Automation Singularity refers to a highly customer-centric and agile oriented state of constant improvement and optimization through the future workforce, opening up an expanded horizon of possibilities. Human specialists drive customer orientation using their creativity and empathy and are complemented by digital workers with extreme productivity and consistency.

Here are some of the specific ways that humans and robots are interacting in the age of Automation Singularity.

Strategic Priorities and Centers of Excellence (CoE)

Business is run top-down. Even the most mundane and repetitive business process (including the types often first automated with RPA), should ultimately align with higher-level company strategies and objectives.

Robots will continue to get ‘smarter’, but humans will ultimately always drive company strategy. RPA tends to focus on building automation road maps for processes that are 1) feasible for automation and 2) drive ROI. However, it will be necessary for companies to have Automation Centers of Excellence (CoE) that ensure the automation roadmap aligns with fundamental company strategies, and that strategic importance is a considering in building automation roadmaps.

Process Discovery

The first step in automation focuses on breaking down a process to prepare it for digital workers. This process discovery phase tends to be a human-driven activity. Subject Matter Experts gather with CoE members and use a whiteboard to map out processes as a basis for robot configuration.

Today, RPA technology actually can (and should) help with process discovery. Allowing your RPA platform to leverage tools to monitor a process over time, then auto-create process maps with statistics can improve the chance for automation success. Process discovery is quickly becoming a human + robot activity, and for the better.

Attended Automation

Despite giant leaps in RPA capabilities, including Intelligent Automation where robots learn and improve with time, some processes are still best performed when a human is involved.

For example, think Customer Service personnel at companies who still want to provide a human interface in their call centers. Robots can significantly improve the productivity and effectiveness of CSRs by providing automation in common and often complicated functions. The human + robot “Singularity” in a call center can provide the best overall experience for the customers it services.

Orchestration

One of the key advantages of digital workers is that they have the capability of achieving 100% accuracy and high levels of productivity. But they also need managing, unlike a popular misconception. While working, robots need direction just like humans, on what to work on, what to do when idle, and how to handle exceptions or problems.

Countless labor management tools have been built to orchestrate humans in working environments (in a warehouse, store, office, etc.). RPA platforms require similar technology to orchestrate robots, and ensure that they are maximized across processes, to deal with exceptions efficiently.

The exciting thing here is that a lot of digital worker orchestration can be automated in itself. We’ve reached a stage where digital workers are being ‘Singularly’ orchestrated by humans + robots.

Trust

Automation is creating a huge transition in work, from humans to robots. This requires a concurrent shift in ‘trust’. The risk management element of RPA is critical as it pertains to security and fraud, which again requires human oversight of digital worker activity and trust.

Automation Singularity thus involves synchronization of governance across humans and robots.

Training and Re-Skilling

There’s a common misconception that successful Automation initiatives manifest value by the replacement or elimination of human workers. Companies having the most success with RPA are getting results with something very different.

The emerging Automation Singularity phase shows us that RPA can maximize the productivity, scalability, and flexibility of both human workers and digital workers. Human workers are optimized and enhanced by robots. Humans can focus on higher-value elements of the business, like customer service and R&D.

This requires a shift in human worker focus, including training to re-skill human workers for their new roles. Successful automation (with robots) generates the most value when humans are optimized through training and re-skilling.

Conclusion — The journey towards Automation Singularity

The impact of automation and digital workers is altering the face of the business landscape forever. Companies on this journey will be completely successful if they build their plans not only around technology but also by focusing on both their human and digital workers. The age of ‘Automation Singularly’ is demonstrating right now that humans and robots can work together seamlessly to co-create the future.

Accelerate your migration to SAP Ariba Solutions with Nia Contracts Analysis

SAP is currently recommending its clients who are currently on Ariba on-premise for indirect procurement to move to SAP Ariba Solutions for strategic sourcing and supplier management. The move to SAP Ariba Solutions on-cloud is a re-implementation of all SAP Ariba components and involves migration of all contracts – both meta-data and actual contracts – which may exist in many forms (pdf, scanned images etc). This frees the clients from needing any SAP S4/HANA components.

While some clients are choosing to move from Ariba on-premise to SAP Ariba Solutions on cloud, there are others who are hesitant. Here are a few reasons why some clients choose not to move to an on-cloud solution:

On the other hand, the biggest advantage of using hybrid cloud encourages customers to move away from highly customized business processes to business standard quality processes, following the industry’s standard best practices.

Whether clients choose to migrate to SAP Ariba on-cloud or any other solution, most of them ask the same questions:

Given the current situation, it is inevitable that many customers will require contracts to be migrated. This is the right time for Nia Contracts Analysis to help clients migrate their contracts from Ariba to SAP Ariba solutions or other platforms.

Nia Contracts Analysis does not get in the way of the operational processes. It in fact accelerates the adoption in the following ways:

Nia Contracts Analysis utilizes advanced Machine Learning (ML) techniques to automate contracts digitization and extraction, enables operational risk analysis, checks compliance and helps in revenues generation. It acts as a single source of truth to answer any kind of contracts related information by using the following:

Let’s discuss more on this if you have any questions. Get in touch with us here.

Automation Singularity: How Human and Digital workforce convergence will transform the future

Automation and AI is no more a buzzword; it’s a reality. From our day-to-day tasks to workplace operations, intelligent machines are altering our way of living. This is just the beginning and economists see this disruption on a large scale and complexity that they have deemed it as ‘The Fourth Industrial Revolution’. (Klaus Shwab, WEF)^1

The world is moving towards a new vision, especially when it comes to re-imagining the workforce for enabling efficient business operations. Gartner predicts that 70 percent of organizations will leverage AI and Automation technologies to enable and assist employees in their task productivity^2. The disruption that RPA brought in back office operations is now cutting those boundaries. Thanks to Cognitive technologies, there is an intelligence explosion in functional front-end operations resulting in digital workers with human-centric capabilities driving critical business activities.

However, this progression of Automation and AI permeating across multiple boundaries in the workplace has its limitations. Intelligent Digital workers, if we may call them so, lack core human traits like Intuition. How many times have you applied a break on the car and avoided an accident just because of an intuition or how many times have you made a business decision based on intuition — probably more often than once and mostly it is combination of Intuition and Intelligence. This combination makes us the most advanced species and lack of this is where Digital workers are more vulnerable to mistakes, when it comes to making decisions.

The advancement in technology is surely removing these gaps and concepts like Singularity are gaining traction – Singularity hypothesis^3 suggests that technology advancement will cause an effect that will not only make machines exceed human intellect but will also see human intelligence grow in a non-biological way. While the singularity hypothesis may be a dystopian world, we are already in an era where the convergence of human and machine is paving the way towards Automation Singularity. In our view, Automation Singularity is not where AI-enabled digital workers will replace humans, but it is where digital workers and humans will collaborate to enhance each other’s capabilities, while slowly converging together to create an intelligent future workforce..

To emphasize the journey towards Automation Singularity and how it will work, let us take a flavor of it with an offbeat example:

For a retail company, ensuring that the right inventory is available at the right location at the right time is most critical. To ensure that, they have planners analyzing the inventory day in, day out to see to it that no high selling product goes out of stock and no customer walks out of the store unsatisfied.

In order to plan and send optimal inventory to the store, planners are using multiple data sources, planning tools and mathematical calculations. Furthermore, not all planners are optimized in their way of working; they might be using different tools and logics to fulfill the demand. It’s not a simple job, it is time-consuming and it needs a deep understanding of business, number crunching and decision making

Every item and store has parameters/attributes associated with it like what period of historical sales should be considered or if there is any minimum or maximum item required in a specific store or set of stores. Planners usually work around these parameters along with multiple reports (like out of stock etc.) and additional data sets (like sizing) before applying a model to predict what inventory needs to go to what store. When we think of the current automation landscape, this is probably not the best candidate but let us bring the journey towards Automation Singularity and re-imagine this with a team structure of Human and Digital workers.

In the journey towards Automation Singularity:

The above example depicts how human and digital convergence can be a win-win for the organization’s core operations. As RPA is becoming part and parcel of organizational processes, the shift towards convergence is the next logical step for organizations. The removal of boundaries and leveraging digital workers will not only enable cost-saving but will also generate revenue and increase customer experience.

References:

https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/

https://www.gartner.com/en/newsroom/press-releases/2019-01-24-gartner-predicts-70-percent-of-organizations-will-int

https://conceptsystemsinc.com/singularity/

How to harness the Value of Procurement Insights

Build your procurement analytics “Muscle”

Business is increasingly motivated by information. Data has become a significant driver of strategy and the main driver of performance reviews in many leading companies. By applying analytics to the millions of data points, these leaders are equipped to make better business decisions through intelligent augmentation and more quickly through intelligent automation. As a result, they’re increasing cost savings, decreasing operating costs, and reducing risk— something all businesses are keen to do.

However, exception to this are procurement organizations that really profit from analytics. In order to run the company effectively, most enterprises still struggle to implement more mature analytics capacities. Our first blog post on ‘Roadblocks towards Effective Procurement Analytics’ displays the challenges faced in various aspects. Many organizations are just starting the analytics journey— which typically begins with descriptive analytics and advances through diagnostic, predictive, and lastly cognitive analytics. Most of the problems facing the current procurement analytics solution fall under two primary classifications to improve actionable ideas; namely: organizational readiness, quality of data, team ability, and deficiencies in technology.

High-performing organizations recognize that procurement health plays an important role in business growth and profitability. Analytics becomes more advanced and harder to conduct with each step of the trip. But the value-added payoff is proportionate with the complexity rise.

Perspective towards Procurement Analytics

Big Value in Big Data

Big data improves spend visibility for procurement teams, helping them handle cost savings and vendor performance risk. This is made possible with the five separate procurement performance parameters that can deliver excellent value:

Once there is clarity in the strategy to deliver value, procurement functions require analytics solutions and support that provide three key features:

Additionally, Flexibility is an important trait of the solution, as is the potential of delivering procurement analytics as a service. Let’s understand these perspectives in detail.

Solution Perspective #1 – Strategic Capability

Need for Definition of Roles

CPOs and procurement teams are often unable to articulate a clear vision efficiently. Supply chain and procurement teams are becoming more engaged in decision-making at the executive level, moving from a mindset of cost reduction to being recognized as a strategic aspect of company activities.

In order to obviously target value drivers and produce insights that contribute to company goals, insights need to be collected and presented to individual roles or individuals. In this way, suitable roles in the procurement function can contribute to the goals of their organization. Therefore, separate dashboards for the CPO, Category Manager, Procurement Operations Manager, Project Manager, each showing perspective appropriate to their users, allow them to act according to their positions.

A procurement analytics solution needs subject matter expert to assist these persona-based insights, who can participate with organizations to allow them to identify the correct role-based questions and analytical insights to deliver value. Senior procurement specialists who have the analytical and domain knowledge to identify actionable ideas frequently provide this capacity.

Solution Perspective #2 – Analytical Capability

Upskill your Team

Supply chain and procurement departments need to reassess their skill sets to become more agile and data driven. Procurement teams are not inherently data scientists who can capture patterns intuitively, mine and discover them. This implies that CPOs need to promote a more data-centered cultural shift within the team, using real-time insights to accelerate better business decisions.

To extract value from procurement information, the significance of analytical talent is essential. This implies that CPOs require access to individuals who can set up information workflows and modeling methods to guarantee extensive insights, provided by analytics. Two main roles in the development of analytical capacity in the tactical acquisition are that of:

In order to consistently offer extra value, new data sources need to be pulled in and integrated with current information. Converging data sources with other organizational sources (e.g. operational / maintenance, HR) enables procurement tasks to obtain value from interactions and trends.

Solution Perspective #3 – Technological Capability

Transform with Actionable Insights

By harnessing the strength of big data, businesses have higher possibilities to predict market trends, spend, consumer behavior and real-time supply chain needs. By mixing historical data with customer insight, a CPO can move away from making reactive fiscal information choices and instead adopt an insight-led forward-looking strategy.

To prevent being overwhelmed by information overload, procurement teams turn to automated solutions as a manner to redirect their focus to unveiling company strategic insight.

Modular in Nature
Cloud-based solutions and current IT infrastructures need to be integrated seamlessly to drive company efficiency, agility, and visibility, and to regulate corporate spending and supplier management. The best solutions for modern and future analytics will be modular and over the top solutions, allowing organizations to incorporate quickly evolving best-of-breed technologies while keeping internal legacy/procurement systems untouched.

Role-Based
The value chain of procurement analytics possesses several features: cleaning, classification, evaluation, and visualization. To make the most of the holistic analytical method, each of these functionalities should be backed by their corresponding best-matched technology.
To meet the needs and objectives of stakeholders, the visualization or dashboarding of procurement analytics insights should be flexible.

Rise of AI & Machine Learning
There are majorly three evolving developments in technology that are likely to affect procurement organizations: The Internet of Things (IoT), predictive analytics, and social sourcing. Over the previous few years, smart technologies, particularly machine learning have made significant progress. As computers are learning to create increasingly better data-driven choices based on historical data patterns, the insights they provide are increasing in value.

Although this technology is not new to spend analytics, supply chain and procurement teams still need to make full use of the projected insights from data patterns, such as spend Insights converged with risk or operational insights. Machine learning technology has an important role to play in procurement analytics due to the sheer data quantity, disparate sources, and the need to recognize data patterns to derive value from converged data sets.

Overarching Needs of Procurement Analytics Solution

Flexibility – The ultimate requirement in Procurement Analytics

Besides the main components of the analytic solutions of the next generation described above, the platform’s thematic requirement is its flexibility to develop and mature with an organization. It must sufficiently be flexible enough to:

Human Insight – Mind trumps over Matter when it comes to Service

Even the latest enhancement of artificial intelligence cannot replace human understanding, at least some degree of which is critical to ensure the accuracy of base data, validating ideas and guiding the evolution of the analytics platform from its inception to maturity. Given the challenge of retaining analysts with distinctive abilities to deliver coherent insights into procurement, the next-generation procurement analytics solutions should also offer optional analytics as a service to guarantee that organizations continuously harness the complete value of the technology. This will be critical for long-term achievement, so one needs to be sure their enterprise is ready.

The Future

The next-generation procurement analytics solutions should also offer optional analytics as a service to guarantee that organizations continue to harness complete authority of the technology. Companies will want their procurement effectiveness to keep pace with their competitors with many processes fit for automation. This will be critical for long-term achievement, so it is important to ensure the enterprise is ready.

AssistEdge RPA 18.0: Empowering Implementation of Automation Blueprint for Tomorrow’s Enterprise

“Tomorrow’s Enterprise needs a boost in employee productivity while reducing operating costs”

Automate your business processes with AssistEdge RPA 18.0

The biggest worry for a typical Fortune 500 enterprise is around its burgeoning processes and application ecosystem. While the dawn of 2020 led to a splurge of applications for every need, enterprises are gradually trying to reduce their IT footprint by way of keeping a lean yet niche application ecosystem which in turn is expected to streamline their business processes too.

The key to a lean IT footprint (and of course, streamlined processes) is to kick things off by identifying processes/tasks that don’t need human intelligence but are still required in the value stream for its hygiene factor. These tasks are then piled up for automation. This ensures the employees dedicated to such workstream(s) are re-purposed for other high-value work yielding better ROI.

AssistEdge RPA 18.0, through its cutting-edge algorithms and cognitive behavioural patterns, can help enterprises magically identify tasks with potential for automation and can stitch together connected tasks (via guided human intervention) to form processes that can be queued up for automation cycles. A purely metric-driven platform, AssistEdge helps automate redundant tasks across the enterprise and continually monitors the automation run cycle for increased productivity and optimized efficiency.

The three core tenets of an enterprise

Employees, Business Processes and Applications are the three core tenets of an enterprise.

The operating cost of an enterprise looms large primarily due to its expanding digital application ecosystem and redundant processes.

Enterprises need to start filtering processes and tasks that do not add value (versus) tasks that provide incredible value. Employees should be re-purposed towards processes and tasks that can boost the ROI rather than spending time on hygiene factors.

 

Five relevant questions enterprises should start asking themselves:

Enterprises should try to dig deeper into business unit specific processes and its relative application touchpoints. The purpose is to measure the relative importance of each process, application (and its corresponding tasks) touchpoints to attain the goal of the Business Unit.

For instance, an HR Business Unit might have a suite of COTS product(s), home-grown applications for handling onboarding processes. However, there might be scenarios where its sub-contracted to external agencies to gather all the essential information and conduct background checks.

In cases like this, the overall usage aspect for the process must be done along with the TCO (Total Cost of Ownership). The unused assets would call for optimization either via automation or through retirement until further need.

The effort in such a case would span across multiple initiatives including — automation, process streamlining, application ecosystem rationalization, while also having a purview at aspects like governance, compliance, auditing, etc.

AssistEdge RPA 18.0: Stitching and Executing an Automation Blueprint

As automation is a journey to bellwether the fruits of cost savings and productivity maximization, AssistEdge helps to empower enterprises with an automation ecosystem to kick-start the journey with a discovery of automation potential for an enterprise; arrive at an automation blueprint; identify processes and tasks with a higher potential for automation — value and efforts (and compare various ways of actual task execution in current state); stitch the missing parts of a process/task (if any) and make it holistic; run automation cycles; manage/optimize and monitor automation across other RPA products too; achieve an integrated aspect of support ecosystem, whereby agents can focus more on maximizing Customer Satisfaction Index than on the customer’s query and indirectly resulting in a reduced AHT.

AssistEdge helps to connect the D-O-T-S where efforts are in vain to maintain everyday hygiene work routine by automating those workflows and thereby re-purposing the skilled workforce for other valuable workstreams.

 

Here’s a list of values packed in AssistEdge RPA 18.0 platform:

And the walnut is about to be cracked: In future, AssistEdge would go beyond and help enterprises to stitch a full-fledged TO-BE SOPs (based on AS-IS findings), create Business Requirement Documents, Software Rationalization, Compliance and so on.

The Benefits:

Interested in learning more about AssistEdge RPA 18.0? Write or talk to us now.

AssistEdge Engage: A Unified, Metric-Driven Experience Platform

“Towards an Intelligent and Insightful Call Center Experience”

Transforming Customer Service Experience

As every enterprise aims to maximize the potential of its after-sales customer service, one of the biggest challenges faced by a present-day call center is around swiftly identifying the support query of a customer and instantly service the query. This will not only reduce Average Handling Time but also boost Customer Advocacy Index.

Let’s take a look at a standard customer service scenario

A Corporate Customer: “Can you clarify when will I get the shipment of my 1000 iPhone X, 500 data dongles and, I need to de-activate an active SIM card, enable corporate roaming for 5 SIM cards?”

Agent Z (Telecom ABC): “Thank you, Sir. First, I would like to introduce myself. Next, I would like to know about you before taking your queries one by one”.

A Corporate Customer: “Well, let me introduce, I am Rick….”

Agent Z (Telecom ABC): “Thank you, Sir. I might have to run you through some security questions”.

Rick: “Well, let’s go through it. But this is the 10th time I am repeating it. I am tired of it”.

! Check: That’s a dip in Customer Advocacy. How does Telecom ABC know about it and address it?

Call center units of Fortune 500 enterprises need to quickly switch off existing applications and transmute to an intelligent/smart, unified informational platform.

 

Many enterprises are facing the same challenge where the process of customer acquisition is proactive, smooth while the after-sales service gets trivial in terms of the following:

Many back-offices have a suite of disjointed applications, and it varies from legacy applications to graphical user interfaces, excel based applications to desktop-based interfaces and so on. Thanks to a varied set of application ecosystems, enterprises are now finding it difficult to manage them operationally and leverage it to address the query of a customer.

In the above mentioned example, a call center agent must navigate around 10 applications to service the query. The agent might have to navigate to an application to know the customer or conduct a quick authentication check, then navigate to a different application to gather background details of past/current requests, navigate to a suite of applications to address the current query and finally, post a resolution, and log a comment with detailed notes for the query that was addressed by the agent.

In such cases, the average call handling time would typically exceed 30 minutes considering each application is backed by a different technological facet.

Hence, the whole experience needs to be reversed. Instead of enterprises focusing on application-driven experiences to address a customer query, the customer’s intent should drive the whole experience.

The experience needs to be transmuted across all service channels including e-mail, chat and service tickets (via call).

Addressing the Need: Unification of Information(al) Facets

While an ideal scenario calls for a UI Portfolio Analysis covering all applications used in a call center, it also needs significant investment costs as the results of portfolio analysis might demand an application consolidation (or) standardization (or) workflow streamlining initiatives that might incur huge investment.

What is the near-term solution then?

The key to address the problem statement is to probably start with the intent of the customer (caller). By placing the intent at the core, the set of informational facets surrounding the intent need to be unified/displayed to the user.

For instance, when a customer calls up, an automatic customer identification process along with the set of products/services owned by the customer can be instantaneously fetched/shown to the user. Hence, even before the customer support agent picks up the phone, he/she should know about the caller.

Post identification, all informational facets falling within the ambit of the support call needs to be quickly retrieved/shown in the same UI.

It will empower the user to spend more time conversing with the caller, build a strong relationship, cross-pollinate products/services rather than keeping the customer on hold and doing a data entry operation and struggling to address the need. Besides, this will also avoid a scenario where the user is required to navigate across multiple applications for various data points.

AssistEdge Engage: One-Up Above the Notch

The objective of AssistEdge Engage platform is to unify key information from multiple applications, enable automation of mundane/repetitive tasks, tap into relevant information with zero-clicks, while for a composite workflow involving a sequence of tasks, allow the user to navigate to the respective application to complete the detailed task flows (but) from within the AssistEdge Engage platform itself.

AssistEdge Engage platform enables enterprises with a set of tools to quickly ideate/build a WYSIWYG 360o dashboard with all the right set of information that is required for every agent’s day-in-a life journey, be it to view self-performance snapshot or getting to know key informational insights to service a customer.

Backed by a powerful services’ support from EdgeVerve Systems Ltd, enterprises can penetrate the AssistEdge Engage platform to quickly ideate/launch a customized call to action dashboard, and address automation needs depending on the specific business and user scenarios.

AssistEdge Engage also goes a step further to enable an agent to run processes queued for automation at the user’s end and to have complete control on the automation execution too.

Backed by a patented technical solution, UI platform with standardized aesthetics and widgets and a detailed think tank on the business needs, AssistEdge Engage platform represents the intersection of the three areas for a quantifiable customer support ecosystem solution. The platform targets to optimize some of the KPIs like Active Waiting Calls, Longest Call Hold, Call Abandonment, Average Handling Time of a Call and of course, Customer Satisfaction Index.

AssistEdge Engage stitches a seamless, unified experience by focusing on a user’s day in a life journey and empathy towards the customer and an agent’s conversation.

 

Key Benefits of AssistEdge Engage platform:

Click here to find out more about the AssistEdge Engage.