For CIOs riding today’s rising wave of robotic process automation (RPA), leading-edge adopters whose mature implementations have paid off can provide invaluable lessons about how to make the best of the technology and where its use can lead.

Telecom titan AT&T is one such enterprise, having began RPA trials in 2015 to reduce repetitive tasks for its service delivery group, which had a large volume of circuits to add at the time, as well as various services in play for provisioning networks, says Mark Austin, vice president of data science at AT&T.

“These things would come in large batches, and they would have Excel files and people were literally typing these things in individually into the systems because they weren’t set up for batch,” Austin says. “We heard about RPA at the time, and we started trying it and all of a sudden we were able to automate one process and then the next process and it kind of grew from there.”

With the technology in its early days, the first thing AT&T IT did was go to its compliance and security experts for guidance on governing RPA, which helped the team make its automation tools stable and secure. The next step was to win the battle for hearts and minds within the company by turning skeptics into believers that automation could make employees’ lives better. Initial efforts focused on addressing unpopular, monotonous tasks such as order entry.

The pilots helped demonstrate how automation could fit into daily operations and workflows.

Within a year, AT&T had implemented 350 automation bots. More than six years into its RPA journey, AT&T has implemented more than 3,000 automation bots. Austin says RPA has helped AT&T recognize hundreds of millions of dollars in annualized value, saved 16.9 million minutes of manual effort each year, and shown a 20x return on investment.

Taking RPA to the next level

Mark Austin, vice president of data science, AT&T

AT&T

With RPA ingrained in its business process DNA, AT&T opted to combine automation with data science and the chief data office because it believes the future is in smarter bots that leverage AI functionality, such as OCR or natural language processing (NLP), an emerging strategy often referred to as intelligent automation.

“Tying those things together is pretty powerful,” says Austin, who runs AT&T’s data science, AI, and automation group.

By way of example, Austin points to what he considers one of the company’s biggest RPA successes: a bot his group has created that uses OCR to scan vehicle registration documents and NLP to understand those documents and any necessary actions AT&T must take in support of more than 10,000 technician vehicles, one of the largest vehicle fleets in the US. If payments are required, the bot can also trigger the payment process.

Being able to create automation bots such as these was invaluable when the COVID-19 pandemic first hit, Austin says.

“There were a lot of customers that were calling and saying they wanted to move the charges from this org to that org in their company,” Austin says. “Someone might call up and say they wanted to move 5,000 lines. What we do now is we have them interface with [interactive voice response (IVR)]. The IVR detects what they want to do and then it triggers a bot to send them a secure form to fill out. They fill out the form, submit that back, and we run the bot to automate the process to get it going.”

The company has also rolled out bots to help customers avoid overage charges. One such bot monitors usage of AT&T’s integrated voice, video, messaging, and meeting services, more than 21,000 records per minute, looking for overage charges above a pre-set amount. If it encounters one, it automatically notifies the customer and the assigned AT&T sales rep.

Codifying RPA best practices

After the first year of pilots, with demand for RPA spreading rapidly through the business, AT&T created an automation center of excellence (COE) to accelerate implementation.

“When you’re the size of AT&T, and you’ve had so many mergers and so many systems, there’s just lots of manual processes,” Austin says, explaining why it was essential to create a COE that could focus on implementing automation throughout the organization.

The centralized automation team now boasts 20 full-time employees and some contractors as well. Austin notes that the real secret to successfully scaling automation is spreading RPA knowledge throughout the organization. The COE helps develop, deploy, manage, measure, and enable automation projects across AT&T. More importantly, it seeks to educate subject matter experts in automating their own tasks and processes.

“Pretty early on, we figured out that if you really want to scale, you’ve got to move to training others how to do it, teach them how to fish, so to speak,” Austin says. “Ninety-two percent of everything we do with the 3,000 bots is done outside of my team. If you’re not an IT person, it’s maybe 40 hours of training.”

The company has trained more than 2,000 citizen RPA developers who have built the lion’s share of AT&T’s 3,000 automation bots. To support them, the company has created a “Bot Marketplace” where citizen developers can “shop” for ready-to-use tools and support to get their automation solutions up and running. The marketplace stores and shares low-code and no-code automation solutions and tools. It now adds roughly 75 new blueprints of reusable automation components every month.

As RPA knowledge has spread, Austin says the lines of business have started forming their own automation teams, creating a hybrid model in which the COE provides tools and support, while front-line teams in the lines of business implement automation.

“They even have some new job titles popping up,” Austin says. “We’ve got a couple process automation managers and automation developers that we’re seeing out there. On our team, we’re continuing to move to automate the process, the platform, and then tie in the data science side.”

When it comes to lessons learned, Austin has some advice for others out there who may be starting their RPA journey. First, start small and get some wins. Second, don’t try to keep things centralized. While the center of excellence has been essential to AT&T’s RPA journey, just as important has been democratizing the effort to scale the proliferation of automation within the company. Finally, evangelization is important. AT&T has created an internal automation summit where groups can present their automation projects to the rest of the company, show off their successes, and help spark new ideas.

Artificial Intelligence, Robotic Process Automation

The “endless aisle” concept isn’t new, but it’s definitely the future for many supply chain operators. This retail strategy enables customers at a physical store to virtually browse and order any products that are either out of stock or not sold in-store and have them shipped to the store or their home. A fulfillment center or another nearby retail location that has the item in stock fills their order.

Increasingly, consumers expect an endless aisle experience. The pandemic has accelerated the transition to digital shopping and fundamentally changed consumers’ purchasing mindset. Today’s consumers regularly buy everything from daily groceries to new cars online or through an app, and they expect fast delivery — even within an hour, in many cases. If the retailer they go to first can’t meet that expectation, the consumer can open any number of apps and purchase the same product from another retailer, either brick-and-mortar or online, and pick it up or have it delivered when they want it.

So, the pressure is on to create the endless aisle. However, supporting this strategy will require most supply chain operators to significantly modernize their operations, including implementing solutions powered by artificial intelligence (AI) and machine learning (ML). It requires a mindset shift for operators — from thinking about technology not only as a tool to help lower supply chain costs, but also as the key to preventing missed sales opportunities, filling more orders faster, and increasing profitability.

Top challenges to building the endless aisle

1. Legacy limitations and lack of insight

Many companies, especially in the retail space, have already focused a lot of attention on creating the front-end experience for the endless aisle, giving their customers various digital options for ordering products from both in-store and online inventories. But it’s on the back end where most businesses fall short on delivering this experience: They can’t get the right products from here to there fast enough.

Several issues can hinder an organization’s ability to achieve a true endless aisle experience:

Outdated facilities, order management systems, and supply chain processesInflexible systems that prevent order fulfillment from multiple warehouse or retail locationsThe lack of true, real-time visibility into inventory status (i.e., what is available, where it is located now and where it needs to be)The inability to project where the next order will most likely originate so that inventory can be staged at the closest location to fill that order at the lowest cost

AI and ML play a leading role in helping supply chain operators overcome these limitations and build a next-generation supply chain. Following is a closer look at how these advanced technologies can enable the endless aisle by helping organizations to develop intelligent warehousing and engage confidently in more predictive decision-making.

2. Creating smarter, more flexible warehouses

Historically, supply chain operators have had dedicated warehouses and distribution centers that serve specific customers or regions. That strategy creates complexities for companies in forecasting the type and amount of inventory needed at those facilities. The result is that companies can’t flex much or at all.

No organization can create smarter warehouses or a more agile, flexible supply chain without updating their back-end technology first. Most will also need to rethink their entire order management process — including whether there’s a different way to handle it rather than with their inflexible, traditional enterprise resource planning (ERP) system, which lets them map specific products only to specific locations and offers very little visibility.

If these organizations have intelligent warehousing systems within their supply chain, they could request and supply any inventory they have to any customer or geography at any time. They could also confidently enable the endless aisle concept while at the same time reducing shipping costs and delays.

To create intelligent warehousing and deliver the endless aisle, many organizations will need to wrap new technologies like AI and ML around their legacy ERP system to improve and extend its capabilities or even completely replace certain functions. Integrating their ERP system and warehouse management system will also be a critical measure to ensure efficiency and timeliness when the business eventually starts shipping inventory from more places to serve customers in any location.

3. Enabling more predictive, proactive decision-making

Predictive modeling, using both AI and ML, lets an organization know how much inventory to stock, and where to place the goods based on historical and current patterns and behaviors. This insight is a must for any supply chain operator that wants to stay ahead of trends, prepare for future sales, and accelerate order-to-fulfillment time.

ML is also an excellent tool for minimizing costs and lost revenue due to obsolescence, excess inventory, and stockouts. And AI tells the organization where future demand is likely to originate and suggests where future inventory should be placed as it arrives. AI also helps supply chain operators avoid costs from excess shipping charges, long transit times, and stockouts and obsolescence.

These advanced technologies are also essential to providing real-time data insights that inform supply chain “digital twins” — logical views of the physical supply chain used for simulation modeling — that allow the business to understand, well in advance, what options it has to fulfill customer requirements when supply chain disruptions inevitably occur.

Many companies that have made significant progress on their journey toward building a next-generation supply chain are also using AI and ML to enhance their forecasting so they can address their “SKU problem.” They are better able to determine what inventory they need to have on hand instead of keeping two of everything on the shelf “just in case.” More organizations are also embracing AI and ML as force multipliers for their supply chain workforce; intelligent automation is helping them overcome current labor shortages while allowing their existing workers to be more productive.

There is no one-size-fits-all approach to modernizing the supply chain, creating intelligent warehousing, and laying the groundwork for the endless aisle. Each company’s journey will vary in scope and duration. Some organizations will choose to augment their existing infrastructure with more intelligent solutions, while others will go so far as to set up entirely new and separate supply chain operations. But the need for change is urgent, and those businesses that act now regardless of any further disruption or uncertainty that may be on the horizon are those that will emerge as tomorrow’s supply chain leaders.

Learn more about Protiviti’s Emerging Technology Solutions and Supply Chain Services.

Connect with the authors:

John Weber

Director – Supply Chain, Protiviti

Geoff Weathersby

Director – IoT and Emerging Technology, Protiviti

Artificial Intelligence, Machine Learning

With employee experience increasingly vital to business success, enterprises are rethinking how they deliver applications to business users to ensure greater productivity and efficiency. Global consulting and IT services company Infosys is one such company doing that at scale.

Rapid digital transformation at Infosys over recent years had resulted in a multitude of applications both homegrown and acquired. However, driving the adoption of these applications by its 270,000 employees had become a burdensome undertaking for the IT giant, which offers its business users access to over 200 homegrown applications alone.

Although the company had a rich bank of information to cater to almost every requirement of its employees, there was no unified channel through which to experience it. Employees had to visit each application separately — a cumbersome process that was sapping productivity.

“Infosys was struggling with many tedious and time consuming internal operational processes that were taking away precious time from employees,” says Narendra Sonawane, vice president and head of information systems at Infosys. “The internal systems and processes were not available on mobile devices, and neither were they intelligent enough to be able help employees anticipate and respond early and be more productive. For instance, a new joiner needed to access seven different web apps to become functional.”

To empower its knowledge workers with efficient access to assets they need to perform their day-to-day work, Infosys created an intelligent core platform that would in turn support several other platforms and applications.

“The company philosophically refers to this core platform as Live Enterprise, a platform with agility built into its DNA so that it can quickly sense changing business needs and continuously evolve in response,” Sonawane says. “The platform was to make Infosys a more agile organization that could respond to business needs impeccably backed by the availability of resources and knowledge workers.”

The platform has won Infosys a 2022 US CIO 100 award for IT innovation and leadership.

Intelligent automation at play

To create a platform for ensuring a unified experience across capabilities such as CRM, ERP, and custom-apps, Infosys leveraged extreme automation, taking a mobile- and cloud-first approach to ensure agility and speed at scale.

The hybrid-cloud strategy for Live Enterprise provided Infosys with the opportunity to move away from proprietary technology in favor of an open-source stack. In addition to Live Enterprise, the resulting Polycloud architecture also supports Infosys Meridian, Infosys Assessment Platform, Infosys NIA, and other key company platforms.

As part of its core, the platform leverages a knowledge graph to identify the right projects for the right talents and to recommend learning options to employees. The knowledge graph, which establishes relationships among objects, events, situations, and concepts, has more than 23 million nodes and 109 million relationships and provides employees recommendations such as adjacent skills to learn, skills popular in their unit, future skills of need, trainings to undertake, career path guidance, and best fitting projects.

In developing Live Enterprise, Infosys also used process mining across 20-plus processes to help streamline business operations. “Several tools were enabled to constantly monitor IT systems against established KPIs and identify root causes of business disruptions and eliminate them quickly,” says Sonawane.

The platform also makes extensive use of AI. For example, with Document AI and optical character recognition, key data points can be extracted from a statement of work to create a draft project plan. Also, with AI-enabled Resume Parser, the company can extract personally identifiable information (PII), skills, educational details, and professional summary from CVs. “With Resume Parser, nearly half a million resumes are parsed every month, thus helping with quick turnaround, saving about two million minutes,” says Sonawane.

A platform for platforms

Live Enterprise itself hosts a range of platforms aimed at enhancing user experience, including InfyMe, a mobile-first self-service digital platform that brings together information and transactions an employee needs together in one place, drawing from Infosys’s 450-plus applications.

Another platform, LEX, gives employees access to learning content from anywhere, from any device, at any time. It contains courses for enhancing technical and professional skills to help engineering students become industry ready.

“With collaboration platforms such as MS Teams and WebEx in place and with the availability of one-stop employee app and learning apps, collaboration, productivity and employee experience was restored and enhanced during the time of the pandemic and beyond,” says Sonawane.

Live Enterprise is equipped with a cloud proxy solution for comprehensive endpoint security. As part of that security strategy, endpoint systems have been integrated with Intune. Microsoft Endpoint Configuration Manager also ensures 95% auto remediation of known vulnerabilities.

The platform also integrates DevSecOps practices supported with tools such as app monitoring, resulting in significant test automation and automated code-quality analysis. Chatbots deployed across applications respond to 31,000 user queries per month, while ticket automation has expedited ticket resolution without manual intervention.

“The compounded outcome of these features was a 4X increase in automated deployments, 80% test automation, and 70% automation in code-quality analysis. These automations have led to a 50% improvement in deployment lead time, 48% defect reduction, and 15% ticket reduction due to better quality releases,” says Sonawane.

Live Enterprise also makes extensive use of robotic process automation (RPA).

“There were more than 500 RPAs implemented across functions in the last year, which resulted in 7,900 plus man hours of effort being saved every month. Also, 44 bots were deployed across applications, with which cumulatively nearly a million queries have been answered to date,” he says.

Employee-centric transformation

Live Enterprise has facilitated the transformation of a wide range of business operations at Infosys, from recruitment to code delivery, by integrating siloed applications and streamlining processes informed by data insights.

“Our digital transformation yielded cost reduction of $10 million. There was a 90% improvement in hardware and software readiness for better utilization and billing,” Sonawane says.

Moreover, the platform has helped Infosys keep its workforce fresh on the necessary skills to push the organization forward.

“Over 25,000 employees were digitally reskilled through the platform,” says Sonawane, adding that Live Enterprise has helped Infosys become a “smarter workplace” by “achieving major-league knowledge sharing,” AI-based continuous learning, and more streamlined business operations through process simplification and automation.

“With the reaffirmed confidence in the organization, we were able to bag some of the biggest deals during the peak of pandemic,” he says. “With many digital transformation journeys going astray after initial momentum, Infosys has been able to achieve set milestones along the way assuring client confidence, employee experience, and operational excellence.” Equipped with these advantages, Infosys is also taking this platform as a go-to-market solution to its clients under the brand name Orbit.

CIO 100, Employee Experience, Infosys

Three years ago, Johnson & Johnson (J&J) set out to apply intelligent automation (IA) to every aspect of its business. As the global COVID-19 pandemic was beginning to spread, the company, one of the world’s largest suppliers of pharmaceuticals, medical devices, and consumer packaged goods, needed to reduce costs, speed up tasks, and improve the accuracy of its core business operations.

Robotic process automation (RPA) was already gaining traction as organizations sought to apply software “robots” to automate rules-based business processes. But organizations like J&J wanted to take automation further. By combining RPA with machine learning (ML) and artificial intelligence (AI), they sought to automate more complex tasks. The opportunity led J&J’s Ajay Anand and Stephen Sorenson to place a very big bet in 2021.

“The one way to get attention in J&J from your very senior leaders is with the size of the impact that you could have,” says Anand, the pharmaceuticals’ vice president of global services strategy and transformation. “Generally, J&J prefers everything in billions.”

Anand and Sorenson, the company’s senior vice president of technology services, supply chain, data integration, and reliability engineering, proposed the creation of an enterprise-wide Intelligent Automation Council that they would chair. And they said they would deliver half a billion dollars of impact over the following three years. The team has already nearly hit that mark. Anand notes that, in a recent review, an executive committee member asked them to double that number based on the current pace.

Early intelligent automation roadblocks

Thanks to the work of the Intelligent Automation Council, J&J is now applying IA to everything from basic business processes, to chatbots that can help employees and customers, to algorithms that can monitor the company’s supply chain and help it adjust to changing conditions — like a doubling of the demand for Tylenol in the early days of the pandemic.

Stephen Sorenson, SVP of technology services, supply chain, data integration, and reliability engineering, Johnson & Johnson

Johnson & Johnson

But when Anand and Sorenson helped J&J take its first steps on its automation journey, they quickly ran into roadblocks.

“We were offshoring and using low-cost labor and trying to simplify our processes, but it was very difficult to scale and turnover was high,” Sorenson says. “We had this scenario where we were constantly retraining people and exception processes were killing us.”

It’s difficult to imagine just how many exceptions a process has until you actually execute on it or train people to do it, Sorenson explains. Exceptions can gum up even seemingly simple tasks, like sending confirmation forms. Typos, a new job title — any little thing could send those forms straight into the error queue, Sorenson says.

“We tried to automate them and what we realized was that people didn’t know their business processes as well as they thought they did,” he explains. “They knew their jobs and they could get work from point A to point Z, but if you tried to automate that, very few of the automations had an easy path to the end.”

It didn’t take long to realize that the traditional approach to mapping business processes — sitting down with employees, understanding how they go about their work, and capturing that — wasn’t going to give the automation team what they needed. To get a complete view of business processes, J&J brought in a task mining tool.

“We picked a handful of employees who were willing to partner with us in the early stages and we went through all of their privacy concerns and trained them, then we put this tool on their desktop to record the actual activity,” Anand explains. “When they were starting a specific process, they would hit record, and then we would capture it on this tool. We ended up creating the swim lane and all the documentation associated with it.”

Rather than interviewing the employees about the process up front, the team took the recordings and reviewed them with employees, asking whether there were any variations that weren’t captured that they wanted to share.

Adopting a digital-first mindset

J&J started using RPA for simple business process tasks such as moving documents, filling out spreadsheets, sending key messages, email integrations, and the like. It grew from there.

Ajay Anand, VP of global services strategy and transformation, Johnson & Johnson

Johnson & Johnson

“When we looked at all of our business processes, we were also very keen on ways in which we might be able to reimagine them with a digital-first lens,” says Anand, pointing to invoice-to-cash as a key example of the company’s new perspective. Like any company, when executing that process, J&J sometimes had errors or disputes with customers.

“By reimagining those processes with a digital-first mindset, we were able to look at things end-to-end and look for places where we are not only just able to automate, but also incorporate some intelligence,” he says. “Can we predict the customers with which we may have some disputes, and can we start taking some steps, proactively?”

By applying intelligent automation to invoice-to-cash, J&J was able to increase cash collection, reduce the error rate, and reduce the number of work hours and dollars spent to achieve the same results.

Anand explains that the core of J&J’s digital-first mindset around intelligent automation is 3E: experience, effectiveness, and efficiency. Does the automation change the experience of employees, customers, and suppliers? Does it make processes more effective and more efficient?

Success flowed from small wins

Sorenson says the team learned that the key to successful automation, as with many IT projects, was starting small, getting wins, and educating people about the possibilities.

“We had a saying, ‘Don’t try to get a home run.’ Just get on base, get the players on base, and we’ll move them around, start getting some hits. And then we’ll start getting some runs,” Sorenson says. “That really helped people think they didn’t have to worry about everything, they just needed to get these few steps automated and then we can see where we can take it from there.”

Sorenson notes that the small wins were able to help the automation team earn trust, but they also generated data that allowed them to show that the digital-first, machine-first mindset led to more accurate results.

“If you thought about it differently, you could actually automate the steps so that they were more accurate and build in detection so that you could find issues where things were failing historically, or even reconciliation steps that allowed us to confirm that things were working all along,” Sorenson says.

Pretty soon, as trust grew, the conversations were no longer about convincing stakeholders about the value of automation; they were about what else the team could do.

Anand notes that managing fears by showing examples to peers and partners was key.

“When people saw those examples, that really inspired them,” Anand says. “There was always this little fear that automation means people are going to lose their jobs. And they were able to see that it actually moved employees to more higher-order work and freed them up to do more innovation.”

Artificial Intelligence, Robotic Process Automation

Since the outset of the pandemic, organizations have been increasingly launching initiatives aimed at automating business processes, turning to technologies such as robotic process automation (RPA) in efforts to reduce costs, speed up tasks, and improve accuracy of core business operations.

Some leading organizations, however, are not stopping there. Seeking to push their automation agendas forward, they are embracing a move toward broader “intelligent automation” (IA), a strategy that weaves capabilities such as artificial intelligence (AI) and machine learning (ML) into standard RPA to enhance its functionality.

In addition to RPA, AI, and ML, intelligent automation strategies can also incorporate a mix of technologies such as natural language processing, chatbots, and others that complement each other, says Lakshmanan Chidambaram, president of Americas strategic verticals at global IT consulting firm Tech Mahindra.

“These technologies together allow us to automate business processes to a larger extent, when compared to simple RPA automations,” Chidambaram says.

As RPA adoption matures, it appears likely that IA will also gain traction within enterprises seeking to improve automation outcomes. An August 2022 report from Gartner projects global RPA software spending to reach $2.9 billion this year, up 20% from 2021. The worldwide RPA software market is expected to continue experiencing double-digit growth in 2023, according to the research firm.

Vendors are rapidly evolving their RPA offerings into broader automation platfors with embedded capabilities for hyperautomation — Gartner’s term for IA. As a starting point toward hyperautomation, organizations will increase their spending on RPA software because they still have many repetitive, manual work tasks. Automating these could free up employees’ time to focus on more strategic work, the firm says.

Here is a look at how leading organizations are bringing intelligence to their automation strategies — and advice for CIOs seeking to do the same.

Embracing intelligent automation

Technology services provider Insight Enterprises is one such company embracing IA, leveraging a variety of automation technologies to support its business processes.

“Our RPA team focuses on internal optimization of highly manual back-office processes and a few client-facing reporting activities,” says Sumana Nallapati, CIO. “The team’s two primary towers currently focus on operations and finance, with a goal to provide a thoughtful approach to tackling manual processes, reducing costs, increasing productivity, and smoothing out error-prone processes.”

The firm is using Automation Anywhere’s RPA platform, which combines the basic RPA functionality with the ML and analysis capabilities of automatic process discovery and process analytics, as well as cognitive technologies such as computer vision, natural language processing, and fuzzy logic.

The initial drivers for deploying RPA at Insight were to optimize operations and enhance critical back-office functions. “We sought to standardize the organization’s critical processes while working to scale and increase productivity across our entire business,” Nallapati says.

Insight understood early on, however, that automation was vital for growth in many business areas, Nallapati says, so it focused on two primary use cases when launching its RPA initiative in 2018: deal registration and sales order entry.

Since RPA was new to the company, Insight began slowly in those two areas to work through the challenges of implementing an RPA strategy and infrastructure, Nallapati says. “Once the teams could show success in standing up an environment and automating more minor activities, we looked to scale the focus of automation,” she says. “Now, we’re working to transform how we maximize intelligent automation’s tactical, strategic, and competitive advantage.”

Insight is expanding its use of automation and IA globally. “As part of our integrated technology roadmap, we have a focused stream on transforming our organization into an ‘automation first’ culture,” Nallapati says. “The goal for this stream is to go from being focused solely on less valuable, individual automation to creating a center for enablement of strategically focused intelligent automation.”

For an organization to maximize the benefits of IA, the effort should be tied to a larger strategic initiative and be strongly linked to process transformation, Nallapati says.

“In the short term, our team is focused on a formal approach that identifies areas of our business that are heavy in manual, resource-intensive processes,” Nallapati says. “Ultimately, our vision is to run a self-service, scalable automation model that allows for teammate-driven development and maintenance of bots/automation.”

Growing the IA strategy

Cloud software provider Freshworks is also deploying IA, as part of a multi-year digital transformation effort, with a focus so far mainly in the human resources department.

For example, the company is using the AI capabilities of its own IT service management software (ITSM) combined with the RPA capabilities of Automation Anywhere’s platform to enhance the onboarding process and give new employees access to the tools they need on their first day of work.

With IA, the company has streamlined areas such as invoice handling, employee onboarding and offboarding, and customer order processing, says Prasad Ramakrishnan, Freshworks CIO.

“This automation has allowed our IT team members to get time back by skipping mundane repetitive tasks, and focus on what they were hired to do,” Ramakrishnan says. “Using AI and RPA technology, all of our employees can find the information they need in a streamlined and efficient manner.”

The need to increase automation of business processes “increased drastically” when all of Freshworks’ employees began working remotely in 2020, Ramakrishnan says.

“When it came to onboarding, different stakeholders needed to come together to achieve a successful day-one experience for our new hires,” he says. “With all stakeholders moving to a remote-first working model, [IA] enabled us to create role sets and streamline approval processes, thus reducing the long cycle times. Once the team started, there was no limit to our imagination on things we could identify and optimize using IA.”

Freshworks has set up task forces across the business to continuously identify new opportunities to implement IA. “We see an increased need to intelligently automate the various tasks that employees perform,” Ramakrisnan says.

Kickstarting a smooth IA journey

Adopting and expanding intelligent automation can be challenging because it involves a number of components and impacts multiple processes. Experts suggest several practices to ensure a smooth move.

One is to understand that automation is a journey, not a destination. “It’s never ‘good enough’; there are always more opportunities to uncover and tackle,” Nallapati says.

A good way to find and exploit those opportunities is to create an “automation first” culture, Nallapati says. “Much of the automation journey is rooted in change management,” she says. “Working with leaders to understand what automation can do, walking teams through the change, and teaching stakeholders how to work with and manage a ‘digital worker’ is critical to the program’s success.”

Automation isn’t a one-and-done endeavor, Nallapati says. “Much like the traditional employee, digital workers need coaching, occasional support, and the best tools and processes to make their work easier and more productive,” she says.

Another good practice is to set bold goals then work iteratively in small, decisive steps toward the big goal, Nallapati says. “Do not try to boil the ocean with automation; you must stay methodical and focused on the outcome you are trying to achieve,” she says. “Show value quickly. Break down a larger goal into smaller pieces that allow you to realize value more rapidly and tie it to a specific, measurable outcome and return on the investment.”

IT-business alignment and collaboration are key

Companies also need to optimize business processes to increase the effectiveness of automation, Nallapati says. “Working together in a partnership, the business unit and the automation teams can leverage their expertise to refine the best approach and way forward to optimize the efficiency of the bot/automation,” she says.

Technology leaders should make sure to get business leaders and users involved in the IA process, Ramakrishnan says. “Educate them about the possibilities and collaborate with them in joint problem-solving sessions,” he says.

One recent example at Freshworks was a joint effort between the automation team and the billing department. “With a large number of customers and a large number of invoices to process every day, any small savings through automation goes a long way in increasing productivity, accuracy, and improving employee and end customer satisfaction,” Ramakrishnan says.

Similar to the type of hackathons that are common in IT organizations today, Ramakrishnan says, “we partnered with the business to have a business-side hackathon/ideathon. We educated the key users from the billing team on the possibilities of automation, and then they were encouraged to come back with ideas on automation.”

IT and billing then jointly reviewed the suggestions for feasibility, prioritized them, and came up with an implementation plan, Ramakrishnan says.

It’s also smart to avoid jumping into IA, and automation in general, without a good reason.

“Never build a solution looking for a problem,” Ramakrishnan says. “Be sure to identify the problem first, and confirm if there’s a way to solve it already before creating more havoc for employees with a new application to deploy, implement, and learn,” he says.

Artificial Intelligence, Business Process Management, Machine Learning, Robotic Process Automation

Digital transformation has reached a critical juncture within the railway industry. As rail operators embrace new trends in intelligence, sustainability and service, aging telecommunications architecture of more than 20 years ago is unable to meet current and future requirements.

The existing GSM-R train-to-ground communication system can no longer provide sufficient capacity for modern railway stations. Instead, operators are turning to the Future Railway Mobile Communication System (FRMCS) with broad bandwidth and a new decoupling architecture based on LTE and the latest technology to improve performance.

“Digital transformation is a long journey and rail operators need to ‘dream big’. Currently, the most pressing challenge for rail operators is to identify both the pain points and benefits and address them with cost-effective digital solutions—to ‘act small,’” said Xiang Xi, Vice President, Aviation & Rail BU, Huawei Technologies Co., Ltd.

As an industry-leading ICT solution provider, Huawei can help with these transformation efforts in three aspects: by reshaping connectivity, reconstructing the platform, and enabling intelligence. At the upcoming InnoTrans exhibition, Huawei will outline the framework for digitalisation of the railway business and best practices for innovation and showcase smart railway related Solution.

Reshape connectivity

With digitalisation, demand for new services such as train automation, smart maintenance, and others is growing. Current narrowband network has insufficient bandwidth to meet complex network requirements to support these services. Innovative solutions such as FRMCS, Wi-Fi 6 and all-optical networks will enable a more digitalised rail infrastructure.

Huawei FRMCS solution enables wireless communications systems for high throughput, low latency, and reliable connectivity. This solution can support new railway services such as multimedia dispatching communications, trackside IoT, and predictive maintenance.

Reliability in connectivity also requires zero interruptions for real-time service. Wi-Fi 6 Train-to-Ground communication, Railway All-Optical Network using native hard pipeline (NHP) and Urban Rail Cloud-Optical Network based on OTN technology with ultra-low latency, enables no-disruption connectivity to operators’ assets, services, operations and maintenance.

Reconstruct platform

Traditional urban rail lines and service systems, including ATS, AFC, and PIS, are relatively independent. The silo construction of IT resources leads to high construction costs, low resource utilization, and isolation of multiple information systems. The unified construction mode of the urban rail cloud changes these issues.

Huawei’s Urban Rail Cloud Platform solution empowers rail operators to maximise IT resources and improve security and efficiency of operations.

Enable intelligence

As rail operations increase in complexity, rail operators need to gain a better situational awareness in order to manage their assets better and expedite incident response time. The challenges becomes even greater as operators look for ways to improve low-carbon development and other parts of their operations to address sustainability objectives.

Huawei’s Urban Rail Intelligent Operation Center (IOC) solution connects digital environments with physical spaces for improved and integrated situational awareness leading to better decision-making and an efficient and collaborative command.

Next-generation communications technologies will play a critical role in addressing the unique challenges of the rail industry. Cutting-edge solutions such as FRMCS, Wi-Fi 6, and all-optical networks are addressing those challenges to empower the rail industry to modernize for improved safety and reliability of rail lines, while opening new opportunities for innovation.

Register now to find out Huawei’s global experience in the rail industry at the 9th Huawei Global Rail Summit on 22nd September at the Grand Hyatt Berlin in Germany.

Digital Transformation

A modern, agile IT infrastructure has become the critical enabler for success, allowing organizations to unlock the potential of new technologies such as AI, analytics, and automation. Yet modernization journeys are often bumpy; IT leaders must overcome barriers such as resistance to change, management complexity, high costs, and talent shortages.

Those successful in their modernization endeavors can expect significant business gains. In Ampol’s case, the transport fuels provider enjoyed enhanced operational efficiency, business agility, and maximized service uptimes.

A vision for transformation, hampered by legacy

Ampol had a clear goal: intelligent operations for improved service reliability, increased agility, and reduced cost. To achieve this, Ampol created a vision centered on “uplifting and modernizing existing cloud environment and practices,” according to Lindsay Hoare, Ampol’s Head of Technology.

This meant having enterprise-wide visibility and environment transparency for real-time updates, modernizing its environment management capabilities with cloud-based and cloud-ready tools, building the right capabilities and skillsets for the cloud, redesigning the current infrastructure into a cloud-first one, and leveraging automation for enhanced operations.  

While Ampol had most workloads in the cloud, it is still highly dependent on its data center. This meant added complexity to infrastructure networking and management, which in turn drove up maintenance and management costs. The need for human intervention across the environment further increased the risk of error and resultant downtime. Its ambition to enable automation across the entire enterprise, at that point in time, felt unattainable as it lacked the technical expertise and capabilities to do so.

Realizing its ambitions with the right partner

Ampol knew it was not able to modernize its enterprise and bridge the ambition gap alone. It then turned to Accenture. “We needed a partner with a cloud mindset, one that could cover the technological breadth at which Ampol operates,” said Hoare. “Hence why we turned to Accenture, with whom we’ve built a strong partnership that has spanned over a decade.”

Accenture has been helping Ampol in its digital transformation journey across many aspects of its IT operations and as such has a deep understanding of Ampol’s automation ambitions.

“We brought to the table our AIOps capability that leverages automation, analytics, and AI for intelligent operations. Through our ongoing work with Ampol, we were able to accelerate cloud adoption alongside automation implementation, reducing implementation and deployment time,” said Duncan Eadie, Accenture’s Managing Director of Cloud, Infra, and Engineering for AAPAC.

Reaping business benefits through intelligent operations

Through its collaboration with Accenture, Ampol was able to realize its vision for intelligent operations which then translates to business benefits.

Visualization and monitoring

Ampol can now quickly pinpoint incidents to reduce the time to resolve. Recently, a device failure impacted Ampol’s retail network and service stations, but a map-based visualization of the network allowed engineers to identify the device and switch over to the secondary within the hour: an 85% improvement in downtime reduction.

Self-healing capabilities

Intelligent operations not only detect failures but also attempt to resolve them independently and create incidents for human intervention only when basic resolution is unsuccessful. As a result, Ampol’s network incidents have been reduced by 40% while business-impacting retail incidents are down by half.

Automating mundane tasks

Automation now regularly takes care of mundane and routine tasks such as patching, updates, virtual machine builds, and software installs. This frees up employees’ time that is otherwise spent on maintenance, enabling them to innovate and add real business value through working on more strategic assignments and business growth.

Future-proofing

As Ampol focuses on the global energy transition, it is investing in new energy solutions in a highly dynamic environment. A cloud-first infrastructure removes complexity, increases the levels of abstraction, and offers greater leverage of platform services, enabling agility and responsiveness. The right architecture and security zoning facilitate critical business-led experimentation and innovation to ensure Ampol continues to place at the front of the pack.

As IT infrastructure becomes a critical enabler across industries, organizations are compelled to embrace modernization. While significant roadblocks exist, a clear vision and the right partner can help overcome challenges and unlock the potential of the cloud, AI and analytics, and automation, to be a true game-changer.

“This is a long journey,” says Hoare, “we’ve been at it for years now… It needs drive and tenacity. But when you get there, you’ll be in a great place.”

Learn more about getting started with a modern infrastructure here.

Cloud Management, Digital Transformation

The data, information, and analytics economy runs on well-curated, structured data. No matter your industry?having good curated data and content is critical. It’s increasingly important as more data and content are generated. Intelligent tools to sift through content are more robust and at the same time, more “needy.” That means modern technology platforms, systems, and even content consumers require well-structured data and content to perform well. As most artificial intelligence (AI) practitioners state?”nothing starts without good data.”