Despite its potential for relieving pressure on the workforce, automation in the workplace is often seen negatively, as a cause of job losses or a growing skills gap. Yet, done well, automation can provide critical support that frees people up to focus on more impactful work — and can lead to happier, more motivated and productive employees.

At a time when burnout has become a major issue — with Future Forum data showing 40% of workers globally experience it — automation can also help employees by simplifying work and saving them time.

So, how can IT leaders help reduce the cognitive load and automate common tasks such as creating sales decks using Salesforce data or raising purchase order requests? One way is through the digital headquarters (HQ).

Making automation a reality with the digital HQ

The key to effective workplace automation is keeping it simple and empowering end users. If a system is too complicated to set-up it becomes a burden on the tech team and is not scalable.

With most businesses still navigating the shift to hybrid, the one office that every employee comes into each day is the digital HQ — a single digital space where workflows between your people, systems, partners and customers. In transforming how teams work, communicate and collaborate, the digital HQ sits at the heart of automation initiatives, with free-flowing conversations built around specific projects or teams taking place in channels.

Heading into a tough economic climate, it’s more important than ever for organisations to keep teams motivated and engaged, so they are able to perform and deliver results quickly. Automation within the digital HQ is a major step towards this — empowering employees to liberate their time from manual tasks and helping them breeze through multiple requests that might otherwise perforate their day.   

Offering a no-code solution that everyone can use, Slack’s Workflow Builder hands control back to the team, boosting efficiency in the process. Just ask telecommunications giant Verizon, who used Slack’s digital HQ — alongside automations — to improve output and employee experience.


Personalised problem-solving

Verizon’s Planning and Engineering team were the first to identify the potential of Slack’s Workflow Builder to bring solutions to, not just their own department, but the whole company. This is because Workflow Builder is an easy-to-use tool that requires no coding experience, with over 400,000 people around the world having built workflows so far — 80% of whom are in non-IT roles. It was therefore easy for Verizon to see its potential to give teams autonomy in solving their own pain points.

Verizon launched the Citizen Builder Programme, encouraging staff to leverage automated workflows to create solutions. This level of personalised problem-solving meant issues were resolved with far greater precision than if another team had been tasked with the job. With one impactful example being how Verizon’s Wireline Network Operations team used Slack’s Workflow Builder to coordinate field technicians for last-mile service calls. Automating parts of this process not only reduced the load on the team but also led to more accurate customer appointment times — all without adding any additional pressure to Verizon’s IT team.

With an expansive telecommunications operation, and a reputation for excellent customer service, Verizon faces a huge amount of admin every day. But with Slack’s Workflow Builder, they have ensured it doesn’t take its toll on workforce motivation, and satisfaction isn’t reserved exclusively for its customers.

For more information on how Slack’s Digital HQ can help your business click here.

Application Performance Management, Change Management, Networking, Remote Work

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


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

By Milan Shetti, CEO Rocket Software

If we’ve learned anything over the last few years facing a global pandemic, stalled supply chains, rising inflation, and sinking economies, it’s that change is the new normal in today’s markets.

In response, organizations have invested heavily in digital transformation. IDC forecasts that global spending on digital transformation will reach $2.8 trillion by 2025 — more than double what was spent in 2020.

As organizations amp up their digital transformation initiatives, which are critical for survival in today’s business climate, they must also consider how to modernize and migrate sensitive data and how it is managed and governed. C-suite leaders must have confidence in the data they have on hand to fuel business processes, deliver customer and employee experiences, and improve their operational analytics and insights.

Given the volume of data most organizations have, they need agile technologies that can provide a vast array of services to streamline content management and compliance, leverage automation to simplify data governance, and identify and optimize all of their company’s valuable data.

Ultimately, when evaluating automation technologies, your business needs software that will enable teams to move quickly and easily identify high-priority, sensitive data and to identify and remove redundant, obsolete, and trivial content (ROT) to remain compliant with complex regulatory demands. 

With organizations grappling with how best to streamline data management and compliance, there are four key considerations in doing it effectively.

1. Identification

Businesses need fast and accurate analysis of all their content. Organizations with content-rich processes should look for flexible and scalable automated solutions that can deliver a broad classification of content — reducing the chances of important information slipping through the cracks and allowing teams to quickly identify more types of sensitive data.

2. Action

To support compliance with a governance-first approach to content-rich process automation, businesses must be vigilant when it comes to managing the retention and privacy of documents. This is achievable by automating as much governance decision-making and manual processes as possible. Utilizing automation technology to automatically govern content-rich processes and eliminate mundane, tedious, and repetitive tasks, teams can eradicate many opportunities for human error and free up employees and resources to increase efficiency. 

3. Access

One of the biggest threats to a company’s sensitive data is accessibility. Easily accessible, less secure data is vulnerable to hackers and malware, which, if breached, can have catastrophic consequences for an organization. Teams must look for automation software that can set time and geography parameters around employee accessibility, deny access should a network be breached, and allow redaction across the entire enterprise. 

4. Lifecycle

To successfully manage the entire content lifecycle, businesses must have the ability to place content on legal hold, manage the over-retention of documents, and enable encryption at rest. Rocket Software’s Mobius Content Services platform  delivers this by not only allowing report management teams to encrypt and quickly put content on legal hold, but also providing storage reduction to avoid over-retention and ROT. Mobius can also easily integrate into many shared drives and collaborative platforms to streamline ROT and site auditing.  

With investment growing in digital transformation, organizations must stay competitive — and, for many, data is becoming the critical differentiator. By implementing the right tools now for data automation governance, organizations will be better positioned to maximize it and stay compliant. To learn more about Rocket’s content management solutions, visit the product page.

Data Management

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

By Milan Shetti, CEO Rocket Software

In today’s volatile markets, agile and adaptable business operations have become a necessity to keep up with constantly evolving customer and industry demands. To remain resilient to change and deliver innovative experiences and offerings fast, organizations have introduced DevOps testing into their infrastructures. DevOps environments give development teams the flexibility and structure needed to drive productivity and implement early and often “shift left” testing to ensure application optimization.

While DevOps testing ecosystems require cloud technology, DevOps modernization software has allowed businesses that utilize mainframe infrastructure to successfully implement DevOps testing processes into their multi-code environments. However, introducing DevOps to mainframe infrastructure can be nearly impossible for companies that do not adequately standardize and automate testing processes before implementation.

The problem with unstructured manual testing processes

The benefits of DevOps testing revolve around increased speed and flexibility. In order to reach the full potential of these benefits and ensure a successful DevOps adoption, organizations should work to unify testing operations and eliminate any threats to productivity long before implementation begins. 

While it is important to equip developers with tools they are comfortable with, businesses working within multi-code environments must shift away from processes that require multiple vendors or lack integration. Operations that force development teams to jump from software to software to perform tasks create a complicated testing environment that can slow processes and create a disconnect between teams and departments. 

Manual testing also creates barriers to optimizing DevOps. While manual processes will still play an essential role in Quality Assurance (QA) testing, the potential for human error and the tedious, time-consuming tasks that come with manual testing make it impossible to create the speed and accuracy required for DevOps testing. And, if your testing is done using a specific developer script, you’re likely not capturing key metrics to improve your software development lifecycle, such as how the code changes the database. DevOps and true “shift left” testing environments demand structure and flexibility throughout operations that can only be achieved through standardization and automation.

Elevating testing with standardized and automated processes

To ensure successful DevOps implementation, businesses must start with an entire audit of their current operations and value stream — which is all the activities required to turn a customer request or need into a product or service. In doing so, teams can determine which software or processes create disconnects or slow operations and where automation can be integrated to enhance speed and accuracy.

Opting for vendors that offer user-friendly, code-agnostic and highly comprehensive DevOps platforms enable teams to create a central point of visibility, reporting and collaboration for processes. This standardized approach eliminates silos between teams, minimizes onboarding and allows teams a common means to rapidly commit, document and test changes to code and applications. Integrating systems and operations into a unified DevOps environment allows development and QA teams to track and schedule testing times between departments effortlessly.

From there, development teams should look to automate as many testing processes as possible. Leveraging automation in testing allows teams to implement automatic, continuous testing that eliminates human error and ensures all bugs are squashed before production. Teams can create multiple test environments and processes like unit testing, integration testing and regression testing. Standardization allows multi-code testing to be done with greater predictability and by different people — reducing the reliance on a few gifted developers and creating a more stable production phase.

Development teams can also create knowledge bases of automated testing templates to quickly pull and use or adjust to fit new and evolving testing needs. And, by leveraging automated DevOps tools, teams can configure software with controls that automatically test and vet any new coding introduced into the environment to quickly identify and address any bugs in the code or changes to the application.

The future of the mainframe and DevOps testing

A recent Rocket survey of over 500 U.S. IT professional businesses showed that the mainframe is here to stay, with more than half of the companies (56%) stating the mainframe still makes up the majority of its IT infrastructure due to its security and reliability. Thanks to highly integrative and intuitive DevOps modernization software, multi-code environments can reap the benefits of increased productivity and enhanced innovation through continuous “shift left” testing methods.

Just as the mainframe continues to modernize, so too does DevOps modernization software. Future DevOps testing software looks to leverage Artificial Intelligence (AI) and Machine Learning (ML) technology to further strengthen and streamline testing environments. Organizations like Rocket Software are working to develop technologies that use AI to study testing processes to help teams identify where testing is required and what needs to be tested more accurately. ML software will be used to track relationships in testing environments to identify patterns that help teams predict future testing needs and take a more proactive approach.

As agility and speed become more important in today’s digital market, the ability of teams working within multi-code environments to implement DevOps testing into operations will become a greater necessity. Businesses that standardize processes and utilize automation throughout testing will set their teams up for success. By creating structured and flexible DevOps testing environments, teams will enhance innovation and increase speed to market to help their business pull ahead and stay ahead of the competition.

To learn more about Rocket Software’s DevOps tools and solutions, visit the Rocket DevOps product page.

Software Development

NetSuite is adding a host of new features and applications to its cloud-based NetSuite ERP suite, in an effort to enhance its automation capabilities and compete with midmarket rivals such as Epicor, IFS, Microsoft Dynamics, Infor, and Zoho in multiple domains including HR, supply chain, banking, finance, and sales.

The new capabilities were announced Wednesday at the company’s annual SuiteWorld conference in Las Vegas.

NetSuite and other ERP software providers have been focusing on automation as CIOs and other C-suite leaders look to navigate challenges such as labor shortages and supply chain issues caused by geopolitical crises and the pandemic, said R Wang, principal analyst at Constellation Research.

Enterprises want to cut down the number of employee hours spent on certain processes, and so are asking for automation, Wang said, adding that automation frees up resources to focus on strategic areas and can cut down errors in repetitive tasks.

NetSuite differentiates itself from its corporate parent, Oracle, by focusing on customers in the midmarket segment that may not be big or complex enough to require, or may not be ready to implement, many separate applications.    

“The addition of the new tools feeds directly into the expansion of the suite strategy as most NetSuite customers prefer to keep all software solutions inside NetSuite,” Wang said.

NetSuite uses the SaaS model, with customers paying a subscription fee based on number of users.  NetSuite ERP is a suite of applications that work together, reside on a common database, and are designed to automate core enterprise business processes. The ERP suite is  available on Oracle Cloud Infrastructure; companies need various tools and connectors to run the system on infrastructure from other cloud providers.

New tools automate routine HR tasks

In an effort to automate some human resources tasks such as wage calculations and attendance tracking, NetSuite has added a workforce management software suite to its HR application, SuitePeople. The software includes tools that can help schedule shifts, calculate labor and operational metrics, and record employee engagement.

A new visual scheduling tool, according to NetSuite, will allow companies to eliminate the use of standalone scheduling applications or spreadsheets.  It also enables teams to use a combination of forecasts, employee schedule templates, labor costs, and labor deployment models to build a staffing plan.

In order to automatically track attendance, the management suite of tools comes with a SuitePeople Time Clock that gives employees various capabilities, available via mobile devices, to record time and attendance. Time Clock comes with options for photo capture and biometric fingerprint verification that eliminate the risk of employees logging in and out of work on behalf of other employees, NetSuite said.

In addition, SuitePeople’s now can also automate wage calculations, as data from other tools such as Time Clock and scheduler can flow directly into the payroll tool for processing.

The SuitePeople Workforce Management suite is presently available in the US, Canada, Australia and New Zealand.  Information on timing of the rollout in other geographies was not immediately available.

Mobile app enhances warehouse operations

NetSuite has also introduced a new mobile application, NetSuite Ship Central, as part of its warehouse management system (WMS) software suite. WMS software is typically architected to optimize warehouse tasks such as manpower allocation and inventory control.

The mobile application, which can also be installed on a kiosk device, is expected to minimize shipping costs and transit times, NetSuite said.

NetSuite Ship Central is being sold now in the US and will be available worldwide in November, the company said.

Automation speeds accounts payable

Looking to help enterprises process bills and pay vendors faster from within NetSuite’s ERP platform, the company has introduced a new tool, Accounts Payable (AP) Automation, inside its SuiteBanking application.

The tool can capture vendor bills using machine learning-based object detection and optical character recognition (OCR), the company said, adding that it comes with a bill-matching and approvals feature to avoid overpayment or fraudulent or duplicate payments.

Automated bill matching ensures vendor bills are two- or three-way matched with the associated purchase orders to ensure details such as unit pricing, quantity, and totals are accurate, NetSuite said.

The new tool comes with a vendor payment automation feature in partnership with HSBC Bank, the company added. This partnership allows enterprises to access payment options such as checks or virtual credit cards, among others.

AP Automation also offers payment reconciliation, designed to improve the accuracy of accounting with the help of a rules engine that matches and reconciles virtual credit card charges while flagging discrepancies for further review by accounting staff. 

“Having core account aspects within the ERP gives the user the opportunity to incorporate all the other data flows within the organization into their accounts payable planning and accounts payable strategy. Using one window to see the entire spend workflow and ERP data saves time and reduces errors,” said Kevin Permenter, research director at IDC.

The software is currently available in the US, and Oracle is offering free implementation, no charges for bill scanning, plus a 50% discount on the subscription fee to its first 1,000 customers for AP Automation.

CPQ tool streamlines sales process

NetSuite also introduced a new add-on sales application, NetSuite Configure, Price and Quote (CPQ). The software, according to the company, will help enterprise sales teams configure, price and quote complex products accurately and reliably, directly from with the NetSuite ERP.

While the guided selling feature is designed to allow enterprises to find the exact products and services needed from thousands of SKUs by providing an e-commerce-like catalog experience and filtering tools, the configurator feature allows enterprises to save time spent on reworking orders by applying customizable rules that ensure every configuration, across product and service features, is correct.

The add-on tool, which is priced separately from the core NetSuite ERP system, is currently being offered only in the North America region and includes features such as a proposal generator, bill of materials calculator and e-commerce integration.

ERP Systems

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

Understanding the student lifecycle isn’t easy. With more higher education institutions attempting to embrace digital learning, there is a growing need for visibility throughout the student journey. By gathering data across every student, faculty and alumni touchpoint, institutions can optimise each stage of the admission and onboarding process. 

The appetite for insights among higher education institutions is such that the global big data analytics in education market is expected to grow from a value of $18.02 billion in 2022 to reach $36.12 billion by 2027.

Unfortunately, many institutions remain reliant on legacy solutions with siloed data, which introduces lots of ad hoc manual tasks that slow the process of attracting and nurturing prospects. 

Automation will play a key role in enabling providers to implement a data-first approach – and better support prospects and recruitment faculties to ensure the student lifecycle runs as smoothly as possible. 

The problem with legacy tools in higher education 

Most higher education institutions today rely on legacy middleware they are familiar with, but that fails to offer visibility over the student lifecycle. These solutions make it difficult to access student records, accommodation, financial data and third party or cloud platforms. 

Data is also isolated and siloed in on-premises solutions, making it difficult to generate insights and optimise the student experience. 

In order to generate concrete insights, data needs to be collected at the edge of the network and across campus to feed into a centralised analytics solution. There it can be processed to develop insights into how to improve operations over the long-term.

How Boomi addresses these challenges 

The answer for these organisations is to undergo digital transformation by migrating datasets to the cloud. Ultimately, this will generate concrete insights to enhance the experience for students and faculties. 

While this transition is already underway, with 54.3% of higher education institutions reporting they were cloud-based in 2021, there are many that still need to migrate to the cloud. 

Integration platform as a service (iPaaS) solutions like the Boomi AtomSphere Platform can help enable this transition by unifying application data to ensure insights are accessible throughout the environment via a single cloud platform. 

Essentially, Boomi offers organisations the ability to connect data from a variety of sources, helping with the process of migrating data to the cloud and connecting data sources wherever they may be.  

Connecting data allows decision makers to generate the insights needed to make faster admission decisions – such as streamlining the onboarding experience for prospects and recruitment faculties. 

The easy way to move to the cloud 

Boomi has emerged as a key provider in enabling higher education institutions to move to the cloud. Boomi supports Amazon Web Services (AWS) data migration and application modernisation to link data, systems, applications, processes, and people together as part of a cohesive ecosystem. 

This approach enables higher education institutions to leverage a growing number of services through AWS, simplify data pipelines and improve transparency for decision makers. 

Ultimately, by providing decision makers with access to high quality data, institutions will not only increase the quality of the student experience but become more cost efficient by maximising retention.

To find out more about Boomi click here.

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Education and Training Software, Education Industry

To help meet the needs of enterprises that are looking to navigate uncertain economic conditions while complying with new data regulations, ServiceNow has released the next iteration of its Now workflow automation platform, dubbed Tokyo, with new features that focus on easing supply chain complexities and optimizing asset and human resource (HR) management.

Tokyo’s release comes just months after the company released the previous version of the Now platform, named San Diego, that focused on personalization and automation of work experiences.

The new release, according to the company, is geared more toward chief financial officers and chief operating officers who are looking for a return on their IT investment.

Simplifying the supply base

The release comes with a new feature, dubbed Supplier Lifecycle Management (SLM), that can read names and other data of suppliers from emails and spreadsheets and move them into a new window inside the Now platform.

Automatically moving these supplier contacts and information, according to ServiceNow, helps enterprises reduce operating cost and allows the supply chain team to focus on creating a more resilient supplier base.

The SLM also offers a supplier-facing interface that can be used to launch queries for the enterprise.

For users in the enterprise itself, Tokyo includes a new tool, dubbed Enterprise Asset Management (EAM), designed to automatically track and help manage the full lifecycle of physical business assets, from planning to retirement, for industries such as healthcare, financial services, retail, manufacturing, and the public sector.

The EAM tool can enhance companies’ strategic planning capabilies as it allows easy visibility into the enterprise asset estate, the company said, adding that EAM can alo help to optimize inventory levels in order to generate maximum efficiency from existing assets.

Automating HR issue resolution

ServiceNow’s Tokyo release also offers features that focus on simplifying human resource management.

One new feature, Issue Auto Resolution for Human Resources (ITSM), is designed to help HR teams manage issues brought up by company staff by applying natural language understanding to analyze employee requests and deliver content through the same channels used by employee. These channels can be Microsoft Teams, SMS or email, the company said, adding that ITSM understands and routes any request to a specific HR representatives in case of pressing matters.

Another feature, dubbed Manager Hub, is focused on employee retention. The feature, which can be accessed via the Employee Center (desktop or mobile), provides a single window for managers within an enterprise to map employee milestones and review them.

The Manager Hub can be used by an enterprise to deliver personalized training to all managers within an enterprise, Service Now said.

Security and Sustainability

The Tokyo iteration of Now also comes with added sustainability-planning and security features.

The new release offers a feature named Vault, designed to secure business-critical ServiceNow applications by using controls such as flexible key management and data anonymization. It also allows enterprises to export their ServiceNow system and application logs at scale and in near real-time, the company said.

Another tool in Tokyo’s arsenal is the Admin Center, which allows system administrators to discover, install and configure ServiceNow tools or features through a self-service interface. Admin Center, according to the company, can take advantage of new Adoption Blueprint features, which in turn can recommend applications to administrators based on criteria such as instance maturity and application entitlements.

In order to help enterprises plan and manage their sustainability goals, the Now platform’s Tokyo release comes with an environmental, social, and governance (ESG) management tool.

The tool, according to the company, can track performance towards goals, collect and validate data for audits and create reports that aligns with major ESG reporting frameworks.

Human Resources, Supply Chain Management Software

Without a doubt, one of the key drivers of the Fourth Industrial Revolution is Robotic Process Automation (RPA). Organizations worldwide have increasingly leveraged RPA technology and are now adopting multi-vendor strategies for a multitude of enterprise automation tools, beyond RPA. From recent conversations with the VOCAL Council, I estimate that almost 40% of the nearly 40,000 customers using RPA are deploying a multi-vendor strategy.

RPA tools have evolved from simple bots that automate single, micro tasks or activities to more complex end-to-end, unattended solutions that can automate entire processes and deliver unprecedented benefits. However, automation management is the automation that automation vendors forgot.

At the heart of RPA is the orchestrator. While orchestrators have improved and some have moved to become cloud-based, several have not been rearchitected. As a result, the design debt built over the years (with a focus on selling bots vs. managing them) has limited orchestrators to basic operational bot metrics.

The high cost of orchestrators prevents organizations from fully capitalizing on the current limited benefits, and integration challenges make it even harder to incorporate multi-vendor orchestrators into the tech stack. The swivel chair approach (input data from one system to another) that automation is supposed to eliminate is back, with precious resources swiveling to manage multiple automation vendors, extract metrics only to populate Excel and PowerPoints, meticulously and manually maintain bot schedules and reschedules, and painfully pray that bot failure may be detected early.

Lack of support, missing automation management capabilities, and inadequate/missing self-recovery are just a few of the serious challenges in managing automations.

Automation vs. orchestration: Same pod, different peas?

The easiest way to understand the automation vs. orchestration divide is in terms of “one” vs. “many”. Automation is often about automating a single repetitive task or activity within a process to run on its own and with minimal (or no) human intervention. For example, you could set up an RPA bot to automatically create IT service tickets, another to launch a web server, and a third to change a line of code in JSON files. Each of these bots would continually execute its specific task until you stop it. Repetition is the name of the game. Intelligence and logic – not so much.

Orchestration, on the other hand, is about automating multiple tasks to work seamlessly together as part of a larger workflow. The effort could involve multiple environments, devices, services, and people. And that’s why it is much more complex than a single bot for a simple task.

This complexity makes it vital to understand the many steps involved during orchestration and how these steps intersect. It also requires seamless coordination to prevent bottlenecks and ensure that enterprises can successfully derive the expected benefits from the effort, whether it’s process optimization, error-free output, accelerated innovation, improved employee experiences, or faster time-to-market.

Process automation is not as simple as automating every task within that process, there is also a layer of orchestration and task interdependency where most judgement calls and other complexities live. Automating and managing that layer is the toughest part of the journey.”

Max Ioffe, Global Intelligent Automation Leader, WESCO Distribution

Optimization vs. orchestration

There are network optimization tools that came following the shortcomings with network orchestrators. Cloud optimization followed a similar path.  One can integrate orchestration and scheduling capability to create dynamic schedules, create task queues, initiate workflows, and monitor and track executions to self-recovering and predictive maintenance. You can also incorporate triggering events and advanced logic to automate multiple tasks within a process and ensure timely, efficient and consistent execution.

Over time, these tools can confer benefits like reduced IT costs, higher-quality output, and limited process downtime and going a bit further even provide self-sustainability. Automation promises to remove the mundane, yet automation management is laden with the manual and mundane.

With automation optimization, your precious resources don’t have to worry about mundane activities like managing, scheduling, or restarting bots. The automation optimization tool steps in and boosts your license, utilization and infrastructure efficiency while driving higher employee engagement. 

“For automation to succeed, having & sustaining optimal utilization, is key. Automation optimization tools that integrate with multiple vendors and offer a single pane of glass to manage automations will lead to a better Total Cost of Ownership and perhaps even increased RPA adoption.”

Akash Choudhary, Director Enterprise Architecture, ServiceNow

Sound without music

Benefits notwithstanding, just orchestration – which is almost always part of the overall RPA solution package – has some limitations. For one, many of these solutions are so complex and prohibitively expensive that companies with small IT budgets and teams struggle just to purchase the solution, much less leverage its benefits.

Costs do vary, depending on the instances you want to deploy and can be a TCO barrier[1]. Support is often an afterthought, which impacts the quality and timeliness of automation maintenance, change requests and incident management. The longer the response time to classify, handle and resolve incidents, the larger the amount of disruption and potentially losses for customers. Qualified support people who understand the architecture of orchestrators are limited and hence often customers face more annoying sound than music, with limited or no recommendations on how to apply appropriate solutions.

Some orchestrators don’t monitor all incidents or provide a holistic view of incidents, which is key for incident monitoring, investigations, and root cause analyses. While it is possible to automate these aspects, many orchestrators don’t provide “automation management” capabilities. Further, they don’t provide a single, centralized “incident box” that can help organizations keep track of all their automation initiatives. Under this scenario, determining the lifecycle metrics of an automation program is almost impossible.

Many customers seek a self-recovery/self-healing capability and sometimes want the orchestrator to simply restart a service that stops or becomes unresponsive. At other times, they need higher-level orchestrated workflows to automatically start up a new virtual machine (VM), check that its services start correctly, update the DNS put it into the load balancer, and even shift services to a different data center. In today’s automation environment, if a Virtual Desktop Interface (VDI) fails, it cannot automatically resolve the underlying problem, much less reboot on its own in the fastest possible time. Additionally, due to this limitation, the enterprise cannot monitor CPU or memory usage or self-clone machines to dynamically scale up capabilities to match peak requirements or scale down (“shutdown mode”) when requirements are low.

Organizations must custom-build an inventory management system for the orchestrator, which can be an arduous and resource-intensive effort. An automation management solution with a built-in or integrated inventory management tool increases the visibility into the automation ecosystem. It provides opportunities to streamline systems maintenance, improve automation efficiency, enhance output quality (lower errors), and reduce downtime.

“You need to reduce costs, streamline, and improve visibility of your RPA bots to scale your automation program. An automation optimization tool to monitor and control bots and derive tangible business, value and automation lifecycle metrics is must.”

Amol Rajamane, Global Digital Automation Leader, DuPont

The rise of automation optimization solutions

In an ideal world, automation workloads – whatever their heritage – should be able to move seamlessly between and be shared among, automation providers, wherever the optimal combination of performance, functionality cost, security, resilience etc. can be found – while avoiding the dreaded “vendor lock-in.” What if your automation can meet your demand without demanding more from you? Automation hopping, at the risk of introducing yet another term in a busy highway of terminology in automation, is not a far reality. Consumption pricing will pave the highway.

Automation optimization is a category that brings together end-to-end automation orchestration and management capabilities to cut cost, increase performance, and measure business impact – across multiple automation tools.

Several niche solutions tend to focus just on the control aspect of automation management, ie addressing portions of the orchestration limitations described above. Automation executives are often looking for a holistic, cost-efficient solution. CFOs are seeking a holistic, cost-efficient solution – an integrated, all-around athlete vs. buying multiple brands of shoes to potentially become an athlete.

An end-to-end automation optimization solution strings together the automation lifecycle: idea generation and classification, document gathering (discovery), building the bot, controlling the bots with dynamic scheduling, license/bots/utilization optimization with dynamic caching and AI-powered bot failure prediction, and deriving key value metrics. This approach provides the necessary operational (bot) metrics, value (KPI) metrics, and lifecycle (idea to value) metrics to determine and communicate the automation value to your organization.

Automation optimization solutions address the shortcomings of existing orchestrators but also offer the ultimate outcome challenging and shaping the automation industry today: improving adoption and scale.

A few solutions have emerged in recent years that deliver all these advantages, address the limitations of older platforms, and allow organizations to easily add or remove bots to match their evolving automation needs.

“The cost of bot license, infrastructure and automation management creates an significant dent in the technology budget. To drive customer centricity, automation technology vendors are better served helping their customers improve their existing license, infrastructure, scheduling utilization rather than pushing more bots. The rise of automation optimization solutions is evidence that there is a major gap in the market.”

Ankit Thakkar, Automation & Finance Digitization Leader, Thermofisher

The future of orchestration and optimization solutions

As automation and orchestration technologies develop, many more cutting-edge solutions will emerge for different enterprise use cases. The best automation optimization solutions will allow organizations to capture all the benefits of large-scale automation at the lowest possible TCO. A solution, for example, like Turbotic (disclosure: I sit on Turbotic’s Board) speeds up bot development by allocating development tasks and approval steps appropriately and systematically. It also clearly shows the entire automation opportunity, all the way from ideation to implementation in a single flow. Further, the solution automatically creates an automation business case, boosts bottom-up pipeline generation, and supports value tracking and monitoring to bring greater transparency into the automation ecosystem.

The best automation optimization solutions also provide visually compelling dashboards and real-time metrics to measure these benefits and assess the true value of the automation initiative across the enterprise. In addition, they will effortlessly integrate with existing systems to minimize disruptions and deliver all the advantages promised by enterprise-wide automation.

Advanced orchestrator solutions enable organizations to implement hyperautomation with support for cutting-edge technologies like AI, ML, and cognitive NLP. These “predictive” orchestrators leverage data and learning to better orchestrate all automation using improved predictions and decision support. These capabilities enable enterprises to optimize their automation licenses and resources in order to optimize costs, throughput, compliance, and ROI, and eliminate the chances of costly SLA breaches.

Today’s companies are operating in a highly challenging business landscape with the great resignation, quiet quitting, lack of qualified automation talent, and more. Using orchestration alone, with its current limitations, is not enough.

Orchestrate or optimize? I say both.

[1] Orchestration is often an upfront cost and impacts ROI until one has a critical mass of process automations and bots in production.

Robotic Process Automation