This article was co-authored by Massimo Pezzini, Head of Research, Future of the Enterprise at Workato.

The uncertain economic environment and rapidly evolving technology landscape have pressured organizations to improve efficiency, innovate, and adapt. Citizen developers have emerged as an approach to bridge the gap between technical expertise and domain knowledge. Those self-taught deeply understand their industry’s needs and pain points, enabling them to create tailored applications that address specific challenges. Citizen developers are a vital resource for organizations looking to streamline processes, increase efficiency, and reduce costs, whilst supporting business innovation and agile change. One can trace the emergence of citizen developers back to end-user computing in the late 20th century. However, the rise of the internet and subsequent development of low-code and no-code platforms, increasingly assisted by AI technologies, are democratizing software creation.

Who is a citizen developer?

Citizen developers are non-technical employees who take the initiative to develop software applications or automate business processes without relying on IT departments or specialized software engineers, if not for training and support. They use “low-code” tools and technologies to address personal or workgroup-level enterprise development and automation challenges that are key for them but not critical enough at the enterprise level to deserve the attention of central IT.

These individuals possess a deep understanding of their industry’s needs and pain points, enabling them to identify opportunities for development and automation that can drive efficiency and productivity within their organizations. Citizen developers are business technologists (that is, they have “enough” IT skills) who can and want to address development and automation tasks alone, by using low-cost or free cloud services. Characterized by the low cost of entry, short learning curves, minimal training requirements, intuitive (often conversational) UIs, and AI-assisted high productivity, these tools empower users to excel.

Their goal is to:

Improve efficiency by automating their own or their workgroup’s formal or informal processes

Respond quickly to opportunities and threats (business agility)

Introduce new creative ways of doing their job (innovation)

Collect and aggregate the data they need to steer their activity or make operational decisions (insights)

Interact with the enterprise systems in a simplified, optimized, and personalized way (experience)

The key point is that they are enabled to achieve these goals at the micro-organizational level (department, workgroup, or even individual), whereas the central IT department focuses on the macro, organization-wide issues.

The implications

Enterprise automation technology providers increasingly offer tools tailored to citizen developers, making them easily and widely accessible through low-cost or free cloud services. While citizen developers can improve micro-efficiency, business agility, and innovation, they also present risks, such as security, compliance, privacy, data quality, duplication of efforts and technologies, and mounting technical debt.

In general, citizen developers may possess different technical expertise than professional developers, therefore the applications and automations they implement might be sub-optimally designed. Furthermore, these applications may need to be designed with scalability, a skill that citizen developers don’t necessarily master, thus potentially creating challenges as the organization grows.

Challenges can also arise in ongoing maintenance and support as the lifecycle of citizen-developed applications may not be rigorously managed. Consequently, outdated applications may remain in place for a long time, potentially harming the organization in the long run.

Additionally, the lack of coordination among numerous citizen developers can lead to fragmented processes, uneven development, and duplication of efforts across the organization.

Lastly, documentation by citizen developers can help others understand, maintain, and modify the applications they create, but usually documenting what they develop is not a priority for them.

Technical debt, that is the accumulation of technical issues, poorly developed and hard-to-maintain code, can arise when organizations do not put in place proper development governance processes. While citizen development has a good deal of merits, the risk of building technical debt looms large on it. Ensuring the appropriate governance guardrails are in place and involving the relevant people is essential for success.

“Strong governance is the cornerstone of successful citizen development, as it ensures that organizations can scale their digital initiatives enterprise-wide while maintaining control and compliance. By meticulously evaluating and choosing the most fitting combination of low/no-code platforms such as apps, automation, analytics, and BPM, organizations can unlock the power of citizen development while reinforcing the crucial role governance plays in fueling sustainable, secure growth.”

Neeraj Mathur, Director of Intelligent Automation, VMware

Citizen development is not a one-size-fits-all solution for building applications and automations. It plays a complementary role in optimizing certain business processes, but professional developers remain crucial for sustainable automation initiatives. Striking the right balance between citizen developers and professional developers is critical to successful enterprise automation.

Organizations that create a foundation of proper governance in citizen development and see the importance of finding the right mix of citizen and professional developers see tremendous success. Professional developers should handle complex, enterprise-wide business processes, while citizen development should focus on locally enhancing customer experience, building customer trust, and driving revenue. By addressing these considerations, organizations can maximize the benefits of citizen development while mitigating potential risks.

Measuring the return on investment (ROI) for citizen development and automation initiatives can be challenging, as they often involve a mix of formal and informal efforts with benefits that are difficult to quantify. Today’s market lacks specific, agreed, and measurable metrics for assessing citizen development success, aside from a number of enabled developers or new applications delivered, which primarily serve to fuel the hype. However, the value of the citizen developer approach can, in many cases, be assessed in terms of faster time to value and improved business agility.

What should CIOs do?  

Chief information officers (CIOs) should recognize that citizen development and automation will happen, whether they want it or not. It is much more effective to view it as an opportunity rather than a threat. Citizen developers can complement professional specialists by addressing the mass of medium or low complexity, local requirements that the few specialists CIOs have in-house will never find the time to tackle. As millennials and Gen Z join the workforce, the number of business technologists will naturally increase. Moreover, as these tools are increasingly enhanced by generative AI technologies, such as ChatGPT and Bard, their learning curve will further shorten, and their use will become even more widely democratized.

“Collaboration between Citizen Developers and IT can bring about the perceived benefits by minimizing the risk of creating flawed automation and receiving support from IT expertise. Although Citizen Developers can address their challenges, they could overlook the interconnectedness of systems and processes, resulting in unintended consequences. IT can provide a broader perspective on enterprise architecture, ensuring all stakeholders comprehensively understand the business.”

Karl Mosgofian, CIO, Gainsight

Therefore, CIOs should collaborate with business leaders to identify potential benefits and opportunities of citizen development and automation and incorporate its support into their enterprise automation strategy. Moreover, they should proactively empower business technologists by providing the right tools, training, mentoring, and support services through a citizen developer facilitation team.

This team should select tools that meet business technologists’ needs while allowing for monitoring, management, and governance. Implementing a marketplace for reusing the developed assets across the citizen developer community, a life cycle management process and proper governance guardrails will help minimize duplication of efforts. Establishing a citizen developer community of practice can foster knowledge sharing and feedback collection.

Periodic reviews of citizen development and automation approaches within the broader enterprise automation strategy will ensure continued effectiveness. CIOs and facilitation teams should aim to establish themselves as trusted providers of a “citizen automation platform as a service,” encompassing technology and enablement services. This position will encourage business technologists to actively utilize the tools provided by Centers of Excellence (COEs) instead of looking at the shiniest new tools that pop up on the Internet.

In today’s race to digital, the demand for customized software solutions will grow, making citizen development more compelling. CIOs and facilitation teams who can strike the right balance between citizen and professional developers and implement a governance framework that addresses risks and challenges will drive enhanced competitive positioning in the digital age. With proper planning, coordination, and oversight, citizen development can drive innovation, agility, and efficiency across the organization.

Developer, No Code and Low Code

At this time of dynamic business and market changes, uncertainty, and quickly evolving consumption models for IT infrastructure, every IT executive understands the benefits and necessity of network agility. Agile networks can respond quickly to changes in the market, customer demands, employee requirements, and technology advances. Yet most businesses haven’t tapped into two major capabilities of truly agile distributed networks: scale and automation.

Obstacles to network automation

Looking back, we can see a few reasons for this. Early efforts to automate were limited to initiatives like cost cutting without taking into account how automating certain functions would impact IT staff or customers. Some initiatives were project-based, often varying by person or department, making it difficult to enable the right business outcomes.

There were also limitations in technology. Artificial intelligence and machine learning (AI/ML) were not advanced enough to accurately capture, organize, and interpret the data to make accurate recommendations.

So, it’s no surprise that only a small number of companies have taken advantage of automation. According to a Gartner study, 41% of companies have automated less than 10% of their network activities (Figure 1).

Figure 1. Percentage of Network Activities Automated

Source: Gartner 2021 Data Networking Poll

Cisco Meraki

The cloud, visibility, data, and AI/ML make simplified automation a reality

A lot has changed in the past few years. Today, automating networks is not only possible and desirable, but also highly trusted by IT. Because most automation initiatives are now focused on the outcomes of network users, including IT staff, employees, and customers, automation is orchestrated across an entire network to help reduce and streamline workflows.

By leveraging one common networking architecture and multiple cloud-based devices, users can view and manage a network from end-to-end through any number of interfaces (e.g., web UI, APIs, mobile). It also provides an easier way to implement and manage automation tools throughout a network. Automation can now be used for broad operational and business use cases, such as automating device configurations, security, and firmware updates, alerting, and other routine maintenance.

Machine learning has also greatly advanced over the past several years. We’ve seen how it can gather and organize telemetry data collected from all parts of a company’s network. In addition, AI solutions from networking industry partners can analyze and interpret this data to provide detailed sights into network metrics, including situations like the health of a device, and also recommend better ways to optimize a network (e.g., turn this application off while not in use to increase network optimization by X percent).

IT staff looking to further reduce workloads have tapped APIs for custom automation. Utilizing data and actionable insights from APIs, businesses can also analyze how and why their network is being used (e.g., how many customers are using a banking drive-through window or use Wi-Fi at a chain of restaurants). By applying this information to a wide variety of professional areas, business and operations analysts can make key decisions and identify opportunities. They can also gain insights from sources outside of traditional networking technologies by gathering valuable information from Internet of Things (IoT) devices like smart cameras, kiosks, gas pumps, and physical security systems.

Benefitting from scale and automation

Networks built for scale and automation rapidly increase time-to-value for businesses and their customers while also enabling a more agile business. What used to take hours can now be accomplished in minutes, giving IT staff more time to focus on strategic initiatives like enhanced customer satisfaction and advancing digital transformation.

Don’t want to build automated apps from scratch using APIs? Cisco Meraki has hundreds to choose from across a range of industries as well as use cases available on the Cisco Meraki Developer Hub. You can also learn more about network scale and automation at the Cisco Meraki website.


Oracle on Wednesday said it is adding new AI and automation capabilities to its Fusion Supply Chain Management (SCM) and Fusion Human Capital Management (HCM) suites to help enterprises increase efficiency across divisions.

The updates to the SCM suite, which have been made generally available, include an AI-based planning tool, an enhanced quote-to-cash process for Fusion applications, and new rebate management capabilities.

The AI-based planning tool, according to the company, is expected to aid enterprises in improving the accuracy of lead time assumptions across their supply chain through machine learning.

“The new feature can improve planning efficiency and results by identifying lead time trends, anomalies, and their potential impact with prioritized actions and resolution suggestions,” the company said in a statement.

The new planning tool has been added to the planning advisor inside Oracle Supply Chain Planning, an application that is part of the company’s SCM suite.

Since last year, Oracle has been gradually adding new capabilities, including some AI and automation features, to its supply chain suite.

Last year in February, Oracle introduced machine learning-based shipping time forecasting along with real-time analytics for the supply chain.

In October, the company released a new supply chain management application customized for healthcare companies, dubbed Oracle SCM for Healthcare. The application offers capabilities such as a supply chain planning service and added capabilities for the Oracle Procurement application to help drive down supply chain costs.

As part of the new updates to its SCM suite, Oracle is offering an enhanced quote-to-cash process across all Fusion applications.

Quote-to-cash (QTC) process is a part of the sales cycle in an enterprise that constitutes end-to-end delivery of a product or service. Typical components of the process include sales, account management, order fulfillment, billing, and accounts receivables functions.

The enhanced QTC process, according to the company, will help enterprises centralize subscription orchestration, comply with accounting requirements, improve order management, reduce costs while decreasing time to market, and improve customer experience.

“The integrated solution connecting Subscription Management (CX), Configure Price and Quote (CX), Order Management (SCM), and Financials (ERP),enables customers to quote, capture, and fulfill orders (of mixed physical goods, subscriptions, and services) more efficiently and recognize revenue accordingly,” the company said in a statement.

Oracle has added new rebate management capabilities inside the SCM suite to aid enterprises to optimize discounts or promotional campaigns targeted toward their customers.

The new capabilities automate the rebate management process, the company said, adding that automation includes rebate calculation, financial settlement, and closing customer claims.

This helps reduce administration costs and improves customer experience, the company said, adding that the new capabilities are being offered as part of Oracle’s Channel Revenue Management application under the SCM suite.

New updates to Oracle Fusion HCM suite

In addition to the new updates to the SCM suite, Oracle has announced a new application, dubbed Oracle Grow, in an effort to add more AI capabilities to its HCM suite.

“Oracle Grow is designed to enhance the employee experience and improve performance by engaging with individuals to discover new growth opportunities and empowering managers to align upskilling and reskilling with business priorities,” Chris Leone, executive vice president of applications development at Oracle Cloud HCM, said in a statement.

To be offered as part of Oracle Me that was released in April last year under the HCM suite, the AI-powered application delivers personalized insights and intelligent guidance across all interactions from Oracle Learning, Oracle Dynamic Skills, and Oracle Talent Management in one interface, the company said.

In order to provide insights, the AI engine inside the application is trained on an enterprise’s data in a controlled environment before being rolled out completely, according to Natalia Rachelson, group vice president of outbound product management for Fusion Applications at Oracle.

“We would start small, and we would see what kind of results the AI is recommending and then we work with customers to fine-tune the models to adjust to their specific sort of datasets,” Rachelson said, adding that the data normalization is done by Oracle data scientists.

This means that for new Oracle Fusion customers, rolling out Oracle Grow’s AI capabilities would require a longer time, the group vice president said.

Oracle Grow’s AI capabilities include growth experience for employees, suggestions for career paths within their enterprise, personalized development suggestions, and managerial skilling.

The AI engine in Oracle Grow, according to the company, can suggest development opportunities that workers need to adapt to changes in their role, discover new growth options, and achieve their career aspirations. “By unifying people data from across Oracle Cloud HCM, Oracle Grow provides personalized guidance on the next steps employees should take based on their responsibilities, career interests, desired skills, individual learning styles, and changes in the business,” the company said in a statement.

ERP Systems, Oracle, Supply Chain Management Software

Automation has long been the lifeblood of IT work. In pockets throughout the organization, the call to automate processes has always been a key driver of IT agendas, whether it be to overhaul targeted processes within the sales or marketing function, or within IT itself.

But the rise of digital capabilities such as AI and robotic process automation, along with the drive to digitize operations across the enterprise via digital transformation, has pushed some IT organizations’ automation agendas into overdrive, heralding a new era of “hyperautomation,” in which all facets of business operations, from mundane tasks, to product development, to manufacturing, are viewed as internetworked processes ripe for automation gains.

Toyota Motor North America, which has embraced the cloud for more than 20 years, is one such enterprise pursuing hyperautomation. Within the past three to four years, the company has been relying more heavily on Amazon Web Services — as well as many independent automation tools — to achieve a ramped-up automation agenda that sees nearly all business operations now automated on the cloud, says Brian Kursar, chief technology and data officer and group vice president of digital technology at Toyota Motor North America.

“The big change now is this hyperautomation,” says Kursar, who oversees about 2,500 IT professionals at Toyota. “I tell my teams that automation is our salvation.”

The mantra seems to be working. Toyota claims hyperautomation has saved the company $10 million thus far, at a rate of roughly $5 million annually. One single team saved 6 weeks of labor through the efforts, representing a savings of $250,000, according to the company, which has at least 100,000 automated scripts running on schedulers on its ETL platforms across thousands of databases alone. 

“It’s definitely a companywide initiative,” Kursar says.

Driving efficiencies with hyperautomation

Gartner coined the term “hyperautomation” nearly a decade ago to refer to automation across all business units.

While more than 80% of businesses employ some automation, relatively few enterprises have achieved hyperautomation, and fewer than five enterprises — Ericsson and Johnson & Johnson among them — globally generate savings from their hyperautomation programs exceeding $100 million per year, says analyst Frances Karamouzis, distinguished vice president at Gartner.

Hyperautomation “is a disciplined approach for doing three things: rapidly identifying, vetting, and automating as [many processes] as possible,” Karamouzis says. “It could be a business process or an IT process. And to do that, people use a whole myriad of technologies. They use AI. They use RPA. They use iPaas. They use low code.”

Toyota isn’t quite there yet, even as the company’s ramped-up automation agenda spans business, development, and manufacturing processes. CTDO Kursar is keenly focused on Toyota’s cloud engineering and development practices, which enable all business units to exploit cloud automation features for their needs in ways that are easy to implement.

By implementing AWS foundational services such as Backstage, for instance, developers and end users can write Python scripts and build applications without worrying about whether “they’ve closed the right ports and set the correct permissions in containers,” Kursar says, adding that having the security of the development platform built-in opens automation efforts to many employees.

Business analysts and developers can also use a wide variety of development tools to create automation workflows, ranging from low-code platform for non-developers to higher-end RPA and AI machine learning model automation tools. Developers also have access to a worklet development kit for the automotive industry, dubbed Chofer.

In addition to providing significant cost savings, Toyota IT’s hyperautomation in the cloud makes far more data — and investment — available to business groups for analysis, creating models, and unlocking data insights, Kursar says.   

“Very early on, we were so focused on keeping the lights on and building platforms. If the business is too busy running the business, we don’t have money for analysis,” the CTDO points out. “The reinvestment [from hyperautomation] has gone into investing in data scientists who can create very complex machine learning models that drive even more cost savings and value.”

And that is “one of the greatest accomplishments of this hyperautomation,” Kursar says. “These smart engineers can focus on driving insights to provide decision support for our business.”

Taking an automation-first approach

Kursar confirms that automation is also used by product engineers in Toyota’s manufacturing process. Toyota — and all other automakers — are far from self-building automobiles, but many are integrating automation into the design, specifications, and quality assurance processes.

One of Toyota’s build tests for vehicles is thermal imaging to test the welding of the frame. It is “very time consuming,” he says, but the implementation of automation not only saves the company millions of dollars but results in a higher-quality output — now every vehicle is tested in this manner.

Toyota operates roughly 15 manufacturing plants in North America and is currently building a new plant in North Carolina with Panasonic to support its electric vehicle (EV) batteries. “Everything going in there is going in with a data-first, automation-first [blueprint],” Kursar adds.

IDC maintains that more than $150 billion was spent on automation between 2017 and 2021. Yet, only roughly a third of enterprises are more than 50% into automation goals for basic software development, IT, and business processes, such as claims processing, according to a recent IDC report detailed at IDC Directions 2023 in March.

“For many companies, automation starts with processes they already fully understand. However, machine learning and AI are allowing engineers to better understand how small nuances in operating parameters can make a big difference in the business outcome,” says Dave McCarthy, a vice president at IDC.

Many C-suite executives are now focused on what IDC terms Enterprise Automation 2.0, defined as a “unified approach to closed-loop automation where artificial intelligence continuously supports decision-making and automated actions that proactively optimize and enrich outcomes to maximize the business value of the automation,” says Ritu Jyoti, group vice president of AI and automation at IDC.

Next-generation automation 2.0 will span an entire organization, and include generative AI and business processes such as process and task mining, RPA, workflow automation, BPM, application integration,  API management, data integration, and event brokers, the IDC report states.

Most of these — including machine learning model creation — are currently in process among many in Gartner’s Hyperautomation 100 Club. But soon, as enterprises continue to use digital transformation processes to save money and differentiate their offerings, it will become ubiquitous at thousands of companies, experts predict.

“Automation is the way we can be better than our competitors,” says Toyota’s Kursar. “By automating as many processes as we can, we can then really focus on high value and differentiating work.”

Automotive Industry, Cloud Computing, Digital Transformation, Robotic Process Automation

By Ram Velaga, Senior Vice President and General Manager, Core Switching Group

This article is a continuation of Broadcom’s blog series: 2023 Tech Trends That Transform IT.  Stay tuned for future blogs that dive into the technology behind these trends from more of Broadcom’s industry-leading experts.

It is clear that artificial intelligence, machine learning, and automation have been growing exponentially in use—across almost everything from smart consumer devices to robotics to cybersecurity to semiconductors. In 2023, there is no doubt that artificial intelligence and automation will permeate every aspect of our lives. As Michael Dell predicts, “Building systems that are built for AI first is really inevitable.”

We’ve come a long way in the past 10 years in developing artificial intelligence—we’ve had breakthroughs that have dramatically increased the capability of artificial intelligence to recognize patterns and relationships. Going forward, we’ll see an expansion of artificial intelligence in creating. As a current example, consider ChatGPT by OpenAI, an AI research and deployment company. This application has been in the news lately due to the quality and detail of its outputs. For example, teachers are even adjusting their curriculums to ensure students are writing original work and not just using ChatGPT to write their assigned essays. It’s also been flagged as a risk: cybersecurity companies have identified bad actors using ChatGPT to create malware. That’s usually a sign that the use of an application has become widespread. But how good can it be? Can artificial intelligence really create thoughtful insight into the future of artificial intelligence and automation?

We decided to test it out—we used ChatGPT to create a perspective on artificial intelligence and automation in 2023. Read the result below:

(From ChatGPT, by OpenAI: an AI research and deployment company.)

Artificial intelligence (AI) and automation are playing an increasingly expanded role in today’s world, with advancements in computing technology allowing for more advanced and specialized forms of processing. Over the last few decades, computing has evolved from general-purpose processing using large, monolithic CPUs to high-parallel processing using many CPUs, graphics processing units, and now, dedicated AI and machine learning (ML) accelerators.

As new types of computing have become available, applications have also adapted to leverage their new capabilities. While applications were previously written as large, monolithic scale-up programs, they have changed to become containerized, scale-out applications based on platforms like Docker and Kubernetes. This change in computing has been enabled by high-speed, high-bandwidth Ethernet networking using leaf-spine architectures.

One company that has driven the industry’s pace of high-speed Ethernet switches is Broadcom. They have enabled networks to handle the demands of AI and ML workloads. Their 51.2Tbps Tomahawk 5 switch chip, for example, provides double the bandwidth of any other networking silicon, and includes features that dramatically accelerate AI and ML workloads.

The very success of capabilities like those on ChatGPT are based on highly scalable networks connecting thousands of accelerators. This allows for AI and ML to be run at scale, enabling new and powerful applications in areas such as autonomous vehicles, natural language processing, and image recognition.

In conclusion, the advancements in computing, including the emergence of dedicated AI and ML accelerators, and the use of containerized, scale-out applications are enabling AI and automation to play an expanded role in today’s world. These advancements have been enabled by high-speed, high-bandwidth Ethernet networks interconnecting the new types of compute elements. As technology continues to advance, Broadcom will play a key role in driving even more exciting developments in the future.

We have to say, ChatGPT delivered a solid overview of our position on artificial intelligence and automation. We agree that we’ve just scratched the surface and it is important to highlight the words “at scale”. We’ll see the true benefits of artificial intelligence and automation once we can consistently and frequently apply it at the scales enabled by the latest Ethernet networking technologies.

To learn more about Broadcom’s innovative focus on artificial intelligence and automation, as well as other tech trends, visit our blog series, 2023 Tech Trends That Transform IT

About the author:

Broadcom Software

Ram Velaga is Senior Vice President and General Manager of the Core Switching Group at Broadcom, responsible for the company’s extensive Ethernet switch portfolio serving broad markets including the service provider, data center and enterprise segments.

Artificial Intelligence, IT Leadership

By Leonard Kleinman, Field Chief Technology Officer (CTO) ) Cortex for Palo Alto Networks JAPAC

Many things challenge how we practice cybersecurity these days. Digital transformation has brought significant adoption of new technology and business models, including cloud solutions, e-commerce platforms, smart devices, and a significantly more distributed workforce. These, in turn, have brought with them an increase in new threats, risks, and cybercrime.

As organizations emerge post-pandemic, many of the risks and uncertainties manifested during that period will persist, including the hybrid workforce, supply chain risk, and other cybersecurity challenges.

Let’s look at some of these cybersecurity challenges and how automation can level the playing field.

Problem: not enough cybersecurity talent

A major contributor to the growing spate of cyberattacks is the lack of skilled cybersecurity personnel. The overall global numbers of experienced cybersecurity practitioners are low compared to the need for such practitioners to handle the cyberthreats that manifest across all industry sectors. While demand for practitioners continues to escalate, the growth in actual numbers is low, leading to the increasing deficit between demand and supply.

This contrasts significantly with the global cybersecurity market, which is expected to expand at a compound rate with more demand for solutions and products. The increasing number of cyberattacks, digital transformation changes, and talent shortages are contributing to this growth, and organizations are expected to acquire/deploy more advanced security solutions to detect, mitigate, and reduce the risk of cyberattacks.

Automation, AI, and vocation

Automation systems are everywhere—from the simple thermostats in our homes to hospital ventilators—and while automation and AI are not the same things, much has been integrated from AI and machine learning (ML) into security systems, enabling them to learn, sense, and stop cybersecurity threats automatically. So instead of just alerting us to a threat, an automated system would be able to act towards neutralizing it.

At its core, automation has a single purpose: to let machines perform repetitive, time-consuming, monotonous tasks. This, in turn, frees up our scarce human talent to focus on more important things or simply things that require the human touch. The result is a more efficient, cost-effective, and productive cyber workforce.

Even threat actors are themselves using automation to facilitate their attacks. The MyDoom worm, one of the fastest-spreading pieces of malware on the internet, uses automation to propagate and is estimated to have caused around $38 billion in damage. It is still spreading, but the surprising part is MyDoom is not new. Released in 2004, it can still be seen trolling the internet.

A persistent fear in cybersecurity is that automation is here to replace humans. While somewhat justified, the reality is that automation is here to augment humans in executing security operations and, in some cases, help organizations supplement and address the growing talent gap. As advanced as it may be perceived, automation will always be reliant on humans, completely configurable, and under the supervision of the security team. If anything, automation and AI are bringing forth new cybersecurity roles such as Algorithm Bias Auditor or Machine Risk Officer.

The benefits of automation

Automation can do many things, from detecting potential threats to containing and resolving threats. These actions take seconds and are largely independent of human intervention. Provided via security orchestration, automation, and response (SOAR), automation gives SOCs a significant boost in execution, significantly improving productivity and response. The Cost of a Data Breach 2022 Report highlights the role of automation in halving the cost of a data breach and reducing the time to identify and contain by 77 days.1

Orchestration provides the ability to activate the many tools in your operational environment, seamlessly connecting them via playbooks to undertake specific actions. This allows for a consistent, repeatable response process together with all the necessary information for your cyber practitioner, all in one place.

Additional efficiencies are derived from the AI/ML engine within SOAR, which can learn attributes from alerts and use that knowledge to prevent future attacks. Every alert and event handled are learned from for future purposes. Automation plays a significant role in terms of enabling an agile, proactive cybersecurity capability.

Most importantly, automation provides a better quality of life to your cybersecurity team, reducing alert fatigue and frustration and giving them back precious time. In the age of the Great Resignation, retention has become a significant issue.2 Retaining staff allows you to increase your ROI on people—acknowledging the significant investment organizations make through recruitment, ongoing training, and tacit knowledge learned on the job.

Automation helps organizations address the talent challenge. It also enables a greater ROI on your current tools and technology, bringing them into play as part of the orchestration process.

Where to start?

A prerequisite for automation begins with gathering and correlating data. Any good automation system requires good data to work efficiently and effectively. The more data sources, the better the quality of operations.

Aim to gather data from all aspects of your business environment, such as endpoint, network, and cloud. The AI/ML system within the automation platform makes analyzing and correlating all this data easier. These two components are what make cybersecurity automation possible.

Next, analyze your current standard operating procedures (SOPs), looking for regularly recurring activities/processes—ones that reduce workload and the risk of an overlooked alert. Look for tasks that do not deviate or vary in an unpredictable manner. These are prime candidates for automation.

Now, identify the tools that need to be orchestrated within those processes, along with the required APIs (or create them) to enable the integrations.

Finally, create your playbook. This gives you control over the process, providing you with the ability to consistently replicate and improve the process over time. Include any specific actions you require, the tool/s to perform, and any other additional tasks, e.g., block, notify, contain, etc.

Don’t drop the ball on automation

Cybersecurity is essential for any business in a digitally transformed world, protecting company data, its people, and its customers. However, just the implementation of cybersecurity will not be enough as our adversaries continue to innovate and get craftier in their approach.

As organizations continue to pursue digital transformation initiatives coupled with technology advances, the automation of cybersecurity is not just recommended—it is mandatory in leveling the playing field.

Learn more about the benefits of consolidation.

1. Cost of a Data Breach 2022 Report, IBM Security, July 2022

2. Paula Morgan, “Top Five Tips For Retaining Employees During The Great Resignation,” Forbes, August 4, 2022.

About Leonard Kleinman:

Len Kleinman is the Field Chief Technology Officer (CTO) – Cortex for Palo Alto Networks JAPAC focusing on critical industry sectors such as Government, Banking and Finance, Utilities, and Education. His mission is to work with executives and business stakeholders to make security a strategic priority that translates into business value and assist in the development of a risk-based cybersecurity culture aimed at protecting our digital lives.

Artificial Intelligence, IT Leadership, Machine Learning

By virtue of their position between IT and effecting business strategy, CIOs can identify what processes their organizations need in order to modernize and automate. When it comes to updating core systems to drive operational efficiencies, they also have to ensure that a sound business case exists to automate them, says Laurie Shotton, VP and analyst at Gartner. That’s not surprising since CIOs typically own IT automation, as well as help drive business automation. But it’s not always a given the two aren’t working at cross purposes.

“For the last 15 to 20 years, organizations have been trying to modernize core systems in order to drive operational efficiencies,” he says. “And quite often, the business case for replacing them doesn’t stack up.”

Automation, the business, and the CIO

Since automation can help improve KPIs and create new channels to help improve end-user experience, it’s one of the primary tools in a CIO’s toolkit to drive the business forward, says Brian Woodring, CIO at Rocket Mortgage. “The biggest challenge is making sure that by automating the business, you’re not just taking a legacy, highly bureaucratic manual process and putting RPA in front of it,” he says. “You may get some short-term wins, but you’re unlikely to deliver durable value. One of the biggest things I’ve learned is you can’t do automation to the business; you have to do it with the business.”

As an example, the technology organization of the pharmaceutical segment at Cardinal Health collaborates closely with business leaders so they can identify current pain points and determine the right processes to automate, focusing on how these tools will improve the customer or employee experiences, says CIO Greg Boggs.

“Our technology organization collaborates closely with business leaders so we can identify current pain points and determine the right processes to automate, focusing on how these tools will improve our customer or employee experiences,” he says. “In general, it’s been straight forward to quantify the business impact of automation initiatives, given they typically have clear before and after business metrics. We’ve matured our practice around automation and built architecture that’s enabled us to be nimble, innovative, and able to pivot quickly in a dynamic, global healthcare environment.”

The challenge of the CIO’s job at a financial institution, however, is to eliminate waste by redefining the entire business process while delighting the client and simultaneously maintaining compliance, says Woodring.

Additionally, businesses that combine automation with AI will be able to make faster decisions, optimize business processes, and drive higher rates of efficiencies, says Subramani Elumalai, VP of application management services delivery at Capgemini.

Other CIOs concur that the business is the central consideration for automation efforts.

At Northwestern Mutual, for instance, the company’s mission — to free Americans from financial anxiety — drives everything it does to inform its business priorities, says Jeff Sippel, CIO and EVP.

From a practical level, the organization is consistently looking to apply automation solutions where they can have meaningful impact. The company measures the success of these efforts by business outcomes, not the success of the automation itself, he adds.

Automation as enabler

Automation and business goals also go hand in hand for Vaibhav Tandon, head of commercial management, Adani Electricity Mumbai Ltd.

Automation acts as an enabler to identify specific processes and achieve business requirements, he says. Customer centricity is also crucial to the power company’s business goals, and automation initiatives ensure it enhances the system’s productivity effectiveness. “It’s become one of the key levers in the client experience, and it plays different roles throughout the lifecycle of that change,” says Sippel.

For the CIO, this requires a broader and longer-term perspective while simultaneously keeping the lights on and innovating to create the best client experience.

“We’re essentially rebuilding the city while we’re living in it, so the CIO is constantly weighing both the strategic and tactical considerations: what are the right tools, and how do we bring them together at the right time and place,” he says.

Jamie Smith says his job as CIO at the University of Phoenix is to evangelize the opportunities for applying automation across all the university’s activities. His perspective is that automation augments human tasks so the university can do more for its students.

The university currently employs a variety of automations including RPA to automate recurring human tasks for efficiency, ML-based automated nudges to facilitate student progression and attendance, and an automated virtual assistant (Phoebe) to broaden the support window for working adult students when they need assistance.

Priorities for CIOs

Automating complex workflows will remain a CIO priority, says Petr Baudis, CTO and chief AI architect at London-based Rossum. The key will be getting such projects to scale beyond departmental silos. A catalyst to make this happen will be the ongoing improvements in AI-enabled data capture.

Fast and accurate data extraction will speed up transactions and automation capabilities, and be the foundational technology within any business intelligence or data analytics platform, enabling better collaboration and B2B communications, he says.

“The types of automation technology we see being vital include RPA along with process and task mining,” says Baudis. “We’re seeing a convergence taking place between all these technologies as enterprises try and scale their automation projects.”

Plus, Adani Electricity this year is continuing with advancements in the areas of distribution management, customer experience, the metering ecosystem, and consumer data analytics, says Tandon.

“We’ve implemented SAS’ AI/ML-based energy forecasting solution to improve our forecasting performance,” he says. “This has helped us achieve a forecast accuracy of around 97%, thereby allowing us to optimize power procurement costs while providing reliable electricity supply to our 2.5 million consumers. We’ll also continue with advancements in distribution management, the metering ecosystem, and consumer data analytics.”

The power company’s flagship automation projects include implementing an advanced distribution management system to create a self-healing grid infrastructure with enhanced visibility and scalability to improve the customer experience. They’re also implementing a cloud-based data lake and analytics solution that will provide what Tandon calls a single source of truth, and drive self-service analytics and data-backed decision-making to help them operate more efficiently.

“Estimated readings for our customers stood at 2.2% three years back, but now we’ve brought them down to 0.3%,” he says. “The whole mechanism was automated so all readings were optically downloaded without any manual intervention. This initiative not only ensured our system accuracy and return of equity (RoE) incentive, but also improved transparency and reduced consumer complaints.”

And at the pharmaceutical segment at Cardinal Health, a main goal is to also boost its efforts in warehouse automation to better serve its customers, Boggs says.

“In IT, we’ll continue to prioritize infrastructure as code, continuous integration and deployment, and AI operations,” he says.

The University of Phoenix has some new automation projects on tap as well. Currently, the institution is developing an enterprise platform that will enable the increased use of ML and automation across a wide range of student and staff journeys, Smith says.

“This engine will be deeply integrated into our data lake to enable truly individualized student support at the right time, through the best channel,” he adds.

The university also plans to continue improving student support by continuing to automate increasingly complex tasks in matriculation, transcript processing, and student financial aid.

“Recent advances in the ability to consume unstructured documents and natural language processing are enabling a whole new crop of complex tasks to become candidates for automation,” says Smith.

His team is creating platforms and systems by which they can effectively scale and govern automation safely and reliably. After all, he says, there’s nothing less effective than automating a process that shouldn’t exist. Automation combined with AI should significantly help businesses make faster decisions, optimize business processes, and drive higher rates of efficiencies, says Elumalai. “It has the potential to improve business KPIs through auto-detect, auto-heal solutions, and create new channels to improve end-user experience,” he says.

Artificial Intelligence, Business IT Alignment, CIO, Data Management, IT Leadership, IT Operations, IT Strategy, Machine Learning

One of the first things Patrick Thompson (pictured) did on becoming chief information and digital transformation officer of specialty chemicals manufacturer Albemarle in 2017 was to introduce an annual survey to gauge employee attitudes toward services IT staff provides. Now he has a self-service bot delivering some of those services through Microsoft Teams, and providing real-time feedback on what employees want its help with, and whether they get it or not.

Albemarle is growing fast. Net sales have more than doubled in the five years Thompson has been with the company, and its goal is to double them again in the next five. One of Albemarle’s biggest businesses is producing lithium, the low-density, highly reactive metal that revolutionized phone and laptop batteries. However, demand for lithium is now being driven by electric car batteries, each one of which contains thousands of times more lithium than a typical phone battery.

Like other lithium producers, Albemarle is scaling up, and with new production facilities and expanding existing ones, the number of workers is growing, as are their demands on the IT department. Thompson’s annual survey has been key in matching resources to those demands.

“We measure three characteristics: people, process and technology,” he says. Employees rate on a scale of 1 to 4 the performance of the IT people that provide the service, the efficiency of the IT department’s business processes, and the technology IT provides — whether it provides all the necessary features, or is sitting on the shelf unused. Those scales, he adds, give great insight into what to do.

While headline numbers provide a helpful indicator of progress, it’s the detailed feedback that employees provided — such as requests for more self-service tools — that enabled Thompson to fix a poor score on the help desk.

“Some of the simplest things like password resets were requiring people to call in and get a person on the phone to make these changes, when they could easily be automated through a bot with a job aid,” he says.

While the initial need was to automate password resets, Thompson also had an eye on the future, and the possibility to automate other mundane tasks, perhaps avoiding the need for employees to log into particular software tools altogether.

In fact, Albemarle had already developed a number of job aids using ServiceNow and other platforms, but they couldn’t capitalize on it from a self-service point of view. “People were having to thumb through catalogs,” he says. “They couldn’t just ask a question and get an answer.”

Several of Albemarle’s existing software suppliers, including ServiceNow and Workday, offered bots that could respond to questions and automate workflows, but they were specific to those applications. “We really wanted a federated solution, something that could be independent of those, and then create APIs,” he says.

Buy before build

Thompson also briefly considered tackling things in-house and building their own bots, but that would have been very expensive. Instead, he settled on Moveworks, developer of a chatbot that integrates with a number of identity management, enterprise software and collaboration platforms.

Albemarle’s first application of Moveworks was automating password resets.

“Our help desk at the time was about a million-dollar budget, and we had about 45 external providers,” Thompson says. “We were wasting a lot of money on password resets and simple things that a bot should be able to do. We drove that down to $100,000 a year and reduced our tickets by 90%.”

The first implementation arrived just as COVID hit, and Thompson saw the number of colleagues working from home jump from 500 to 5,000 within a month. “Now all of a sudden, the self-service capability had to come faster,” he says. “Moveworks really helped us accelerate that.”

The potential for automating non-IT functions also became apparent during that time. “Everybody [was asking if they could] have a robot for HR, facilities and accounts payable, So all these other departments are now using Moveworks as well.”

Over time, Albemarle has enriched the capabilities of its bot ALbot to include federated workflow approvals, a move that’s also enhancing adoption of other platforms even though users now rarely have to log into them.

An exercise in consolidation

“We deal with a lot of different systems like SAP, Ariba, Concur, Workday, Salesforce,” says Thompson. “What Moveworks allowed us to do is federate some approvals so we can have one experience around approvals instead of having to log into multiple systems. They just get an approval request through the bot.”

Maintaining these isn’t placing an additional workload on the IT department either, Thompson says. He has an application management services agreement with Moveworks, and one-and-a-half staff supporting the platform from a technical perspective. For the rest of it, he says, it’s not about extra effort but leveraging what you already have, and pointing it in the right direction. By that he means ensuring that documentation, like training manuals or job aids, is available in a form ALbot can ingest, or devising processes that ALbot can participate in.

ALbot is helping in other ways, too. When an employee asks a question it can’t answer, the information is logged. “We’re constantly doing this root cause analysis: somebody asked a question and there wasn’t an answer,” he says. “We’re taking that data, categorizing it, and then sending it out to the right content experts and saying, if you answer this question, if you build some content we can point to, this will no longer show up in the queue any more.”

These days, some of that analysis is handled by ALbot itself, he says. “I don’t have to go through all the questions that the bot didn’t answer,” he adds. “It’s putting it in an analytic portfolio for me to give to the right managers.”

The bot has also helped the centralized helpdesk respond to the demands on it in other ways, as it can offer 24/7 support in multiple languages for the questions it’s been trained to answer. “We’re a global company,” says Thompson. “We’re completely covered around the world with the language capability. That’s another powerful adoption accelerator.”

Paths of progression

Thompson has learned some lessons about how automation can save time and cost from his work with ALbot, but his experience with automation — and with Albemarle — goes back much further. His first contact with Albemarle was as a CIO of the company that built one of its plants, industrial construction contractor Turner Industries. That role, and his work automating parts of Turner’s project planning process, earned him a place on the March 15, 1999, cover of CIO magazine,’s predecessor.

Over that long career, he says, 37 of the people who worked for him have gone on to land roles as CIOs, CTOs or CISOs. The part he played in their advancement is one of the accomplishments he’s proudest of, he says.

“I’m a big fan of investing in people, not only on a business level, but a personal level, and teaching techniques and skill sets to make those kinds of things happen. It’s the same playbook each time — a clear vision of basic IT transformation first.”

Thompson’s playbook has three chapters: First IT transformation, then business transformation, and finally digital transformation.

“First get your IT right: your network, infrastructure, security, collaboration tools, and cloud strategy,” he says. Then tackle the business side of things. The goal here, he says, is “One ERP, a single instance, with no code change, no custom code.” It’s also the time to “get your back office synergized, and payroll centers and accounting offices optimized.” The third chapter is digital transformation in customer excellence, the supply chain, manufacturing, and the back office. “Once people are taught that framework, and you back it up by aligning their performance goals with the strategy and empowering them to help enable that strategy, people want to do that,” he says. “They’re motivated. The playbook is there. It’s all in the execution.”

Application Management, CIO, Collaboration Software, Data Management, Document Management Systems, Employee Experience, IT Leadership, Remote Work

Enterprise software and workplace management orchestrator ServiceNow announced rosy revenue numbers in its Q4 2022 earnings call Wednesday evening, saying that total revenues topped $1.9 billion, which represents a 20% year-on-year increase.

IDC analyst Stephen Elliot noted strong corporate management and the company’s expansion into the workplace experience market as contributing factors in the reported growth.

Most of ServiceNow’s revenue came from service subscriptions, which rose to $1.86 billion in the quarter, a 22% year-on-year rise. The company’s current remaining performance obligations, which represent contract revenue that will be recognized as such in ServiceNow’s numbers within the next 12 months, rose to nearly $7 billion as of the reporting date. That’s a 22% increase compared to the fourth quarter of 2021.

Chairman and CEO Bill McDermott was bullish on the company’s performance, saying that the market conditions that have helped grow ServiceNow’s revenues should remain strong in the foreseeable future.

“Our Q4 surge in new business shows that the secular tailwinds of digitization aren’t going anywhere,” he said in a statement accompanying the results. “The world works with ServiceNow as the end-to-end platform for digital transformation.”

ServiceNow’s substantial growth exceeded profitability guidance, according to CFO Gina Mastantuono, who credited net-new annual contract value gains for much of the surge.

“What’s more, our results were generated with a lower mix of early renewals from 2023, providing us more opportunities to drive further expansion throughout the year,” she said in the statement.

Despite the growth, ServiceNow’s stock price dropped nearly 8% in after-hours trading, for reasons that weren’t immediately clear. McDermott, however, has vowed “absolutely no layoffs in 2023,” according to a report from Bloomberg, bucking a trend among technology vendors of late.

IDC’s Elliot, who is group vice president of I&O, cloud operations, and DevOps, credited a healthy corporate culture for ServiceNow’s continued success, saying that, while rapid growth can sometimes cause companies to lose some of their strengths over time, ServiceNow has managed to avoid that.

“I’d say that they’re hitting on all cylinders,” he said. “I also think that they have been very consistent and focused on what customers are looking for, and translating that into investments in the company.”

This isn’t a surprise, Elliot added, given the strong leadership across ServiceNow’s management ranks. He credited McDermott, in particular, for helping to minimize internal politics and other distractions that can sap a company’s momentum as it expands into new business areas.

“They’ve had so much success with the IT management business,” he said. “And over the course of the past five years, the expansion into field service management, HR, employee experience businesses; [their focus] has continued to drive them.”

ServiceNow, Technology Industry

First Tech Credit Union is a San Jose-based financial institution with more than $16 billion in assets. As the eighth largest in the country, it primarily serves tech companies and their employees, but still has a lot of manual processes in place.

“We’re very early in our automation journey,” says Mike Upton, the organization’s digital and technology officer.

First Tech had been deploying some robotic process automation, trying to replace paper forms, as well as using for other automations. But these efforts all fell short.

The first problem was many of the bank’s processes cut across organizational and technological silos. Its existing point automation solutions were often unable to do the hand-offs.

For example, the process to send a domestic wire involved 105 different manual steps. “When we started mapping all that, we realized how many touch points and hand-offs there were.”

So First Tech began a new approach last summer using a low-code automation platform from Pegasystems. The vendor was selected specifically because of its cross-silo capabilities. But having the right technology in place wasn’t enough.

In some cases, even when the processes were well documented, one department might not fully understand how their workflow impacts another team, Upton says.

“The technology is very powerful, but the way people think is very challenging,” he says. “They’re comfortable with what they know. Having to re-imagine, re-engineer and re-think processes turned out to be one of our biggest challenges.”

In addition to the hand-offs, the credit union sometimes had to get everyone’s agreement on whether to automate at all.

“There were challenges getting the different business team partners to agree on where automation could be applied and where they had to have manual controls in place,” he says.

And there were many things that could’ve derailed the project that had less to do with technology and more with business processes, change management, and controls.

“Thankfully, we were able to avoid complete catastrophe,” he says. “But we’re seeing this more frequently as we take on other RPA projects.”

Eventually, the drawn-out wire process was cut to just five steps, saving hundreds of labor hours. The bank also reduced average call handling times by 40% and eliminated all data entry errors by auto-filling forms with relevant case data. That time saving now allows employees to focus on higher-value tasks, and help the credit union grow without needing to add additional staff in a tight labor market.

But First Tech is not unique. Issues like these are common to most companies embarking on automation journeys.

The who, what, and why of automation

According to a 2022 survey by Salesforce and Vanson Bourne, demand for automation by business teams has increased over the last two years, said 91% of respondents. And according to Gartner, the RPA software market grew 19.5% last year compared to 2021, and is expected to grow 17.5% in 2023. And by 2025, 70% of organizations will implement full automation in infrastructure and operations, an increase from 20% in 2021.

But automating a bad process can make things worse as it can magnify or exacerbate underlying issues, especially if humans are taken out of the loop.

In some cases, a process is automated because the technology is there, even if automation isn’t required. For example, if a process occurs very rarely, or there’s a great deal of variation in the process, then the cost of setting up the automation, teaching it to handle every use case, and training employees how to use it may be more expensive and time-consuming than the old manual approach.

And putting the entire decision into the hands of data scientists, who may be far removed from the actual work, can easily send a company down a dead end, or to end users who might not know how automation works, says James Matcher, intelligent automation leader at Ernst & Young.

That recently happened at a company he worked with, a retail store chain with locations around the US.

The retailer approached people on the front lines, and employees and managers working on the shop floors, for suggestions about manual processes that should be automated.

“They ended up with a long list of use cases along the lines of, ‘How do I upload this Excel spreadsheet,’” says Matcher.

But these were minor issues that didn’t scale across the whole operation.

“These were little tactical things that you couldn’t repeat,” he says. “So there was no definitive value coming out the back end of the exercise.”

So they spent six months going to individual stores getting ideas, wasting thousands of hours before deciding on a different approach of putting together an internal lean team, bringing in consultants, and taking a holistic, role-based approach to automation.

“We spent about four months during the persona-based mapping,” says Matcher. “That was quite a rigorous exercise to get right.” Then came two months for designing the technology, and the first use cases went into production three months later.

After all, customers have a wide range of demands and each needs different kinds of help—and different kinds of automation to serve their needs that might involve actions by different employees or different corporate systems.

Other tasks currently handled by employees could be replaced by self-service tools. For example, a customer looking to return a product could start the process on their smartphone app, eliminating the need for excessive manual data entry.

“We went through the process of matching customer personas and employee personas, and got a huge amount of optimizations,” he says.

One key factor to set up the right automations is to match them to the right business objective. For example, companies looking to automate in order to reduce headcount or labor costs might miss the main objective: to improve customer service and grow the business.

Matcher says he recently saw this happen with another client, a manufacturing company looking to reduce the number of customer service representatives with automation.

The business unit started the automation process last spring, then went back to the CFO for additional funding to continue the project in the summer because they were able to free up several thousand person-hours.

“And the CFO says, ‘I don’t see any adjustment in your headcount in the new budget,’” says Matcher. “‘Where’s all the money I spent?’”

In fact, the customer service reps used the time they saved to cross-sell and up-sell customers, and double their revenues.

“Ultimately, the gross margin level is more beneficial to the organization,” says Matcher. “But if we hadn’t shown the bridge between the two, they probably wouldn’t have continued the automations in that domain. They would have looked at the ROI and stopped the program.”

The when and where of automation

When it comes to automation, people become more important, not less. Forgetting this can be a big mistake.

“You have to put in a lot of conscious thought,” says Sanjay Srivastava, chief digital strategist at Genpact.

By automating simple, repetitive processes, enterprises still need human experts to handle complex and unusual cases, requiring upskilling. But more than that, automation can enable new business activities. For example, someone working in accounts receivable may spend less time generating routine invoices and more on solving customer problems. But they’re also in a position to recognize that a customer is spending more than usual and may be ready to buy additional products or services than they were before. That will require a different set of skills.

“The operating model has to change, and that’s a bigger question about business management,” says Srivastava. “We all know it’s easy to get software implemented, but it’s hard to get business outcomes achieved. There’s a big journey between the two and we mustn’t fool ourselves that we’ve achieved results just because we got the software.”

There are only so many productivity gains remaining to be made, he adds. But there’s unlimited growth potential in finding new business opportunities made possible through automation.

For example, companies can use automation to improve existing service offerings. “If you improve the stickiness, you improve the durable advantage and your competitive position,” says Srivastava. “And that gives you the ability to have a more sustainable business in the long run.”

Next, you can use the improved relationships with customers to expand products and services or create new ones, and to cross-sell customers.

“Then, you’ve expanded your revenues,” he says.

In addition to that, there are also other downsides to removing humans from the loop prematurely.

Many models, for instance, require human supervision and training to fine tune and improve them, says Craig Le Clair, VP and principal analyst at Forrester Research, Inc., and author of a recent report about the perils of automation.

In December, for example, Hertz agreed to pay $168 million to settle disputes related to false theft reports. Customers would return a car late, Hertz’ automated systems would report the car stolen, and the next person who rents that car would be arrested for vehicle theft.

There were more than 360 legal claims filed against Hertz by customers related to such false arrests. In one case, according to law firm Pollock Cohen LLP, a NASA employee was pulled over, surrounded by police with guns drawn, and arrested in front of co-workers.

“Here’s an area where they removed humans in the loop prematurely,” says Le Clair.

Another example of too much automation was Zillow’s plan to value homes with AI and make purchase offers they called “Zestimates.” When the offers came in too high because, say, there were undisclosed problems with leaky basements, human sellers would jump on it, and Zillow wound up with too many bad bets. When the AI erred the other way and offered payments that were too low, buyers would naturally go elsewhere to sell their house.

“If you had a crack in your foundation, the algorithm wasn’t going to pick up on that,” says Le Clair. “You certainly can solve a lot of problems by keeping humans in the loop.”

So companies can have too much confidence in data and algorithms, he says. Just look at the online chatbots without human backups.

“You don’t have an easy escalation,” he says. And when there are humans online to take over when problems arise, the systems often lose context of the conversation and the customer has to start over with the agent. “So we don’t do human well, and that’s a critical element as we move forward.”

In fields like medicine and finance, there are regulatory restrictions to automation, says John Carey, MD in the technology practice at consulting firm AArete.

“There will be a high watermark for some automation because the legal framework needs to evolve,” he says.

But even in industries without heavy regulations and compliance requirements, companies should keep an eye on ethics and standards when it comes to rolling out automation, especially when new technologies like OpenAI’s ChatGPT are making AI tools dramatically more intelligent and capable. “These smart tools are fantastic,” says Carey. “But the challenge for us is to be aware that they are double-edged swords. We have to figure out how to use and leverage them in ways that are legitimate, and build solutions that are ethical for clients and end users.”

Data Center Automation, IT Leadership