CAS-PA thinking - ServiceNow Certified Application Specialist - Performance Analytics Updated: 2024

I've written about ServiceNow (NYSE:NOW) a couple of times, and I am continually impressed every time I revisit them. I first bought shares in 2018, with that lot up 280% from purchase. I've added to that position, and the company is in my kids' long-term portfolios. Among software companies, NOW ranks with Adobe (ADBE) and Salesforce (CRM) in relative safety while maintaining double-digit revenue growth. The strength of the company's growth combined with its wide-moat and financial stability commands a premium in the market.

The performance has been incredible, and the metrics look great, so what could go wrong?

What I am looking at is an ultimately inevitable growth cliff. When a company like NOW hits 85% of customer penetration in the Fortune 500, the next question is where the company goes next.

For one thing, as new software launches in generative AI bolster the company's offerings in areas like HR, customer service, and operations, upsell continues to be a strong suit for NOW. Typically, successful software as a service (SAAS) companies operate on a land-and-expand strategy, where the initial offering (in this case, IT services management) gets the foot in the door and the company continues to roll out and upsell.

Looking above, with a historical 97-99% retention rate for customers and high net revenue retention, early customer cohorts have continued to grow their subscriptions over time. This provides a nice tailwind to revenue growth to make up for any churn and smooth out new customer acquisition.

Looking over the past 10 years, though, revenue growth has slowed. It's inevitable, and as NOW has grown into a mega-cap, it's highly likely we see revenue growth rates continue to trend down slowly over the medium-to-long term.

However, recent deal flow has remained impressive. The company reported in its most recent quarter closing 83 deals with over $1M in annual contract value (ACV), with 14 of the top 20 deals including all 7 workflows (ITSM, ITOM, ITAM, security and risk, customer, employee, and creator). There's no stronger sign of a company's reputation than new customers' willingness to dive in on the whole stack. Additionally, the company closed 19 Federal deals >$1M in ACV with 3 of those over $10M including a deal with the Air Force ranking in as their third largest in company history. Although opportunities with the government aren't going to break open a company-altering growth avenue, churn is negligible with a 99% retention rate.

With that deal flow, subscription revenue growth came in at 24.5% yoy, with Federal growing 75%. Management discussed opportunities in industry-specific solutions like legal, finance, and procurement. I think industry-specific solutions are nice, but they also aren't going to set the world on fire for growth, considering the company's size at this juncture. However, internationally, the company generates around 26% of revenues in EMEA and 11% in APAC at this point, so strong traction outside the US is good to see for long-term growth. Some additional color from the earnings call:

You know, one thing that might be of interest to you is, in America, the number of 5 million-plus deals actually more than quadrupled year over year. And the number of 10 million-plus deals doubled year over year. And I think, as CJ laid out beautifully, technology and employee workflows were enormously successful. And in EMEA, our 1 million-plus deals grew 70% year over year, which means that the platform and multiple components of the solutions that are great engineering team builds is resonating.

And we're seeing particular uplift now in government and manufacturing. And one, you know, interesting fact, we have these world forums coming up in London, Paris, Frankfurt, and Rotterdam, and we have a billion-plus pipeline that's registered for those events. So, we feel good about that. And APJ, when you think about it, the 1 million-plus deals increased 40% year over year.

And Japan is continuing to impress us with the unprecedented opportunity of the world's third-largest economy, as -- Germany as well. So, we're seeing lots of real growth opportunities on the global stage, and I think Gina pointed that out earlier as well.

The company's incorporation of generative AI into its offerings is good to see for maintaining its edge going forward. All the talk about growth would be for naught if NOW's offerings lost their lead against the competition. The company's website has some good videos showing use cases, but it's about what you'd likely expect. Customers, employees, etc. are able to use a chat box (AI assistant) to accomplish tasks that may have required scrolling an FAQ page or talking to a live person before. Additionally, they are leveraging AI to auto-summarize tickets, articles, etc. which I think could have some pretty substantial productivity gains. Here's some discussion on AI from the earnings call:

What's unique about ServiceNow is digital transformation can deflect so many of the cost-intensive, labor-intensive procedures that companies have to deal with to properly serve their market. On top of that, you have one-third of the productivity of knowledge workers getting torn apart by swivel-sharing between, on average, 13 individual applications a day. Now, you add the productivity tailwind of generative AI on this once-in-a-generation ServiceNow platform and you have achieved a very important business transformation. And I think, right now, CEOs are focused on business transformation. And when you can supply them one common UX that is consumer grade that integrates with the half-a-century-old legacy mess that they have to contend with, and we can get the actions that they need done done done to achieve cost out productivity and the growth on, they're all about ServiceNow now. And that's why you're seeing these results, and they are sustainable.

All that is to say NOW continues to impress me. My main concern as the company branches out into customer service and HR is the specter of competition from other major software companies, notably CRM. I think NOW has proven itself capable of competing, but growth will not be as easy against entrenched SaaS-native operators as it was stealing share from the legacy offerings.

That being said, I don't see a growth cliff in the near term. Deal flow remains strong, and the company's new offerings continue to push out the TAM further and further.

Looking at valuations across the space, the high-growth cohort has ticked back up into the tail end of the year, but overall valuations are about average compared with the long-term averages. NOW falls into the mid-growth cohort (just shy of the 25% cutoff for this grouping).

Looking at the scatter plot of growth versus valuation (against sales) above NOW falls out above the mean, but deservedly so. For one thing, the company is actually profitable on a GAAP basis, which is not the norm. Looking at some comps, the company is growing way faster but is priced about the same as ADBE. It's also growing faster and priced about the same as PLTR and TEAM, as well. NOW's operating metrics against PLTR's aren't even comparable, but I'll leave you to draw whatever conclusions you want from that. Most of the companies residing below the curve are more speculative and less safe.

As far as profitability, NOW's GAAP operating margin is only at 8%. The company still spends 37% of its revenues on sales and marketing and 24% on R&D, which are aggressive but relatively in line with other SaaS companies. Its G&A expenses are at 10% of revenues against the median of 16%, which is a good sign of cost controls. However, stock-based compensation of 18% of revenues is still pretty substantial and shows the company is heavily investing in personnel to maintain its growth rates.

I'd anticipate there are many levers the company can pull to Boost profitability further once growth slows down, but ultimately the company is still trying to dominate as many areas as it can. I am happy to see management has a $1.2B share repurchase authorization in place to offset dilution for shareholders from SBC.

Earnings growth rates have been strong from a small base, and the company has historically traded for high-flying 100X earnings.

The concern with a company trading for such a lofty multiple is the ease with which it could compress. In the blink of an eye, the company could go from trading at 67X earnings today to 30X earnings, and some would still call the company expensive. This is a risk you'll have to swallow to invest in many of these types of companies, and it's a very real one. All it takes is a big enough speed bump in the company's growth rates or a bad outlook. However, at current projections, if the company maintains its current P/E ratio, earnings should grow at around a 20% per year clip.

In order to illustrate the valuation risk, if the company were to trade at 47X earnings in 2025, despite the incredible earnings growth rates, investors would get zero return out of the stock.

Despite that risk, NOW is a great growth story. I've got the company in all my portfolios, and I intend to continue holding it for the long term. The valuation has ticked up a little in the recent past, but it's a buy with the long-term in mind.

© 2024 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

© 2024 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

Lightstep’s software is used by DevOps teams to analyze system-wide metrics, tracing data and logs in real time to understand the cause and effects of changes to application performance and reliability, accelerating cloud application development.

Digital workflow software vendor ServiceNow is acquiring Lightstep, a developer of system observability technology, in what ServiceNow says is one of its biggest acquisitions.

Monday ServiceNow announced an agreement to buy Lightstep but did not disclose the financial terms of the deal. ServiceNow expects to complete the acquisition by the end of the current quarter.

Lightstep, based in San Francisco, develops next-generation application monitoring and observability tools used by DevOps engineers to build, deploy, run and monitor cloud-native applications. The company’s software is used by GitHub, Spotify and Twilio, according to ServiceNow

[Related: ServiceNow ‘Quebec’ Brings Workflow Development, AI Capabilities To Wider Audience]

Lightstep was founded in 2015 by CEO Ben Sigelman, Chief Operating Officer Ben Cronin and Chief Architect Daniel Spoonhower. Team members at the company advanced modern observability through work at Google on tracing and metrics monitoring and created both the OpenTracing and OpenTelemetry open-source projects.

The underlying technology of the Lightspeed software analyzes system-wide metrics, tracing data and logs in real time to understand the cause and effects of changes to application performance and reliability and accelerate “development velocity” without compromising quality, according to ServiceNow.

“Today, observability primarily benefits the DevOps teams that build and operate mission-critical apps,“ said Ben Sigelman, CEO and co-founder at Lightstep, in a statement. ”We‘ve always believed that the value of observability should extend across the entire enterprise, providing greater clarity and confidence to every team involved in these modern, digital businesses.”

“By joining ServiceNow, together we will realize that vision for our customers and help transform the world of work in the process – and we couldn‘t be more excited about it,” Sigelman said.

By adding Lightstep’s capabilities to its technology portfolio, ServiceNow can extend its reach beyond IT service management and IT operations management to a customer’s DevOps teams.

ServiceNow also will apply Lightstep’s observability capabilities “across the enterprise through digital workflows that convert real-time insights into action across all the technologies, people and processes that enable digital businesses,” the company said in a statement.

ServiceNow’s Now Platform coordinates technical and team responses and actions around workflow changes that drive digital transformation.

“Customers will be able to more easily monitor and respond to critical signals and indicators of software health using Lightstep’s capabilities with ServiceNow’s IT workflows solutions’ ability to weave disparate elements into a seamless digital fabric,” ServiceNow said in a statement. “This gives business the confidence and clarity to drive faster innovation and better outcomes across the entire digital experience.”

“Companies are betting on going digital in order to thrive in the 21st century, but the transition is often challenging to navigate,“ said Pablo Stern, senior vice president, IT workflow products at ServiceNow, in a statement. ”With Lightstep, ServiceNow will transform how software solutions are delivered to customers. This will ultimately make it easier for customers to innovate quickly. Now they‘ll be able to build and operate their software faster than ever before and take the new era of work head on with confidence.”

Altogether ServiceNow has made nearly 20 acquisitions going back to 2013, including four AI technology-related acquisition is 2020. In March ServiceNow struck a deal to buy Intellibot, an India-based developer of robotic process automation technology.

By Sophia Velastegui

C200 member Sophia Velastegui is the Chief Product Officer of Aptiv, a pioneering automotive and autonomous tech company. Sophia has served as Chief Technology Officer for AI at Microsoft within the Business Applications group, where she played a role in advancing traditional AI and OpenAI/ChatGPT. She has held significant roles at tech giants Google/Alphabet & Apple. Sophia also serves as board director for Blackline (NASDAQ:BL).

As we continue to navigate the dynamic intersection of technology and business, it is essential for corporate leadership to reflect on the advancements that continue to redefine the business landscape. At the forefront of this transformation stands ChatGPT, an innovation that not only warrants acknowledgment but demands a thoughtful analysis of its ripple effects on all business models.

ChatGPT: A Brief Overview

ChatGPT, the most prominent example of cutting-edge Generative AI, has emerged as a game-changer in the AI landscape. Developed by OpenAI, it represents a remarkable leap forward, pushing the boundaries of what was once thought possible in AI language models and beyond.

This conversational model’s achievements are nothing short of groundbreaking. Within just two months of its launch in January 2023, ChatGPT attained an impressive 100 million monthly active users, securing its position as the fastest-growing consumer application in history—a testament to its widespread adoption and societal impact. To put this into perspective, TikTok took approximately nine months to reach the same user milestone after its global launch, while Instagram achieved this in a comparatively longer span of 2-1/2 years.

Generative AI: Shaping the Future of Business

ChatGPT's influence extends to the broader landscape of generative AI. No longer confined to a mere tool, it has evolved into a driving force behind the future of business—affecting a paradigm shift where generative AI is not just an aspiration, but a strategic imperative for sustainable growth and competitive advantage.

This influence is palpable in the transformation of business operations; ChatGPT's integration has led to streamlined communication, elevated customer interactions, and a redefined landscape of efficiency and productivity. Its capacity to decipher and generate human-like text not only expedites decision-making but also has opened avenues for innovation and creativity previously inaccessible by AI.

The Broader Ecosystem: Beyond a Singular Tool

ChatGPT is not an isolated phenomenon. It exists within a broader ecosystem of events and products that have collectively shaped the business landscape. Executives need to keep informed of this interconnected web of advancements, ensuring that strategies encompass the entirety of the evolving business landscape.

However, the ubiquity of ChatGPT hasn't translated uniformly across the population. As revealed by a Pew Research Center survey from May 2023, only 59% of American adults are aware of ChatGPT, and a mere 14% have engaged with this innovative platform. These statistics underscore the challenges and opportunities that lie ahead as ChatGPT continues to shape the AI landscape.

The Future of Generative AI

AI typically focuses on a single type of input (e.g., text). However, the future involves accommodating multimodal signal types, meaning the AI system can process and interpret information from different sources and types—understanding not only text, but potentially images, audio, or other forms of data like your biometric signals from your Apple Watch.

This hints at a future where personalized digital assistants may redefine the very fabric of our daily lives. Imagine a world where your assistant intuitively understands you, providing a 360-degree view of your preferences, needs, and habits. This vision is already underway beyond text-based interactions, with advancements like DALL·E, designed for image generation, paving the way for a richer, more immersive and layered AI experience. By integrating advancements, future iterations of generative AI will become more intuitive—meeting you where you are instead of requiring precise language to comprehend your instructions, and incorporating elements such as biometrics and environmental signals to gain additional context.

Expanding AI Capabilities

As AI capabilities expand, there's an anticipation that generative AI will extend its applications to various data types beyond text.

By the year 2030, According to the World Economic Forum, the integration of AI into healthcare systems will allow it to access and analyze information from multiple sources, detecting complex patterns in chronic conditions to enhance treatment strategies.

The transformative impact extends to predictive analytics, where AI will empower healthcare systems to forecast an individual's risk of specific diseases. This foresight will enable proactive measures, allowing for the swift implementation of preventative interventions.

AI-powered predictive healthcare networks are also expected to aid in decreasing patient wait times, improving staff workflows, and reducing the ever-growing administrative burden by the year 2030. The collective result will be an elevated patient experience, illustrated by personalized care pathways and improved overall efficiency.

Evolving From Broad Resource to Specialized Solution

These expectations are reminiscent of the early days of the internet when companies relied heavily on comprehensive solutions like Oracle for ERP, considered the go-to solution for a wide array of business needs. Over time, however, domain-specific solutions evolved in the form of specialized SaaS products like Workday for HR and ServiceNow for IT and customer service.

In a parallel manner, ChatGPT serves as the Swiss army knife of generative AI, offering a broad-ranging solution across many applications—allowing companies to leverage its benefits without developing and maintaining their own complex systems specific for their domain.

As generative AI technology matures, and understanding continues to grow, we can anticipate further evolution and branching, likely leading to the development of specialized solutions catering to specific industries and use cases. Similar to the emergence of domain-specific SaaS products, we can expect the rise of new market leaders, each excelling in their niche and contributing to greater adoption of generative AI technologies across industries.

Complexities and Challenges of Generative AI

Generative AI, epitomized by the emergence of ChatGPT, presents exciting possibilities while maintaining a complex landscape beneath the surface. The allure of instantaneous content generation is undeniable, but concerns arise, particularly in the form of hallucinations—instances where the system produces inaccurate or fictional information that may sound entirely plausible.

AI systems consist of three key components: computational resources, data, and AI models; their effectiveness depends on synergy among these elements. In generative AI, training data quality is critical, requiring diligent curation to rectify biases and inaccuracies. Additionally, limitations in planning and backtracking reveal deficiencies in contextual and strategic thinking within these systems.

Factual accuracy is another issue. The absence of a predetermined truth framework in generative AI responses emphasizes the need for meticulous fact-checking. Despite lacking a concept of truth and accuracy, ChatGPT intentionally conveys responses with unwavering confidence, creating a blurred distinction between fiction and reality. This inherent design limitation raises concerns as the model processes all information as truth.

Another challenge arises in the significant demand for computational resources these systems require, frequently leaning heavily on costly highly performant GPUs (graphic processing units) rather than the more cost-effective CPUs (compute processing units) especially designed for generative AI workload. This limitation is further intensified by a shortage of GPUs to keep pace with the increasing demand.

Governance and Regulation in the Era of Generative AI

In June 2023, the European Parliament approved the EU Artificial Intelligence Act (EU AI Act), establishing accountability for AI developers, providers, and users to ensure safe implementation. Aligned with the European Commission's risk-based regulatory framework, the EU AI Act categorizes AI applications into four risk levels: unacceptable, high, limited, and minimal. Unacceptable risks, posing clear threats to safety, livelihoods, and rights, will be banned.

While there currently are no comprehensive AI regulations in the US, the recent AI executive order on testing indicates a step towards ensuring the safety and reliability of its use, with numerous legislative and regulatory initiatives also being considered at both federal and state levels.

Effective governance requires collaborative efforts with regulatory bodies, industry stakeholders, and policymakers to formulate standardized guidelines that balance innovation with societal well-being. Our responsibility as company leadership lies in understanding these regulations, actively contributing to discussions, promoting ethical AI practices, and championing the establishment of regulatory frameworks that foster innovation while safeguarding societal interests.

Additional Concerns With Using Generative AI

As the capabilities of generative AI continue to advance, additional concerns related to potential misuse and vulnerabilities have surfaced:

1. Jailbreaking: Generative AI models may face the risk of jailbreaking attempts, where malicious actors seek unauthorized access to the underlying system or exploit vulnerabilities.
2. Prompt Injection: Unwanted or harmful instructions injected into the AI system through manipulated prompts can lead to the generation of undesirable or inappropriate content.
3. Poisoning: Poisoning attacks involve manipulating the training data to introduce biases or distort the behavior of the generative AI model, leading to biased or unreliable outputs.

Addressing these concerns requires a multi-faceted approach involving rigorous security measures, continuous monitoring, and ongoing research to stay ahead of emerging threats. This proactive stance will contribute to the responsible and secure deployment of generative AI technologies in diverse domains.

ChatGPT: a Catalyst for Generative AI Adoption

ChatGPT is more than a standalone innovation; it is a catalyst for the broader adoption of generative AI. Its success has paved the way for similar technologies, creating an environment where businesses are increasingly open to explore and integrate generative AI into their business.

The profound advancements in generative AI are propelling us into a future laden with unprecedented possibilities. Yet, this progress prompts an examination of the ethical challenges that accompany it. The potential pitfalls, ranging from unintentional biases to privacy concerns, underscore the critical importance of implementing safeguards throughout the development process.

ServiceNow strikes deal to buy Mapwize, a developer of indoor-mapping and wayfinding software, used to help workers navigate offices and utilize workplace resources.

Workflow software company ServiceNow will acquire Mapwize, a French developer of indoor-mapping and wayfinding software, under an agreement ServiceNow announced Wednesday.

ServiceNow plans to build the capabilities of Mapwize’s applications into its flagship digital workflow Now Platform and Workplace Service Delivery Suite to assist businesses and organizations that have adopted hybrid work practices.

“In the new world of hybrid work, the role of workplace services has never been more critical in creating great employee experiences,” said Blake McConnell, ServiceNow senior vice president of employee workflows, in a statement. “With Mapwize, ServiceNow will power the future of employee experiences by making it easier for people to navigate their work environment and access the workspace information and workplace services they need to remain productive.”

[Related: ServiceNow Nabs Deloitte All-Stars Larry Quinlan, Erica Volini]

ServiceNow said it expects to complete the acquisition of Mapwize sometime during the current (third) quarter of 2021. Terms of the acquisition were not disclosed.

Mapwize develops “indoor mapping” applications used by employees to navigate offices and reserve conference rooms, seats, workspaces and other workplace resources, according to the ServiceNow acquisition announcement. The Mapwize capabilities also help workplace teams “manage and update workplace floor maps based on usage trends and evolving real-estate trends.”

The Mapwize acquisition is the latest of several ServiceNow has made this year including Intellibot, a developer of robotic process automation software, and Lightstep, a provider of system observability technology.

Mapwize, based in Lille, France, was founded in 2014 by CEO Mederic Morel and CTO Mathieu Gerard.

The acquisition of Mapwize and its applications is particularly timely as many businesses and organizations adopt hybrid in-office/work-from-home practices. The applications, which run on laptops and mobile devices, are designed to “support agile and flexible workplaces,” according to ServiceNow.

ServiceNow said Mapwize’s “mapping solutions, product features and technical talent will complement and enhance” the capabilities of ServiceNow’s Workplace Service Delivery Suite, including the Workplace Space Mapping, Workplace Reservation Management, Workplace Space Management, Workplace Visitor Management, and Case and Knowledge Management applications and the Safe Workplace Suite.

“With ServiceNow, we have the unique opportunity to scale our technology to help global organizations navigate hybrid work environments,” Morel said in a statement. “Our technology, paired with the Now Platform and Workplace Service Delivery Suite, will remove the stress and time of navigating the workplace, allowing employees to stay agile and efficient in any environment, on any device.”

Programs use data, known as ‘values’. Variables hold values. Each variable in a program must have a data type. Sometimes a programmer needs to store a lot of related data. To do this they use structures such as arrays.

Beginning with its first edition and through subsequent editions, Thinking and Deciding has established itself as the required text and important reference work for students and scholars of human cognition and rationality. In this, the fourth edition, Jonathan Baron retains the comprehensive attention to the key questions addressed in the previous editions – How should we think? What, if anything, keeps us from thinking that way? How can we Boost our thinking and decision making? – and his expanded treatment of courses such as risk, utilitarianism, Baye's theorem, and moral thinking. With the student in mind, the fourth edition emphasizes the development of an understanding of the fundamental concepts in judgment and decision making. This book is essential practicing for students and scholars in judgment and decision making and related fields, including psychology, economics, law, medicine, and business.

“… An impressively broad and serious guide to the psychology of thinking and deciding.” Choice