How to Navigate the Shift to Generative AI with PagerDuty

How to Navigate the Shift to Generative AI with PagerDuty

How to Navigate the Shift to Generative AI with PagerDuty

As the kickoff to our first fully digital SaaStr AI Day, Jennifer Tejada, the CEO of PagerDuty, chatted with Jason Lemkin, SaaStr CEO and Founder, about navigating the shift to Generative AI and what Artificial Intelligence in SaaS might look like in the next 6 months, as well as years to come.

Jennifer has been at PagerDuty since 2016, a disruptive company that took automation to the next level before AI was hot in 2024. Before diving into all things AI, Jason asked Jennifer, “How do you keep going long in this industry when so many CEOs have left?” 

How to Buck Up and Go Long in SaaS?

”I’m eight years into a three-year gig,” Jennifer jokes. How do you go long in SaaS? Start with your motives and why you want to be a CEO. Jennifer emphasized the importance of having the right motives and values as a leader, such as growing people, expanding markets, and building value for others, to sustain long-term success. 

One reason Jennifer still does this after a couple of decades in business is because she loves seeing a person achieve something they didn’t think was possible for them. As a CEO, you constantly ask people to do things they might think are unreasonable or impossible, and then they achieve it. It all comes down to what you believe and what you’re willing to stand behind over time. Tejada still sees a lot of value in helping people achieve their full potential and the fulfillment that comes from building a diverse and high-performing culture.

Where’s the Line Between Automation and AI?

It’s a little blurry for people. PagerDuty has a long history with AI because they’ve used analytical AI and a foundational model long before Jennifer even joined the company. The three co-founders were gathering data in hot storage to respond more effectively to instances with the benefit of historical and technical context. 

“Context is magic, but it’s often hard to come by when solving really complex problems,” Jennifer says. 

When we talk about GenAI today, there’s a lot of low-hanging fruit for reducing some of the junk on your desk, neat consumer apps that make you look prettier on Instagram, and generating a first draft of your resume. 

But the real value is surfacing deep context that helps humans to do incredible things at a much faster rate. That’s where true automation will be, in the disruptive work that people are freed up and enabled to do and do more effectively and quickly due to having the assistance and rich context. 

As a startup, you can look at it a few ways. 

  1. How do I leverage GenAI in my operations to become more efficient? 
  2. How do I leverage GenAI in my product to drive competitive advantage? 
  3. How about bringing more value to customers in a much shorter time frame? 
  4. Or supporting customers by enabling GenAI? 

Most of PagerDuty’s customers are Enterprises, and they’re in varying places on the continuum in their journey of adopting GenAI. They differ from startups because they’re Fortune 100 companies with a lot of legacy, incumbency, and regulation.

Are Enterprise Leaders Excited About GenAI?

“GenAI is absolutely accelerating the appetite for automation,” Jennifer says. CIOs now understand that if they don’t figure out how to use automation as a lever to improve margins, customer experiences, and innovation with fewer resources, they’ll be the ones without a job at the end of the year. 

There is an openness and appetite from CIOs, COOs, and Heads of Revenue saying, “Help me figure out how to replace some of the work my humans are doing so I can free them up to move up the value chain,” or “Help me improve my margins so I earn the right to be here in a few quarters.” 

That shift in appetite and willingness to experiment are pronounced. There is an expectation that GenAI will do more than just automate simple stuff. Eventually, it will build its own code, repair its own code, and manage customer conversations in a way that the customers might not be able to tell if it’s a human or intelligence. 

All of that is realistic, but most GenAI adopted now still requires a human in the loop for oversight and judgment. Experimentation and learning still have to happen, and there is a need for smart, strategic employees to monitor how AI is working to improve the product market fit, application, and safety for their customers. 

Can You Be Competitive as a Vendor Without AI? 

It depends on the utility of your product and platform as it is. PagerDuty knew right away that fidelity would be important. Their customers trust that when they’re orchestrated into an incident process, it’s not going to be a false positive or negative. 

So, if you start spraying AI-generated junk at them and reduce the integrity of the entire platform, that’s no good. They told customers early that they would go slow to go fast to make sure they weren’t shipping garbage and eroding trust. 

Delivering that level of fidelity at scale is a technically and architecturally challenging problem to solve for. 

  1. You have to understand what customers expect from you already and continue to deliver on your value prop. 
  2. There’s a lot of low-hanging fruit that’s low value with GenAI. That stuff will become table steaks quickly. 

The most interesting innovation is often from the teams that understand the problem the most. Usually, they don’t have the most data scientists or experience in AI. Yet, they can articulate a big, hard problem and start chipping away at how GenAI can help them solve it. 

PagerDuty just launched a survey to Fortune 100 CIOs, and 100% of them said they have concerns about the security, privacy, and data risk of the technology. As startup and tech leaders, we need empathy for where they’re coming from. 

We can take higher risks in a less regulated industry than a peer in an investment bank or healthcare business. 

51% said they’ll only adopt GenAI after they have the right guidelines in place. Those guidelines are still evolving in terms of how you create a framework for responsible AI in a company that has tens or hundreds of thousands of employees. 

98% actually paused AI experiments to establish policies, and yet only 29% feel like they have the right guidelines in place. 

These stats show how early the big businesses are in this journey. Leaders in the Fortune 100 know that cybercriminals are getting increasingly more sophisticated and can leverage GenAI, too. 

Zero trust isn’t enough. 

If Zero Trust Isn’t Enough, What Is? 

How you respond is what matters now—anticipating the unexpected and being ready for it. One thing customers talk about isn’t downtime and outages. It’s disruption. Someone is getting access, sitting dormant for a while, and blowing you up with a supply chain attack when they want to. 

Beyond zero trust is anticipating that and being prepared to respond to an event quickly to prevent it from becoming a material business impact. 

Today, public companies are required to file material events within four business days, but how do you define a material event? How we classify and communicate these things with customers matters. Gone are the days of building a moat and trying to keep everyone out. Folks don’t want to react to something. They want to use AI to seek out problems and find root causes before they rise to the surface. 

It Takes Time to Change a Culture

It takes customers time to change their culture. PagerDuty’s customers have an operations chasm to cross. They’ve modernized a lot of their tech stack and people, but their work still looks a lot like it did after World War II — a military-designed command and control with authority at the top, approval loops, etc. 

That’s not how consumers think. They want a perfect experience now, and if they don’t get it, they’ll go elsewhere. We have to cross the chasm of the old way to how consumers expect it to work, and GenAI is the only answer right now. 

How Have Conversations Changed Over the Past 18 Months?

PagerDuty customers are worried about three things. 

  1. Protecting revenue
  2. Improving margins
  3. Mitigating risk

There isn’t a structured roadmap for freeing up resources to invest in GenAI that can solve some of these problems, and there’s a lot of noise for decision-makers about where they will see value. 

One recommendation Jennifer has for every product leader is to ensure you have a way to surface value realization in product. Do like Doordash and tell your customers in the app how they just saved $12 on their order. 

Over the last 18 months, on earnings call, Jennifer noted that:

  • Sales cycles are longer because more approvals are required. 
  • Decisions are also centralized — the bring your own tool to work jig is up. CIOs and CFOs who have done the work to centralize decision-making around technology investments won’t redistribute that authority anytime soon because that control gives them margin leverage. 
  • Customers are being much more cautious, not buying ahead of their needs or being able to anticipate things like headcount growth or budget certainty as effectively as they had in the past. 
  • Contracts are going all the way up to the CEO for signature, which is almost unbelievable. The way you engage customers has to change, and you have to be able to show what the returns are before renewal time. 

Will Humans Be in the Loop in 2025, ‘26, and ‘27?

”There will be humans in the loop and fewer jobs to be done,” Jennifer says. Roles will evolve into higher-value work over time. Looking back in history at every other disruptive automation or technical step-change, whether the smartphone or Cloud computing, people stepped up their game and built more creative and interesting things. 

Everything in the industry runs on software. More of those jobs over time will need less supervision, which frees up smart people to do higher-value things. 

GenAI will make for better software, too. There will be new problems to solve, and we can already see that GenAI is driving a faster proliferation of complexity, which means more events and incidents. While GenAI should reduce some of that complexity, it’s more about how leaders evolve these roles for people ahead of the jobs being subsumed by GenAI. 

“I haven’t seen any great companies with fewer engineers. They just build better software,” Jason says. “It’ll be a step function in quality, but we won’t have a lot of one-person, billion-dollar companies.” 

While software is showing up in places we didn’t expect, it’ll still require customers to be willing to try it. This is why this time in history is so exciting. What will it mean for society? And what are the big problems that will come alongside it, like any new technology? 


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