While automation saves time and boosts productivity, the complexity of developing automation can be a limiting factor and bottleneck. Generative AI is a paradigm shift here, in that it brings consumer-style simplicity to assisting in the development of enterprise-grade automation.

With the new interface of generative AI, organizations can democratize automation and therefore increase the number of individuals that are contributing to authoring and harnessing automation.

To help our customers achieve their goals with automation, we are excited to announce public Early Access for AI-generated Runbooks. Starting today, Runbook Automation users can write the task they wish to automate in plain-English and let AI build a template of automation for that particular task.

AI-generated Runbooks lower the barrier to entry to new automation developers and speeds up the time to create new automation for experienced automation authors. This feature works seamlessly with the user’s preferred scripting language, offering a low-code solution for what used to be a high-code task.

Simply sign up for the PagerDuty Runbook Automation Trial if you are not an existing Runbook Automation user. For existing Runbook Automation customers, App administrators can enable this feature at any time.

Tangible Benefits from Leveraging AI-generated Runbooks

Better Development timelines

Are you a seasoned automation engineer? AI-generated runbooks will help you save time and effort.

Creating self-service automation for tasks involves sifting through documentation, identifying the right calls/commands, and then transposing them into individual job steps manually.
With AI-generated runbooks, authors can generate these on the fly and faster than ever before.

Here’s a quick look at what provisioning access to apps in Okta looks like with and without AI-generated Runbooks:

Faster Onboarding

Get good fast with example jobs for reference. Start with tasks you’re familiar with and see how these tasks operate within the Runbook Automation platform.

Democratize Technical Automation

With AI-generated Runbooks, less experienced automation-authors can quickly go from thought to development to implementation. This broadens the scope of people within an organization that can leverage a technical tool such as Runbook Automation.

Conquering Blank Slate Problems

A typical conundrum for users in the face of automation is “Where do I start?”. With AI-generated runbooks, users can now hit the ground running by creating baseline versions of Jobs for their variety of use cases.

Build with Best Practices for Optimal Results

The AI-generated Runbooks use fine-tuned “prompt engineering” that embeds the known best-practices from the engineers here on the Process Automation team at PagerDuty.
For example, all Jobs are created with a ReadMe that explains the prerequisites for invoking that Job. And all secrets used within the Job are retrieved from Key Storage – rather than requested from the user.

GenAI – Security & Data

A common concern we hear from our customers considering adopting generative AI is around the security of their data, and the potential of giving competitors advantages through model training. PagerDuty AI-generated runbooks feature is an opt-in, meaning you need to enable it to be able to use it.  Furthermore, as stated in the feature documentation:

The only data sent to the generative AI model is the text entered into the prompt field. No other data about your environment, existing Jobs or the source of the prompt is sent to the AI model. Furthermore, the AI model is not trained on the text entered into the prompt.

Read our Guidelines for the Safe and Secure Use of Generative AI to learn more about how we’re working with and building our AI-powered  features.

See AI-generated Runbooks in Action

AI-generated Runbooks propel automation into a new realm, where your operations are empowered like never before. Embrace the future of automation with PagerDuty, and witness the transformation it brings to your operational landscape.

Sign up for the PagerDuty Runbook Automation Trial today!

The post Democratize Automation with AI-Generated Runbooks appeared first on PagerDuty.

As we’ve seen across market landscape evaluations, there are a number of ways that solutions can be implemented. Despite this, the problems AIOps solutions aim to address remain fairly consistent: fewer incidents and faster resolution. But which teams can stand to benefit from this powerful technology and how will AIOps help them achieve their desired business outcomes?

Understanding how different teams can implement best practices to see a reduction in MTTR, total incidents, and time to adopt automation will help ensure that each team is taking value from your investment. Here are three teams that stand out as having much to gain from leveraging AIOps: Network Operation Center (NOC) teams, Major Incident Management (MIM) teams, and distributed service owning teams. Let’s cover each.

NOC teams

If you have a NOC, it acts as your central nervous system. You may also be in the middle of undertaking modernization efforts to reduce both cost and risk.

Many of our NOC customers tell us about challenges such as:

• Eyes-on-glass operational style causes incidents to go undetected
• Catch and dispatch means too many escalations to SMEs or routing incidents to the wrong team
• Manual work drives up MTTR
• L1/L2 teams experience high turnover and blame culture is common

To move beyond this, organizations can create L0 automation. This is automation that serves as the first responder, only bringing in humans when necessary. For well-understood, well-documented issues, L0 automation can auto-remediate incidents without a responder intervening. But for other more complex issues that require a hands-on approach, NOC teams can create L0 automation that immediately pulls in diagnostic information before the responder looks at an incident, routes incidents intelligently according to event data, and populates the incident notes with pertinent documentation and runbooks.

PagerDuty AIOps helps NOCs modernize and move away from eyes-on-glass methods. These NOCs are a center of excellence within their organizations, spearheading data-driven optimization, enabling best practices, and ensuring incident readiness.

MIM teams

When critical, customer impacting incidents happen, you don’t have time to waste. But, with complexity and noise on the rise, how do Major Incident Management teams improve to meet growing customer expectations?

We see MIM teams with common challenges such as:

• Finding out about major incidents from overwhelming customers/users calling in or delayed team escalations
• Lack of context as initial triage takes too long to assess severity and business impact
• Long MTTR waiting for the right people, the right diagnostics, the right runbooks, etc
• Disjointed tooling leading to communication barriers for responders and corresponding teams

MIM teams can overcome these challenges with a variety of automation and ML tactics. First, organizations can create automation that immediately routes high priority or severity incidents to a MIM team and tags in the appropriate teams needed via incident workflows. Additionally, ML can gather key context such as how rare an incident like this is, if it happened before and how it was resolved, and change events that might be correlated to the failure.

PagerDuty AIOps helps MIM teams detect major incidents faster, improve MTTR and customer experience, and save SMEs time. This reduces the cost of each incident and mitigates risk.

Distributed service owning teams

DevOps and distributed service owning teams are under more pressure than ever to deliver exceptional customer experiences. But with competing priorities and fewer resources, this is easier said than done.

Many of our customers share challenges they are facing such as:

• Disparate monitoring tools with no central pane of glass
• Too much noise leading to incorrect escalations and false incidents
• Lack of context and information silos
• Toil and time taken away from value-add initiatives

For service owning teams looking to overcome these challenges, an AIOps tool that can aggregate data from all the monitoring sources in the technical ecosystem can help bring clarity to incident response. Additionally, with ML, teams can reduce noise by automatically grouping together alerts based on context, time, and previous event data that the model has trained on. With this and the ML-surfaced triage information, incident response is streamlined so teams can get back to innovating faster.

PagerDuty AIOps helps service owning teams spend less time firefighting, reduce MTTR, and create exceptional customer experiences. This improves culture and team retention while increasing revenue for the entire organization. 

Ready to get started?

With PagerDuty AIOps, teams like the ones we looked at see 87% fewer incidents, 14% faster MTTR, and 9x faster automation adoption. This helps organizations move faster, focus on the work that matters most to customers, and reduces risk and team burnout. Best of all, teams from dev to IT can see value from PagerDuty AIOps.

PagerDuty AIOps works in conjunction with the rest of the PagerDuty Operations Cloud to help organizations manage their operations by leveraging AI and automation to supercharge their digital transformation. With over 700 integrations, GenAI capabilities, and end-to-end event-driven automation, PagerDuty gives customers a 400% ROI and the right tools to leapfrog the competition.

To try PagerDuty AIOps out yourself, you can take an interactive product tour or try us for free for 14 days.

The post Three Teams That Can Use AIOps to Work Smarter, Not Harder appeared first on PagerDuty.

Despite what movies like The Terminator may lead you to think, AI is not something to be afraid of or avoid. Instead, AI can be used to improve upon many of the services and apps we use in our daily lives, while also encouraging innovation. Currently, artificial intelligence and machine learning (ML) are helping to fuel major changes in IT Operations. With AIOps—or Artificial Intelligence in IT Operations—tech companies are finding new ways to streamline and automate many internal processes, while ensuring an optimized user experience for their customers.

With the new year well under way, let’s take a look at the top 5 biggest AIOps trends to watch out for in 2021.

AIOps Trend #1: Multi-Purpose Tools

One of the most appealing trends coming to AIOps is the presence of multi-purpose tools. Currently, many of the available AIOps tools are only able to handle a single data type at a time – be it metrics, logs, etc. This means having to use multiple tools and combine data points in order to achieve a given task.

However, in 2021, we’ll be seeing new AI algorithms get created to handle multiple data types at once and by a single application or tool. This will allow these tools to view all of the given data (metrics, logs, transactions, events, etc), analyze how they relate and interact with each other, and help reduce alert noise by grouping them together whenever it makes sense. Most importantly, the presence of multi-purpose AIOps tools will ultimately save companies time and money.

AIOps Trend #2: Faster Incident Response

An area where AIOps truly shines is incident response. AIOps provides teams with much faster root cause analysis by automatically performing analysis procedures for events, logs, other metric data, and providing relevant and real time context to responders to accelerate triage. Teams can be equipped with information such as past incidents that look similar to the one at hand, or pointed to relevant incidents happening at the same moment in time. What this means is much faster incident response times and a more reliable service. AIOps takes all of the data to predict possible issues much sooner, allowing your team to respond much more quickly—often before an incident even occurs.

With proactive incident detection and AI-backed event management, response times are faster than ever, and we only expect this to improve in 2021 with multi-purpose tools and smarter algorithms.

AIOps Trend #3: Heavier Reliance on AI for Remote Work

With everything that’s happened in 2020, one thing that became very apparent is that remote work is here to stay. Due to the pandemic, many tech companies were essentially forced to have to close down their offices and have their employees work from home. This eventually led to companies like Facebook and Twitter to adopt permanent work from home policies.

What remote work means for AIOps is data is now being collected from a wider area of locations rather than single clusters (ie: an office building or classroom). There are now many unique data generators, which will require new intelligent algorithms to help predict new incidents with employee productivity and the remote use of the service. These changes can help to predict problems before they occur as many of us continue to adjust to a fully remote workspace.

AIOps Trend #4: Better Security and IT Integration

In 2021, we’ll be seeing much more integration between security and IT as a means to more quickly detect and prevent problems, threats, and vulnerabilities. The data sets for securing your infrastructure and IT operations are nearly identical. AIOps can help automate the interaction between security and operations algorithms, allowing the system to almost immediately stop cybersecurity threats in their tracks.

AIOps Trend #5: Preventative and Automated Remediation

AI algorithms can help with preventative and auto remediation of incidents. With AIOps, incident detection and resolution can be automated to detect anomalies and prevent a problem from occurring. This will free up time for IT operations teams to innovate and focus on providing their customers with the best possible experience.

As we see new tools in 2021 with multi-data point collection, proactive incident detection and resolution, and smarter algorithms that include a focus on remote work, we’re excited about the new ways AIOps can help teams work more efficiently and creatively.

Artificial Intelligence in Tech: What is AIOps?

As Gartner defines it, “AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination.” Simply put, AIOps allows teams to focus far less on completing or assigning individual tasks and more on what they do best—innovating and creating better products and services. Plus, with AI-backed insights and intelligence, teams and processes are made more efficient and services more reliable.

In the tech world and for IT Teams, AI has brought along new ways to further increase speed and efficiency to provide users with seamless experiences that meet their rising expectations in a world of “digital everything.” You’ve seen how DevOps has completely changed how development and IT Operations work together. AIOps takes things a step further, using data science and AI to further automate several processes for faster service delivery, reduced costs, and overall improved quality.

Want to learn more about AIOps and how you can bring it to your company? Read up about PagerDuty Event Intelligence or check out this interview with PagerDuty’s Director of Product Marketing, Julian Dunn, to find out What is AIOps, Exactly?

The post 5 AIOps Trends for 2021 appeared first on PagerDuty.

This transition toward individualized healthcare has prompted the industry to start moving to new technologies like AI and machine learning to improve treatment methods and patient experiences while reducing costs. However, healthcare organizations still face obstacles such as complex legacy infrastructure investments, as well as regulatory compliance and security risks. Additionally, many practitioners still rely on outdated technologies (e.g., using pagers to notify on-call staff) that create process bottlenecks and hinder organizations from being as effective as possible.

In a nutshell, the healthcare IT industry is rife with both unprecedented opportunities and unique challenges. Here are some of the key trends that are shaping this space.

Trend 1: Healthcare Is Moving Toward Omnichannel
Healthcare consumers today demand fast, frictionless, convenient, and personalized experiences, driving increased competitiveness and digitization across the healthcare industry.

The ability to deliver healthcare tools and information to consumers so they can be more knowledgeable about their own health is just one aspect of the personalized healthcare experience. Easy accessibility and convenience will be a key differentiator for companies within the healthcare industry—according to Bright.md, 90% of patients feel no obligation to stay with a healthcare system that doesn’t offer digital tools.

Additionally, as indicated by the rising number of investments in remote patient monitoring (RPM) devices, telehealth platforms, public employee retirement systems (PERS), and mHealth applications, Forbes estimates that digital health tech spending for out-of-hospital settings will exceed $25 billion globally by the end of 2019, a growth rate of 30%.

These new business models empower consumers to make their own decisions and take control of their own health. With the increase of new business models in the healthcare industry, it’s crucial to ensure that both the physical and digital aspects of the patient journey are seamless. Healthcare organizations that treat patients as consumers who expect great experiences across digital, telehealth, and other contexts will likely be well-positioned to create an omnichannel experience that can improve patient engagement.

Trend 2: Value-Based Care + Improving Patient Experiences Using AI and Machine Learning
In this industry, the right or wrong action can mean life or death in a matter of seconds, so healthcare delivery and payer organizations need to be able to proactively monitor patients and employ the right technology. But many organizations are struggling to monitor health behaviors and adhere to treatment protocols.

To address these challenges, the industry is looking to machine learning and AI, with 94% of healthcare organizations stating they believe AI can improve patient experiences and increase cost efficiencies. Due to the increasing use of AI and machine learning in healthcare, a healthcare approach called precision health has emerged and is focused on proactively diagnosing and lowering the risk of future illnesses.

Additionally, Gartner found that in clinical care, organizations are increasingly investing in machine learning and predictive models for detecting the risk of clinic deterioration, mortality, or readmission. This adoption of machine learning has the potential to empower healthcare companies to improve patient outcomes while reducing costs.

Finally, there is a regulatory driver for this trend. Rising healthcare costs have incentivized more states to shift toward value-based care models to improve quality and reduce costs, and avoid the risk of funding loss due to the Affordable Care Act (ACA). Value-based care models aim to reduce the prevalence of over-expenditure in healthcare and incentivize physicians to make the most out of the patient experience. Several organizations are modernizing their tech stacks and capabilities through SaaS providers in order to deliver this value-based care model.

Trend 3: Modern Health IT Ecosystem Increases Vulnerability to Security Threats
According to J.P. Morgan, data security remains a large part of IT expenditures for healthcare organizations since they manage a large amount of private patient health information. The digitization of data has made protected healthcare information (PHI) more vulnerable, highlighting the importance of security in the industry. Due to the sensitivity of PHI, healthcare organizations must always prioritize protection against security and data breaches and remain compliant with standards such as HIPAA and NIST.

Yet, most healthcare providers currently do not track patient data access history. Verizon’s 2018 Data Breach Investigation Report states that healthcare is the only industry where insiders cause more data breach damage than external users (58% of healthcare security breach attempts involve inside actors). This puts patient data privacy at risk because security teams don’t have the visibility they need in order to predict and prevent internal patient privacy breaches.

To counter more sophisticated data exploitation attacks such as ransomware and phishing emails, new techniques like machine learning will also play important roles in protecting medical data. Emerging businesses like Protenus are attempting to build AI solutions to protect patient privacy and assist risk mitigation to alleviate market pressure from exposed health record costs. For the hundreds of companies that participated in a Ponemon study, the average total cost of a healthcare data breach was $4 million, which underscores the significance of prioritizing intelligent solutions to proactively maximize cyber and data security.

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PagerDuty’s Solutions for Healthcare Help Improve Patient Experiences and Operational Cost Efficiency
With the transformation of the healthcare industry, it is crucial for healthcare organizations to ensure maximum visibility and reliability across their evolving technology stacks so they can take real-time action to protect patients while simultaneously improving efficiency.

To achieve this, healthcare organizations must implement solutions that facilitate digital transformation so that they are positioned to adopt cutting-edge technologies such as AIOps, automation, and data analytics. Organizations should leverage automation and analytics tools to align with key drivers such as reliability, HIPAA compliance, modernization, and more. As pressures increase around value-based care and the consumerization of healthcare, the stakes around modernizing healthcare IT will continue to increase exponentially.

To learn more, check out PagerDuty’s solutions for Healthcare and get started with a free trial.

The post Healthcare IT Trends and Challenges appeared first on PagerDuty.