As we close out 2023, we at DevOps.com wanted to highlight the most popular articles of the year. Following is the latest in our series of the Best of 2023.
Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.
This week: Amazon Prime Video has ditched its use of microservices-cum-serverless, reverting to a traditional, monolithic architecture. It vastly improved the workload’s cost and scalability. (more…)
As we close out 2023, we at DevOps.com wanted to highlight the most popular articles of the year. Following is the latest in our series of the Best of 2023.
AI is buzzing again thanks to the recent release of ChatGPT, a natural language chatbot that people are using to write emails, poems, song lyrics and college essays. Early adopters have even used it to write Python code, as well as to reverse engineer shellcode and rewrite it in C. ChatGPT has sparked hope among people eager for the arrival of practical applications of AI, but it also begs the question of whether it will displace writers and developers in the same way robots and computers have replaced some cashiers, assembly-line workers and, perhaps in the future, taxi drivers.
It’s hard to say how sophisticated the AI text-creation capabilities will be in the future as the technology ingests more and more examples of our online writing. But I see it having very limited capabilities for programming. If anything, it could end up being just another tool in the developer’s kit to handle tasks that don’t take the critical thinking skills software engineers bring to the table.
ChatGPT has impressed a lot of people because it does a good job of simulating human conversation and sounding knowledgeable. Developed by OpenAI, the creator of the popular text-to-image AI engine DALL-E, it is powered by a large language model trained on voluminous amounts of text scraped from the internet, including code repositories. It uses algorithms to analyze the text and humans fine-tune the training of the system to respond to user questions with full sentences that sound like they were written by a human.
But ChatGPT has flaws—and the same limitations that hamper its use for writing content also render it unreliable for creating code. Because it’s based on data, not human intelligence, its sentences can sound coherent but fail to provide critically informed responses. It also repurposes offensive content like hate speech. Answers may sound reasonable but can be highly inaccurate. For example, when asked which of two numbers, 1,000 and 1,062, was larger, ChatGPT will confidently respond with a fully reasoned response that 1,000 is larger.
OpenAI’s website provides an example of using ChatGPT to help debug code. The responses are generated from prior code and lack the capability to replicate human-based QA, which means it can generate code that has errors and bugs. OpenAI acknowledged that ChatGPT “sometimes writes plausible-sounding but incorrect or nonsensical answers.” This is why it should not be used directly in the production of any programs.
The lack of reliability is already creating problems for the developer community. Stack Overflow, a question-and-answer website coders use to write and troubleshoot code, temporarily banned its use, saying there was such a huge volume of responses generated by ChatGPT that it couldn’t keep up with quality control, which is done by humans. “Overall, because the average rate of getting correct answers from ChatGPT is too low, the posting of answers created by ChatGPT is substantially harmful to the site and to users who are asking or looking for correct answers.”
Coding errors aside, because ChatGPT—like all machine learning tools—is trained on data that suits its outcome (in this case, a textual nature), it lacks the ability to understand the human context of computing to do programming well. Software engineers need to understand the intended purpose of the software they’re creating and the people who will be using it. Good software can’t be built by cobbling together programs with regurgitated code.
For example, ChatGPT cannot understand the ambiguity in simple requirements. Although it is clear that if one ball just bounces and comes back but another bounces and then bounces again, the second ball has traveled further, ChatGPT struggles with this nuance; that nuance will be needed if these systems are ever to take over from developers.
It also has trouble with basic math, such as when it’s asked to determine which is greater and offered a choice between a negative and positive number. ChatGPT confidently tells us a correct summation of the space, but cannot understand that -5 is less than 4. Imagine your thermostats going haywire because the heating kicks on at 40 degrees Celsius instead of at -5 degrees Celsius because the AI program coded it that way!
Pre-trained AI code generation also raises some legal questions with regard to intellectual property rights; it cannot currently distinguish between code that is licensed in a restrictive or open fashion. This could expose people to licensing compliance risk if the AI borrows a prewritten line of code from a copyrighted repository. The problem has already prompted a class action lawsuit against a different OpenAI-based product called GitHub Copilot.
We need humans to create the software people rely on, but that’s not to say there couldn’t be a place for AI in software development. Just like automation is being used by security operations centers for scanning, monitoring and basic incident response, AI could serve as a programming tool for handling lower-level tasks.
This is already happening, to an extent. GitHub Copilot allows developers to use ChatGPT to improve their code, add tests and find bugs. Amazon offers CodeWhisperer, a machine language-powered tool designed to help increase developer productivity using code recommendations generated by natural language comments and code in the integrated environment. And someone has created a Visual Studio code extension that works with ChatGPT.
And one company is testing AI for developers. DeepMind, which shares a parent company with Google, released its own code generation tool, dubbed AlphaCode, earlier this year. DeepMind published the results from simulated evaluations in competitions on the Codeforces platform in Science magazine earlier this month under the headline Machine Learning systems can program too. Headline grammar aside, AlphaCode achieved an estimated rank within the top 54% of participants by solving problems “that require a combination of critical thinking, logic, algorithms, coding, and natural language understanding.” The abstract for the paper says: “The development of such coding platforms could have a huge impact on programmers’ productivity. It may even change the culture of programming by shifting human work to formulating problems, with machine learning being … responsible for generating and executing codes.”
Machine learning systems are becoming increasingly advanced each day; however, they cannot think like the human brain does. This has been the case for the past 40+ years of study into artificial intelligence. While these systems can recognize patterns and increase productivity for simple tasks, they may not always produce code as well as humans. Before we let computers do code generation en masse, we should probably see systems like AlphaCode rank in the top 75% of participants on a platform like Codeforces, though I fear this may be too much for such a system. In the meantime, machine learning can help with simple programming problems in the future, allowing developers of tomorrow to think of more complex issues.
At this point, ChatGPT won’t be disrupting any field of technology, especially not software engineering. Concern about robots displacing programmers is vastly overstated. There will always be tasks that developers with human cognition can do that machines will never be capable of.
How can this be? Here we are, right before Christmas, and it will be a new year before we know it. Time to put 2023 in our rearview mirrors. I, for one, say, good riddance. I’m sorry, but 2023 will not go down as a great year for me, and I think it was a tough year for many of our friends in the tech world, too. In fact, to quote Queen Elizabeth (who we lost in 2022), for me, 2023 was in many ways an annus horribilis.
I say that because 2023 saw the sometimes-savage impact of the cycles of the market up close. For the first time in a long time, many of our friends and colleagues were the victims of layoffs. In fact, according to Crunchbase, over 191,000 U.S.-based tech workers were the victims of layoffs. That doesn’t include non-U.S.-based workers, which could easily drive that number to over a quarter of a million. That is on the heels of 2022, when about half that number of workers were laid off. So, in the last two years we have seen 350,000 or so tech workers laid off. For many of these folks, finding their next gig was not as easy as it was in the past. There weren’t recruiters breaking down their doors with interview opportunities. Employers became more selective and demanding. If you are looking for a job that is 100% remote so you can work from wherever, that gets much harder. Employers want employees in the office again. Probably every one of you reading this was either laid off at some point in the last two years or you know someone who was. But it wasn’t just the layoffs. The very foundation of our tech infrastructure suffered seismic shocks, too.
For many of us, the Silicon Valley Bank failure in March was a splash of cold water in the face that signaled business as usual was no longer going to be business as usual. I have been in tech for 30+ years, and for much of that time, SVB was a mainstay of venture-backed startups. Most of the venture-backed companies I helped found were customers. If they could fail, who and what else was suspect? We soon found out what else was not as rock solid as we had led ourselves to believe. The entire VC ecosystem that the tech world relied upon was shaken to its core. With the unprecedented rapid rise in interest rates by the Fed, the underlying business fundamentals of the tech VC market were broken. Who would take a risk investing in non-profitable startups when you could get tax-free bonds that paid as much, guaranteed? We actually saw VC funds close up shop, not raise new funds and cut back. This resulted in over 3,200 venture-backed companies failing in 2023. For those companies who held on, many received the message from their boards that they shouldn’t expect to raise more money at “up valuations” until sometime in 2024 or beyond. They should hold on to their cash. It seems reasonable, but you know, with VCs, there are always two sides (or more) to the coin.
These companies are usually not profitable. They burn money every month. When they cut, they do so first with marketing and sales, and generally, their growth slows. Slow growth makes it difficult to raise funds at an increased valuation. So, it becomes a death spiral. They aren’t growing enough to raise money at a decent valuation, but they are being told to shut down the marketing spend. It’s a Catch-22.
In addition to all the macroeconomic news, 2023 was also a year where war seemed to be breaking out everywhere. I am reminded of the old saying, “War is not healthy for children and other living things.” It isn’t great for tech either, as it turns out. Especially when those war zones are also in places that support thriving tech economies.
So, besides macroeconomic slumps and wars, what else can we say about 2023? In terms of DevOps, we saw a year where DevOps seemed to move from front-and-center to blending in. I think most organizations today are using some sort of CI/CD process in terms of developing and deploying software. Many of the fundamentals of DevOps have seeped into the very fabric of our IT infrastructure. Some may see this as “the death of DevOps;” heck, even Gene Kim is renaming DOES into something else. But as I will explain in my next article, the rumors of DevOps’ demise have been greatly exaggerated. To the contrary, DevOps is beginning to see its promise fulfilled. You will have to read my next blog to get more of my views on this topic.
But 2023 (like all black clouds) also had a silver lining. As Dustin Hoffman in The Graduate said (sort of), I have two words for you: AI. With all due respect to Taylor Swift, my Person of the Year was not really a person at all. I would say it’s AI. You could not swing a golf club around tech people in 2023 without hitting AI in the head. AI has the potential to be a civilization-changing technology. On par with the internet itself, the cellphone and the like, AI could change the lives of everyone. This is as opposed to technologies like the cloud or methodologies like DevOps that were technology-market-changing. AI is attracting literally billions of dollars in investments and is moving forward at warp speed, in spite of those who say it should slow down. It promises to invade and disrupt every corner of IT as well as one business vertical after the next. A perusal of our Techstrong.ai site will give you all you need to know. The impact of AI on DevOps is just starting to be understood. Operationalizing AI for IT will be a major theme for us here at Techstrong in the year ahead, so stay tuned.
While 2023 has been a difficult year in tech, the great thing about cycles is they cycle through. What went down will come up. We are already starting to see signs of an uptick. I am personally optimistic that 2024 will be a great year for the tech world and I pray for the world in general with fewer wars, less death and less sickness. How about more peace, more commerce, more goodwill, better health and, of course, more DevOps?
Happy holidays and happy new year to all of our Techstrong community!
Senser is extending the reach of its artificial intelligence for IT operations (AIOps) platform to now include an ability to define and maintain service level agreements (SLAs) and service level objectives (SLOs).
SLOs are a set of internal performance goals that require access to telemetry data from service level indicators (SLIs), while an SLA is a formal commitment to maintaining specific levels of service.
Senser CEO Amir Krayden said the company’s AIOps platform collects data from SLIs and then applies predictive AI models to enable IT teams to achieve SLOs and SLAs. The Senser AIOps platform leverages extended Berkeley Packet Filter (eBPF) and graph technology to gain visibility into the entire IT environment versus requiring IT teams to deploy agent software. Machine learning algorithms are then used to aggregate and analyze that data to define thresholds for predicting performance in addition to recommending benchmarks for tracking SLOs and SLAs.
That approach provides a single source of truth for identifying the actual level of service being provided based on a topology of the infrastructure, network, applications and application programming interfaces (APIs) that makes it possible to identify the root cause of issues and the potential impact of an outage for degradation of performance.
IT teams have been attempting to achieve and maintain SLAs and SLOs for decades, but given all the dependencies that exist in a distributed computing environment, it’s difficult to achieve that goal. Senser is making a case for applying AI within the context of a platform for automating the management of IT to define and maintain SLOs and SLAs to make it possible to consistently manage SLAs and SLOs in a way that reduces the level of cognitive load that would otherwise be required. Senser is also working toward adding generative AI capabilities to provide summaries that explain what IT events have occurred.
Collectively, the goal is to provide IT teams with a more efficient holistic approach to monitoring and observability that legacy platforms are not going to be able to achieve and maintain, said Krayden.
At the core of that capability is eBPF, a technology that allows software to run within a sandbox in the Linux microkernel. That capability enables networking, storage and observability software to scale at much higher levels of throughput because they are no longer running in user space. That’s especially critical for any application that needs to dynamically process massive amounts of data in near-real-time.
As the number of organizations running the latest versions of Linux continues to increase, more hands-on experience with eBPF will be gained. IT teams may not need to concern themselves with what is occurring in the microkernel of the operating systems, but they do need to understand how eBPF ultimately reduces the total cost of running IT at scale.
Ultimately, the goal is to reduce the current level of complexity that today makes effectively managing highly distributed computing environments all but impossible for IT teams to manually maintain in an era where the pace at which applications are being built and deployed only continues to accelerate.
Digibee is leveraging artificial intelligence (AI) to make it simpler to migrate to its integrated platform-as-a-service (iPaaS) environment by converting code into a JavaScript Object Notation (JSON) format.
A translator tool created by Digibee invokes large language models (LLM) to convert that code into a JSON file that can then run on the Digibee Integration Platform.
Initially, that capability is being made available to facilitate migrations from the Mulesoft integration platform made available by Salesforce, with support for other legacy integration platforms planned for 2024.
Digibee CTO Peter Kreslins Junior said this approach reduced the level of time and effort required to switch platforms by using generative AI to map and refactor legacy integration code. That refactoring process doesn’t completely automate the reverse engineering of workflows, but it does substantially reduce the total cost of switching platforms by, for example, automating data migration and simplifying the transfer of configurations, he noted.
Generative AI also eliminates the need to rely on documentation or maps that either may be limited or non-existent, added Kreslins. Instead, all the integration patterns used can now be automatically discovered by an LLM, he added.
IT teams will still need to vet and test the code created by the translator tool, but overall, the speed at which any migration effort is made should be accelerated, thanks to the rise of generative AI, in 2024. Many organizations are locked into various platforms today simply because the effort required to rework the code that has been created to extend them has required either too much effort on their part or the contracting of expensive consultants. A migration project that might have taken a year or more can now be accomplished in a few days, said Kreslins. The overall goal is to reduce the current level of pain IT teams experience when migrating between platforms, he added.
It’s not clear if the rise of generative AI platforms that make converting code into other formats and languages simpler will drive a wave of migrations, but there are many organizations relying on legacy platforms that are not nearly as easy to use as more modern alternatives. As the time and effort required to make those transitions is reduced, more IT teams will be, at the very least, more open to considering their platform options. The providers of those legacy platforms will naturally have to invest in new capabilities much faster to discourage customers from migrating to another platform.
In the meantime, the amount of code that will need to be integrated is about to exponentially increase as developers rely more on LLMs to generate code. The downstream implications of that increased pace of application development for DevOps teams are profound. It’s not clear to what degree those teams can increase the level of scale at which they today manage application deployments.
The one thing that is clear is there is no going back because, so far as application development and deployment are concerned, the proverbial AI genie is out of the bottle.
Opsera is leveraging a generative artificial intelligence (AI) model it built to enable DevOps teams to migrate from one platform to another.
Gregg Holtzrichter, chief marketing officer for Opsera, said the Hummingbird AI model makes it simpler to migrate source code from, for example, a Bitbucket on-premises continuous integration/continuous development (CI/CD) platform to GitHub.
Opsera originally developed Hummingbird as an orchestration engine that integrates DevOps workflows spanning multiple tools and platforms. The primary goal is to make it possible for DevOps teams to unify their workflows, said Holtzrichter.
At the same time, Opsera is now making it possible to leverage a generative AI model to reverse engineer the source code a DevOps team may have developed to extend the capabilities of a specific platform. That new code then makes it possible to significantly reduce the total cost of switching DevOps platforms, said Holtzrichter. As part of such an effort, Opsera is also now providing support for organizations looking to specifically migrate to GitHub Copilot and GitHub Actions.
It’s not clear how many DevOps teams might be looking to switch platforms, but with the rise of generative AI capabilities for writing code, interest in platforms such as GitHub has increased, noted Holtzrichter. In addition, support for platforms such as the on-premises edition of Bitbucket is being sunsetted by Atlassian as part of an effort to drive more customers toward the cloud edition of the CI/CD platform.
Regardless of the underlying platforms involved, generative AI promises to reduce the ability of any vendor to lock customers into a specific platform. As DevOps teams extended DevOps platforms over the past decade, many of them have discovered that the integrations they created and maintained made it all but impossible to switch platforms. With the ability to refactor much of that code based on the insights surfaced by the Opsera platform using generative AI, the time and effort required to switch platforms has been substantially reduced, noted Holtzrichter.
At its core, Opsera makes it possible to apply analytics to DevOps workflows as they become more complex. The number of dependencies that exist between various application modules, for example, makes it exceedingly difficult for DevOps teams to manually identify the impact a delay in one project might have on any number of other downstream projects.
The Opsera platform surfaces that intelligence by scanning all repositories and branches, including commit history and generating alerts and notifications whenever KPI thresholds or compliance requirements are exceeded. DevOps teams can also use the platform to assess their overall DevOps maturity using, for example, the DevOps Research and Assessment (DORA) metrics defined by Google. In addition, many organizations are keen to discover what best practices should be adopted by other application development teams.
Now that AI is being applied to that analytics capability, it’s become possible to extend the reach of the Ospera platform beyond analytics to include source code migration. Each organization will still need to determine whether such an effort is worth the remaining time and effort still required, but at the very least, most DevOps teams can take comfort in the fact they can now keep their options a lot more open.
Sonar has added a secrets detection capability to its portfolio of tools for analyzing code and DevOps workflows.
Designed to be applied to both finding secrets in code repositories and as applications are developed using an integrated development environment (IDE), this capability is being added to SonarLint, SonarQube and the managed SonarCloud service to identify, for example, passwords, application programming interface (API) keys, encryption keys, tokens, database credentials and other private information.
Sonar co-CEO Olivier Gaudin said the goal is to eliminate secrets that are accidentally or maliciously stored in source code to improve software supply chain security.
While it’s important to discover secrets in codebases, Sonar is making a concerted effort to make it simpler for developers to discover secrets before they find their way into a codebase that is one step away from a production environment, noted Gaudin. The SonarLint static analysis tool the company provides can now discover secrets in microseconds using the syntactic and semantic analysis engines the company has now extended, he added. No developer is going to wait five to 10 minutes for a code scan to be completed, said Gaudin.
A Learn as You Code tool also explains how each secret found in a code segment poses a security risk. The overall goal is not only to remove secrets but, ultimately, reduce the number of instances where secrets are discovered in code, said Gaudin.
Most organizations today are at varying stages of embracing DevSecOps best practices to improve software supply chain security. It’s not clear how readily secrets management is being embraced as part of those modernization efforts, but as cybercriminals routinely scan for application secrets stored in clear text that can be used to compromise application security, the need to encrypt secrets becomes a higher priority.
There is, of course, no shortage of tools for detecting secrets. Sonar is now simply making a case for managing secret detection as part of a larger effort to reduce vulnerabilities in code that cybercriminals can exploit. That approach should help reduce the total cost of securing a software supply chain at a time when forthcoming regulations are going to make organizations that deploy software more accountable for application security.
In the meantime, thanks to the rise of generative artificial intelligence (AI), the volume of code that needs to be scanned will likely exponentially increase. The issue with general-purpose AI platforms such as ChatGPT is they were trained using code of varying quality that often contains vulnerabilities that are now sometimes incorporated into the code they generate. On the plus side, however, many developers have little to no application security expertise, so it’s just as feasible that ChatGPT will create more secure code than a human developer. Regardless of how the code was created, however, no one will know for sure how secure it is without scanning it first.
Hopefully, as AI progresses, the quality of the code being generated will only continue to improve. Until then, DevOps teams should continue to assume all code is suspect until proven otherwise.
Massdriver has made available tools for tracking cloud costs and generating a cloud infrastructure bill of materials (Cloud IBOM) to internal developer platforms (IDPs) as part of an ongoing effort to streamline platform engineering workflows.
The Cloud IBOM makes it possible, with a few clicks, to map hybrid cloud resources such as databases, machine learning pipelines and serverless computing frameworks.
Massdriver CEO Cory O’Daniel said collectively, these tools reduce expenses by preventing misconfigurations that result in unexpected costs. Rather than solely providing a summary of costs incurred, the tools provided by Massdriver surface how much cloud services are going to cost at the time the developer is invoking them, he noted.
In the absence of that context, developers will nearly always overprovision cloud resources to ensure maximum availability regardless of cost, says O’Daniel. The overall goal is to help organizations reduce the total cost of IT by providing developers with insights into costs in real-time, he added.
Earlier this year, the company launched an IDP that comes with platform engineering tools that, for example, make it possible for DevOps teams to build a catalog of infrastructure components that developers can access within the context of guardrails they define. The platform also includes observability and visualization tools that make it simpler to track metrics and monitor the usage of cloud resources.
Massdriver is making a case for an IDP that provides built-in tools and workflows that enable organizations to embrace platform engineering as a methodology for managing DevOps workflows at scale. The goal is to reduce the level of expertise required to build and deploy applications in cloud computing environments, said O’Daniel. One of the primary reasons there are not more applications running in the cloud today is that these environments are still too complex to successfully manage, he added. An IDP abstracts away that complexity within a set of workflows that have been vetted by a DevOps team, he added.
There are, of course, multiple approaches to building an IDP, but rival approaches require too much effort to set up and maintain, said O’Daniel. A proprietary platform provides a more turnkey approach that doesn’t require DevOps teams to either build their own or customize an open source platform, he added.
It’s not clear at what pace organizations are embracing platform engineering, but there is a clear need to strike a balance between delivering IT services managed by a centralized team and the desire a developer might have to embrace new tooling whenever they see fit. Today, too much responsibility for provisioning IT environments has been pushed left toward developers who now have less time than ever to write code.
There is, naturally, no guarantee that reducing the cognitive load for developers will increase productivity. Building applications requires streaks of inspiration, but, in theory, an IDP should provide more time for inspiration to strike. DevOps teams, as part of that effort, should be continuously working toward eliminating any bottlenecks that have emerged in their DevOps workflows.
DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted to better serve our audience.
Our goal in these challenging economic times is to make it easier for DevOps professionals to advance their careers.
Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role it tends to create an opportunity for others.
The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and, naturally, the pay scale being offered.
We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration:
Indeed.com
MedStudy
Remote
Senior DevOps Engineer
$125,000 to $165,000
SimplyHired.com
Raytheon
Tewksbury, Massachusetts
Technical Fellow – DevSecOps/Cloud Native
$122,000 to $155,000
CareerBuilder.com
State Street Corp.
Quincy, Massachusetts
Azure DevOps Engineer
$120,000 to $202,500
Dice.com
USEReady
New York, New York
Senior Azure DevOps Engineer
$120,000 to $160,000
Northrop Grumman
Dulles, Virginia
Senior Principal DevOps Engineer
$107,300 to $199,600
Via an early access program, ScienceLogic this week made available a Hollywood update to its artificial intelligence for IT operations (AIOps) platform that, among other capabilities, provides root cause analysis capabilities that can be invoked via a natural language interface.
For the first time, this release of the ScienceLogic SL1 platform incorporates predictive and generative AI technologies the company gained with the acquisition of Zebrium in 2022.
Michael Nappi, chief product officer for ScienceLogic, said Zebrium provided ScienceLogic with an AI model that surfaces insights in a way that is simpler for IT teams to understand and act on. In addition to recommending automated workflows to run, the platform gives IT teams the option to automatically run them when, for example, there is an IT incident that an existing workflow has been defined to address, he added. Each organization will need to determine its level of comfort in automating those processes based on the level of potential risk to the business, noted Nappi.
In addition, the SL1 user interface displays IT operational information at the business service level to provide IT teams with a guided experience that makes it simpler to prioritize tasks based on their relevance to the business, noted Nappi. There is also now an SL1 toolkit that DevOps teams can use to build or customize PowerPacks templates for monitoring specific processes and services.
Finally, SL1 is now integrated with Slack and WebEx to streamline collaboration across IT teams. Previously, the platform only supported Microsoft Teams.
In effect, SL1 now provides IT teams with a cockpit through which they can invoke AI to autonomously manage a wide range of tasks, said Nappi.
It’s not clear how much advances in AI might one day soon democratize the management of IT, but it is clear the level of expertise required to manage complex IT environments is declining. The overall goal is to reduce dependency on IT professionals, such as software engineers, who are hard to find and retain.
The rate of change being made to complex IT environments is now also occurring faster than IT teams can track without the aid of AI, noted Nappi. ScienceLogic, in the longer term, is working toward making it possible to interact with a chatbot in real-time to enable IT teams to meet that challenge, he noted.
Each IT organization will need to decide for itself how heavily to rely on AI to manage IT functions, but in time, many IT professionals are not going to want to work for organizations that don’t provide some level of AI to reduce the level of toil they regularly encounter today. Rather than having to manually perform a series of monotonous tasks, AI should enable IT professionals to act more like supervisors in an IT environment.
It may be a while before that aspiration is fully realized, but in the meantime, IT teams would be well-advised to start identifying which tasks will soon be automated using AI because roles and responsibilities will evolve. The challenge and the opportunity now is determining how IT teams can add more value to the business as those transitions occur.