As modern companies enter the digital transformation era, the old way of doing IT Operations is certainly not enough to ensure operational efficiency and business success. For digital enterprises and DevOps to transform into a resilient, efficient, and flexible business unit, they should harness the power of artificial intelligence (AI). One cutting-edge approach that leverages the power of AI and machine learning to streamline business processes and address IT complexities is Artificial Intelligence for IT Operations ( AIOps).
SourceForge recently had the chance to speak with Gabby Menachem, the Chief Executive Officer at Loom Systems, to discuss AIOps and how this new business paradigm is reshaping modern IT operations. Menachem also offers his insights on how to successfully integrate AIOps in business decision-making and shares how Loom Systems’ award-winning AIOps solution, Sophie, enables modern businesses to efficiently predict and solve IT incidents to ensure successful digital transformation.
Q: First and foremost, can you share with our readers a brief overview of your company? When was Loom Systems established and who are the brains behind the company?
Gabby Menachem, the Chief Executive Officer at Loom Systems
A: Founded in 2015, Loom Systems’ mission is to improve customer experience by predicting and preventing incidents in the digital enterprise using AI and machine learning. Headquartered in San Francisco with offices in New York and Tel Aviv, Loom Systems’ award-winning AIOps solution, Sophie, automatically parses logs to detect IT incidents within any type of application and provides recommendations on exactly how to fix those incidents.
There are many brains behind the company and the list is growing all the time. The founders are: Dror Mann, our VP of Product, with over a decade of experience in product and project management consulting on multiple projects with top Israeli companies, including IAI (Israel Aerospace Industry), Matrix, and the IDF on the implementation of big data analytics and intelligence projects.
Ronny Lehmann is Loom’s Chief Technology Officer. He began his career with 8 years in the IDF’s elite 8200 unit leading R&D teams in cyber and algorithms projects, and implementations. He was also the VP of R&D at BioCatch, a leading provider of behavioral biometric, authentication, and malware detection solutions through machine learning and signal processing.
As for myself, Gabby Menachem, I am the Chief Executive Officer of Loom Systems. Bringing over 15 years of technology innovation and entrepreneurship experience, I was previously Co-founder and CTO of Voyager Analytics, a product that analyzes social network data with a range of customers that include leading financial institutions. Prior to that, I served as General Manager and VP R&D in a microwave engineering startup.
Q: What industries do you serve and who are your current customers?
A: We cater to Fortune 2000 companies from various industries with a focus on financial services, telecommunications, and consumer packaged goods. Essentially, Loom Systems drives the most value in larger organizations, which deal with high volumes of data and are undergoing a digital transformation or cloud migration. Our market penetration in the Fortune 500 category has been close to 8% this far, and we see that number increasing about 25% in the next two years.
Q: For those that are unfamiliar with AIOps, can you explain this approach in the simplest of terms?
A: AIOps, a term coined by Gartner, stands for Artificial Intelligence for IT Operations, and refers to solutions that use AI and machine learning to automate tasks and processes. AIOps itself is neither a solution nor a philosophy. It’s an overarching framework for how to address increasing performance demands and IT complexity with the development of new and powerful technologies that have the potential to transform IT Operations and digital businesses at large. We describe it as the use and ongoing adoption of AI in day-to-day IT Operations, reducing the workload of IT teams by automating manual or skill-heavy tasks.
For Loom, Sophie is the intelligence layer that powers AIOps. Using Artificial Intelligence, Sophie is able to automatically and continuously analyze logs – the most detailed sources of historical and real-time data – correlate them with all the layers in the IT stack, and pinpoint root-causes. Sophie’s machine learning then enriches these IT incidents with insights from its proprietary, ever-growing knowledge base, written in plain English. These recommended actions allow IT teams to solve issues faster and with fewer escalations, thereby reducing the overall mean-time-to-resolution by 45%.
Q: Gartner predicts that by 2020, approximately 50% of enterprises will be actively using AIOps platforms. Why do you think AIOps is gaining momentum and why do modern businesses need this revolutionary approach to ensure successful digital transformation?
A: Digital transformation has become a top priority for enterprises and, in some cases, a necessity to survive. For IT Operations to transform into a highly effective, resilient, yet flexible business function, it needs artificial intelligence. The current generation of monitoring tools only works with prior, manual configuration. This is why AI is a crucial layer that, added to IT Operations, really transforms business capabilities.
Loom’s AIOps intelligence was built to support modern organizations in replacing a broad range of their IT Operations processes and tasks by connecting the dots between IT and business. Using Loom’s intelligence Sophie, modern businesses are able to successfully predict and solve IT incidents, leading to successful digital transformation.
Q: As the leading AI-based log analysis company, what is your advice for organizations looking to deploy AIOps technology? How can they choose the best products in the AIOps marketplace and get the most return on investment (ROI)?
A: As a next-gen IT monitoring solution, AIOps not only shifts IT Operations from reactive to proactive, but it also makes IT faster, less costly, and a lot smarter. Enterprise companies suffer from IT incidents impacting revenue and customer experience and have grown to use a reactive model to handle these threats to the business. In order to improve efficiency and protect their services revenue, companies use Loom Systems’ solution Sophie as an AI layer to continuously and proactively find incidents and help solve them with a crowdsourced knowledge base, before customers are affected.
And this leads to the realization that implementing AIOps is a business decision, with the goal of improving customer experience through prediction and resolution of issues. I get asked a lot about where to start with AIOps, and the first thing I advise to do is to look at where predicting failure can get you to more revenue. The move to become a digital enterprise has to support revenue growth, and AIOps is the way to shift from firefighting issues to becoming predictive.
The introduction of an AIOps solution needs to cover the span of applications, infrastructure, and network monitoring to enable IT to deliver growth and stability to the business, using the same team in an easier, more scalable process. We have recently conducted a financial study on the expected ROI from an AIOps solution and derived some interesting insights. Based on the analysis of Fortune 2000 companies, the top three financially quantified contributors of AIOps are IT productivity improvements (reducing MTTR in incident process), prevention of outages, and increased efficiency through automation. These are the areas that anyone who is looking at AIOps should focus on to deliver the most ROI for the organization. By predicting IT incidents and providing IT teams with the recommended actions to fix those incidents, Loom Systems is able to save more than $13 million in three years for a typical, or what we call “The Winner Organization”.
Q: Tell us about your AIOps-powered log analytics solution. How exactly does Sophie empower IT teams and help enterprises predict and prevent problems in the digital business?
A: Using AI and machine learning, Sophie automatically parses logs, detects IT incidents within any type of application, and alerts you immediately. The intelligence builds from there; Sophie correlates across all the layers in the IT stack, pinpoints root-causes, enriches them with insights, and tells your IT team exactly how to fix these incidents.
By providing the explanation and the resolution of incidents within the business context, Sophie’s intelligence layer empowers L1 operators to fix IT issues without having to escalate it to Engineers and SMEs. This, in turn, frees L2 and L3 teams to focus on higher-value IT tasks, increasing the overall capacity of IT teams by 18% over three years.
To date, clients that use Loom’s intelligence Sophie reported over 40% prediction rate and a 45% decrease in mean-time-to-resolution (MTTR) in their daily IT Operations.
Q: What makes Sophie stand out over other similar solutions in the market today? What are its key features and capabilities? Can you please provide us with sample use cases.
A: We all know logs – the black screen full of text that no one likes. Whenever there’s a problem, everyone looks at the logs to try and understand what’s going on, which is completely reactive. The best we’re able to do today is look at visualizations that are part of a methodology. This is why our AIOps solution is unique in the market; Sophie is an intelligence layer that understands logs automatically, without configuration, and is able to take that understanding all the way up to the insight level, explaining the business impact of the incident and the exact action needed to correct the issue.
Once implemented on cloud or on-premise – in a matter of minutes – Sophie starts ingesting logs (structured, unstructured, semi-structured) and begins providing proactive alerts, automated root-cause, and recommended actions, based on its existing intelligence network.
Mostly Fortune 2000 companies like SoftBank are using Loom Systems effectively to predict, prevent and resolve IT incidents before they impact the business. This not only keeps their operations running smoothly and improves business productivity, but also alleviates the tedium of reading logs, and frees up time for operations to concentrate on other important IT tasks. Using Loom Systems, enterprises are able to predict over 40% of priority-1 Incidents and reduce mean resolution time by 45%.
Q: Looking ahead, what market movement, trends, and technologies are you seeing that will impact AIOps in the coming years?
When things break, businesses are at risk of losing their reputation and revenue. This creates a big technological barrier and skill set gap for the people who solve issues in all systems. The role of AIOps is going to grow exponentially because these problems can’t be economically solved without a technology that has a cognitive layer to it. This is why I believe that AIOps is fascinating, and it’s interesting to see that Fortune 2000 companies are among the largest innovators, embracing these exciting new capabilities.
About Loom Systems
Loom Systems is a premier provider of advanced AIp-powered analytics solution that predicts and prevents problems in the digital business. Built for low-touch operational simplicity and usability, Loom Systems’ cutting-edge solution empowers DevOps, IT, system admins, security specialists, and NOC teams by transforming reactive users into proactive power-users. Loom Systems specializes in predictive analytics, machine data, big data, artificial intelligence, AIOps, IT operations analytics, and more.