“Swish.ai is like putting a Ferrari engine in your Volvo”, said one of our enthusiastic customers. How can Swish.ai have such an impact on your existing ITSM system? Let’s take a look under the hood…
What is the most dominant factor affecting the time it takes to process and resolve
an ITSM ticket? When I ask this question, the common answer I get is, “It depends on the nature of the ticket”. Indeed, the nature of the ticket is an important factor, but it’s not the single most important factor.
The single most dominant factor is ‘wait time’.
Going digital is not about investing in the latest and greatest technologies. It’s about adopting transforming the people, processes and systems in place to solve business problems. Digital transformation is often seen as a logical and expected end-goal of deploying new technologies.
The enterprise IT segment is going through an age of digital disruption. Organizations need the right skills, processes and tooling to launch new business models and digitize interactions with their internal workforce and end-users alike. Yet, most digital transformation projects fail in terms of delivering the promised Returns on Investment (ROI). According to a recent McKinsey report, 70 percent of all transformation projects fail in this regard.
In this article, we will discuss how your ITSM organization can improve the ROI on your existing tooling investments, especially in the IT Service Desk applications.
Artificial Intelligence is redefining the role of ITSM in data-driven business organizations. ITSM users expect new solutions to deliver on the promises of service excellence, instant and effective delivery of highly personalized ITSM capabilities. However, many CIOs continue to experience a disconnect between the marketing hype and real-world performance due to underoptimized technology enablement. In this article, we take this discussion to some of the leading industry experts, who weigh in on the best approach to deploy AI technology solutions to optimize ITSM delivery:
In this post, we will focus on ticket routing operations within the IT Service Desk domain. Organizations typically model business decisions against three key constraints: Problem Identification, Ticket Distribution and Workforce Assignment. The goal of an autonomous ticket routing technology is to optimize for all three of these key pillars.
It started with a creative approach to finding the perfect VP Engineering for DeepCoding, and ended with an important discussion on gender equality and language
It is common knowledge that technical service and support is a high turnover industry. In fact, global benchmarking data from 2020 shows that annual agent turnover was about 40% for enterprise agents and a staggering 97% for managed service providers! This is problematic for numerous reasons, including the high cost of turnover and the loss of knowledge and expertise that accompanies agent turnover.
In this article, we will discuss the difference between automation and autonomy in context of ITSM technology solutions and how Swish.ai has taken the charge of the AI revolution in ITSM.
In this blog, we will focus on understanding the waste processes, the subsequent IT firefighting loop and how intelligent ITSM technology capabilities can help go truly lean with IT Service Desk operations.
Lean IT has failed to present itself as a simple fix for every organization struggling to manage operational bottlenecks and productivity challenges. In fact, Lean IT has been far from delivering the promised rewards.
Since many organizations are pursuing digitization efforts, it’s important to first understand what digital transformation really means. According to Sloan MIT, digital transformation refers to “the use of technology to radically improve performance or reach of enterprises”. The performance refers to customer-facing interactions as well as internal operations that are inherently driven by technology as part of the ITSM framework. The latter determines the state of an organization to prepare for the digital disruption — to build up the knowledge and capabilities necessary for keeping pace with digitization efforts as an agile organization.
In the IT Service Management domain, service desk and support departments are seemingly riddled with productivity challenges and failed technology investments. And the repercussions extend beyond the negative impact to customer satisfaction. Especially for internal IT Service Desk use cases in large enterprises, the overwhelmed and unproductive support structure limits the ability of teams and individuals working on large-scale mission-critical projects, ultimately causing significant loss in business opportunity – a soft cost often overlooked by IT.
The IT Service Desk uses a range of metrics to understand the true impact of an IT incident, each presenting a different cost perspective IT incidents. In this post, we will discuss an important IT Service Desk metric, Mean Time to Resolve (MTTR) and its importance in understanding the true business impact of unresolved IT support requests.
Efficiency alone doesn’t ensure success, but inefficiency is a sure way to fail. You know who knows about efficiency? Ants. Yep, these tiny creatures, though infamous for ruining picnics, operate in an incredibly systematic and effective way. The data scientists at DeepCoding, a Tel Aviv-based AI startup, draw their inspiration from these ant colonies’ models of efficiency for their machine learning model. And it works! DeepCoding’s Delivery Intelligence platform has already helped large enterprises to increase the efficiency of aspects of their operations by 80%, and their customer circle among Fortune 2000 companies is constantly growing.
In the context of DeepCoding and the IT/software work, the first revolution will be a transition from the existing “systems of record” to “systems of intelligence”. This means that today’s systems are today keeping records of all the work done by the IT/software teams – typically systems such as ServiceNow or JIRA,There is now a transition to new systems such as DeepCoding that leverage the data contained in the Systems of Records and leverage AI/ML to predict risks and make recommendations to managers about how to reorganise work in the most efficient way in order to mitigate those risks.