Swish, a Tel Aviv, Israel-based startup developing automation technologies for IT service management, today emerged from stealth with $13 million in a series A round led by Dell Technologies Capital with participation from Skywell, Samsung, StageOne, and AxessVentures. The funding will be put toward expanding the company’s headcount and for supporting go-to-market efforts, CEO Sebastien Adjiman says, as well as bolstering Swish’s product stack.
Enterprise help desk support is one of the most labor-intensive — and costly — IT functions. A 2020 BMC study found that the cost of manually handling a help ticket averages $22. Exacerbating the challenge, the acceleration of digital transformation during the pandemic has increased help desk ticket volume. In addition, the IT labor shortage is limiting the ability of enterprises to staff up to meet this growth. According to the latest data, U.S. IT job growth slowed in October because of too few candidates.
Swish uses AI to automate ticket orchestration in existing IT service management workflows. With Swish, tickets can be automatically routed to relevant, available agents based on skill set, load, and cost criteria, ideally improving the resolution time. The platform also provides management with analytics to help identify optimization opportunities in the organization, generated by a combination of natural language processing (NLP), business process mining, and machine learning algorithms.
“We founded Swish with the belief that [the] real value of [automation] isn’t just simple efficiencies but is instead the ability to turn the avalanche of data that’s being generated by today’s digital interactions into autonomous decisions which are smarter, faster, and more accurate,” Adjiman told VentureBeat via email. “We believe Swish is the perfect solution for any enterprise service and support leader who’s looking for a way to quickly utilize the benefits of [automation] to help them reinvent their current ticket process.”
Swish scores IT service reps on their expertise, knowledge, strengths, and weaknesses. Using an AI system, the platform automatically groups similar tickets based on the data contained in tickets, such as ticket descriptions and resolution notes.
Swish looks for inefficient patterns of behavior such as “ping pong,” “rework,” “pending abuse,” and poor workload allocation. It also flags service types leading to high or low satisfaction and costs among customers, informed by sentiment analysis from feedback forms.
“The Swish platform’s core AI engine consists of a unique combination of proprietary machine learning, NLP and business process mining algorithms, which are trained on all the historical tickets that are archived in the existing tools used by our clients,” Adjiman explained. “This historical goldmine of data is then used dynamically to train the models to capture insights about our client’s unique environment, even as it evolves. For example, our service language understanding goes beyond NLP to explore service-specific terms — improving the understanding of each underlying ticket issue and thus identifying the next best action more accurately.”
Against the backdrop of Swish’s relaunch, companies are looking broadly to increase their use of automation technology as a result of the pandemic. The BMC survey found that by automating help ticket desk resolution, 22% of tickets can be resolved at practically no cost — in part because of improved error handling and analysis tools like reporting. This is key, given that 95% of customers cite help desk support as important in their choice of and loyalty to a brand.
“The core use case of Swish’s platform is its autonomous ticket orchestration capability. Swish … suggests and provides resources to ensure [agents] have everything at their fingertips to resolve a ticket without the need to re-route or pause it,” Adjiman explained. “Since it’s agnostic by design, it can be deployed on any enterprise ticketing system such as ServiceNow or BMC. Once deployed … Swish can then be connected to additional workflow systems to accelerate any service and support area, such as customer service, HR, and facility management tickets.”
Of course, the employee monitoring aspects of Swish might be discomfiting to some companies. While 78% admit to using monitoring software to track their employees’ performance, 59% of workers say that they feel stress or anxiety as a result of their employer monitoring them, while 43% say that it feels like a violation of trust.
But 35-employee Swish pitches its analytics as a means to provide targeted training. Low-performing reps can be afforded opportunities like tutorials, guidance, and coaching, Adjiman says, or shifted to an area of service for which they might be better suited.
“Service management is an obvious target for the emerging [automation] industry due to the rapidly growing ticket volumes and labor-intensive processes enterprises rely on today,” Dell Technologies Capital managing director Yair Snir said. “The Swish team has already proven the value of [automation] for some of the largest companies in the world.”
Swish — which has raised a total of $15 million in capital and has 15 customers, including Fortune 500 companies — competes with a number of startups in the IT service automation space including Moveworks, Capacity, Electric, and Spoke. Underlining the segment’s growth, Zendesk recently acquired Cleverly, a service automation startup that creates AI-powered tools to solve common customer problems, for an undisclosed amount.
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