AiThority Interview with Arka Dhar, Co-founder and CEO at Zinier

Know My Company

What is your experience about the journey into Artificial Intelligence? What made you found Zinier?

Before starting Zinier, my co-founder Andrew Wolf and I founded and sold Lift12, a data-driven platform for lifestyle brands. During that time, we provided retailers with recommendations on what inventory to stock, popular trends, and more. Unfortunately, these companies struggled to actually implement these recommendations across their various locations.

After selling Lift12, Andrew and I put our heads together and started thinking about other logistics- and process-driven industries facing a similar challenge. Almost every industry we looked at was dealing with similar issues around operational visibility and execution in the field. Much of this came from field service workers both technicians in the field and back-office coordinators – being entirely overwhelmed by inefficient processes, or working with technology that actually hindered their effectiveness instead of amplifying it.

For example, many field service organizations today rely on hundreds of disparate systems to plan, schedule, and execute work. And technicians in the field are often forced to cycle between 20 or more systems to actually deliver service. On the back end, the ratio between back-office coordinators and field technicians can be as low as 1:4. This means the back office is putting out fires all day, from dispatching technicians to handling service escalations and avoiding potential SLA violations. To add another layer of complexity, field service workforces are often a mix of badged employees and contractors.

This is where Zinier comes in. We use Artificial Intelligence to automate routine tasks for both field technicians and the back office, alleviating that 1:4 ratio and establishing clearer visibility across field service organizations. Ultimately, we’re shifting the paradigm from field service management to field service automation.

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What are your customer-focused AI products?

We recently announced ISAC (Intelligent Service Automation and Control), an AI-driven platform that helps field service organizations infuse AI into their existing processes. ISAC uses Machine Learning and analytics to help organizations optimize every aspect of their field service operations, resulting in the highest levels of automation-driven outcomes.

By comparing a constant flow of field data against historical trends, ISAC is able to recommend the best course of action at any given time. And with a flexible, open architecture, customers can easily build their own use cases for AI, whether it’s scanning a closeout package for anomalies or recommending a stock transfer by predicting parts required for a work order and an individual technician’s parts on-hand.

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How can enterprises leverage on Zinier’s Mobile Workflow Studio?

Traditionally, most field technicians have to use multiple mobile apps to find the job site, understand the task, input data, and upload closeout packages at the end of a project. Not only that, but it can take weeks or even months before a change to an installation or maintenance process is reflected in the corresponding mobile workflow.

Zinier’s Mobile Workflow Studio enables field service organizations to create step-by-step mobile workflows to guide technicians through each task. This is especially impactful for enterprises that rely on new hires and outsourced partners, who would otherwise need to be trained or monitored closely to ensure a consistent level of service. With Zinier, customers can easily create, modify, test, and deploy mobile workflows in hours, not months.

How have you worked on AI-automation in Field Force Optimization?

The actual execution of fieldwork is a fairly complex process, from the creation of a work order to the work being done to the closeout of a work order. Our focus is on how we can apply automation and AI to all the different aspects and stages of this operation to help drive efficiency and lower operational costs.

One of these examples is our ability to automate just-in-time scheduling. In the past, when a customer called in with an issue, the back-office coordinator had to manually figure out which parts might be needed to solve an issue and then look through a long list of technicians to determine who was available and equipped to take on the task. Zinier automates most of this manual work. It can take the issue and compare it to an entire history of similar work that’s been performed, or even the history of work done to that particular asset, to more accurately predict the potential issue and parts needed. Zinier then matches the task to the best technician for the job, evaluating their past work completed, skillset, proximity to the job site or even parts on-hand.

By automating this specific aspect of the delivery chain, we are able to put more time back in the hands of coordinators, improving first-time-fix-rates and driving down operational costs.

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How does Zinier apply AI in service insights? How does technologically optimized data help enterprises come to better consensus?

With thousands of moving parts and people, it can be difficult to pinpoint where issues start and what actions to take next. Our AI-driven platform can help uncover performance trends as they emerge, eliminating guesswork and helping people identify issues before it’s too late. Once operational kinks are worked out, organizations can set optimized processes in motions with intelligent automation that continuously adjusts based on workforce behavior.

With custom dashboards for every level of an organization, Zinier helps field service organizations visualize the metrics that matter most. By weeding out the false flags from the relevant data, organizations can spend less time crunching numbers and more time turning insights into action. They can also improve their ability to forecast accurately by monitoring the overall performance of field teams and equipment, then drilling down to each individual site and technician.

Is Zinier planning to develop IoT solutions for the utility industry? How can IoT solve the problems of capital-intensive industries?

We are a pure enterprise SaaS company, and our expertise is in helping companies accomplish more with automation and AI. We don’t have plans to create and manufacture IoT devices. Instead, we’ve built an open software platform with ISAC, allowing our customers to connect any IoT device they’d like using APIs or even creating custom apps to control connected devices.

We currently work with global telecom providers, which is an extremely capital-intensive industry. What we’re seeing with a lot of wireless providers is an initial foray into IoT where they stream real-time data and alerts from wireless towers back to the Network Operations Center (NOC). However, there is a disconnect between alarms coming into the NOC and the actual deployment of technicians to solve issues. This is where Zinier can help. Whenever an IoT device triggers an alarm, our AI-driven platform looks at historical trends and site records to determine the next step, whether it’s attempting a remote fix or dispatching a technician. And with more data informing each decision, companies are able to reduce the amount of time spent visiting sites and triaging issues.

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How do you seek information on Artificial Intelligence related topics?

I consume information from a variety of sources, from podcasts and traditional media sources to industry reports and trade publications. I also try to attend trade shows and other events where I can interact with entrepreneurs and thought leaders.

What makes deploying Artificial Intelligence capabilities so hard?

One of the biggest hurdles is getting buy-in from all stakeholders. Most enterprises already have a number of solutions in place and are reluctant to invest in new platforms and additional training.

Once the decision to invest in AI has been made, enterprises still need to deploy a custom solution. Because there are so many different systems in place, an AI implementation for one company may look entirely different from another. On top of this, an AI engine from one provider may not work across the board, causing siloed workflows and limited visibility. To combat this, we purpose-built a platform-agnostic, end-to-end solution that would help organizations infuse AI into their existing processes.

Where do you see AI/Machine Learning and other smart technologies heading beyond 2025?

I think we’re still in the early stages of understanding how AI and ML can be applied in business to help streamline operations and drive productivity. There’s been a fear of how automation and robots are going to take over all jobs, but we just don’t see that happening in the near future. However, the definition of a company’s “workforce” is going to change. Today, it consists of only people. Over the next five years and beyond, it will actually evolve into a “blended” workforce of people, AI, and automation. This is especially true in field service, where the “last mile” of execution still has to happen with people.

The other major technology evolution just starting today is 5G. The promise is ubiquitous connectivity, and everything around us coming online, from autonomous vehicles to smart cities. This means millions of more devices constantly streaming data, and the only way to make sense of it all is through AI/ML. We’re certainly in an interesting time!

What start-ups and labs are you keenly following?

I like to keep track of adjacent industries and technologies, so any startup focused on Robotic Process Automation or Business Intelligence is of particular interest. Also, as an MIT alum, I follow a couple of its cutting edge labs doing research on AI, including the Computer Science and Artificial Intelligence Laboratory (CSAIL) and Data Systems and AI Lab (DSAIL).

What technologies within AI and computing are you interested in?

I’m always interested to see the interplay between people, AI, and automation. We often hear about machines replacing traditional workforces, but the most successful companies over the next decade will be the ones that augment their current workforce with Machine Learning.

It’s also fascinating to see the role automation will play in the era of hyper-connectivity – especially when it comes to the development of new technologies. We like to imagine a future of smart cities and autonomous vehicles, but don’t always think about the infrastructure required to support these ventures. The ultra-low latency and massive data capacity of 5G networks will radically transform everything from VR to self-driving cars, but it’s also forcing field service organizations to streamline their operations and work faster than ever.

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Thank you, Arka! That was fun and hope to see you back on AiThority soon.