The useful thing about a pair of thick work gloves is that they are merciless product critics. Tiny buttons become absurd. Fussy menus turn hostile. A beautiful dashboard, designed in climate-controlled calm, can become pointless beside a noisy machine in bad weather. Prateek Chakravarty learned this before anyone called him a technology executive. He learned it as a Schlumberger field engineer, working across Indonesia, France and Russia, where the distance between software’s assumptions and physical work was measured in more than pixels.
Those assignments supplied the first chapter of a career that would wander through utility strategy, venture investing, energy analytics and enterprise software. Yet the plot has stayed tidier than the résumé suggests. Chakravarty keeps returning to the people who install, inspect and repair the infrastructure everyone else notices mainly when it fails. Electricity, broadband, industrial equipment and the crews that keep them going are not glamorous until the lights go out.
Today he is CEO of Zinier, a field service management software company headquartered in California. Its products coordinate technicians, assets, dispatchers and workflows. The vocabulary now includes AI agents, predictive maintenance and no-code automation. The governing question is older and plainer: does this help someone finish the job?
The world according to a work order
On isolated oil rigs, ordinary administrative tasks acquired teeth. Status reports had to be filed. Parts and supplies had to be requested. Schedules had to be organized. The worker doing all this might be far from reliable connectivity and very far from anyone who designed the system. Chakravarty has since used examples like heavy gloves, extreme noise and remote locations to argue that developers need to immerse themselves in a field worker’s actual experience.
It is a modest idea with radical consequences. Most business software grew up around office workers, so the desk became its invisible furniture. The keyboard, the large monitor and the stable connection were taken for granted. Shrinking the same logic onto a mobile screen did not magically make it field-ready. The context had changed; the design often had not.
“Continue to be obsessed with the problem to be solved.”Prateek Chakravarty, on AI readiness
Chakravarty’s solution is not romance about the rugged worker. It is proximity. Watch the job. Learn the language. Notice which screen a dispatcher must click through to find a part, what knowledge vanishes when a veteran retires, and what happens when a supposedly elegant workflow meets an emergency repair. Software earns its keep in those particulars.
An operator learns capital, then data
After mechanical engineering at the Indian Institute of Technology, Kharagpur and his years at Schlumberger, Chakravarty attended Harvard Business School from 2006 to 2008. He then joined Pacific Gas and Electric Company. His work there included leadership roles, commodity sales and marketing, strategy around smart grids and electric vehicles, and corporate venture activity. He helped initiate a project-finance business that invested in rooftop solar and was involved with investments in SolarCity and Sunrun.
The move from an oil field to a utility boardroom could look like a departure. It was more like changing altitude. He could now see the infrastructure system around the worker: regulated markets, capital decisions, customer demand and new energy technologies. The physical world was still there. It had simply acquired a spreadsheet.
Next came Bidgely, then an early startup applying data analytics and artificial intelligence to utility meter data. Chakravarty joined its founding team, led business development and marketing, later ran worldwide sales, and ultimately served as Chief Revenue Officer. In 2013, he was already presenting work on energy disaggregation: extracting appliance-level patterns from aggregate consumption data so utilities and customers could act on something more useful than a monthly total.
Bidgely connected the two halves of his education. Infrastructure produced oceans of data; software could translate it into decisions. But adoption still depended on people, incentives and trust. A clever model was only the opening move. Someone had to turn it into a product a utility could buy and a customer could understand.
The return to field service
Chakravarty joined Zinier in 2020 as Chief Business Officer. He built global customer success and solution delivery teams, along with an enterprise sales organization. In March 2021, he succeeded co-founder Arka Dhar as CEO. His appointment announcement spoke of building a “human-machine workforce,” language that sounds futuristic until one remembers the work orders. The machine would coordinate, predict and assist. The human would still climb the pole, inspect the asset or make the repair.
His public explanation of Zinier has remained focused on companies that install and maintain physical, communications and digital assets. The common need is coordination under changing conditions. A telecom crew, utility technician and manufacturing service team do different work, but all wrestle with skills, schedules, inventory, asset histories and the surprise that arrives at 4:47 on a Friday afternoon.
No-code tools matter in this setting because the workflow refuses to sit still. A rigid system forces the operation to conform to the software. A configurable one can follow local processes and change without beginning another grand IT pilgrimage. Chakravarty’s bias is toward shorter releases that demonstrate value, an adaptation of agile methods for physical industries that may not share a software company’s rhythms.
AI comes down from the cloud
Generative AI gave Zinier’s original mission a new mechanism. Chakravarty talks about agents that can retrieve knowledge, recommend actions and automate portions of field and back-office work. His examples are almost comically uncinematic: find a part, preserve a retiree’s expertise, help a less experienced technician diagnose an issue, reduce the screens a dispatcher must visit. No chrome robot strides through the presentation. Mostly, someone gets an answer before losing patience.
He also argues that the large language models themselves are becoming commoditized. The advantage lies in identifying a meaningful problem and solving it inside the user’s context. This is why he favors vertical expertise and forward-deployed engineers who work closely with customers. A field service company should not have to become an AI laboratory merely to improve scheduling.
“We’re making their lives easier one problem at a time.”Prateek Chakravarty, on field workers and AI agents
That line contains his managerial temperament. Grand visions are permitted, provided they submit to small proofs. In 2025 he described implementation as a collaborative journey from a minimum viable product toward more autonomous operations, with measurable returns released in stages. He advised leaders to remain open to change while resisting infatuation with the technology itself. The problem is the compass; the model is equipment.
A more appealing industrial future
Chakravarty worries about the industrial skills gap. When he began his career, major industrial employers competed strongly for technical talent. The status economy later swung toward technology companies and office work. Experienced field workers are retiring, fewer young people are replacing them, and years of tacit knowledge can disappear at once.
His answer is not to remove people from the picture. It is to make their work easier to learn and more satisfying to do. AI can carry context, surface the next step and handle routine coordination. Used well, it can give a field technician some of the leverage that software has long given a knowledge worker. Productivity matters, but Chakravarty also talks about employee engagement. The worker should feel more capable, not merely more measured.
There is an aspiration behind the pragmatism. In a podcast, Chakravarty named Sal Khan of Khan Academy as an entrepreneur he admires, pointing to technology used for broad social impact. His own chosen constituency is the deskless workforce. The comparison is revealing: access before spectacle, usefulness at scale, and technology as a way to widen someone’s options.
Off duty, public biographies describe him as an avid sports fan and adventure globetrotter. He speaks English, Hindi and Bengali, plus some Russian acquired along the route. His comfort-watch sitcom is Friends, a pleasantly ordinary choice for an executive occupied by emergency dispatch and industrial automation. In a 2026 personal essay, an abruptly rerouted flight became an occasion to write about mindset and the strangers who helped one another during an unexpected three days in Dubai. Even the travel story bent toward people solving the situation in front of them.
The stubborn detail wins
Careers are often narrated as escapes: from the field to the office, from operations to strategy, from old industry to new technology. Chakravarty’s makes more sense as a return. Each stop gave him another instrument for the same work. Engineering supplied the physical facts. Business school and PG&E exposed the economics. Bidgely added data and startup go-to-market. Zinier brought him back to the technician, now carrying a software platform instead of a paper form.
The appealing irony is that his AI thesis depends on refusing to be dazzled by AI. Talk to the customer. Understand the pain. Speak the industry’s language. Release value in pieces. A model may be able to generate a maintenance plan in seconds, but somebody still has to know whether the plan survives rain, gloves, a missing part and the last appointment of the day.
That is where Chakravarty’s story remains grounded. Infrastructure is abstract only from a distance. Up close, it is a person, a tool, an asset and a job that needs finishing. The future he is building has plenty of intelligence in it. It also remembers to check the weather.