Based on the current McKinsey report on the state of AI, 78% of surveyed organizations use AI in no less than one enterprise perform, up from 55% in 2023. However whereas most corporations are nonetheless experimenting with AI implementations, some visionaries have already remodeled complete industries with groundbreaking options that save thousands and thousands of {dollars} and serve a whole bunch of 1000’s of customers every day.
Meet Ankit Agarwal – the architect behind AI methods that course of orders for McDonald’s and Starbucks clients worldwide, automate warehouse operations dealing with thousands and thousands of Amazon packages, and have remodeled how over 450,000 eating places run throughout essentially the most difficult time in hospitality historical past. As a founding engineer of Voice AI at DoorDash and a former senior software program engineer at Amazon, Agarwal has spent greater than a decade turning formidable AI concepts into real-world options that generate billions in financial worth.
What does it take to construct AI methods that world manufacturers belief with their core operations? How do you scale from prototype to enterprise deployment, serving thousands and thousands of customers? And what important errors do most corporations make when implementing AI that forestall them from reaching transformative outcomes?
On this unique interview, Agarwal shares the insider methods behind creating AI options that function at scale, recounts hard-won classes from main groups of fifty+ engineers on cutting-edge initiatives, and explains why the following wave of AI adoption will both make or break small companies and what leaders can do at present to place themselves on the successful facet.
Lately, AI and automation have been reworking industries in an unprecedented approach. Out of your perspective, what are essentially the most vital alternatives and challenges for companies adopting AI at present?
As AI adoption grows, its advantages change into apparent each for enterprise-level corporations and for smaller companies. As an illustration, AI-driven innovation permits corporations a wide selection of alternatives for optimizing sophisticated processes, similar to conserving warehouse stock underneath management or processing a number of orders. Nonetheless, for AI integrations to be helpful, companies want a acutely aware strategy to adopting new applied sciences. Operational processes ought to be studied first, so the improvements do not stay superficial and change into actually built-in into enterprise operations. Consequently, funding within the worker workforce schooling and adaptation ought to go hand in hand with implementing new technological options.
Your profession contains a number of initiatives of integrating progressive options into enterprise workflows. It started at Samsung Analysis Institute and took you to Amazon and DoorDash, the place you led transformative AI initiatives. How did these experiences form your strategy to Innovation?
I understand each process I work on, no matter its scale, as a possibility to grasp new applied sciences and purchase new information and expertise that I’ll take additional to my subsequent initiatives. As an illustration, my early expertise at Samsung supplied me worldwide publicity and taught me to work in cross-functional environments whereas holding as much as business requirements. These abilities proved helpful later, at Amazon and DoorDash, the place I labored on large-scale and impactful initiatives. One other key ability that proved helpful many occasions all through my profession was figuring out ache factors and discovering essentially the most environment friendly solution to apply novel applied sciences to resolve them.
Your work at DoorDash, the main meals supply firm within the US, is a transparent instance of such an strategy. There, you led the event of GenAI-powered voice automation methods and ordering platforms that at the moment are utilized by a number of world manufacturers. What do these improvements inform us about AI reshaping buyer expertise and operations?
AI-based options possess the potential to resolve the issues eating places and meals deliveries are going through at present, similar to labor shortages, together with rising workload. The AI-powered voice system allowed eating places to automate voice orders and customer support, decreasing the workers workload and bettering service pace. The voice-ordering system built-in with POS terminals was carried out for phone-ordering in 2023 after which expanded to drive-throughs in 2025, turned one of many pioneering options of this type, and was later adopted by main meals chains like Donatos and Rooster Categorical. This instance demonstrates that AI-based options, when performed proper, profit clients, companies, and workers alike, as they scale back workload and enhance customer support.
Your work for Amazon, the worldwide retail big that processes thousands and thousands of orders per day, additional demonstrates that automation just isn’t restricted to processing orders and could be built-in into totally different points of the enterprise operation, from warehouse administration to fraud detection. What classes did the large-scale initiatives educate us about scaling AI initiatives for affect?
In comparison with the earlier instance of implementing AI in customer-facing providers at DoorDash, the initiatives I’ve developed for Amazon primarily targeted on the corporate’s inside processes. The automation required each putting in new {hardware} and sensors similar to cameras and scales for monitoring, and creating software program for processing this newly acquired knowledge, which was used for real-time warehouse administration, predictive analytics, and environment friendly logistics. Because of this, the system allowed for a discount in labor prices and minimized errors instantly, but additionally offered the mandatory sources for e-commerce development that adopted in 2022 and past.
As a workers software program engineer at DoorDash, you formed an environment friendly crew of a number of dozen engineers who work collectively to create progressive options. What recommendation would you give aspiring tech leaders who need to drive improvements at their organizations?
The principle lesson to attract from this expertise is that collaboration is essential for innovating and reaching technical excellence. For large and small groups alike, it’s obligatory to determine an atmosphere the place folks with totally different backgrounds can voice their views and share concepts. Furthermore, keep in contact with folks in your organization in addition to your rapid crew: perceive what affect your innovation could have on their workflows and make sure you handle the challenges which will come up from that. Keep away from innovation for the sake of it: analysis your person ache factors and give attention to them. As an illustration, understanding the challenges small eating places confronted helped us to create a platform that’s actively utilized by them.
As AI-driven options like these we’ve got mentioned proliferate in numerous industries, what do you concentrate on their affect within the close to future? Will they continue to be a prerogative of enterprise-scale corporations, like Amazon, or have they got the potential to change into extra accessible for smaller companies as effectively?
As AI know-how advances, it turns into extra accessible for smaller companies, for instance, via cloud-based and low-code options. At DoorDash, our work confirmed how AI-driven instruments like voice automation can empower smaller eating places to compete in a digital-first financial system. Trying forward, I need to advance AI-driven restaurant automation even farther. Combining it with logistics and autonomous supply innovation, I plan to scale these platforms worldwide and make the service extra accessible for underserved communities, driving financial and social fairness
