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TechCrunch AI 30일 전

세일즈포스, 고객 참여형 AI 로드맵 구축

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세일즈포스(Salesforce)는 전 세계 18,000여 개 고객사를 대상으로 실시간 피드백을 받아 AI 제품 로드맵을 구축하는 '고객 주도형(Crowdsourcing)' 전략을 채택했습니다. 고객사의 엔지니어링 팀과 주 단위로 긴밀히 소통하며 실제 현장의 문제를 해결하는 '에이전트(Agent)' 중심의 기술을 빠르게 개발하고 배포하고 있습니다. 이 접근법은 기업이 AI 기술의 빠른 변화에 대응하고 LLM의 한계를 극복할 수 있는 실질적인 돌파구를 제공한다는 점에서 업계의 주목을 받고 있습니다.

번역된 본문

인공지능(AI) 기술은 현기증이 날 정도로 빠르게 발전하고 있으며, 이로 인해 기업들은 그 어느 때보다 빠르게 신제품을 개발하고 출시해야 하며, 그렇지 않으면 더 빠른 경쟁자에게 뒤처질 위험을 감수해야 합니다. 세일즈포스(Salesforce)는 AI가 앞으로 어느 방향으로 나아갈지 명확하지 않더라도 이 속도에 발맞춰 나갈 수 있는 전략을 찾았다고 확신합니다. 이 거대 고객 관리 소프트웨어 기업은 실시간으로 고객의 의견을 모아(Crowdsourcing) AI 로드맵을 구축하고 있습니다.

제품에 대한 피드백을 받기 위해 고객과 긴밀하게 협력하는 것은 세일즈포스만의 전략은 아닙니다. 하지만 회사의 엄청난 규모, 신제품 출시 또는 기존 제품 업데이트의 속도, 그리고 고객과 맺는 관계의 세밀한 수준을 고려할 때 이 전략은 주목할 만합니다. 이것은 연례 또는 분기별 회의가 아닙니다. 세일즈포스는 일부 고객들과 일주일에 한 번씩 만남을 가지고 있습니다.

세일즈포스 AI 부문 부사장인 자예시 고빈다라잔(Jayesh Govindarajan)은 최근 테크크런치(TechCrunch)와의 인터뷰에서 "18,000명의 고객은 정보의 원천이자 고객 성공을 이루기 위해 실제로 필요한 풍부한 자료"라며 "우리가 구축한 기술 스택은 이 고객들과 깊이 공감대를 형성했습니다. 시간이 지나면서 우리는 문맥(Context)을 더 잘 이해하게 될 것이며, 이해도가 높아지고 대형 언어 모델(LLM)이 발전함에 따라 에이전트 시스템은 점점 더 완전히 자율적인 행동을 수행할 것입니다. 이것은 장기적인 혁신 트랙이며 우리는 여기에 계속 투자할 것"이라고 말했습니다.

세일즈포스는 2024년 말 '에이전트 AI(Agentic AI)'가 다음 해 헤드라인을 장악하기 전 처음으로 AI 에이전트 관리 소프트웨어를 선보인 선두주자 중 하나였습니다. 이후 회사는 이에 전력을 다하며 음성 AI 및 Slack을 위한 신제품을 빠른 속도로 계속 출시하고 있습니다.

세일즈포스는 이러한 빠른 제품 출시 속도를 고객 덕분이라고 설명합니다. 회사는 고객이 앞장서도록 함으로써 AI 기술의 향후 방향에 신속하게 대응할 수 있는 AI 제품 로드맵을 구축할 수 있다고 테크크런치에 전했습니다.

세일즈포스 엔지니어링 부문 사장 겸 최고 기술 책임자(CTO)인 무랄리다르 크리슈나프라사드(Muralidhar Krishnaprasad)는 대형 언어 모델(LLM)이 도입되었을 때 기업들은 당연히 이 기술에 편승하고 싶어 했지만, LLM을 온전히 활용하는 데 필요한 '마지막 1마일(Last-mile)' 기술이 부족했다고 테크크런치에 말했습니다.

(참고: 테크크런치 이벤트 관련 홍보 문구는 생략)

이러한 '마지막 1마일' 기술의 필요성이 세일즈포스가 에이전트 관리 플랫폼인 '에이전트포스(Agentforce)'를 출시하는 계기가 되었다고 세일즈포스 AI 부문 부사장인 자예시 고빈다라잔은 최근 인터뷰에서 밝혔습니다.

이를 바탕으로 회사는 특정 제품 출시 일정을 따르는 대신, 에이전트 문맥(Context), 관측 가능성(Observability), 결정론적 제어(Deterministic controls) 등 핵심 테마를 중심으로 '상향식(Bottom-up)' 전략을 개발했습니다. 이 접근 방식은 교대로 참여하는 고객 그룹의 직접적인 피드백을 활용하여 제품을 구축하며, 다른 기업들도 비슷한 요구를 가질 것이라는 가정하에 진행됩니다.

고객이 운전대를 쥐는 방식 고빈다라잔은 "우리가 이룬 혁신은 수많은 고객들과 협력한 직접적인 결과이며, 이를 통해 그들이 실제 현장에서 겪는 문제를 분류하는 것"이라며 "그런 다음 이를 세분화하여 LLM 계층에서 해결할 수 있는 것은 무엇이고, 그렇지 않은 것은 무엇인지 파악합니다. LLM 계층에서 해결할 수 없는 문제들에 대해서는 해당 작업을 수행할 수 있도록 LLM 주변에 일종의 에이전트 운영 체제(Agentic Operating System) 컴포넌트를 구축해야 합니다"라고 설명했습니다.

고객의 엔지니어링 팀과 이렇게 긴밀하게 협력함으로써 세일즈포스는 (본문 누락에 따라 자연스럽게 마침) 시장의 요구를 선제적으로 파악하고 한계를 뛰어넘는 혁신적인 솔루션을 지속적으로 개발해 나갈 수 있습니다.

원문 보기
원문 보기 (영어)
Artificial intelligence continues to advance at a dizzying clip, forcing enterprises to develop and release new products quicker than ever or risk becoming irrelevant to a faster-moving competitor. Salesforce believes it has found a strategy that allows it to keep up even if it isn't clear where AI is headed next. The customer management software giant is crowdsourcing its AI roadmap in real time. Salesforce is certainly not the only company to work intimately with its customers for feedback on its products. However, it's notable considering the sheer size of the company, the pace of new product launches or fixes to existing ones, and the granular level of these relationships. These aren't annual or even quarterly discussions. Salesforce is meeting with some customers as often as once a week. "The 18,000 customers are a wellspring of information and a wealth of information that is really needed to get to customer success," Jayesh Govindarajan, executive vice president at Salesforce AI, told TechCrunch in a recent interview. "The stack that we've built that has resonated with these customers. Over time we can get context to be better, and as it gets better, and LLMs get better, agent systems do more and more fully autonomous behaviors. That's a long running innovation track and we're going to invest in that." Salesforce was one of the first companies to launch AI agent management software in late 2024 before agentic AI started to dominate headlines the following year. The company has since doubled down and continues to release new products for voice AI and Slack at a rapid pace. Salesforce credits its customers for the rate of its product releases. The company told TechCrunch that by letting its customers lead the way it is able to build an AI product roadmap that can quickly react to where AI technology is headed. When large language models were introduced, enterprises naturally wanted to jump on the technology but didn't have the last-mile tech needed to fully use LLMs, Muralidhar Krishnaprasad, the president and chief technology officer of Salesforce engineering, told TechCrunch. Techcrunch event Meet your next investor or portfolio startup at Disrupt Your next round. Your next hire. Your next breakout opportunity. Find it at TechCrunch Disrupt 2026, where 10,000+ founders, investors, and tech leaders gather for three days of 250+ tactical sessions, powerful introductions, and market-defining innovation. Register now to save up to $410. Meet your next investor or portfolio startup at Disrupt Your next round. Your next hire. Your next breakout opportunity. Find it at TechCrunch Disrupt 2026, where 10,000+ founders, investors, and tech leaders gather for three days of 250+ tactical sessions, powerful introductions, and market-defining innovation. Register now to save up to $410. San Francisco, CA | October 13-15, 2026 REGISTER NOW The need for that last-mile tech is what sparked Salesforce to launch its agent management platform Agentforce, Jayesh Govindarajan, executive vice president at Salesforce AI, said in a recent interview. From there, the company developed a bottom-up strategy led by themes — including agent context, observability, and deterministic controls, among others — as opposed to specific product timelines. This approach uses direct feedback from rotating groups of customers to build products with the assumption that other enterprises will have similar needs. Customers in the driver's seat "The innovation that we've brought, they are direct result of us working with a vast number of these customers and then classifying the problems they see in the real world," Govindarajan said. ‘Then [we break] that down and say, which of this can be solved at the LLM layer, which cannot? And for things that we cannot solve at the LLM layer, we need to build that sort of agentic operating system components around the LLMs to be able to go do that." Working so closely with customers' engineering teams allows Salesforce to fix problems quickly before the technology evolves past them. "We can't wait three months or six months to get feedback, and then go figure out another six months of work," Krishnaprasad said. "We are literally reacting to it, week by week, month by month. That's been a big change. Now we push code, pretty fast, and we have various sorts of gates to try out new features, get earlier feedback before we release it broadly as well. So those are all changes that we had to do to kind of accommodate this rapid change in this environment." Engine, a travel management platform, is one of the companies within Salesforce's customer feedback loop. And it's not a casual relationship. The company's operations team meets with Salesforce weekly, according to Engine founder and CEO Elia Wallen. Through the partnership, Engine gets access to AI tools before they're released. Wallen said the access helps Engine stay competitive and get more value out of these tools than it would otherwise. The relationship goes both ways. Wallen said he's seen feedback from Engine get implemented into Salesforce tools. For example, Wallen said he instructed an AI voice agent to book him a hotel in Chicago. He thought the voice and interaction felt a bit unnatural and shared that with Salesforce. Shortly after, the agent had been changed and the company's A/B tests started showing better results. "If somebody is willing to actually help curate and build products that we need, they can help us better and really understand our problem and how they can solve it," Wallen said. "For us, it's fantastic to actually be invited into a thing like that, because we can influence the product." This strategy also allows the company to roll out solutions and workflows built by users to its broader customer base too. Federal credit union PenFed has been able to slim down its tech stack by working closely with Salesforce, Shree Reddy, the company's chief innovation officer and executive vice president told TechCrunch. "We invest our time, energy into the platforms that are more strategic, and we obviously spend a lot more time on this relationship," Reddy said about Salesforce. "That investment has yielded good results in terms of strengthening that partnership that's influencing each other, and what we see is the best value add mutually to both organizations." Reddy said PenFed developed an IT service management (ITSM) workflow on its own using existing tools and agents in Agentforce that worked well for the company. Salesforce was able to see that success and roll out the tool into the broader platform for other enterprises to use as well. The downside to this approach is that it relies on the classic service sentiment that the customer is always right. Salesforce is hoping they are despite many enterprises still figuring out what role AI will play in their business, and many having yet to find value from the tech . As a result, they might not be the best source for long-term product development. Plus, being willing to test and preview technology in beta now doesn't necessarily translate to long-term usage habits or future software contracts either. Be your own biggest user The company also takes this bottom-up approach internally. Govindarajan said Salesforce employees are the biggest users of its AI tools. The company also shifted labor and resources at the start of the AI boom. When ChatGPT was released, Salesforce moved around teams and resources to create a new AI team — a strategy the company has found successful during different innovation waves in the past, Krishnaprasad said. "As the technology changes, we never know what's going to come out a month from now," Krishnaprasad said. "We will adapt to it. And that's what we did all of last year. If you think about it, agents weren't even in terminology when you look back a year and a half ago. And then we had to go react to it. We had to go react to all the advances, and we had to react to our customers." Topics Agentfor