기업들이 본격적인 AI 도입을 시도하면서 직면한 가장 큰 과제는 '파편화된 데이터 인프라'입니다. 성공적인 엔터프라이즈 AI 구축을 위해서는 분절된 시스템을 벗어나 통합되고 엄격하게 관리되는 개방형 데이터 기반을 마련해야 합니다. 궁극적으로 기업은 AI를 단순한 혁신 프로젝트가 아닌 핵심 비즈니스 지표와 직결된 전략적 자산으로 활용해야 합니다.
번역된 본문
후원: Infosys Topaz와 함께합니다.
인공지능(AI)이 이사회 최우선 의제로 자리 잡고 있지만, 많은 기업은 실질적인 AI 도입을 가로막는 가장 큰 장애물이 바로 자사의 '데이터 상태'라는 사실을 깨닫고 있습니다. 소비자 지향적 AI 도구가 빠르고 편리한 사용성으로 사용자들을 사로잡은 반면, 기업 리더들은 대규모로 AI를 배포하려면 겉으로는 화려하지 않지만 훨씬 더 중요한 작업, 즉 통합되고 거버넌스가 적용되며 목적에 부합하는 '데이터 인프라'가 필요하다는 것을 알게 되었습니다. AI에 대한野心과 기업의 준비도 사이의 이러한 간극은 차세대 디지털 전환의 핵심적인 과제 중 하나로 떠오르고 있습니다.
Databricks의 바베시 파텔(Bavesh Patel) 수석 부사장의 말을 빌리자면, "AI의 품질과 효과성은 조직 내 정보에 크게 좌우됩니다." 하지만 많은 기업에서 이러한 정보는 여전히 레거시 시스템, 분절된 애플리케이션, 그리고 단절된 포맷에 파편화되어 있어, AI 시스템이 신뢰할 수 있고 풍부한 맥락을 담은 결과물을 생성하는 것이 거의 불가능합니다. 파텔은 "실제로 대부분의 조직에서 가장 큰 경쟁 우위는 자체 데이터와 여기에 추가할 수 있는 서드파티 데이터"라고 말합니다.
엔터프라이즈 AI가 가치를 창출하려면 데이터를 개방형 포맷으로 통합하고, 정밀하게 관리(거버넌스)하며, 모든 부서에서 접근할 수 있도록 만들어야 합니다. 이러한 기반이 없다면 기업은 파텔이 거침없이 표현한 '끔찍한 AI(AI)'를 얻을 위험이 있습니다. 이는 분절된 SaaS 플랫폼과 연결되지 않은 대시보드에서 벗어나, 정형 및 비정형 데이터를 결합하고 실시간 컨텍스트를 보존하며 엄격한 액세스 제어를 시행할 수 있는 통합된 개방형 데이터 아키텍처로 나아가야 한다는 것을 의미합니다.
이러한 기초 작업이 제대로 마련되면 조직은 측정 가능한 성과를 거두어 효율성을 높이고, 복잡한 워크플로우를 자동화하며, 완전히 새로운 비즈니스 라인을 출시할 수 있습니다. 특히 기업들이 비즈니스 결정을 주도하는 산출물의 정확성을 추구함에 있어 이러한 가치에 집중하는 것이 매우 중요하다고 Infosys의 단 기술 책임자(Rajan Padmanabhan)는 강조합니다. 선도적인 기업들은 AI 이니셔티브를 단순히 분리된 혁신 프로젝트로 취급하는 대신, AI 배포를 비즈니스 지표와 직접적으로 연결하고 거버넌스 프레임워크를 활용하여 어떤 것이 성과를 내는지, 무엇을 신속하게 폐기해야 하는지 결정하고 있습니다.
파텔은 "비즈니스 사용자들의 AI 리터러시 측면에서 큰 기회를 보고 있습니다. 그들은 AI를 어떻게 생각해야 하는지 이해하는 데 매우 열정적입니다."라며, "본질을 들여다보았을 때 AI는 무엇을 의미할까요? 기술, 교육, 역량 강화 측면에서 어떤 조각과 빌딩 블록을 마련해야 할까요?"라고 덧붙입니다.
앞으로의 가능성은 상당합니다. AI 에이전트가 공동 파일럿(Copilot)에서 워크플로우와 트랜잭션을 관리할 수 있는 자율적인 운영자로 발전함에 따라, 승리하는 조직은 바로 지금 올바른 기반을 구축하는 곳이 될 것입니다. 파드마나반(Rajan Padmanabhan)은 "우리가 보는 새로운 사고방식은 실행 시스템(System of execution)이나 참여 시스템(System of engagement)에서 행동 시스템(System of action)으로 전환하는 것입니다. 이것이 우리가 내다보는 앞으로의 새로운 길입니다."라고 언급합니다.
기업 내 AI의 미래는 파편화된 정보를 더 현명한 의사결정과 완전히 새로운 운영 방식을 모두 지원할 수 있는 전략적 자산으로 전환할 수 있는지에 달려 있습니다.
이 에피소드는 Infosys Topaz와 협력하여 제작된 Business Lab입니다.
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Megan Tatum: MIT Technology Review의 메건 테이텀입니다. 연구실에서 나와 시장으로 진입하는 새로운 기술을 비즈니스 리더들이 이해할 수 있도록 돕는 쇼, Business Lab입니다. 이 에피소드는 Infosys Topaz와 협력하여 제작되었습니다. 최근 AI의 발전으로 매력적인 새로운 산업 애플리케이션이 등장했지만, 부적절한 데이터 모델에 대한 의존은 많은 기업이 벽에 부딪히게 만들고 있습니다. 특히 AI 및 에이전틱 AI(Agentic AI)는 데이터에 완전히 새로운 요구 사항을 부과합니다. 이 기술이 효과적으로 작동하려면 더 높은 수준의 접근성, 컨텍스트 및 가드레일이 필요합니다. 기존 데이터 모델은 종합 부족합니다. 너무...
Sponsored In partnership with Infosys Topaz Artificial intelligence may be dominating boardroom agendas, but many enterprises are discovering that the biggest obstacle to meaningful adoption is the state of their data. While consumer-facing AI tools have dazzled users with speed and ease, enterprise leaders are discovering that deploying AI at scale requires something far less glamorous but far more consequential: data infrastructure that is unified, governed, and fit for purpose. That gap between AI ambition and enterprise readiness is becoming one of the defining challenges of this next phase of digital transformation. As Bavesh Patel, senior vice president of Databricks, puts it, “the quality of that AI and how effective that AI is, is really dependent on information in your organization.” Yet in many companies, that information remains fragmented across legacy systems, siloed applications, and disconnected formats, making it nearly impossible for AI systems to generate trustworthy, context-rich outputs. “Really, the big competitive differentiator for most organizations is their own data and then their third-party data that they can add to it,” says Patel. For enterprise AI to deliver value, data must be consolidated into open formats, governed with precision, and made accessible across functions. Without that foundation, businesses risk “terrible AI,” as Patel bluntly describes it. That means moving beyond siloed SaaS platforms and disconnected dashboards toward a unified, open data architecture capable of combining structured and unstructured data, preserving real-time context, and enforcing rigorous access controls. When the groundwork is laid correctly, organizations can move toward measurable outcomes, unlocking efficiencies, automating complex workflows, and even launching entirely new lines of business. That value focus is critical, says Rajan Padmanabhan, unit technology officer at Infosys, especially as enterprises seek precision in the outputs driving business decisions. Rather than treating AI initiatives as isolated innovation projects, leading companies are tying AI deployment directly to business metrics, using governance frameworks to determine what delivers results and what should be abandoned quickly. “We see this big opportunity just with AI literacy with business users, where they're very eager to understand how they should be thinking about AI,” adds Patel. “What does AI mean when you peel the covers? What are the pieces and the building blocks that you need to put in place, both from a technology and a training and an enablement standpoint?” The possibilities ahead are substantial. As AI agents evolve from copilots into autonomous operators capable of managing workflows and transactions, the organizations that win will be those that build the right foundation now. “What we are seeing as a new way of thinking is moving from a system of execution or a system of engagement to a system of action,” notes Padmanabhan. “That is the new way we see the road ahead.” The future of AI in the enterprise will be determined by whether businesses can turn fragmented information into a strategic asset capable of powering both smarter decisions and entirely new ways of operating. This episode of Business Lab is produced in partnership with Infosys Topaz. Full Transcript: Megan Tatum: From MIT Technology Review, I'm Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace. This episode is produced in partnership with Infosys Topaz. Now, recent advancements in AI may have unlocked some compelling new industrial applications, but a reliance on inadequate data models means that many enterprises are hitting a brick wall. AI and agentic AI in particular place a whole new set of demands on data. The technology requires greater access, context, and guardrails to operate effectively. Existing data models often fall short. They're too fragmented or siloed. Data itself often lacks quality. To bridge the gap, they require an AI-ready upgrade. Two words for you: data reconfigured. My guest today, are Bavesh Patel, senior vice president for Go-to-Market at Databricks, and Rajan Padmanabhan, unit technology officer for data analytics and AI at Infosys. Welcome, Bavesh and Rajan. Rajan Padmanabhan: Thank you. Thanks for having us. Bavesh Patel: Thanks for having us. Megan: Fantastic. Thank you both so much for joining us today. Bavesh, if I could come to you first, when we talk about AI-ready data, what exactly do we mean? What new demands does AI place on data, and how does this impact the way it needs to be structured and used? Bavesh: Yeah. Great question. Appreciate you hosting us today. I think that obviously the whole world is enamored with AI because of all of the power that we can all see as users. AI is now democratized across hundreds of millions of users. And when we think about enterprises and businesses using AI, the quality of that AI and how effective that AI is really dependent on information in your organization, and that's data. And what we found is that most enterprises, their data is kind of locked away in these different applications and different systems. And it's very difficult to get a good view of, what is all my data? How trustworthy is it? How recent and fresh is it? And all of that is being injected into the AI. Unless you have a proper understanding of your data, the ability to ensure that it's data that's accurate and that can be used so that the AI can take advantage of it, you're actually going to end up having terrible AI. We see a lot of customers spend time on cleansing their data, organizing their data, making sure it's access controlled correctly, and that tends to be the fuel of good AI. Megan: Yeah. It's such a foundational thing, isn't it? But it can be missed, I think, quite easily. Rajan, what difference can having AI-ready data really make for enterprises as they unlock that full potential of AI and its applications? Rajan: First and foremost, thanks for having us. It's a pleasure. I think in continuation of what Bavesh talked about, see, data and AI is pretty synonymous. And similarly, the consumer AI and enterprise AI and enterprise agentic AI are different because first and foremost, the business needs to have the context. That context from your enterprise information, which is not only structured, both structured and unstructured and user-generated contents and all forms of data is going to be very, very critical to really get the context right, and really get any model that you pick. That's where the platforms like Databricks really help with the plethora of models or whether you want to build your own models or whether you want to ground the model based on your data. That is going to be very, very critical. That is where getting the data for AI is going to be very, very critical. The third critical part, and this actually will be one of the roadblocks for adoption of AI. That's why if you see the AI adoption on the consumer side is skyrocketing, but on the enterprise side, the enterprises are struggling is primarily around the precision of their output, because you are taking a business decisions where you are taking a buy decision, you are taking a sell decision, or you are trying to recommend something, recommend the content. It could be 20 different use cases. For that, the precision is going to be very critical. We are seeing our customers, the successful customers, definitely for the precision to be more than 92% is not aspiration, that is a must-have. If you have that, definitely being that AI data is going to be the entrepreneur right now for that. Megan: And I suppose if we've outlined there how critical this is, where should enterprises start then, professional perhaps, the level, what are the foundations when it comes to building an AI-ready data model? Bavesh: Yeah. And I think Rajan hit the nail on the