While AI hype certainly abounds, real-world AI success can be rarer. For Yaron Haviv, founder and CTO of AI company Iguazio, which was acquired by McKinsey & Company in 2023, the grit of turning AI projects into real business impact is a daily reality. That’s why he put his faith in MongoDB.
“People go read some buzzy articles and go ‘Oh, it's so easy to go and implement that,’” he said. “Sometimes we see the vendors in the space trying to get higher adoption and saying ‘Now it's really easy to do all these things.’”
Haviv—who has extensive experience operationalizing and derisking machine learning and gen AI applications at scale—is more than a little cutting on some of the sound and fury surrounding the generative AI space. He’s also a fan of hard-won knowledge and is happy to share it. As soon as Haviv and his team at Iguazio begin asking questions around how risk is governed, whether observability is built into the application, and what happens when large language model (LLM) updates break applications, it becomes clear that achieving tangible results requires a considered data strategy.
“If you don't build the right architecture, the model doesn't provide good results. You can start with preparing the data properly, putting in guard rails and direct prompts. All those issues need to be addressed,” he added.
Strategic implementation
With only one in 10 AI projects making it from experimentation to production, preparation is central to unlocking the full benefits of AI tools. Iguazio’s clients tend to be multinational in scale and are typically working on multiple use cases, meaning the focus is firmly on building the right infrastructure that will serve multiple goals as their plans mature.
The first task is to build a platform for AI that will serve the various aspects of the lifecycle, and then to implement the initial use case and continue forward with more use cases. “It's not really a one off,” Said Haviv. “Building the right technologies for data pipelines and data storage, building the automation for how to move from development to production and CI/CD is what we do.”
Many Iguazio and McKinsey clients operate in the customer service sector, with Haviv finding that automating how people engage with chatbots or call centers can have powerful benefits for customer service businesses. “You can shrink the call duration by at least 50%,” he said, based on his recent experience.
Not only do these improvements translate to the operation of a more cost-effective call center, but executed well, measurably higher levels of customer satisfaction also accompany these AI-backed changes. The journey is far from over when the production stage is reached. Clients usually have new use cases in mind, or even want to increase the sophistication of their current solutions, he adds.
“They’re often trying to build an MVP. So they will, for example, introduce a documentation bot or something for internal use that answers questions about their content,” said Haviv. “They'll have multiple iterations on the same project. Maybe the next revisions will index more documents or allow them to ask more sophisticated questions.”
Mature data landscape
There’s no question that data is the foundation for any successful AI, machine learning or gen AI application. Essential questions around how to store, process and access data must be fully answered upfront, with the real-world applications of the project needing to remain at the forefront at all times.
In many gen AI use cases that Haviv has seen, he believes they could be deployed using MongoDB. “It's very convenient that I don't need to install MySQL and then install the new bus, or whatever vector database.
“A lot of focus today is on vector databases, but in the real world, you'll have all the different models of access to a given database. I don't need to work with five different types of databases, each one with its authentication.
“MongoDB has the enterprise maturity for serving different data types, and has just extended it to vector data, authentication and versioning—all those fundamentals that you need to get production ready, without patching. And you get to choose from an a la carte menu of data access patterns.”
MongoDB meets the most important factors that are non-negotiable for Haviv in the deployments he works on. As he sums up: “I trust MongoDB because it has security, enterprise functionality and reliability.”
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