Merck and Mastercard are seeing real agentic AI results. Both say the plumbing came first.

Merck says its AI agents are already producing measurable results, including cutting one drug discovery cycle by 33% and speeding compliant marketing material delivery by 70% to 80%. VP Sean Finnerty says those gains depend on building the underlying infrastructure first, rather than deploying disconnected one-off agents that become technical debt.

The article underscores a broader enterprise AI lesson: useful agentic AI depends less on flashy demos than on identity, security, data access, and orchestration layers that can scale. Companies that standardize this plumbing early may move faster as agent deployments multiply across clouds, edge locations, and fragmented data systems.

Merck is using AI agents to cut drug discovery cycles by a third and ship compliant marketing materials up to 80% faster — but VP of Digital Platforms Sean Finnerty says the only reason it's working is because they built the infrastructure first.And the pharmaceutical manufacturer is seeing promising early results: AI is generating marketing drafts that are “99% right” when it comes to compliance, shrinking review cycles from months to days and accelerating delivery by 70% to 80%. In the company’s medical research, meanwhile, one AI-assisted discovery cycle was reduced by 33%.Still, agentic AI only works if companies first build the underlying “plumbing,” Finnerty said of digital platforms and services at a recent AI Impact Series event. “If we do one-offs, we're gonna end up with thousands and thousands of things that are ultimately just gonna be debt that we'll have to deal with later,” he said. “And that's gonna be a drag on any further innovation.” Starting with the plumbingMerck’s plumbing-first strategy comes from lessons learned during the early days of cloud in the 2010s “when nobody knew what the heck was going on,” Finnerty said. Getting the cloud right meant building from the ground up; at Merck, that infrastructure now supports 2,500 AWS accounts, numerous Microsoft Azure subscriptions, and new Google Cloud Platform (GCP) integrations. “AI is gonna be the same exact thing,” Finnerty said. “We're going to have thousands and thousands of agents.” The questions then pile up: How do you register them? How do you secure them? How do you ensure they're connected to the right tools, and have access to the right data and the right context? Context delivery is also critical; Merck works with three hyperscalers and has forty-seven edge locations and hundreds of databases. “Many, many petabytes” of structured and unstructured data are stored in Oracle databases, SQL databases, Excel spreadsheets, phone transcripts, and other repositories, Finnerty said. His team is building scaffolding to deliver meaningful context in various situations, he explained. Data must be organized and ingested into various platforms, because “there’s no one solution to solve every single problem.” Sometimes it's Databricks, other times it's Amazon Redshift, “plus four other things.” The goal is: “Let's make that easy and frictionless for people to do, and secure it, and make sure it's well integrated with MCP [model context protocol], and A2A [Agent2Agent], and upstream compute,” Finnerty said. “If you wanna run stuff on GCP or you wanna run stuff on AWS, we've got the plumbing in place so you can run your adjacent workloads wherever you want.” How Merck is using agentsAs it builds out its technical plumbing, Merck is experimenting with agents across regulated enterprise operations, scientific discovery workflows, and app modernization. Notably, AI is accelerating drug discovery. Finnerty explained that scientists look at molecular structures and disease states to determine if a given condition is druggable. But even if a disease state is known, developing a drug to target it can take years. Now with AI, teams are starting to see “very promising things,” such as cutting one particular research cycle down by one-third. “That's a year off of the life of the discovery cycle,” Finnerty said. “Which means, theoretically, we can get it to a patient who needs that therapy a year faster.” Once developed and approved, these products are regulated and marketing materials around them must be clearly and explicitly articulated. “The way you communicate that information per market, per country, per state, per region, is all very carefully governed and regulated,” Finnerty said. It’s also variable: An ad campaign for a vaccine in the state of Georgia looks much different from one launched in Canada. Historically, humans did the due diligence to make sure the company complied with various laws. Draft materials go through iterations of reviews; when a mistake is discovered, it gets “kicked back to the beginning, and it goes through it again, and then it takes another however many weeks and months,” Finnerty said. But now, AI can do that “much, much more effectively,” and the process is increasingly evolving from a human-in-the-loop to essentially a "human-as-governor." With human oversight, AI can deliver a first draft in a day or week that is 99% there, allowing teams to ship materials up to 80% faster. Meanwhile, when it comes to app modernization, AI can discover architecture, document data interactions, APIs, network paths, and do authentication checks and authorization; it can also write code for Terraform for deployment and refactor JavaScript into Python. Where the company would have previously spent weeks and months and hundreds of thousands of dollars to update one application, Finnerty said, agents are now handling the work through prompts.Running into "wackiness" That’s not t