Dentsu: From Proof of Concept to Production in Under 12 Weeks, Time to Insight Down 90 Percent

Advertising and media group Dentsu built a predictive analytics copilot on Azure AI Foundry and Azure OpenAI Service and moved it from proof of concept to production in under 12 weeks. The conversational assistant cut overall time to media insight by 90 percent and analysis time by 80 percent, according to the official Microsoft customer story published in November 2024, which names five members of the team that shipped it.

Weeks of waiting for every media question

Dentsu runs marketing for global brands on a foundation of media and consumer analytics. Forecasting, budget allocation and channel optimization drive every campaign decision, and before the copilot each of those answers took specialist analysts weeks to produce. In media buying, timing is margin: a forecast that arrives after the campaign window closes has a value of zero. Becca Kline, Senior Director of Analytics at Dentsu, described the cost of that delay in the customer story: “Delayed campaigns slow customer service and may result in missed opportunities.” Demand for insight grew faster than analyst capacity, and every new client question joined a queue behind the last one.

The company chose generative AI as the route to scale. Callum Anderson, Global Director for DevOps and SRE, framed the intent plainly: “We look to generative AI for efficiency and performance solutions.” The goal was a chat interface where planners ask questions in natural language, from optimal budget allocation to channel performance, and receive data-driven answers in minutes.

The decision: a microservice inside the suite, instead of a standalone chatbot

The choice that made the 12-week horizon possible was architectural. Dentsu engineers designed the copilot as a loosely coupled microservice inside the company's existing application suite, integrated through APIs and the GraphQL protocol, rather than launching a separate chatbot product.

That single decision delivered three compounding advantages. First, the copilot inherited the shared governance modules Dentsu already applies across its client applications, so responsible AI controls arrived built in rather than bolted on. Second, users met the new capability inside tools they already knew, with a consistent look and feel, which removed an entire adoption battle. Third, the platform work was already done: the team deployed on Azure Kubernetes Service with Azure API Management, and blue/green deployment brought the copilot into production with zero downtime. Under the hood, the copilot runs as directed acyclic graph flows written in Python, connected to an Azure AI hub through prompt flow as a third architectural tier.

Speed came from templates as much as from architecture. Lead DevOps Engineer Simon Ransom credited the GitHub GenAIOps starting point: “It was great to get a head start with the scripts that were part of the Azure AI prompt flow template.” DevOps Engineer Jon Crocombe and Director of Project Management Katie Jenkins round out the named team; Jenkins summarized the collaboration with Microsoft engineers simply: “It was really cool to see Microsoft and Dentsu engineering brains coming together.”

The result, with full context

The documented outcomes: analysis that previously took weeks now completes in minutes, an 80 percent reduction in analysis time; overall time to insight fell 90 percent; and analysis costs dropped alongside. The system went from proof of concept to production in under 12 weeks and has been live since late 2024.

The source is equally clear about the trade-offs. The compressed timeline deferred systematic response evaluation to a later phase, and after launch the team reorganized the copilot's logic to raise answer accuracy. That sequencing (ship a governed v1 fast, then measure and refine in production) is a real engineering choice the customer story documents openly, and it is part of what makes this case credible rather than promotional.

The pattern has held since publication. Independent coverage by AI Expert Network in March 2025 documented the copilot in operation as a decision-support system integrated with Dentsu's business applications, alongside a widening AI portfolio that includes Salesforce-based retail media tooling and Adobe GenStudio integration. Dentsu also channels its copilot learnings back into the GitHub template, turning an internal build into a reusable blueprint.

What other organizations can learn

The transferable lesson: integration design beats greenfield builds. Many enterprise AI programs stall between demo and deployment because the demo lives outside the systems where governance, identity and user habits already exist. Dentsu inverted that pattern: the copilot entered production as one more microservice in a suite that already carried governance, monitoring and deployment discipline, and the 12-week timeline is the direct result.

Replicating it requires specific conditions. An existing application suite with shared governance modules gives the copilot something to inherit. Platform templates, in this case the Azure AI prompt flow template on GitHub, compress the first mile of engineering. DevOps maturity, blue/green deployment on Kubernetes, turns release day into a routine event. And a deliberately scoped first release, with evaluation and logic refinement planned for the production phase, keeps the schedule honest. Organizations that hold those four assets can treat 12 weeks as a realistic target; all others now know exactly which infrastructure to build first.