New delivery squad model combines industry expertise, technical leadership, and mission-focused execution to help enterprises move AI from pilots to production

DataArt, a global data and AI transformation partner delivering tech-enabled business outcomes for enterprises, today introduced Domain Deployed Engineering™ (DDE), a delivery model that embeds small, industry-fluent teams inside client organizations with accountability for the business result an AI system produces, not the list of deliverables it ships against.

Enterprises face three obstacles on the way to generating value from AI: technology moving faster than they can adapt, stalled pilots that never reach production, and vendors measured by inputs rather than outcomes. Gartner research finds that “finding the right use cases to invest in is consistently cited as a top barrier to AI adoption and once launched, only a little over 40% of selected use cases make it successfully into production.”¹ Gartner also states: “By 2028, 70% of AI initiatives will underdeliver — not because of model limitations, but because business units lack the capacity to operationalize them.”² In DataArt’s experience, both trace back to the same gaps: domain expertise to choose the right problems, and a single owner accountable for reaching production. DDE is built to close both.

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DDE builds on the forward-deployed engineering approach now spreading across the AI industry, with one deliberate difference: deployment succeeds on fluency in the client’s business, not just presence inside it. The model combines industry fluency, engineering credibility across modern architectures, data, and AI, and a focus on business outcomes. Because those qualities rarely sit in one person, DataArt deploys them as lean DDE squads: agentic AI engineers, industry experts, and change management leads assembled around a single time-bound client mission, with one accountable lead and success criteria agreed with the client sponsor before work begins.

Squads scale with the client’s maturity, from one embedded engineer backed by DataArt architects to a full team redesigning how a business unit operates once AI handles part of its work. Their scope spans the full lifecycle of AI adoption: shaping AI transformation programs, building products and solutions, bringing agentic delivery into the client’s own engineering organization, and establishing the governance to run AI reliably in production. Whether a mission runs on a client’s existing systems or builds new ones, squads stay vendor-neutral in what they deploy. Using Artisyn, DataArt’s AI-enabled delivery platform, squads accelerate routine engineering work such as prototyping, testing, and deployment. Every mission is designed to end with the client in control: the squad hands over the working system and the skills to run it to the client’s own teams.

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“AI has introduced more opportunities than our clients can realize, and at the same time it has introduced more complexity than they can adopt,” said Roman Chernyshev, Chief Revenue Officer. “Getting a working solution into production takes people who know the client’s business as well as the technology. Domain Deployed Engineering Squads offer an agile team structure designed precisely for that purpose.”

For one global financial group, a DataArt DDE squad delivered a secure internal AI platform to 73,000 people in five months. For a global contract research organization, a compliance-ready AI platform returned 500% ROI within 30 days. In a compliance-bound agentic engagement, change request cycles fell from seven weeks to about 10 days. Each began the way a DDE mission begins: a problem defined in the client’s own terms and a success measure agreed before the first line of code.

“Across industries, clients are telling us the same thing: they do not need more engineers nearby, they need people who understand their business and own the result. A lot of what is being sold as forward-deployed engineering will not pass that test,” said Scott Rayburn, Chief Marketing Officer, DataArt. “DDE is not our reaction to a trend — it is how we have worked for years, finally given a name and a structure. And we measure it the way clients do: by whether the mission succeeded, not by hours billed.”