Matillion positions Maia as the AI Data Automation platform

The expectations placed on data have fundamentally changed.

AI has moved from experimentation to execution. As 88% of enterprises deploy AI, data consumption has shifted from batch schedules to continuous, reliable availability at scale.

Yet outcomes have not kept pace with ambition.

Despite widespread AI adoption, most organizations fail to realize value, with 58% of those initiatives stalling before delivery. Leaders point to data readiness as the blocker, citing slow pipelines, fragile systems, and manual governance.

Traditional ETL no longer keeps up with AI demands

For years, ETL pipelines formed the backbone of analytics and reporting. They worked when demand was predictable, refresh cycles were slow, and consumers were mostly human. In that world, manually built pipelines and batch delivery were enough.

That world no longer exists.

AI-driven systems generate continuous demand, pushing new consumers into the stack faster than pipeline delivery can keep up. Constant change ripples across the data stack, while data teams remain constrained by manual workflows designed for a slower era.

Over time, the symptoms compound:

• Manually created pipelines are hard to maintain
• Schema changes trigger reactive fixes
• Documentation and lineage drift out of sync
• Teams spend more time maintaining what exists than building what’s next

As demand increases, backlogs grow, and delivery slows. AI initiatives stall while teams keep the lights on. Costs rise because manual effort scales linearly with demand.

Teams bring capability and ambition. Traditional ETL creates constraints by failing to scale or move fast enough.

What data teams actually need now

Data teams do not need incremental improvements to the old model. They need room to move. They need to scale delivery without sacrificing trust, transparency, and governance.

Most importantly, they need to stop rebuilding pipelines and start owning reusable, governed data products that evolve with the business. Data product ownership and governance replace pipeline maintenance as the center of data work.

Data teams breaking free from ETL constraints

The shift toward automated data work is already underway.

Rather than requiring teams to wire pipelines together, monitor them, and keep systems aligned over time, the platform handles repetitive operational work automatically. Data teams regain control over how time and effort are spent.

• Less effort in maintaining the infrastructure
• Greater ownership of transparent, reusable data products
• Governance and quality enforced by design
• Documentation and lineage stay aligned as systems evolve

Greater autonomy allows data teams to do more and begin building data products without limits.

Introducing Maia, the industry’s first AI Data Automation platform for building data products without limits

Maia was introduced last year to help data teams accelerate delivery and reduce manual work, easing day-to-day friction as data demand continued to rise. Early adoption delivered strong ROI, with teams moving faster and spending less time on maintenance—in many cases reducing manual data work by over 90% and accelerating delivery from weeks to hours. Broader usage expanded how teams relied on Maia, moving beyond individual task automation to end-to-end data operations and product delivery, helping customers execute their agentic blueprint and naturally shaping Maia into an AI Data Automation platform. 

At the core of Maia AI Data Automation platform consists of tightly integrated components, each designed to support data teams as they move from maintenance to ownership.

• Maia Team brings an always-on set of expert AI agents that handle the repetitive, time-consuming data work—building, modifying, optimizing, and maintaining pipelines as systems evolve—freeing teams to focus on higher-value outcomes without losing control.
• Maia Context Engine captures business rules, standards, and institutional knowledge, ensuring data products remain transparent, reusable, and deterministic as they evolve.
• Maia Foundation provides the enterprise backbone, with security, governance, observability, and scale built in from the start. This design ensures compliance with data regulations and maintains trust, so teams can move fast without compromising control or risking data security.

Together, these components remove the constraints that slow data teams down. Teams gain the capacity to test more ideas, ship more data products, and turn effort into business impact without bottlenecks. Maia handles data work end-to-end, enabling data teams to scale delivery quickly while preserving governance, trust, and control, supported by fast onboarding and dedicated support.

From maintenance to momentum

When automation becomes part of the foundation, the work itself feels different.

Data teams no longer depend on ticket queues or heroic effort to deliver results. Teams stop rationing capacity and start prioritizing progress. Teams can respond to demand as it arises, test more ideas, and turn more of those ideas into real business impact. Instead of fixing yesterday’s breakages, teams focus on what comes next.

That shift—from infrastructure upkeep to data product ownership—is what allows data teams to keep pace with AI-driven demand.

What customers are achieving with this approach

As organizations adopt this way of working, the impact becomes tangible.

Data products are delivered up to 100× faster, moving from weeks to hours. Manual data work drops by more than 90%, particularly in maintenance and migration. Data teams recover thousands of hours each year without increasing headcount and reduce costs through reuse and consolidation.

These outcomes don’t come from pushing teams harder.
 They come from removing the limits that held them back.

See what freedom looks like in practice

The AI era raises expectations for data teams, but it does not require more manual effort. It requires a better foundation for how data work gets done.

Maia expands what data teams can achieve, accelerating delivery and enabling data products without limits as demand continues to grow. See Maia tackle a real data challenge in a live demo, or explore what’s next at maia.ai.