AWS is still chasing a cohesive enterprise AI story after re:Invent

AWS kicked off re:Invent 2025 with a defensive urgency that is unusual for the cloud leader, arriving in Las Vegas under pressure to prove it can still set the agenda for enterprise AI.

With Microsoft and Google tightening their grip on CIOs’ mindshare through integrated AI stacks and workflow-ready agent platforms, AWS CEO Matt Garman and his lieutenants rolled out new chips, models, and platform enhancements, trying to knit the updates into a tighter pitch that AWS can still offer CIOs the broadest and most production-ready AI foundation.

Analysts remain unconvinced that AWS succeeded.

“We are closer, but not done,” said David Linthicum, independent consultant and retired chief cloud strategy officer at Deloitte.

Big swing but off target

Garman’s biggest swing, at least the one that got it “closer”, came in the form of Nova Forge,  a new service with which AWS is attempting to confront one of its strategic weaknesses: the absence of a unified narrative that ties data, analytics, AI, and agents into a single, coherent pathway for enterprises to adopt.

It’s this cohesion that Microsoft has been selling aggressively to CIOs with its recently launched IQ set of offerings.

Unlike Microsoft’s IQ stack, which ties agents to a unified semantic data layer, governance, and ready-made business-context tools, Nova Forge aims to provide enterprises raw frontier-model training power in the form of a toolkit to build custom models with proprietary data, rather than a pre-wired, workflow-ready AI platform.

But it still requires too much engineering lift to adopt, analysts say.

AWS is finally positioning agentic AI, Bedrock, and the data layer as a unified stack instead of disconnected services, but according to Linthicum, “It’s still a collection of parts that enterprises must assemble.”

There’ll still be a lot of work for enterprises wanting to make use of the new services AWS introduced, said Phil Fersht, CEO of HFS Research.

“Enterprise customers still need strong architecture discipline to bring the parts together. If you want flexibility and depth, AWS is now a solid choice. If you want a fully packaged, single-pane experience, the integration still feels heavier than what some competitors offer,” he said.

Powerful tools instead of turnkey solutions

The engineering effort needed to make use of new features and services echoed across other AWS announcements, with the risk that they will confuse CIOs rather than simplify their AI roadmap.

On day two of the event, Swami Sivasubramanian’s announced new features across Bedrock AgentCore, Bedrock, and SageMaker AI to help enterprises move their agentic AI pilots to production, but still focused on providing tools that accelerate tasks for developers rather than offering “plug-and-play agents” by default, Linthicum said.

The story didn’t change when it came to AWS’s update to vibe-coding tool Kiro or the new developer-focused agents it introduced to simplify devops, said Paul Nashawaty, principal analyst at The Cube Research.

“AWS clearly wants to line up against Copilot Studio and Gemini Agents. Functionally, the gap is closing,” said Nashawaty. “The difference is still the engineering lift. Microsoft and Google simply have tighter productivity integrations. AWS is getting there, but teams may still spend a bit more time wiring things together depending on their app landscape.”

Similarly, AWS made very little progress toward delivering a more unified AI platform strategy. Analysts had looked to the hyperscaler to address complexity around the fragmentation of its tools and services by offering more opinionated MLops paths, deeper integration between Bedrock and SageMaker, and ready-to-use patterns that help enterprises progress from building models to deploying real agents at scale.

Linthicum was dissatisfied with efforts by AWS to better document and support the connective tissue between Bedrock, SageMaker, and the data plane. “The fragmentation hasn’t vanished,” he said. “There are still multiple ways to do almost everything.”

The approach taken by AWS contrasts sharply with those of Microsoft and Google to present more opinionated end-to-end stories, Linthicum said, calling out Azure’s tight integration around Fabric and Google’s around its data and Vertex AI stack.

Build or buy?

For CIOs who were waiting to see what AWS delivered before finalizing their enterprise AI roadmap, they are back at a familiar fork: powerful primitives versus turnkey platforms.

They will need to assess whether their teams have the architectural discipline, MLops depth, and data governance foundation to fully capitalize on AWS’s latest additions to its growing modular stack, said Jim Hare, VP analyst at Gartner.

“For CIOs prioritizing long-term control and customization, AWS offers unmatched flexibility; for those seeking speed, simplicity, and seamless integration, Microsoft or Google may remain the more pragmatic choice in 2026,” Hare said.

The decision, as so often, comes down to whether the enterprise wants to build its AI platform or just buy one.

This article first appeared on CIO.com.