Gartner: AI Platforms Market to Hit $64 Billion in 2026 — Up 63%
Worldwide end-user spending on AI models and platforms is projected to total $64 billion in 2026, up 63.4% from $39 billion in 2025, according to Gartner's latest forecast. The acceleration reflects enterprise AI moving from experimentation to deployment at scale. For marketing and product leaders, this isn't just a budget line item — it's validation that AI infrastructure is now a baseline competitive requirement. The companies investing in foundation models, agent frameworks, and AI-native tooling today are building the operational advantage that will define the next five years.
Read on AppleWorld Today →eMarketer: Marketing's AI Agents Are Only as Good as the Data They're Built On
Nick Craig from Rokt mParticle delivers the uncomfortable truth: "Downstream agents, while extremely valuable, tend to be limited by the quality of inputs they receive." The insight cuts to the heart of marketing AI's adoption gap. Most AI innovation is happening at the tool level — email platforms, DSPs, creative tools — but these agents can only work with the data they're given. If your customer data is siloed, stale, or incomplete, your AI agents inherit those limitations. The real competitive advantage isn't having AI; it's having the upstream data infrastructure that makes AI actually work. Craig argues marketers should "start with the use cases, start with the value, then let AI take hold as the enabler." Technology second, strategy first.
Read on eMarketer →Oracle Unveils AI-Native Application Builder — No-Code to Pro-Code
Oracle launched AI Agent Studio for Fusion Applications, bringing AI-native development to enterprise workflows with support for no-code, low-code, and professional development approaches. The Agentic Applications Builder lets non-developers use natural language to build applications, while AI Studio Skill gives developers familiar tools like VS Code and CLI integration. Oracle's Chris Leone frames the shift: "Enterprise software is moving beyond systems that record work to systems that actively drive and execute outcomes." The platform includes templates and starter projects on a public GitHub repo. For enterprise teams, this lowers the barrier from AI pilots to production deployments — the gap Deloitte's Mauro Schiavon calls "the critical pain point" for enterprise AI adoption.
Read on Small Business Trends →Observe.AI and AWS Announce Multi-Year Partnership for Enterprise CX Agents
Observe.AI announced a multi-year strategic collaboration agreement with Amazon Web Services to help enterprises deploy AI Agents for customer experience at scale. The partnership combines Observe.AI's real-time and post-interaction analytics with AWS's cloud infrastructure and go-to-market reach. The two companies will co-develop solutions targeting customer service operations — the department where most enterprises first feel AI's impact. For enterprise buyers, this signals continued consolidation around proven AI platforms. The message: AI agents for CX are no longer experimental, they're infrastructure.
Read on Business Wire →HubSpot Expands Breeze AI with Agent Hub and Agent Builder
HubSpot expanded its Breeze AI platform with two major additions: Agent Hub for centralized AI agent management, and Agent Builder for custom agent development. Agent Hub gives marketing, sales, and customer support teams a single dashboard to configure, monitor, and manage AI agents — addressing the growing governance challenge as teams deploy more autonomous systems. Agent Builder lets organizations create custom agents using a low-code interface, upload internal documentation, and connect agents directly to CRM data. Administrators can now set monthly execution limits and monitor credit consumption. The release continues HubSpot's strategy of embedding agentic AI directly into the CRM rather than relying on third-party integrations.
Read on MarTech →💡 My Take
Read this one: The eMarketer piece on data quality limiting marketing AI. It's the story no one wants to hear because fixing upstream data infrastructure is hard, unglamorous work. But Craig nails it: if you're sending limited data sets to individual platforms, that's all the AI has to work with. The companies winning with AI aren't the ones with the fanciest tools — they're the ones who spent years building clean, unified customer data. Data quality is the new competitive moat, and most organizations are still pretending the moat is the AI itself.