How Enterprise Agentic AI Platforms Are Rewiring Business Data, Commerce, and Service

The rise of enterprise agentic AI platforms is changing how companies connect data, automate work, and engage customers. In the past week, major moves from Oracle, Salesforce, Walmart, and Anthropic showed that businesses are racing to stitch generative models into core systems. Therefore, leaders must pay attention to data platforms, new commerce channels, and lighter models that cut cost and latency. This post explains what changed, why it matters, and what leaders should plan for next.

## Why enterprise agentic AI platforms matter now

Enterprise agentic AI platforms promise to let AI act on behalf of users and systems. Oracle’s recent announcement is a clear signal of that shift. Oracle revealed a new Oracle AI Data Platform with a collective investment of more than $1.5 billion. The platform aims to bridge generative AI models and enterprise data safely. In practice, that means companies can connect their customer records, product catalogs, and operational data to AI tools while keeping governance controls in place.

However, connecting LLMs to sensitive corporate data is not trivial. Therefore, the platform emphasizes secure integrations and data governance. This is important because agentic systems often need to read and act on enterprise data—whether drafting a customer reply, updating a record, or orchestrating a workflow. Additionally, centralizing data and governance makes it easier to scale AI across teams without exposing the business to risk.

As a result, enterprises that adopt robust data platforms will be better positioned to deploy agentic capabilities widely. Looking ahead, expect vendors to make data governance a core selling point. For businesses, that means planning for unified data, clear access policies, and tests for AI behaviors before rolling agentic flows into production.

Source: CX Today

How enterprise agentic AI platforms change retail: Walmart and ChatGPT shopping

Walmart’s partnership with OpenAI shows how agentic AI platforms can create new commerce channels. The two companies announced that consumers and loyalty members will be able to shop through ChatGPT. This move aims to combine Walmart’s product assortment and delivery capabilities with generative AI’s conversational interface. Therefore, shopping can shift from web searches and apps to natural, chat-based interactions.

For consumers, this means they can ask ChatGPT for recommendations, get help finding the best value, and place orders in a conversational way. Importantly, Walmart highlighted that loyalty members will have particular benefits. Meanwhile, retailers gain a new entry point to reach customers where they already are—inside AI assistants. Additionally, this integration can streamline queries about availability, delivery windows, and returns, because the agent can access product and logistics data.

However, this change also raises practical questions for retailers and brands. For example, how will product ranking, sponsored placements, and data sharing be managed? Therefore, companies should evaluate how agentic commerce affects margins, customer experience, and loyalty programs. In the near term, expect retailers to experiment rapidly with AI-driven shopping flows. For leaders, the opportunity is clear: embracing conversational commerce now could unlock new revenue and reduce friction in the buying journey.

Source: Digital Commerce 360

Enterprise agentic AI platforms in CRM: Salesforce’s Agentforce 360 and ChatGPT access

Salesforce’s Agentforce 360 shows how agentic AI platforms will be embedded into daily workflows. The company announced the general availability of Agentforce 360, an enterprise platform designed to integrate agentic AI across its business software. As a result, both employees and customers can access more autonomous assistance inside CRM, service, and sales workflows. Additionally, Salesforce detailed plans to make Agentforce 360 accessible through ChatGPT, broadening how agents are reached.

Therefore, businesses using Salesforce can expect tighter AI-driven automation for case handling, lead follow-up, and customer interactions. The platform’s aim is to let AI perform tasks—drafting emails, routing issues, and summarizing records—while leaving oversight to human supervisors. Importantly, the integration with ChatGPT also suggests cross-vendor collaboration. For example, Salesforce and OpenAI plan to use an Agentic Commerce Protocol to coordinate actions securely between systems.

However, success depends on thoughtful design. Companies must configure guardrails, define escalation paths, and measure outcomes. Additionally, partners and integrators will play a big role in customizing agent behavior. In short, Agentforce 360 signals a move from AI as a feature to AI as an active participant in enterprise systems. Therefore, organizations should prepare governance, training, and metrics to get real value from these agentic capabilities.

Source: Digital Commerce 360

Faster, cheaper customer-facing AI: Anthropic’s Claude Haiku 4.5

Anthropic’s Claude Haiku 4.5 targets the other side of enterprise needs: real-time customer service. Two weeks after releasing a larger model, Anthropic introduced Haiku 4.5 as a lightweight option for customer-facing tasks. The company positions Haiku 4.5 as faster and more economical, making it suitable for chatbots, automated service workflows, and real-time interactions.

Therefore, teams that need low latency and predictable costs may prefer a smaller LLM for frontline use. Additionally, lighter models reduce infrastructure demands and help maintain responsiveness during peaks. For example, a support chatbot that must answer within seconds benefits from speed and efficiency. Meanwhile, larger models can be reserved for tasks that need deeper reasoning or creative synthesis.

However, choosing the right model is about trade-offs. Smaller models may sacrifice some nuance but deliver consistent performance. Consequently, enterprises must map use cases to model capabilities. Also, vendors will continue to offer a range of options, enabling hybrid deployments—fast models for immediate tasks and larger models for complex work. In the coming months, expect businesses to test lightweight models widely to cut costs and improve customer experience.

Source: CX Today

What these moves mean for enterprise architecture and partners

Taken together, these announcements show a pattern: vendors are building ecosystems that connect data platforms, agentic software, commerce channels, and optimized models. Oracle’s $1.5 billion AI data platform tackles the foundational need for secure data access. Salesforce’s Agentforce 360 brings agentic AI directly into CRM and opens access through ChatGPT, which expands channels. Walmart’s ChatGPT shopping proves that agentic commerce is viable at scale. Anthropic’s Haiku 4.5 offers practical performance for real-time service.

Therefore, enterprises must update architecture, vendor strategies, and partner plans. First, invest in a data platform that enforces governance and traceability. Second, design agentic workflows with clear human oversight and metrics. Third, evaluate lightweight models for latency-sensitive use cases while reserving larger models for complex tasks. Finally, work with partners who can integrate across systems and manage cross-vendor protocols.

Also, these changes create new roles for integrators, security teams, and product owners. As a result, successful organizations will combine careful risk management with fast experimentation. In short, the future of agentic AI platforms will be less about single-vendor lock-in and more about interoperable layers that respect data, deliver fast service, and open new commerce channels.

Source: CX Today

Final Reflection: Building practical, trusted agentic systems

We are at an inflection point where agentic AI moves from demos to everyday business. Major vendors are converging on a simple idea: connect models to data safely, expose agentic services where customers and employees already are, and tune models for the job at hand. Therefore, organizations that balance ambition with discipline will gain the most. Invest in data platforms and governance first. Then, pilot agentic workflows in low-risk areas. Simultaneously, test lightweight models for speed and cost savings. Finally, treat new commerce channels as strategic experiments, not just features. If companies follow this path, agentic AI can drive real productivity, better customer experiences, and new revenue streams—while keeping control and trust intact.