Agentic AI for Enterprise: Platforms, Models, and Retail in 2025

Agentic AI for enterprise is moving from research demos into real business systems. Therefore, companies are rethinking software, collaboration, and the economics of models. In this post I weave five recent stories into a clear picture. Additionally, I show what leaders should watch and how this wave will change IT, operations, and customer-facing teams.

## Oracle Expands Agentic AI Platform: What it Means for IT Leaders

Oracle recently expanded its agentic AI platform with new features. Therefore, the move looks like part of a wider software strategy. Additionally, Oracle appears to line up software with its ambition to be a big AI hardware infrastructure provider. This combination is notable because it means software and infrastructure choices will come packaged together more often.

For enterprise IT teams, this trend matters. First, it simplifies vendor relationships, because one supplier may offer both platform tools and the compute to run them. However, it also raises integration questions, since firms will need to connect agentic services to legacy apps. Furthermore, operational teams will need to plan for automation that isn’t just about chat, but about agents that act on systems and workflows.

Therefore, expect more procurement conversations that mix hardware, software, and implementation services. Additionally, companies should assess how vendor roadmaps align with their data governance and security posture. In short, Oracle’s push signals that agentic AI is becoming a packaged enterprise capability, and CIOs will need to adapt procurement, operations, and skills accordingly.

Source: AI Business

Salesforce and Slack as Agentic OS: A New Hub for Work

Salesforce is positioning Slack to become a centralized platform where humans, agents, and AI work together. Therefore, Slack may evolve beyond messaging into a kind of agentic operating system for business processes. Additionally, this approach aims to unify notifications, automation, and task execution in one place.

This shift affects how teams collaborate. First, it means agents could act on behalf of users inside channels, triaging tasks, updating records, or launching workflows. However, it also requires careful design to prevent noise and to keep human control clear. Furthermore, enterprises will need policies about what agents can do, and audit trails to track actions.

For business leaders, the opportunity is clear: tighter integration of people and agents can speed decisions and reduce manual handoffs. Therefore, pilot projects should focus on measurable workflows like approvals, incident response, or sales coordination. Additionally, IT must ensure data flows securely between Slack, CRM systems, and agent platforms.

In short, Salesforce’s push suggests the future of work will be co-managed by humans and autonomous agents inside shared collaboration hubs. Consequently, organizations should experiment now, while defining guardrails that protect data and preserve human oversight.

Source: AI Business

Cheaper Small Models: Anthropic’s Claude Haiku 4.5 and Cost Choices

Anthropic introduced Claude Haiku 4.5, a smaller and cheaper model that is meant to be used alongside Sonnet 4.5. Therefore, vendors are offering a tiered model strategy that balances cost and capability. Additionally, this change highlights that enterprises can mix large and small models depending on use case.

This matters for product teams and cloud finance. First, cheaper small models reduce the marginal cost of serving many real-time queries. However, they may not match large models on complex reasoning tasks. Therefore, teams should design systems that route routine work to small models and reserve larger models for high-value or high-risk tasks.

Moreover, bundling small and large models together encourages hybrid architectures. For example, an agentic application could use a small model for fast decisions and call a larger model only when deeper understanding is required. Consequently, this pattern lowers operational costs while preserving quality for critical moments.

In short, Anthropic’s approach points to a practical path for enterprises: optimize cost by using the right model for each job, and build orchestration layers that switch models as needed. Therefore, expect more mixed-model productization and clearer cost-management techniques in 2025.

Source: AI Business

Trillion-Parameter Open Model: Ant Group’s Ling-1T and the Open Strategy

Ant Group announced Ling-1T, a trillion-parameter model, and released it with an open strategy. Therefore, this step marks a broader shift as large, reasoning-focused models become part of public ecosystems. Additionally, Ant positions Ling-1T as balancing computational efficiency with advanced reasoning.

For enterprises, open releases change vendor risk calculations. First, open models can be inspected, adapted, and hosted internally. However, they also shift competitive dynamics because more teams can experiment without heavy licensing costs. Furthermore, an open trillion-parameter model may accelerate research into agentic behavior and integrated workflows.

Yet, there are trade-offs. Large models need infrastructure and tuning to be safe and reliable for business use. Therefore, companies should evaluate readiness to host or fine-tune such models based on their data, talent, and security needs. Additionally, partnering with vendors or research labs can speed safe adoption.

In short, Ling-1T’s open approach signals that the frontier of model capability is becoming more accessible. Consequently, organizations should prepare governance, infrastructure, and talent plans to responsibly explore what high-capacity models can do for reasoning and decision support.

Source: Artificial Intelligence News

Retail Pilots: Ellis Shows Real-World Revenue and Planning Impact

Retailers are piloting Ellis, a retail-focused large language model that promises to turn consumer signals into real-time pricing and planning decisions. Therefore, sector-specific models are proving their value in live operational settings. Additionally, Ellis demonstrates how tailored AI can directly touch revenue and supply-chain choices.

This trend matters for merchandising and operations teams. First, a model tuned for retail signals can analyze demand shifts faster than traditional reporting. However, success depends on clean data, clear KPIs, and cross-functional buy-in. Therefore, pilots should measure not just model accuracy but the end-to-end business impact: revenue lift, reduced stockouts, or faster decisions.

Moreover, retail copilot projects often reveal process changes. For example, planners may shift from static forecasts to dynamic guidance, and pricing teams may adopt automated rules with human oversight. Consequently, retailers that combine model insights with strong change management will capture the most value.

In short, Ellis and similar pilots show that verticalized LLMs can move from experimentation to measurable outcomes. Therefore, retailers should prioritize pilots on high-payoff workflows, while building guardrails and feedback loops to keep models aligned with commercial goals.

Source: AI Business

Final Reflection: Putting the Pieces Together

Across these stories we see a coherent picture: agentic AI for enterprise is maturing from concept to practical toolsets. Therefore, platform vendors like Oracle and Salesforce are shaping where agents live and act. Additionally, model vendors are offering a spectrum from cheaper small models to trillion-parameter open releases, which changes cost and access dynamics. Meanwhile, vertical pilots such as Ellis show tangible business outcomes in retail.

This convergence matters because it shortens the path from idea to revenue. However, it also raises governance, integration, and skills questions that executives must address. Therefore, companies should align procurement, security, and operations strategies now. Finally, the future looks collaborative: humans and agents will share workflows, and organizations that build clear guardrails will benefit fastest.