How enterprises are moving from pilots to production with enterprise agentic AI adoption

Enterprise agentic AI adoption is shifting from experiment to real-world operations. Across cloud tooling, infrastructure design, internal apps, and vendor deals, businesses are adapting to software that acts autonomously on their data and processes. Therefore, leaders must understand how new developer tools, data stacks, and vendor relationships change speed, cost, and risk. This post walks through five current developments and what they mean for business teams and IT.

## Google’s Vertex AI Agent Builder and enterprise agentic AI adoption

Google’s new Vertex AI Agent Builder tools aim to make it easier for companies and developers to build and manage agentic systems. The announcement highlights Google’s push into tooling that reduces friction for teams creating agents that can act, plan, and connect to internal systems. Therefore, the changes are less about a single new model and more about the developer experience and integration points.

For enterprises, developer tooling matters because it shortens the path from idea to a working agent. Simpler builders can reduce the need for large, specialized teams. Additionally, they help standardize security and data access patterns. However, tool bets also create lock-in risks if integrations favor one cloud or LLM provider. Teams should evaluate how builder tools handle governance, logging, and connectors to existing systems.

In the near term, expect faster prototyping and more internal agent pilots. Over time, standard builder features — such as debugging, access control, and audit trails — will determine which tools succeed in enterprise settings. Therefore, organizations should treat new builders as part of a broader modernization plan, not a one-off project.

Source: AI Business

IBM Fusion, NVIDIA AI platform, and enterprise agentic AI adoption

IBM Fusion’s implementation of the NVIDIA AI Data Platform at UT Southwestern shows how infrastructure and data services unlock agentic use cases. IBM’s announcement describes a full-stack approach: RTX PRO 6000 Blackwell GPUs, NVIDIA Networking, and NVIDIA AI Enterprise software combined with IBM Fusion’s content-aware data services. This setup helps teams process and index unstructured medical data so agents can answer semantic queries quickly.

Practically, that means researchers and clinicians can query vast, multimodal datasets with better precision. IBM Fusion automates data preparation—indexing and vectorizing—so agents can work against AI-ready stores rather than raw files. Additionally, integrations with tools like NVIDIA NeMo Retriever Microservices make it easier to pull relevant context at inference time.

The UT Southwestern deployment highlights two enterprise truths. First, complex agentic AI systems often require deep integration between storage, compute, and retrieval services. Second, domain-specific value—like drug research or imaging—comes when data pipelines are designed for continuous ingestion and indexing. Therefore, enterprises should consider content-aware services that make data agent-ready, not just faster hardware.

Looking forward, expect more reference designs and partner stacks that pair compute and data services for agentic workloads. These designs will matter because agent performance depends as much on data plumbing as model size.

Source: IBM Think

From pilots to practice: BBVA’s internal playbook for scaling agentic tools

BBVA’s experience with ChatGPT Enterprise shows how broad adoption looks in a large, regulated bank. According to the report, BBVA embedded ChatGPT Enterprise into daily work, saving hours per week per employee, creating more than 20,000 Custom GPTs, and achieving efficiency gains of up to 80% in some workflows. These numbers are social proof that agentic-style assistants can move beyond experiments when supported by governance and internal tooling.

The lesson for other firms is practical. First, empower business teams with low-code or custom GPT options so they can tailor assistants to specific tasks. Second, measure time saved and quality gains early to build momentum. Third, maintain guardrails: access controls, privacy rules, and oversight keep regulators and internal risk teams comfortable.

However, scale also exposes new needs. Large numbers of custom agents require lifecycle management, versioning, and training data oversight. Therefore, successful deployments invest in a small central team that supports, audits, and templates for line-of-business builds. Over time, this hybrid model—central standards, distributed creation—creates durable, scalable productivity gains.

Source: OpenAI Blog

enterprise agentic AI adoption and platform dependency: the Apple–Google report

Reports that Apple may use a custom Google Gemini model to power a big Siri upgrade highlight a different dimension: vendor and model dependency. Bloomberg and other outlets say Apple could pay Google roughly $1 billion per year for access to Gemini capabilities that improve summarization and planning. If true, this deal shows how strategic functionality can move across company boundaries.

For enterprises, the lesson is clear. Relying on a third party for a core capability can deliver speed and features. However, such dependence raises negotiation, privacy, and continuity concerns. For instance, contract terms, data usage rights, and price volatility become operational risks. Additionally, companies must assess whether they can switch providers if costs or performance change.

Therefore, firms should approach large vendor deals with care. Negotiate clear SLAs, data-handling terms, and exit plans. Also, consider hybrid approaches: keep sensitive data and core orchestration in-house while outsourcing compute-heavy or generative tasks. In short, platform deals can accelerate agentic features, but they also require strategic safeguards.

Source: Artificial Intelligence News

IBM’s quantum benchmarking progress and the long view on compute for agents

IBM’s move to Stage B of DARPA’s Quantum Benchmarking Initiative points to a longer-term shift in compute strategy. The program evaluates whether large-scale, fault-tolerant quantum computers can be built and whether they offer computational value. IBM’s selection for Stage B shows ongoing public-private collaboration to test ambitious computing approaches.

For enterprise leaders planning agentic AI systems, the immediate takeaway is modest. Quantum computing is not a plug-and-play solution for today’s agents. However, the initiative matters because it shapes long-term R&D and procurement planning. If quantum proves viable for certain classes of problems by providers and funders by 2033, enterprises will need strategies to evaluate quantum-accelerated services as they emerge.

Additionally, Stage B emphasizes rigorous benchmarking and independent validation. Therefore, businesses should ask for transparent metrics and third-party verification when choosing new compute technologies. Over the next decade, hybrid architectures—classical accelerators plus potential quantum services—may appear. Enterprises that track standardized benchmarks and pilot selectively will be better positioned to adopt novel compute without disruption.

Source: IBM Think

Final Reflection: Building practical paths for enterprise agentic AI adoption

Taken together, these stories form a practical roadmap. Google’s new builder tools reduce developer friction and speed prototyping. IBM’s Fusion plus NVIDIA shows that data plumbing and reference infrastructure unlock real agentic value in demanding domains. BBVA proves that strong governance plus local customization scales productivity. Apple’s reported Gemini deal is a reminder that vendor choices shape cost and control. Finally, IBM’s DARPA progress shows compute frontiers that may matter over the next decade.

Therefore, business leaders should pursue a balanced strategy. Invest in developer-friendly tools and content-aware data services. Enable distributed creation while centralizing standards and audit. Negotiate vendor deals with clear terms and contingency plans. And, importantly, follow emerging compute benchmarks so long-term investments align with technology readiness. With this mix, enterprises can move from pilot projects to reliable, responsible agentic AI systems that deliver measurable value.