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In the last 100 years, IBM Many different technical trends have seen an increase and decline. What happens to win, where you like there, there are such techniques.
But VB Transform 2025 Today, the VP of the AI platform at Armand Ruiz, IBM expanded how much Big Blue Generative AI is thinking and how its enterprise users are actually deploying technology. A major topic that Ruiz has emphasized is that at this point, it is not about choosing a single large language model (LLM) provider or technology. Rapidly, enterprise customers are systematically rejecting single-sellers AI strategies in favor of multi-model approaches that match specific LLMs with target use cases.
IBM has its open-source AI model with the Granite family, but it is not the right choice for that technique, or even for all workloads. This enterprise behavior is not running the IBM as a Foundation model contestant, but as Ruiz has referred to as a control tower for AI workload.
“When I sit in front of a customer, they are using everything they do everything,” Ruiz explained. “For coding, they love anthropic and for some other use cases such as arguments, they prefer O3 and then for LLM adaptation, with their own data and fine tuning, they either prefer either our granite chain or their small models, or even Lama … It is matching LLM only in the case of correct use.
Multi-LMM Gateway Strategy
The IBM’s response to the reality of this market is a new released model gateway that provides venture with single APIs to switch between various LLMs while observing all deployment and regaining governance.
Technical architecture allows customers to run open-source models on their own estimate stack for sensitive use cases, as well as reach public APIs such as AWS bedocks or Gemini of Google Claude for less important applications.
“Gateway is providing our customers a single layer with an API to switch to our customers from one LLM to another and add observation and governance throughout all the time,” Ruiz said.
The approach directly refutes the general seller strategy of locking customers in ownership ecosystems. IBM model is not alone in taking a multi-select view for selection. Many devices have been revealed in recent months for model routing, aimed at directing the workload to the appropriate model.
Agent Orcastration Protocols emerge as important infrastructure
Beyond multi-model management, IBM is dealing with the emerging challenge of agent-to-agent communication through open protocols.
The company has developed ACP (Agent Communication Protocol) and contributed to the Linux Foundation. ACP is a competitive attempt for Google’s Agent2AGENT (A2A) protocol, which was only contributed to the Linux Foundation by Google this week.
Ruiz said that the purpose of the two protocols is to reduce communication convenience and custom development work between agents. They hope that ultimately, different -different approaches will converge, and currently, the differences between A2A and ACP are mostly technical.
Agent orcastation protocols provide standardized methods for interacting on various platforms and vendors for the AI system.
Technical importance becomes clear when considering the enterprise scale: some IBM customers already have more than 100 agents in pilot programs. Without standardized communication protocol, each agent-to-agent interaction requires custom development, which creates an unstable integration burden.
AI is about changing the workflows and the way it is worked
In terms of how Ruz affects AI today, he suggests that it should actually be more than only chatbots.
“If you are just chatbott, or you are only trying to save cost with AI, you are not AI,” Ruiz said. “I think AI is actually about changing the workflow completely and the way the work is done.”
The difference between AI implementation and AI change centers is how deeply the technology is integrated into existing business processes. IBM’s internal HR example shows this change: Instead of employees asking chatbots for HR information, special agents now handle questions about compensation, rent and promotion regularly, automatically root for appropriate systems and only increase humans when necessary.
“I used to spend a lot of time talking to my HR partners for a lot of things. I now handle it with the HR agent,” Ruiz explained. “Depending on the question, if it is something about compensation or it is something about it only to handle isolation, or hiring someone, or to make a promotion, then all these things will connect with different HR internal systems, and they will be like different agents.”
This represents a fundamental architectural change for computer-interaction workflow work from human-computer interaction patterns. Instead of employees learning to interact with AI tools, AI learns to execute the entire business processes.
Technical implications: Enterprises need to move beyond API integration and transfer the signal engineering towards deep procedure instrumentation that allows AI agents to autonomally execute multi-step workflows.
Strategic implications for enterprise AI investment
IBM’s real -world deployment data suggests several important changes for the Enterprise AI strategy:
Chatbot-first leave thinking: Organizations should identify full workflows for changes rather than adding interactive interfaces to existing systems. The goal is to eliminate human steps, not to improve human-computer conversation.
Multi-model architect for flexibility: Instead of being committed to single AI providers, enterprises require integration platforms that are able to switch between models based on the requirements of the case of use while maintaining regime standards.
Invest in communication standards: Organizations should prefer AI tools that support emerging protocols like MCP, ACP, and A2A, rather than for ownership integration approaches that make sellers lock-in.
“There is a lot to build, and I say everyone needs to learn AI and especially business leaders need to be the first leader and understand the concepts,” Ruiz said.
