Google is spearheading the adoption of agentic AI through a new interoperability protocol
Google just set a new standard for scaling agentic AI across cloud platforms. They introduced an agent interoperability protocol. That may sound wonky, but here’s what it means: it’s upgrading the plumbing behind AI agents so they can communicate and operate across different systems without friction. Think of it as setting clear rules so AI agents can “talk” across company apps, clouds, and data stacks, no confusion, no bottlenecks.
This protocol was unveiled at Google Cloud Next, and it already has support from heavy hitters like Accenture, Deloitte, and KPMG. These are partners with serious enterprise clout, and they’re not wasting time. This is aimed directly at enterprise leaders under pressure to justify AI investments. Companies want something real, not another demo.
AI agents that can work across cloud environments open the door to automating repetitive and low-value workflows, think admin tasks, support queues, and basic analysis. This protocol helps integrate those agents quickly into the tools your teams already use. For enterprises juggling thousands of processes across departments, that means faster deployment and measurable results.
What stands out here is Google’s banking on open collaboration instead of isolated stacks. They realize long-term adoption won’t happen unless customers can choose best-of-breed and have everything connect. For those making decisions at the top, the message is clear: AI agents are here, and they’re being built to scale in environments like yours. If your teams are still stuck in pilot mode, this is your cue to accelerate.
IT service firms are repurposing general-purpose large language models
Across the board, we’re seeing something predictable but still powerful: IT service firms are taking general-purpose large language models, typically trained by cloud giants like Google, Microsoft, and AWS, and transforming them into highly focused enterprise tools. Instead of catching headlines with flashy demos, these firms are tuning the models to solve specific problems in procurement, customer experience, HR, compliance, and more.
This shift is real, and it matters. For decades, businesses have struggled to make legacy systems smarter or more efficient. Now, firms like Accenture, Deloitte, and KPMG are deploying AI agents directly into enterprise workflows. These agents are integrated into systems that already exist across major sectors like healthcare, financial services, and public infrastructure.
We’re no longer in the proof-of-concept phase. Cloud providers are pouring tens of billions into data center infrastructure to support AI workloads. The compute power is there, the models are trained, and the building blocks are maturing fast. Enterprises are starting to realize that adopting AI doesn’t mean redesigning their entire tech stack. What it means is injecting targeted, performant tools into high-priority workflows that make a measurable difference.
For the C-suite, speed and result-oriented execution are now the key. Use cases that once took 12 months to prototype can now ship in weeks. These service firms are monetizing the shift by embedding AI across a customer’s processes and verticals, driving top-line productivity, and giving companies the chance to lead instead of trail. What’s changing is how quickly and precisely AI can be applied to produce value. That’s what enterprise-grade looks like.
Accenture is integrating Google’s Gemini model family
Accenture has taken Google’s Gemini model family, one of the most advanced large language model sets available, and applied it to real problems in legacy IT infrastructure. Specifically, they’re integrating it into GenWizard, their mainframe modernization platform. This is part of their broader effort to make AI useful in systems serving billions of transactions and serving critical business logic.
Mainframes still run key operations inside banks, governments, and Fortune 500s. The problem is many of these systems are rigid and costly to maintain. Now, Accenture is using Gemini-powered agents to reduce friction in those environments. That includes automating routine modernization tasks, accelerating code analysis, and enhancing development productivity in environments that were previously hard to optimize.
For C-suite leaders managing legacy infrastructure, this should be a signal. These platforms don’t need to be replaced overnight, but they do need to evolve. AI can now serve as a force multiplier, more precise project forecasting, fewer hours burned, and better results for business-critical systems. That’s what Accenture is aiming for here: immediate operational impact powered by a proven AI foundation.
Deloitte has rolled out a comprehensive suite of agentic AI tools
In collaboration with Google Cloud and ServiceNow, Deloitte deployed more than 100 standalone agentic AI tools. These are not broad-stroke platforms or aspirational ideas. Each agent is purpose-built to address a function inside the enterprise, sales, marketing, procurement, HR, and customer service. The intent is clear: embed intelligence everywhere that business happens.
Deloitte’s tools are active in healthcare, financial services, consumer industries, and the public sector. The value comes from precision. Each agent is designed with a focused mandate to solve friction-heavy tasks or augment human workflows with relevant, real-time data processing. This makes AI a practical tool, not a weighty transformation exercise.
The scale of this initiative shows that agent deployment doesn’t have to stay small. With the groundwork laid across multiple industries and departments, the implementation curve is shortening. Enterprises don’t need to wait years to see results. Efficiency gains can be delivered in months if the target area is well defined and the agent is aligned with business outcomes.
For executives focusing on operational performance and experience metrics, this is an inflection point. If processes are predictable and repeatable, they’re candidates for automation or augmentation. Deloitte is showing what that looks like at scale, real tools solving real problems, immediately. That’s what pushes a market forward.
KPMG is focusing on developing specialized AI solutions for the banking and financial sector
KPMG is narrowing its focus and moving with clarity. They’re building AI tools specifically for finance, starting with the recent release of a commercial lending AI assistant. It’s designed to improve how banking clients evaluate, process, and manage loans. The tool accelerates decision-making, enhances accuracy, and reduces the manual workload across complex lending workflows.
This effort goes beyond external offerings. Internally, KPMG is adopting Agentspace, Google Cloud’s framework for building and managing AI agents, to integrate agentic capabilities into their own operations. That internal commitment matters. Firms that adopt what they sell tend to move faster, understand limitations more clearly, and improve product quality through direct use.
What’s important for financial and banking sector leaders to note is the strategic alignment here. KPMG is engineering solutions for known high-value problems in financial services. These are areas where regulation, risk evaluation, and speed intersect. That creates complexity, but also opportunity. AI built for these use cases must be compliant, auditable, and trustworthy.
The direction is focused: help financial institutions operationalize AI in a way that aligns with existing systems, meets regulatory obligations, and delivers faster throughput. It’s practical, targeted adoption. For any executive in finance looking to deploy AI with a clear ROI and low disruption, KPMG’s move underlines a smart path forward, focused, compliant, and performance-centered.
Main highlights
- Google sets AI integration standard: Google’s agent interoperability protocol accelerates enterprise-grade AI deployment across clouds. Leaders should assess cloud strategies now to ensure future compatibility with scalable agent frameworks.
- Enterprise AI shifts from pilot to production: Service firms are customizing large language models for industry-specific tools, backed by major hyperscaler infrastructure investments. Executives should identify high-impact workflows ready for AI automation.
- Accenture boosts modernization with Gemini: By embedding Google’s Gemini models into its GenWizard platform, Accenture is modernizing legacy systems faster and with higher accuracy. CIOs overseeing mainframe tech should explore AI-driven upgrade paths.
- Deloitte shows scale in AI deployment: With over 100 AI agents launched across core business functions, Deloitte is proving rapid, cross-specialty deployment is viable. Leaders should benchmark internal adoption plans against this scale to remain competitive.
- KPMG targets finance with focused AI tools: KPMG’s tailored solutions, including a commercial lending AI assistant, position agentic AI for immediate financial sector impact. Financial executives should prioritize targeted AI solutions that align with compliance and operational goals.
