Legacy system replacement
When an organization examines its end-to-end processes, it will likely discover workflows that span multiple systems, including legacy software. This has implications for core modernization strategies. As we discussed in last year’s Tech Trends report, AI is increasingly able to learn and understand the essential business rules and workflows that define a business’ operations. Organizations should carefully consider what constitutes their true core systems and determine whether to use traditional application modernization when agents can effectively bridge legacy system gaps.
At Toyota, teams are using an agentic tool to gain better visibility into the estimated time of arrival of vehicles at dealerships and will soon start using agents to resolve supply issues. The process used to involve 50 to 100 mainframe screens and significant hands-on work from supply chain team members. Now, an agent delivers real-time information to staff on vehicles from pre-manufacturing through delivery to the dealership, all without anyone having to interact with the mainframe.
Going forward, the team plans to empower agents to identify delays in vehicle shipments and draft emails to try to resolve the issue.
“The agent can do all these things before the team member even comes in in the morning,” says Jason Ballard, vice president of digital innovations at Toyota. “We’ve made that critical decision to just go ahead and invest in this area a bit further. We feel like that’s where the differentiator is going to be going forward.”11
Managing the mixed silicon- and carbon-based workforce
Perhaps the most significant shift when implementing AI agents involves recognizing that agents represent a new form of labor, one that may share some similarities with the human (or carbon-based) workforce. Some organizations are beginning to think beyond using agents as simple automation tools and are starting to explore ways to integrate them with their human workforce.
This evolution represents a fundamental reimagining of what work means, how it’s performed, and who performs it. At the heart of this shift is a recognition that AI agents and human workers have different skill sets. While agents excel at defined processes, humans remain essential for navigating the shifting ground of business requirements and complex problem-solving scenarios.
This transformation creates two primary areas that human workers are moving toward.
- Compliance and governance: Humans increasingly focus on validation, oversight, and building guardrails for agent operations.
- Growth and innovation: They also concentrate on reimagining operations and identifying new opportunities that emerge from agent capabilities.
At insurance company Mapfre, AI agents are used across the organization, including in claims management, where agents handle routine administrative tasks like damage assessments. And when it comes to more sensitive tasks like customer communication, a person is always in the loop. Maribel Solanas Gonzalez, Mapfre’s group chief data officer, says she carefully considers which tasks to delegate to agents, ensuring that they are tasks that agents can complete safely and efficiently. Anything that may carry risk still goes through a human worker. This is beginning to change the nature of jobs. The company has published an AI manifesto that prioritizes well-governed, respectful, and safe AI.
“It’s hybrid by design,” she says. “With the high level of autonomy of these agents, it’s not going to substitute for people, but it’s going to change what [human workers] do today, allowing them to invest their time on more valuable work.”12
Other enterprises are going even further. Biotech company Moderna recently named its first chief people and digital technology officer, essentially combining its technology and HR functions. The move was a strategic step to evolve Moderna’s operating model by integrating people and technology to accelerate how work gets done.
“The HR organization does workforce planning really well, and the IT function does technology planning really well. We need to think about work planning, regardless of if it’s a person or a technology,” says Tracey Franklin, chief people and digital technology officer at Moderna.13
Specialized vs. broad automation
Successful deployments focus on specific, well-defined domains rather than attempting enterprise-wide automation. Broad automation remains possible but requires multiple specialized agents working in an orchestrated fashion rather than single, monolithic solutions.
Organizations face critical build-versus-buy decisions that often depend on technical maturity and specific use case requirements. Research indicates that pilots built through strategic partnerships are twice as likely to reach full deployment compared to those built internally, with employee usage rates nearly double for externally built tools.14
Multiagent orchestration
The first wave of generative AI in the enterprise consisted largely of general-purpose chatbots, which, while useful as productivity tools, often don’t deliver the kind of opportunities to automate that businesses need to drive new efficiencies. With AI agents, organizations can develop highly specialized tools that automatically execute specific tasks. When these specialists are deployed in an orchestrated manner, they can automate entire workflows. This approach is enabled by evolving standards and protocols that facilitate agent interaction.
Model Context Protocol (MCP): Developed by Anthropic, MCP standardizes how AI systems connect to data sources and tools, providing a universal interface for agents to access enterprise resources.15 While promising, MCP faces limitations in handling complex enterprise security requirements and integrating legacy systems.
Agent-to-Agent Protocol (A2A): Google’s protocol enables direct communication between different AI agents across platforms, handling agent discovery, task delegation, and collaborative workflow.16
Agent Communication Protocol (ACP): This is an open protocol that enables agents to communicate with each other through a RESTful API, allowing agents to collaborate regardless of the environment in which they were built.17 ACP may face hurdles due to limitations on the number of agents that can coordinate in a single network and the complexity of integrating with existing enterprise tools.18
These protocols represent the foundational layer for what experts describe as a “microservices approach to AI”: deploying numerous smaller, specialized agents across various platforms closer to where workflow instructions and data reside. This approach offers several advantages, such as reduced complexity (because smaller agents are easier to debug, test, and maintain); scalable orchestration, where multiple specialized agents can be combined for complex tasks; and platform flexibility that allows agents to run on different systems while maintaining interoperability.
FinOps for agents
As agents operate continuously, poorly configured agent interactions can trigger cascading actions like unpredictable resource consumption and ballooning costs, making cost management critical. Organizations need specialized financial operations frameworks (or FinOps) to monitor and control agent-driven expenses and account for token-based pricing models. These frameworks help track costs in detail through resource tagging, real-time monitoring, automated resource management including autoscaling and rightsizing, and strong governance frameworks to manage AI-specific expenditures.19
Five questions to drive agentic AI implementations
As organizations begin their agentic journey, they can consider five strategic questions to help drive their adoption, both now and into the future.
- What agents will be deployed, and what functions will they perform?
- What are the cost profiles relative to human employees?
- Which processes will be automated and at what level of efficiency?
- What will be the optimal mix of human and digital workforce over the next four years?
- Will agents eventually take over entire operational areas beyond the five-year horizon?
Most enterprises ready to implement AI agents today are likely to have prepared answers for the first three questions. However, things get hazier as they consider the latter two. A lot depends on how agentic technology and the underlying generative AI models develop in the future and how this development drives changes in workforce makeup and operational priorities.