Tech Trend #2: Unleashing agentic AI’s true potential - Strategic approaches for a silicon-based workforce
New research from the Deloitte Tech Trends report reveals that while agentic artificial intelligence (AI) promises autonomous operations and intelligent execution, many enterprises are struggling to move beyond pilot projects. True value creation stems from fundamentally redesigning operations and managing AI agents as a “silicon-based workforce” that complements human talent—rather than simply automating existing processes.
The agentic reality gap: Bridging promise and practice
Enterprises are rapidly exploring agentic AI, with Gartner predicting that 15% of daily work decisions will be made autonomously by agentic AI by 2028, a significant increase from virtually none in 2024. Despite this momentum, many organizations face substantial challenges in translating agentic pilots into production-ready solutions. Deloitte’s 2025 Emerging Technology Trends study indicates that only 14% of organizations have deployable solutions, and just 11% are actively using these systems in production.
Several core infrastructure problems help explain this gap. Many enterprise systems were never built for real-time, autonomous AI agents. As a result, they often slow projects down or block them entirely. Gartner predicts that more than 40% of agentic AI initiatives will fail by 2027 because they cannot work properly with legacy systems.
Data infrastructure is another major obstacle. Many organizations still rely on batch-based Extract, Transform, Load (ETL) processes, which are too slow and rigid for AI agents that need fresh data and fast decisions. This creates friction when deploying and scaling agent-based systems.
There is also a design issue. Instead of rethinking how work should be done in an AI-first environment, companies often try to automate processes that were originally designed for humans. This approach limits the benefits of AI and can even make things worse. In some cases, it leads to “workslop,” where poorly designed agentic tools add complexity and increase operational workload rather than reducing it.
Redesigning operations for a human–digital future
Leading organizations are addressing these challenges by adopting a systematic approach to agentic transformation, moving beyond simply layering agents onto legacy workflows. This requires stepping back to analyze end-to-end processes and leveraging unique agent capabilities, such as continuous, high-volume task execution without human constraints like breaks or working hours.
“Now is the perfect moment to map your value streams and understand how workflows should operate, compared to how they actually work today. Don’t just improve outdated processes for the sake of convenience. Instead, use this new wave of AI to rethink how agents can best collaborate with people, enhance productivity, and streamline operations across the business,” - said Aleksandar Ganchev, Director Technology Strategy Transformation.
This strategic redesign is already taking shape across industries. At HPE, an AI agent called Alfred supports internal operational performance reviews. Toyota uses agentic tools to gain real-time visibility into vehicle arrivals at dealerships and is planning to empower agents to resolve supply chain issues - bypassing the need for human interaction with complex mainframe systems. These examples signal a shift away from traditional application modernization toward enabling agents to bridge legacy system gaps directly.
Perhaps the most significant change is the recognition of agents as a new category of labor - a “silicon-based workforce.” Organizations are increasingly integrating these digital workers with their human, or carbon-based, workforce, enabling people to focus on higher-value activities such as governance, compliance, and growth strategy. Mapfre, for example, deploys AI agents to handle routine administrative tasks in claims management, while keeping humans in the loop for sensitive customer interactions. Moderna has gone a step further by merging its technology and HR functions under a Chief People and Digital Technology Officer to better integrate talent and technology.
“If we think of agents as digital skills, their real value emerges when they operate collectively. Most composite processes don’t exist solely within the enterprise. Trustworthy, secure interworking between agents is critical,” -added Dimitar Dimitrov, Senior Manager Technology Strategy Transformation.
Effective implementations also leverage specialized agents orchestrated at scale, automating entire workflows through emerging standards such as the Model Context Protocol (MCP), Agent-to-Agent Protocol (A2A), and Agent Communication Protocol (ACP). Together, these approaches enable a “microservices model for AI,” reducing complexity while supporting scalable orchestration and platform flexibility. At the same time, organizations are introducing FinOps frameworks for AI, essential for monitoring and controlling agent-driven costs, particularly in token-based pricing environments.
The shift toward agentic AI represents more than a technological evolution; it is a fundamental organizational transformation that will reshape how enterprises operate, compete, and create value. Organizations that master agent-native process design, multi-agent orchestration, and silicon workforce management will be best positioned to thrive in an increasingly automated economy. Ultimately, success will depend on creating new forms of human–AI collaboration that capitalize on the complementary strengths of both human and silicon-based workers.
To explore how organizations can strategically embrace agentic AI and prepare for the future of work, read the full Deloitte Tech Trends report.
