Agentic AI: Why 2025 is the Year Enterprises Must Move Beyond the Buzz
By 2028, a staggering 33% of enterprise software will incorporate agentic AI – a leap from less than 1% today. But the current scramble to adopt this powerful technology risks repeating the mistakes of past tech booms, like the stalled promise of Blockchain. The key isn’t simply *if* to implement agentic AI, but *how* to do so strategically, avoiding costly missteps and maximizing real-world impact.
The Allure – and the Pitfalls – of Autonomous Intelligence
Agentic AI, unlike traditional AI, doesn’t just respond to prompts; it proactively identifies goals and executes tasks autonomously. From automating complex workflows like procurement and recruitment to bolstering cybersecurity and streamlining customer support, the potential applications are vast. This has fueled a surge of interest, with Capgemini reporting that 50% of business executives plan to invest in AI agents by 2025, a dramatic increase from just 10% currently. However, as Matt McLarty, CTO at Boomi, cautions, many organizations are “struggling to get out of the starting blocks” – and worse, are tempted to run before they can walk.
Learning from Blockchain’s Cautionary Tale
The rush to embrace agentic AI echoes the early days of Blockchain technology. Following its decoupling from Bitcoin in 2014, a wave of investment and hype surrounded Blockchain 2.0, promising a revolution in decentralized ledgers. Yet, a decade later, Blockchain adoption remains limited, hampered by technical challenges and a lack of clearly defined, compelling use cases. “I do see Blockchain as a cautionary tale,” McLarty explains. “The hype and ultimate lack of adoption is definitely a path the agentic AI movement should avoid.” The core issue? Organizations often struggled to identify problems where Blockchain offered a superior solution – or even a necessary one.
The risk with AI agents isn’t a lack of capability, but a disconnect between the technology and genuine business needs. As McLarty points out, agentic AI *can* perform tasks beyond the reach of other solutions, particularly in areas requiring contextual reasoning and dynamic execution. But technologists, understandably excited by the possibilities, sometimes lose sight of the fundamental business problem they’re trying to solve.
A Phased Approach: Starting with “Low-Hanging Fruit”
Instead of attempting ambitious, large-scale deployments, McLarty advocates for an iterative approach. Focusing on “low-hanging fruit” – smaller, incremental use cases – allows organizations to build experience and demonstrate value before tackling more complex projects. This means prioritizing the development of “worker agents” – the foundational components that will eventually form more sophisticated, multi-agent systems.
Practical Applications for Early Adoption
What does this look like in practice? Consider these initial applications of agentic AI:
- Automated Invoice Processing: An agent can autonomously extract data from invoices, verify information, and initiate payment approvals, reducing manual effort and errors.
- Tier 1 IT Support: AI agents can resolve common IT issues, such as password resets and software installation, freeing up human support staff for more complex problems.
- Proactive Cybersecurity Monitoring: Agents can continuously scan for vulnerabilities and automatically implement security measures, enhancing threat detection and response.
- Personalized Customer Onboarding: Agents can guide new customers through the onboarding process, providing tailored support and resources.
These initial deployments aren’t about achieving full autonomy; they’re about demonstrating the value of AI-powered automation and building a foundation for future growth. They also provide valuable data and insights that can inform the development of more sophisticated agentic AI systems.
The Future of Agentic AI: Multi-Agent Systems and Beyond
The long-term vision for agentic AI extends beyond individual worker agents to complex, interconnected systems. Imagine a supply chain managed by a network of AI agents, each responsible for a specific task – from sourcing raw materials to optimizing logistics and managing inventory. Or a healthcare system where agents collaborate to diagnose patients, personalize treatment plans, and monitor health outcomes. Gartner predicts that these multi-agent systems will become increasingly prevalent in the coming years, driving significant improvements in efficiency, productivity, and innovation.
However, realizing this vision requires careful planning, a strategic approach, and a willingness to learn from the past. The key is to focus on solving real business problems, starting small, and building a solid foundation for future growth. The potential of agentic AI is undeniable, but only those who avoid the hype and embrace a pragmatic approach will truly unlock its transformative power.
What are your thoughts on the biggest challenges facing organizations adopting agentic AI? Share your insights in the comments below!
