A Practical Guide for CEOs to Build and Take Advantage of AI Agents

Companies are eagerly adopting generative AI with the hope of bringing in a significant return on their investment. Yet, for many companies, they haven't been able to gain the return they wished for. A recent McKinsey report, "Seizing the Agentic AI Advantage," sheds light on this perplexing issue, which it terms the "gen AI paradox": the widespread deployment of AI with minimal bottom-line impact.

The report argues that the problem lies not with the technology itself but with how it's being used. It is a practical guide for CEOs to build and take advantage of AI agents. While many organizations have successfully implemented horizontal AI tools like generative AI chatbots and copilots, these applications often generate diffuse, hard-to-measure efficiency gains. 

The report suggests that the real value lies in vertical, function-specific AI agents that can automate and reinvent the core business processes from the ground up.

Seizing the Agentic AI Advantage

The report reveals a striking statistic: while nearly eight in ten companies are using generative AI, a similar percentage report no significant impact on their earnings. This disconnect stems from an imbalance in AI strategy.

Horizontal tools, which are relatively easy to deploy, have scaled quickly. However, more transformative vertical applications designed for specific business functions are often stuck in the pilot phase. In fact, fewer than 10% of these high-impact use cases ever make it to full deployment.

The reasons for this are multifaceted, ranging from fragmented initiatives and a lack of mature, packaged solutions to the inherent limitations of first-generation large language models (LLMs). These early models, while impressive, are fundamentally passive. They require human prompting and struggle with complex, multi-step workflows, making them ill-suited for the kind of deep, autonomous integration that drives real business value.

The solution, according to McKinsey, is a move toward agentic AI. These are not your average generative AI chatbots. Agentic AI systems are designed to be autonomous, goal-driven collaborators.

The agentic systems can:

• Understand objectives,

• Break them down into subtasks,

• Interact with both humans and systems,

• And adapt their approach in real-time, all with minimal human intervention.

This is made possible by combining LLMs with additional components that provide memory, planning, orchestration, and integration capabilities. By embedding these AI agents and agentic systems into the core business processes, companies can move a step further than simple task automation and begin to truly reinvent how work gets done.

Key Features of Agentic AI

The McKinsey report outlines several key features that distinguish agentic AI from its predecessors. These capabilities allow AI agents to work as proactive, goal-oriented partners rather than reactive tools.

• Autonomy and Planning: 

AI agents can independently break down complicated goals into smaller, manageable steps and then plan and execute these steps in a logical sequence, adapting as new information becomes available.

For example, in a legacy system modernization project, a team of AI agents can be tasked with retroactively documenting the existing application, writing new code, and integrating it into the new system, all in a coordinated manner.

• Memory and Context: 

Agentic AI systems have persistent memory, unlike simple chatbots that treat each interaction as a new event, and AI agents can retain context across multiple sessions and queries, allowing them to build a deeper understanding of the task at hand.

This ability of AI agents to have persistent memory and context retention allows them to correlate internal data, like sales figures, with external factors, like weather patterns or supply chain disruptions, to provide more subtle and deep insights.

• Orchestration: 

You may have experienced that in a business environment, works are rarely finished in isolation. Agentic AI systems can orchestrate complex workflows that include multiple agents, systems, and data sources.

This "agentic AI mesh" can act as a connective layer, allowing seamless collaboration between custom-built and off-the-shelf AI agents.

• Human Oversight: 

The involvement of agentic AI doesn't make humans obsolete. Instead, it changes our role from manual execution to strategic oversight.

For example, a relationship manager at a bank might review a credit memo generated by an AI agent rather than spend weeks drafting it from scratch. This frees up human workers to focus on higher-value activities that need critical thinking and complex decision-making.

Real-World Applications

The report highlights several case studies that show the practical benefits of agentic AI.

Example 1:

In one example, a market research firm was struggling with data quality issues. The manual process of gathering, structuring, and analyzing data was not only time-consuming but also prone to error, with clients identifying a staggering 80% of mistakes themselves.

The firm was able to automate the process of identifying data anomalies and explaining shifts in sales or market share by implementing a multi-agent solution. The system analyzed both internal signals and external events, surfacing insights that would have been difficult for human analysts to uncover. The result was a potential productivity gain of over 60% and an estimated annual savings of more than $3 million.

Example 2:

In another example, a retail bank was looking to improve the process of creating credit-risk memos. Relationship managers were spending weeks manually extracting information from more than ten different data sources to draft these complex documents.

The bank developed a proof of concept that used AI agents to help with data extraction, memo drafting, and confidence scoring. The adoption of AI agents changed the role of the analyst from manual drafter to strategic overseer, leading to a potential 20-60% increase in productivity and a 30% improvement in credit turnaround time.

A practical guide for CEOs to build and take advantage of AI agents:

This McKinsey calls itself a CEO playbook to solve the gen AI paradox and unlock scalable impact with AI agents. To successfully be able to transition to agentic AI, the report advises CEOs to take a strategic, top-down approach.

• The focus should change from optimizing isolated tasks to reinventing entire business processes.

• This means asking not "Where can I use AI in this function?" but "What would this function look like if agents ran 60% of it?"

The report also emphasizes the need for a new delivery model. The traditional, isolated approach to AI development is no longer sufficient.

Instead, companies should create cross-functional teams composed of business experts, process designers, AI engineers, and data scientists.

• These teams should be tasked with building custom agents that are deeply aligned with the company's unique logic and data, creating a competitive advantage that is difficult to replicate.

Finally, the report suggests that CEOs bring the era of AI experimentation to a close because the time for tinkering is over. Now, the time is for a focused, strategic transformation that will change how organizations operate, compete, and create value.

Conclusion

Agentic AI systems aren't just a new toolset but a fresh operating model. It requires a total change in mindset, a willingness to reinvent long-standing processes, and a commitment to building a new kind of collaborative relationship between humans and artificial intelligence (AI). This McKinsey report is a practical guide for CEOs to build and take advantage of AI agents. Only when companies get away from the "gen AI paradox" and start adopting agentic AI advantage can they start to understand the full potential of this powerful technology.