Articles

The Economic and Strategic Impact of Agentic AI: Beyond Efficiency to Structural Transformation

Embedding intelligence, autonomy, and speed into the very fabric of operations.

October 10, 2025

9 minutes

Artificial intelligence is entering a new era, as the overall discourse is also slowly shifting as technology gets better. This is no longer about tools that will summarize content, generator of nightmare images or passive chatbots. Agentic AI is here, agents that can perceive, reason, act, and learn autonomously to achieve complex goals. This isn't a distant future concept; as highlighted in recent industry discussions, large global enterprises are already systematically building the platforms to harness this power, moving beyond one-off use cases to foundational transformation.

In a recent discussion during one of executive roundtables, a panelist emphasized that the first question to any enterprise building an agent should be: "Is there an evaluation framework in place?" Without a quantitative way to measure performance and safety, agentic systems risk becoming unmanageable black boxes.

However, this shift is exposing a critical fault line within organizations. The challenge is no longer simply on a technical level, where it might be easier to tackle and solve it, instead it is deeply human and organizational. A smooth and working transition to Agentic AI demands a fundamental workforce and skill evolution, and many companies might find themselves dangerously unprepared.

Two worlds in collision

The core of the challenge lies in the inherent nature of Agentic AI. For decades, the discussion towards AI seems to have operated in two very distinct and parallel paradigms:

The deterministic world of software engineering: Built on established DevOps practices, this world is built on good ol’ predictability. Input X will inevitably produce output Y. Software engineers are masters of this domain, building robust, secure, and scalable systems with clear cause-and-effect relationships.
The probabilistic world of data science: the world of the MLOps, this is a realm that accepts non-determinism. Data scientists understand that models operate on statistics and probabilities; they are comfortable with uncertainty and focus on evaluation metrics and confidence scores.

Agentic AI exists at the unexplored intersection of these two worlds. An AI agent must leverage the non-deterministic reasoning of a large language model (domain of the data science) to perform deterministic, secure actions through tools and APIs (domain of the software engineering).

This creates a critical skills gap. Current approaches seem to be failing because software engineers are overloaded. When tasked with building agentic systems, software engineers often struggle with the "AI safety mindset". They expect prompts and tools to work predictably and can be blindsided by the inherent unpredictability and novel security risks of LLMs.

On the other side, data scientists are in a perfect position to deeply understand model non-determinism but can (and often do) lack the engineering rigor for API security, authentication, and building production-ready, secure pipelines. Their focus is on the model's behavior, not the security of the entire action-executing system.

From Google Cloud's viewpoint, the solution is investing in a new kind of platform that merges DevOps rigor with the flexibility of MLOps. This involves creating tools for robust evaluation, red teaming, and layered security, what can be defined as a "Swiss cheese model" of defense. Google is also investing in democratizing access through no-code/low-code agent builders and exploring futuristic capabilities like the "computer use case", where agents can autonomously operate any software interface, from SAP to legacy mainframes, dramatically expanding their potential application.

The new AI hybrid roles

If companies are looking to bridge this gap, a new class of hybrid roles must emerge. Organizations need professionals who can speak both languages: that of software engineering and also probabilistic AI.

We are seeing the early outlines of these critical positions:

Product Managers: these are individuals who can translate business problems into agentic workflows, understanding both the capabilities and the underlying limitations of the technology. They are tasked with defining what the agent should do and, perhaps even more importantly, what it should NOT do.

Evaluators & Red Teamers: the most crucial function, as emphasized by experts, is a rigorous evaluation framework. This goes beyond simple unit testing. It involves continuous, automated testing and proactive "red teaming", which means actively trying to break the agent, jailbreak its instructions, or trigger unintended tool use. This role is about stress-testing the agent's behavior across thousands of scenarios, not just a handful, to see what happens in those critical and borderline situations.

Safety Engineers: perhaps the real quintessential hybrid role. These engineers possess a software engineer's mastery of security and infrastructure, combined with a data scientist's understanding of model behavior. They architect the "Swiss cheese" model of layered security, which is all about implementing guardrails on inputs and outputs, validating tool authentication, and ensuring that human-in-the-loop safeguards are present where needed.

Let’s make things clear. This isn't about hiring a handful of geniuses or finding unknown skills in desperate job postings. Instead, this is about training existing teams to adjust their skills and defining these new career paths to create a balanced, multi-disciplinary AI workforce.

From Google Cloud’s perspective, the real differentiator isn’t simply how mature the technology is, but how quickly organizations can reorient their workforce. It’s why Google stresses that evaluation and governance must become everyday skills, not niche practices. A software engineer who understands AI safety or a data scientist who grasps production security is no longer an exception, instead they are the new baseline of an AI-ready enterprise. To that end, Google Cloud has invested in training programs and frameworks designed to accelerate this skill convergence inside enterprises.

A truly human bottleneck

The most surprising insight from industry panels is that the biggest bottleneck is not on a technological level. The pace of innovation from Google, OpenAI, Microsoft, and others is staggering. Where the biggest obstacles lie, and snags are happening, is in organizational adoption.

Agentic AI technology will be capable of independently managing complex systems or predicting failures long before organizations are willing to let it. The barriers are all strictly human and revolve around the following big issues:

Management: implementing Agentic AI is quite different than installing a new software in your organization. It represents a fundamental shift in how work is done. Employees need to understand how to collaborate with agents, when to intervene, and how their own roles will evolve, without having to fear for their own job. This requires comprehensive training and a clear change management strategy.

Trust: Would you trust an AI agent to autonomously manage a supply chain or handle a customer complaint without any oversight? If you would, then you are already on the wrongth path. You shouldn’t trust it, and rightly so. Trust is earned through transparency, reliability, and demonstrable safety.

Regulation: Who is liable when an agent makes a costly error or who is to blame when a security breach occurs through an automated action? All of these questions need to be answered before Agentic AI is made to make decisions in your company. This applies even more to regulated industries such as finance and healthcare.

In some way, it would be fair to say that the Agentic AI adoption curve mirrors the internet's rise: while technologists embrace it immediately, broader organizational uptake requires time, training, and a shift in culture. You can have the most advanced agentic platform in the world, but if your people don't trust it or know how to use it and your customers aren’t happy about it, it provides zero value.

Ready to build an AI-ready company?

So, how can organizations navigate this shift? The solution lies in a deliberate focus on readiness, starting from education and upskilling. This is not a simple technological shift where investing in “new machines” will suffice, companies should instead invest that same time and money in their people. Begin upskilling software engineers in AI ethics and safety principles. Train data scientists in secure engineering practices. Introduce all leaders to the strategic implications of Agentic AI.

Secondly, but as important, ever since day one evaluation and governance should be your guiding priorities. Before setting up your first agent, an evaluation framework should be established. This means being able, again from day one, to measure the agent’s performance, safety, and reliability. Finally, define and explain those lines the agent should never ever cross.

But as much as it is possible to build the perfect safeguards, those agents should never be left alone to act. Instead, think about a phased approach where, at the beginning humans should examine closely everything those agents are doing, to then shift to the classic Human-in-the-loop approach. Use agents to handle routine tasks and provide summaries and recommendations, keeping humans firmly in the decision-making loop. This builds trust and provides crucial learning data for both the AI and the organization.

But all of this is quite a big task to tackle alone, especially in regulated sectors where even the slightest mistake might cost reputation and money. Instead, think about finding the right partner that can help in this critical transformation and can help accelerate your journey and lead you through the right steps to take.

Google Cloud likens this workforce transition to the early days of cloud adoption. The companies that thrived weren’t simply the ones that adopted new infrastructure, instead they redefined their culture and processes around it. Agentic AI requires a very similar leap, by embedding continuous learning, evaluation, and human-in-the-loop practices into everyday workflows. Without that huge cultural shift, even the most advanced AI platforms remain underutilized, leaving organizations stuck in pilot projects rather than industry transformation.

How Xebia enables Agentic AI Transformation

The economic and strategic impact of Agentic AI is one that will traAt Xebia, we understand that adopting Agentic AI is not simply a technical transformation, but a shit in how your organization works and thinks. If you choose Xebia for your technological partner, we will help you through this shift by:

Our AI readiness programs: Xebia will assess your organization's current maturity for the upcoming transformation and and craft a tailored strategy for workforce and skill development.

Specialized training: we offer training programs designed to build those critical hybrid skills, from AI safety engineering to agentic product management. We are also open to customizing training for your particular organization’s needs.

Change management & adoption frameworks: Xebia has all of the tools and expertise you need to guide your people through this change, fostering a culture of trust and collaboration with AI.

The age of Agentic AI is not coming; it is here. The winners will be those who recognize that their most important investment isn't in the models themselves, but in the people and processes that will harness them safely and effectively. The time to build your hybrid, AI-ready workforce is now.

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