The biggest risk of Artificial Intelligence isn’t that its models “hallucinate.” It’s not even the cost.
The real existential risk is that your competitors adopt it first—and do it better.
AI has stopped being a futuristic debate and has become the new competitive battleground. It is no longer a nice-to-have; it is the accelerator that will determine who leads the market and who becomes obsolete. AI has evolved from something we could integrate into our business to something we must incorporate into our application stack if we want to stay competitive. Treating it as a passing trend is not miscalculation—it’s a sentence.
Assuming every company already has some level of AI experimentation underway, we can identify the following set of challenges as a thought framework for evolving AI within the organization. This is not truly a “best practices guide”—it is a strategic survival map.
1. The Foundational Challenge: Data and Process Governance
The first step is introspective: is our organization prepared to integrate AI into the core of the business, rather than as a peripheral assistant?
To implement models effectively, it is critical to identify what data can be used to feed and train them—whether deep learning, machine learning, or other AI approaches. We must also understand where in our value chain these models can be applied to improve performance, and how we will measure that impact—cost reduction, increased availability, risk control, shorter delivery times, and more. Strong data and process governance is the cornerstone of any initiative aimed at becoming a data-driven company.
2. The Strategic Challenge: The Deployment and Expansion Model
There is no single path to adopting AI. The approach depends on factors such as the end user, the technical team developing the solutions, and reliance on third-party services. This leads us to the second major challenge: defining the operating model.
Two main approaches—compatible, but ideally explored in sequence during early phases—tend to emerge:
• Business-Oriented Approach:
Deployment based on generalist tools (such as N8N) or more specialized solutions for specific use cases (such as Gumloop, Relay.app, Zapier). These are often cloud-based, pay-per-use, and rooted in RPA (Robotic Process Automation).
• Technical Approach (In-House Agents):
Direct implementation of AI agents within the enterprise environment using engines like GPT, Bedrock, or Gemini, trained privately or publicly depending on subscription and data sensitivity.
3. The Financial Challenge: Cost Control and Return on Investment (ROI)
The previous step leads directly to the third challenge: controlling operating costs. Before moving into production, it is essential to estimate the costs associated with the system’s usage under real-world conditions.
It is also considered best practice to implement tools that allow for cost monitoring—alerts, quotas, and thresholds—depending on the business criticality and continuity requirements of the process where AI has been integrated.
4. The Operational Challenge: Ensuring Accuracy and Consistency
The first three challenges focus on deploying AI, but the work doesn’t stop there. Once models are in production, we must ensure that their outputs remain accurate and reliable over time.
A widely known phenomenon, “hallucination,” occurs when a model deteriorates and begins to make irrational decisions. To prevent these hallucinations—which can pose serious business risks—we must incorporate validation and monitoring mechanisms tied to our AI agents. This is the first major post-deployment challenge, and its cost must be accounted for from the beginning.
5. The Future Challenge: Evolution and the Cost of Change
Finally, there is a more aspirational—but constant—challenge: ongoing evolution and the cost associated with it. The AI landscape is extraordinarily dynamic. Although this concept is broad and subjective, it must remain part of our mindset as a driver for continuous improvement. It should not paralyze initial deployment, but it must be integrated into long-term strategy to avoid technological obsolescence.
Conclusion: AI as a Strategic Necessity
In the end, the evolution of the market makes AI adoption not an option, but a short-term necessity. To navigate this journey successfully, the best strategy is to define a clear roadmap based on measurable, well-structured steps. Only then can we look toward the future with confidence, leveraging AI as a true engine of transformation.
