The promise of staggering productivity gains has led companies of all sizes to invest significant amounts in Artificial Intelligence, especially given the growing hype surrounding the rise of generative AI. But the reality of the market makes it clear that, in the end, the numbers don’t add up, with most pilot projects being abandoned before reaching their potential for scale, and one-off initiatives yielding an unmeasurable ROI. What we’ve seen is that the main cause of this mismatch between investment and return is the lack of an adequate operational framework.

Generic co-pilots and standalone tools may generate temporary spikes in efficiency without changing the essence of the processes, but applying this technology in a piecemeal manner—without rigorous governance, clear business metrics, and on top of unstructured workflows—tends to increase frustration with the innovation initiative, as shown in the MIT report, which notes that 95% of corporate GenAI pilot projects failed to deliver measurable returns.

What steps should be taken when modernizing systems?

Preparing an organization for Artificial Intelligence to reach its full potential requires a continuous drive toward technological modernization. Attempting to incorporate intelligent agents into rigid, legacy architectures—in addition to integration issues—creates serious operational risks.

The first step toward modernization is to eliminate structural technical issues, ensuring the smooth flow of corporate data. Next, end-to-end observability should be implemented, and applications should be migrated to the cloud.

Modernization progresses when legacy systems undergo refactoring, processes are automated, integration is continuous, and data governance is enforced—creating an environment ready to support real-time hybrid workflows with the security required by the corporate environment and regulations.

What is the AI Operating Model (AIOM), and how does it work?

The AI Operating Model (AIOM) is an operational model developed by Inmetrics to structure the seamless and governed collaboration between human teams and intelligent agents in real time.

This model operates using the R³ Framework, which consists of six strategic initiatives:

• • Reflect: Understand the company's workflows, roles, decisions, metrics, bottlenecks, and maturity.

• • Redesigning with AI: Redesigning operations to account for a hybrid workforce of humans and agents, with new roles and control points, and building a data foundation for architecture, pipelines, quality, and governance.

• • Reorganize: Define responsibilities, routines, governance, KPIs, and agency roles in a continuous cycle.

• • Adopt and implement: carry out controlled cycles, with documentation and monitoring.

• • Review and learn: review performance, the lifecycle, and agent roles. Adjust the KPIs as needed.

• • Scaling with maturity: scaling AI usage through governance, templates, playbooks, and operational maturity.

The technological foundation of AIOM is based on Matrix OS, an operating system divided into two complementary components: DRYVN, which manages day-to-day operations by monitoring data flow, system integrations, technical performance, and model stability in real time; and Matrix Center, which ensures business governance by maintaining full traceability and allowing for immediate human intervention when necessary.

Do humans and agents work together at AIOM?

Within AIOM, we created MaxForce, an agent-based workforce composed of agents with clear business objectives. These are the Agent Roles. In this model, each agent has specific responsibilities, transparent goals, a defined scope of work, quantifiable efficiency metrics, and alignment with the company’s objectives.

Basically, we treat the agents the same way we treat human employees. They are guaranteed autonomy, but with active human supervision, which increases the company’s productive capacity in a predictable manner.

Case Zero: Inmetrics as Proof of the Model Itself

To validate the effectiveness of the AI Operating Model (AIOM), we used our own operation as the baseline case. We applied this model to our business processes, reorganizing operational workflows and implementing a combined approach involving human professionals and agents.

The results we achieved demonstrate the impact of this methodology on productivity. In our sales operations, we recorded a 17% increase in new monthly opportunities, a 23% increase in time freed up for our teams, a 60% improvement in the quality of CRM data, a 37% increase in forecast accuracy, and a 35% increase in the sales conversion rate. This proves that sustainable efficiency stems from control, quality, and predictability.

How can you begin an operational transformation using AI at your company?

Scaling the use of enterprise artificial intelligence requires moving away from one-off solutions and adopting a robust operational model with a proven track record of success. This journey begins with an accurate assessment of technological maturity and current processes.

Learn more about the AI Operating Model and schedule your assessment with one of our experts.

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