26 Jan Strategic AI consulting: how MPL turns artificial intelligence into financial results

At MPL, we combine more than 40 years of tradition in technology and business innovation with a mature practice in artificial intelligence applied to business, process automation and integration with ERPs such as JD Edwards. Our team combines technical expertise certified by partners global companies such as Oracle, experience in more than 1,000 projects delivered and the use of Low Code and No Code tools, which allows us to implement solutions of AI in an agile, scalable way that is aligned with the reality of each company. We work to turn AI into measurable financial return, supporting data-based decision-making with security, governance and productivity.

The challenge for companies with artificial intelligence

Many companies are already investing in artificial intelligence, But they still come up against a familiar scenario: various pilots, proofs of concept and isolated initiatives that don't translate into a clear financial impact. Without a strategic AI consultancy, it's common to see projects disconnected from the strategy, priority disputes between areas and difficulty in measuring returns, which fuels skepticism and wasted budgets.

What differentiates organizations that reap consistent results with AI is not access to technology, but the ability to choose good use cases, prioritize objectively and turn experiments into continuous operation. It is precisely at this intersection between strategy, prioritization and recurring execution that MPL's AI consultancy is positioned.

AI as a business project, not just an IT project

MPL's strategic AI consultancy starts from an essential principle: artificial intelligence only generates value when business leaders understand the potential, recognize their own pains in the use cases and participate in prioritization. That's why the first pillar is not the AI model, but the training of the business areas, in clear language, focused on the capabilities, possibilities and limits of the technology.

According to research by MIT, companies are 5.9 times more likely to achieve a financial return when their employees see value in AI and understand how to apply it on a daily basis. This reinforces the importance of an AI consultancy that goes beyond the hype and translates technology into P&L impact, productivity and competitive advantage.

The MPL method: training, survey and prioritization with decision quadrant

Instead of starting by talking about tools, MPL's AI consultancy starts with real business pains. We use a survey framework based on a simple questionnaire, built on business pains mapped out in several clients and sectors, which makes it easier to identify opportunities of AI in areas such as operations, legal, customer service, HR and finance.

For each pain identified, we evaluate the potential impact on the business and the complexity of implementation, generating an objective score for both dimensions. With this data, we produce an executive report that places each initiative in a prioritization quadrant (Priority, Strategic, Quick Wins and Avoid), in a model inspired by established project portfolio approaches.

This “magic quadrant” turns the topic of AI into an objective debate, where CFO, COO, CIO and area leaders can clearly see which AI projects should start now, which need additional preparation and which don't make sense at the moment.

Squad AI as a Service: from strategy to continuous execution

Strategy without execution becomes just a pretty document. To avoid this, the MPL connects strategic AI consulting to a model of Squad of AI as a Service, responsible for getting the roadmap off the ground. This squad is made up of specialists in generative AI, data science and engineering, BPM, integrations and web and mobile development, with flexible composition depending on the phase of the project.

The professionals are allocated on demand, preventing the company from having to set up a large internal team specializing in AI right at the start of the journey. The same team that implements solutions also monitors, measures results, adjusts models and applies FinOps practices to optimize cloud costs and the use of AI models, ensuring continuous evolution of the project portfolio.

In practice, this means that one quarter could be focused on AI agents with GenAI, document automation and integrations, while the next quarter prioritizes machine learning models for demand forecasting, advanced segmentation of clients or risk models.

Sertrading case: Strategic AI applied to the business

A concrete example of this approach is the case of Sertrading, which sought to organize and prioritize its artificial intelligence in a clear roadmap, with a focus on results and risk control. By training the business areas, using the survey framework and prioritizing in quadrants, the company was able to see clearly where AI would have the greatest impact and which projects should be tackled first.

According to Alberto Basile, CIO at Sertrading, “the biggest gain from the training was to level the understanding within the company. MPL was able to translate artificial intelligence to a business language, without excessive technicality, and this changed the quality of the conversation between IT and the areas. Instead of just discussing tools or technical terms, we started talking about real problems, indicators and the impact on results. The leaders began to clearly recognize where AI generates concrete value and where it doesn't make sense, and no longer see the topic as something distant or exclusively technical.”

In the executive's view, the formula for successful AI implementation is simple and replicable: start with the problem, empower leaders with a clear understanding, prioritize with an objective method and validate each initiative with well-designed proofs of concept. This combination reduces risks, increases clarity for decision-making and brings speed to implementing AI in a strategic and sustainable way.

With the roadmap defined using this methodology, the MPL acted to transform these opportunities in solutions real, integrated into existing systems and monitored continuously, consolidating Sertrading as a success story for MPL's strategic AI consultancy and reinforcing its ability to transform artificial intelligence in measurable business value