Why choose Humanativa


Artificial Intelligence (AI) and Machine Learning (ML) are having their “Hype” phase. There is a lot of expectation and the market demand is therefore increasingly growing.

The change of pace for the customer who wants to transform his organization into AI-Driven will be to follow a path of adaptation and technological, functional and organizational growth. So, beyond the hype, how to help a customer identify the right path for his needs?

With this first article we explain why to choose Humanativa and inaugurate the section of our portal dedicated to the insights concerning our Machine Learning Services and the Methodological path that supports our ML solutions.


The AI and ML market according to Gartner 

Gartner predicts that the global market for Artificial Intelligence and Machine Learning software will reach $62 billion this year, with the fastest growth in the Knowledge Management industry. According to the company, 48% of CIOs surveyed already have AI/ML and machine learning implementations or plan to do so within the next 12 months.


But what happens today when the customer relies on ML service providers?

The following image shows us synthetically three points of view (the IT Manager, the Business Manager and the Top Management) and the “distorted” vision that can be generated when the supplier fails to provide the necessary support to understand principles and processes with the right “language” at all levels of the Customer, and when the Customer, struck by the Hype mentioned in the introduction,  wants an AI-Driven solution, immediately, without facing a correct path that guides him to change.

There is essentially a “latent” risk that is the loss of credibility in Machine Learning Services and in the benefits that can instead be obtained by following a path of innovation.


The support of Humanativa to undertake an AI-driven path

Humanativa aims to support organizations that intend to embark on an AI-driven path to achieve significant transformations and innovations and proposes itself as a catalyst for digital transformation:

  • enabling technology
  • managing process change
  • introducing a culture based on data and its robustness and reliability.

Our job is to help and accompany the Customer to approach the world of ML in order to give the Customer operational and decision-making autonomy and at the same time accompany him with ML Consulting Services for any more complex components.


Enhancement and governance of ML solutions

At the base of Humanativa’s support to the Customer there is the Enhancement and Government of ML solutions. Giving “value” to ML solutions means that the results of our ML services cover different aspects of the so-called “value”:

  • End Customer Value is perhaps the hottest topic for organizations that sell products/services to which we provide solutions. Knowing the end customer means being able to customize services to the end customer himself and attribute an “economic value”. For example, in Humanativa we have methods, techniques and algorithms of Behavior through which to collect the “behavior” of the end customer and give the behavioral prediction to finally give an economic value to the end customer.
  • The Value of Decisions. Business Intelligence products are not enough to make decisions, it is necessary to give “up to date” forecasts that are always updated. Our know-how on Advanced Analytics is in knowing how to interpret the result of ML forecasting elaborations that today allow rapid decision-making both for prevention, risk and innovation.
  • The Value of Indications for the Organization. Often ML tools are used to understand how an organization copes with a service even in its internal processes. In Humanativa we provide indications to support a company through the continuous monitoring of the results of ML Models, basically giving support to redesign or requalify a certain business model and providing forecasting indications also in resource planning activities.


  • The Value of the Government of the Costs of ML. For a company that embarks on a path of enhancement of its data, processes and services through AI & ML services, a key point is to govern its cost. If, for example, we think of Cloud services, major suppliers such as Google, Amazon and Microsoft help customers to choose technologies and services and their appropriate service methods, but the key point is that: no provider of these services can answer the question: how much does it cost “your” company the ML service to be put in the Cloud.

The costs, in this case, are not only of “initialization” of an ML service in Cloud, but above all of “monitoring” during the life of the service itself. In this field, Humanativa has experience and tools to monitor the cost of the ML service and constantly give indications on the cost of the various component modules of the service workflow, in order to be able to give indications on the optimization and / or reduction of the costs of the ML process itself for a certain service in delivery.

Moreover, the concept of ML Costs is so permeated in our Humanativa structure that Data Scientists are the first to design a certain ML service, to collect information on the drivers of the current and desired service, on the principle of continuous “updating” of the Models, on the re-training times of a model,  on the “weight” of false positives of the results, in order to help the Customer find the “right” cost function. We will talk more about this aspect in the article dedicated to the 3 key problems that a customer must solve to become an AI-driven company.

  • The Value of Know-how. In the AI and ML “world” we almost never talk about “transfer” of know-how to the Customer but about “sharing” with the Customer. ML technologies and algorithms are always changing and readapting, so we need:
    • Basic know-how: Training, Consulting, Best Practices to make the Customer “aware” and with an increasing degree of autonomy
    • AutoML: In addition to know-how, automatisms are also needed to make BUs autonomous and the so-called “domain experts”, to whom an ML service is intended, to experiment with AutoML mechanisms.
    • Monitoring of Services: In addition to know-how, in addition to AutoML, today the “rules”, MLOps procedures must permeate the DevOps team of the Customer who manages the products being delivered.


Article written by Maurizio Mansueti