Artificial Intelligence: 5 steps for a successful adoption process

5 min

Many companies choose to embark on a path of adoption of artificial intelligence. Yet, with data in hand, few succeed in implementing it successfully. A large portion gets stranded along the way due to lack of expertise, costs to be incurred, or an ineffective initial strategy. As with any journey, in which you want to reach your destination as best as possible, it is essential to know the way and the various stages useful for regaining strength. In this case, economic. Here are 5 steps that might help you along the way.

Adoption of Artificial Intelligence: how to make an informed choice

Whenever we approach a new project, it automatically triggers the question: why should I invest time and resources in this innovation? Although today we hear about Artificial Intelligence in every context, choosing to adopt it in a functioning business is always a challenge. Therefore, having a plan and properly evaluating the pros and cons is the ideal approach to reducing the risk rate.

First and foremost, it pays to monitor the AI maturity level and the applications that increase business productivity. In addition, you may choose to rely on a tool to define the company’s level of readiness to embrace AI, namely the AI Readiness Index (AIRI): an online test that assigns a final score depending on the degree of readiness to embrace such innovation.

These aspects serve to outline the pros, but what might be the cons? The barriers to AI adoption are related to several factors. IBM research found that the top five factors hindering the successful adoption of AI in the enterprise are:

  • limited AI skills, competencies, or knowledge (34%);
  • the price is too high (29%);
  • the lack of tools or platforms to develop models (25%);
  • projects that are too complex or difficult to integrate and scale up (24%);
  • the excessive complexity of the data (24%)

Artificial Intelligence AdoptionStep 1: The first move is always to define a strategy

Having ascertained that AI is the perfect technology to make a performance leap for the company, it is now time to define an implementation strategy. At this stage, you need to identify which departments, processes, and activities could benefit most from AI.

If, for example, we have a major slowdown in processing inquiries, which come to customer service, we can activate Chatbots. Such virtual assistants offer initial answers by leveraging AI trained based on frequently asked questions.

The projects to start with concern those activities and processes that ensure a higher and more immediate ROI to obtain economic resources to reinvest. Such as example, the use of AI software for massive data analysis or the development of predictive models.

Having outlined an initial strategy we must, then, compare it with the pre-existing corporate business strategy. In this way, we can verify that the objectives, human resources to be involved and economic resources to be made available for new projects are available and consistent with the estimated time frame.

Step 2: The experts to be involved

As seen above, one of the barriers to the full adoption of AI is the lack of appropriate skills and knowledge for such a complex technology. This is because the figures to be involved will need to have not only business knowledge but also complex skills such as Data Science. This is the only way to fully exploit the potential of intelligent software.

Forming a center of excellence for AI in-house is not easy, yet the number of experts required to put on new projects depends on the organization and the scope of its initiatives. A key role is also played by the managers who will have to lead the teams and choose from time to time the skills needed and in line with the growth of the projects. If resources do not grow at the same rate as projects, they may slow growth and become bottlenecks.

Decidedly simpler is the issue concerning the transfer of top-down knowledge, that is, from managers to the resources in the operational levels who will have to use the software already programmed. In that case, a well-structured training course may be sufficient to achieve a good result in a relatively short time. However, one will have to take into account the innate resistance to change that often works against the adoption of new technologies. Especially those resources that have been working in a certain way for years will find it more difficult to accept changes in procedure.


Artificial Intelligence represents a great resource for new business models. To adopt it successfully, it is essential to carefully follow all the key steps. Click To Tweet

Step 3: Identification of data to be analyzed

To date, Artificial Intelligence is successfully acting in the field of data analysis, which is increasingly numerous and complex to manage. From manual surveys, we have moved to IoT devices that, in total autonomy, collect raw data for processing. If we use, in the processing phase, software with Artificial Intelligence, the results we obtain can be used to build a history of events but also create relevant future predictions.

To make this process work, it is essential to check that the input data come from reliable sources, that they are sufficient for the information we want to generate, and most importantly that they are correct and trusted. Outlining, specifically, the sources and data that will make up a dataset is the first step, we then need to create an infrastructure that monitors the cleansing of the data over time and its storage. Ensuring that these actions are done correctly requires companies to staff themselves with artificial intelligence engineers and data scientists.

The more data we possess, the greater the likelihood of machine learning development models that are accurate and will be able to solve problems that are complex to the human mind.

Step 4: Design new operating models

When we introduce new technology into the company, it is incumbent upon us to design new operating models as well. Only in this way can the chosen innovation improve efficiency, increase value, and gain new market share.

The first thing to do is to assess which departments, processes, or individual activities could benefit from AI. The second step will be to define new goals and allocate a congruent budget. Artificial Intelligence, by the way, is a technology that relates well to the other key innovations of Digital Transformation. It acts as the glue between IoT devices, automation, and industrial robots, going so far as to unlock their potential.

Let us not overlook, that new business models will have to be congruent with the company’s core business and align with the entire ecosystem, both internal and external. Innovation will have to increase the efficiency and profit generated by the company’s core business activities and, at the same time, set up an architecture that can scale the potential business generated by AI. New scenarios and new profit-making opportunities will then open up, leading to different stakeholder and customer relationships.

Step 5: Performance evaluation

Such significant changes will have to be accompanied by new performance assessments. The KPIs used in regular business management will have to be joined by new evaluation metrics that, especially in the initial phase, are critical to correcting the course and building the right path forward.

Monitoring progress will allow predictions to get made for the next strategies to be implemented to make processes even more efficient.

The metrics to be evaluated will change from industry to industry and will be customizable and specific to each type of business. As with automation in general, it is good to evaluate these correctly to avoid coming to inaccurate conclusions.

The true potential of Artificial Intelligence is expressed in new business models rather than as an accelerator of traditional business productivity. When making an investment, and it has enormous potential, exploiting only a fraction of it is a waste. Therefore, it is so important, after deciding to implement AI, to involve all business figures, from top management down to operations. Only in this way do we ensure a return on investment commensurate with the potential of the innovation.

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