After six years of working with companies to support activities such as pricing, forecasting and segmentation, using technology based on mathematical models and artificial intelligence, we have learnt to assess the AI readiness of the companies we deal with.
That is why, at the end of each cognitive meeting with a potential customer, we produce a short report, for internal use, giving value to the answers that the customer has given us. As every good salesman teaches, a good part of the meeting is dedicated to key questions that can help us focus on the client’s need, the company’s organisational model and its inevitable criticalities, and the market ecosystem in which it operates.
At the end of the meeting, our team must be able to master, at least at a high level: the business model, the company’s short- and medium-term objectives, the cultural context in which top management operates, the structure of the decision-making processes relating to the areas in which our technologies will be grafted, and Data Science maturity.
Everything we do for the well-being of a client focuses on increasing the margin through Data Science. For this reason, we match the potential customer to one of five categories that make up our “AI Route 6”, a six-step path that all companies, in today’s scenario, are going through or will have to go through, more or less consciously, to remain competitive in the market, increasingly dominated by those who can make Data Science a key element in their decisions.
AI Route 6
Stage 1. At this stage, companies still lack the means to travel our Route. Outdated IT systems, difficult data management and a total lack of or inconsistency in historical data. Moreover, not only are there a lack of professionals capable of dealing with the subject, but top management still has little awareness of the issue. The first step, however, has been taken. Asking us for a meeting showed a nascent curiosity towards this topic.
Stage 2. Companies at this stage are familiar with Artificial Intelligence but have not yet tried to introduce it into their processes. Typically, the managers of companies at this stage have had the opportunity to train themselves on this topic, or they come from previous work experience in companies further down the line. This is the most difficult stage to overcome: there is an awareness of the importance of data science, but a cultural leap is needed, as well as the stubbornness to start a modernisation process in data management.
Stage 3. These companies declare themselves ready and enthusiastic to implement Artificial Intelligence in their processes. They have already approached Data Science with varying degrees of success, applying more basic mathematical and statistical models (and often sufficient for their goals!), but they want to see what lies beyond. Often these managers talk about it more than they know. They formulate ideas rather than strategies on how to use AI in their business and are often just fascinated by the idea of being able to tell the market that they are the first or the best at it.
Stage 4. The fourth stage is quite interlocutory and is usually the best one to intervene by providing our support to these companies. In these companies, there are some people trained on the subject who is trying to “evolve” the data culture to the highest level of awareness. The data scientists in these companies have a fair amount of management confidence and are testing AI to find the right formula to implement more advanced algorithms. Often at this stage, external support can help convince top management to invest in this path again and can give more confidence to the more operational staff that they will be able to rely on the experience they already have in the same or related markets.
Stage 5. Companies on the fifth step of the path have already adopted machine learning in their daily functions. They usually have a structured ML team and are using these technologies to speed up various operations, with some information processing tasks not directly impacting decisions, but stopping at an earlier step. A company, at this point in the journey, is in the best position to prove to itself, in the time frame it sees fit, how much benefit Data Science can bring to its daily operations and business success.
Stage 6. These companies are using AI in a new way to disrupt their business model. They mastered the tools and understood the potential of the technology that brought them to where they are now. The Data Science team is not only extremely trusted by top management, but is the one that can influence strategic decisions the most: all these companies use Machine Learning to fine-tune their algorithms, improve their product offerings, optimise their system infrastructure, and dialogue with customers. At this level, AI is no longer a support, but a strategic partner.
Are you curious about where your company is on the journey and how you can speed up your AI Route 6? You can do so by getting in touch with the Premoneo team.
