Artificial Intelligence (AI) will probably be the great revolution of the next years and it is already affecting many aspects of our daily lives. Think about how you book your holidays, buy things online or invest your savings. It is likely that part of those actions is regulated or executed by Artificial Intelligence, whose algorithms can, for instance, suggest the best hotels, the ideal product or the financial portfolio that best fits our investment goals.
Many big companies have already started investing billions in Machine Learning algorithms and AI tools, but is in the next years that these technologies will spread worldwide and become the field on which competition will be played[1].
In this regard, it is crucial that firms willing to pursue a significant competitive advantage begin to introduce and adopt these technologies as soon as possible.
Gartner has identified 5 levels of “AI maturity” in firms that could be thought as the 5 fundamental steps for AI implementation in your business.
Let’s see them.
Sommario
Step 1 – Awareness
In this phase Artificial Intelligence comes up in conversation although not in a strategic context and there aren’t project or experiments underway. The major risk in this phase is to not understand opportunities and threats posed by AI.
Step 2 – Active
Practical applications and possible projects start emerging more clearly, concepts are understood with more confidence and there are specific meetings in which AI and its deployment are officially discussed.
Step 3 – Operational
The first project has entered the devlopment stage. The firm has access to technologies, skills and best practices regarding AI. Moreover, AI has a dedicated budget and an executive to supervise the activity.
Step 4 – Systemic
The company is considering to implement AI technologies in every new digital process. Most of new products and services employ AI as most of the employees who work on these products understand the technology. Artificial Intelligence is also employed within the organization.
Step 5 – Transformational
AI is part of the company DNA and has a role in every business area. Every employee knows AI strengths and weaknesses.
If your company does not fall into anyone of these phases it is likely that is still in the “level zero” in which there is no consciousness and AI and its applications are not discussed (even informally). Unfortunately, this is the most common scenario, especially if your business operates in a traditional industry in which innovation advances at a slower pace.
How can we perform the abovementioned steps in order to take a full advantage of AI opportunities?
The first step
The first step, which brings the company from the ground level to AI awareness is relatively easy to perform. In most cases we can simply organize a workshop which involves at least a business unit to introduce Machine Learning, Predictive Analytics, Big Data, Internet-of-Things and other Data Science related topics, presenting possible use cases and examples in the industry in which we are operating. At the same time, it is important to remember that those topics are quite often discussed in a superficial way and many times they represent just buzzwords. Many “innovation gurus” may use them to create a lot of hype while having no idea of what they’re talking about. Therefore, in order to avoid misurderstandings and false expectations it’s important to carefully choose the right people to introduce these topics in a clear manner.
From Awareness to Active
This is the crucial step that enable your firm to pass from theory to practice. First and foremost, before launching our first Artificial intelligence project it’s necessary to check if we possess the proper data to train our Machine Learning algorithms. It’s almost impossible that a company already possesses its data cleaned and tied up, ready to be used for AI experiments. Before starting our tests it’s necessary to clearly define which data will be used for our models, and ensure that they respect data quality standards and are available to Data Scientists. It’s in this phase that Data Science expertise becomes crucial: in order to move forward in AI implementation we must either form our Data Scientists team or find the right partner (which may be Dataskills) to guide us in this journey.
Becoming Operational
In this phase the team (external or internal) starts working on Machine Learning models, adopting an iterative framework in order to communicate with the business unit involved in the project. Feedback is crucial and every area should actively participate in the construction of the AI solutions, proposing business rules and testing the ML algorithms to provide useful recommendations. To avoid resource waste, we suggest starting from smaller projects, in order to get confident with technology and develop the required expertise to later move to more ambitious goals.
Employ Artificial Intelligence at every level
The last step, which enables us to get at the Systemic and Transformational level, is to use AI for a broad range of activities, including the development of new products and services and the optimization of existing processes. This is the passage that requires the most expertise and proven experience. Therefore, the company should have its own Analytics team in order to manage the automation solutions in several business areas. In order to attract the best talent in the Data Science field, a company should create an attractive environment, with a ‘test-and-learn’ culture and continuous training for its employees. Thus, is desiderable to build up a “data-driven” culture which grants to every employee a certain degree of freedom in testing its own ideas and exploring new solutions to existing problems, thereby accepting the possibility of failure.
For more on this topic:
3 Steps for a successful Data Strategy
[1] Deloitte (2019). “AI leaders in financial service: Common traits of frontrunners in the
artificial intelligence race.”
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