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AI, 9 steps to implement it correctly in business processes

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AI, 9 steps to implement it correctly in business processes

Despite the difficult international context, for Artificial Intelligence it has been a record-breaking year thanks to the explosion of the use of Generative AI and Natural Language Processing (NLP) software such as ChatGpt. In Italy the market reached 500 million euros, with a growth of 32%. In terms of implementation in the corporate context, over 6 large companies out of 10 have already started at least one project based on Artificial Intelligence, while among SMEs penetration still reaches 15%. The numbers tell how these technologies will increasingly permeate markets, starting even just with applications limited to some processes of our companies. One of the most critical aspects (lies in the management of the change of existing working processes that involve planning, collaboration, control, reporting and much more. To support companies who are now approaching the introduction of these technologies, here are 9 useful tips for companies that are going to experience the use of Generative Artificial Intelligence, Causal AI and prescriptive analysis, to efficiently implement it.

1- AI - Training or Preparation
Be aware that in the company it’s necessary to have a spokesperson able to understand these technologies, at least at a high level. Otherwise, the risk of failing to govern well is much higher than the potential benefits. If you are planning to adopt these technologies, leave a quarter to your most suitable colleagues to dedicate time to training. Some universities, such as Stanford, offer online documents and videos about AI techniques and principles. It will be time gained during the implementation phase!
2- AI - Identification of the use case
Once you build the knowledge base, the next step is to identify what AI can do for your business, such as improving a service, speeding up a process, or boosting a business. During this phase it’s essential to limit your ideas to a specific use case: in this way it will be much faster and more effective to deal with the first impact with AI in your business.
3- AI - Assigning economic value
Once use cases are identified, it’s important to assess the potential business impact of the project and plan the financial value of the identified AI implementations. Linking an economic objective to AI initiatives will allow you not to get lost in the details and always put the results at the center of the overall evaluation. Another factor to consider will be the T factor: the time that our customers save in doing a certain type of activity, since the introduction of the new technological tool.
4- AI - Identify skill shortages
Once the priorities of AI initiatives are established, it’s time to check if there are enough skills to bring POC to success. Before launching a complete implementation based on AI, it’s correct to evaluate your internal capacity, identify skills gaps to understand who to entrust control of these activities, whether it is appropriate to hire specific resources or choose the support of specialized companies.
5- AI - Be joined, at least at the beginning
Once you feel you are ready as a company to integrate an AI project, it’s essential to approach it with a design mindset, making sure you don’t lose sight of your business objectives. In order for the pilot project to be successful, it’s important to rely on a mixed team of internal people and external support from consultants or companies, who have already attended similar experiences. A third point of view, at this stage will ensure that you maintain a certain impartiality regarding the success or otherwise of the initiative.
6- AI - Wipe the data
A high quality and large data repository is the core of a successful AI/ML implementation. For this reason, starting an AI project is a great opportunity to focus on cleaning and categorizing data, a key step to achieve better results. Usually, companies' data are located at different points of aggregation, often on different systems. It will be essential to create a repository that allows you to integrate different data sets, overcome inconsistencies and ensure that the output data is of the highest quality.
7- AI - Step by Step
All the great revolutions started with little subversive acts. So when you start, we suggest you do it by taking little steps. Applying AI to a small data set will allow you to take extensive tests. Then, gradually, you will increase the volume and allow the scaling of these activities.
8- AI - Planning for storage
Once your small data set is up and running, you need to start thinking about the storage of the data that will be generated. Performances of the algorithm are just as important as its effectiveness. To manage large volumes of data and achieve greater accuracy in analysis, you need a high-performance solution supported by fast, optimized storage-a support you probably have not yet implemented in your IT environment.
9- AI - Calculate the impact
AI provides great opportunities for growth, but it will be a big change for colleagues dealing with new decision-making processes. Some managers, by nature, are more suspicious than others, and you will need to make sure that they accept change positively and do nothing to oppose it ex ante. In many cases, a change management activity may be required through specific training that introduces the new artificial intelligence solution explaining all the advantages it brings.
Implementing AI won’t be a breeze, but this has always happened for every technology that impacts your enterprise’s operations. Focus on data sensitivity and your colleagues' confidence in the path you are about to take: these will be the two pillars on which your new AI project will rest.

More than 6 out of 10 large companies have launched at least one AI-based project

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