AI, 9 tips for implementing it effectively


We have spoken many times about the applications that Artificial Intelligence is bringing into the lives of companies. According to some estimates, the global AI market will reach a turnover of more than $190 billion by 2025, with an annual growth rate of more than 30%.

These numbers testify to how these technologies will increasingly pervade the markets, entering even with applications limited to certain processes into the lives of most of our companies. One of the most critical aspects is the change management of existing work processes involving planning, collaboration, control, reporting and much more. This is why, thanks to our experiences at Premoneo, we have compiled a list of 9 useful tips for companies that are about to experiment with the use of Machine Learning or related technologies.

Artificial intelligence can disrupt critical business processes and in this article, we will discuss ways in which organisations can efficiently implement it.

  1. AI – Preparation. Be aware that you need to have people in the company who understand these technologies, at least at a high level. Otherwise, the risk of not being able to govern them well is much higher than the potential benefits. If you plan to adopt these technologies, leave a quarter to your most suitable colleagues to spend time on training. Some universities, such as Standford, offer papers and videos online on AI techniques and principles. It will be time gained in implementation!
  2. AI – Identifying the use case. Once the knowledge base has been built, the next step is to identify what AI can do for the business, such as improve a service, speed up a process or enhance activity. At this stage, it is crucial to circumscribe your ideas to a specific use case. This will make it much faster and more effective to deal with the first impact of AI in your business.
  3. AI – assigning an economic value. Once the use cases have been identified, it is crucial to assess the potential business impact of the project and project the financial value of the identified AI implementations. Tying an economic objective to AI initiatives will allow one not to get lost in the details and always put results at the centre of the overall assessment. Another factor to take into account will be what we at Premoneo call the t-factor: the time our customers save in doing a certain type of activity, since the introduction of the new technological tool.
  4. AI – Identifying skills gaps. Once AI initiatives have been prioritised, it is time to check whether there are enough skills to make the POC successful. Before launching a full AI-based implementation, it is a good idea to assess your internal capacity, identify skills gaps and figure out who to entrust with the control of these activities, whether to hire specific resources or choose the support of specialised companies.
  5. AI – Get support, at least in the beginning. Once you feel that you are ready as a company to integrate an AI project, it is crucial to approach it with a projectual mentality, making sure that you do not lose sight of the business objectives. For the pilot project to be successful, it is necessary, in our opinion, to rely on a mixed team, consisting of people within the company and external support from consultants or companies that have already had similar experience. A third point of view at this stage will ensure that you maintain a certain impartiality with regard to the success or otherwise of the initiative.
  6. AI – Clean data. A high quality and numerous data repository is the basis of a successful AI/ML implementation. Therefore, the start of an AI project is a great opportunity to focus on data cleansing and processing, a key step for better results. Typically, companies’ data are located in different aggregation points, often on different systems. It will be crucial to create a repository to integrate the different data sets, overcome inconsistencies and ensure that the output data is of the highest quality.
  7. AI – Step by Step. All great revolutions started by small subversive acts. Therefore, when starting out, we suggest doing it by taking very small steps. Applying AI to a small data set will allow for thorough testing. Then, gradually, you will increase the volume and allow scaling of these activities.
  8. AI – Plan 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. The performance of the algorithm is just as important as its effectiveness. To handle large volumes of data and achieve greater accuracy in analysis, you need a high-performance solution supported by fast, optimised storage – something you probably have not yet implemented in your IT environment.
  9. AI – Calculating the impact. AI provides great opportunities for growth but it will be a big change for colleagues who have to deal with a new way of doing things. Some managers, by nature, are warier than others and you will have to make sure that they accept the change positively and do nothing to oppose it out of hand. In many cases, change management may be necessary, through specific training that introduces the new AI solution and explains to them all the positive elements it brings.

The implementation of AI will not be a walk in the park without difficulties, but this has always been the case for any technology that has impacted the operations of your company. Focus on data sensitivity and the trust of your colleagues in the path you are about to take: these will be the two pillars on which your new AI project will rest.

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