It is a fact that Artificial Intelligence has reached a level of sophistication that will significantly change the way certain tasks are performed. Many refer to it as the Fourth Industrial Revolution.
Understanding how Artificial Intelligence can help your organization and how it will impact your industry sector is key to extracting the maximum value from this technology.
Every new technology has an impact on business processes, offering companies the opportunity to redesign them, improve their competitive position, and grow ahead of the competition. Artificial Intelligence can have a positive impact on a wide range of areas of organizations, with several common characteristics.
In this article, I’m sharing my experience on how to start an Artificial Intelligence project impacting business processes and the keys to its success!
- Choosing the Right Process
The first and fundamental step of the project is to choose the business process. Although this may seem a trivial step, several aspects must be considered:
In general, the processes achieving a major impact with the incorporation of Artificial Intelligence have the following characteristics:
- Involve a significant number of people.
- Have a high load of documentary analysis or data in general.
- Involve many interactions with third parties (internal or external).
- Are repetitive and/or well-defined and delimited.
- Within sensorized workspaces, machines could be optimized to operate at the optimum level specific to the material or task for which they are intended.
In my experience, choosing a process of medium complexity with a high impact on business objectives allows the organization to learn how to implement AI and then extend it quickly to other business processes.
Definition and scope
The process must be well-defined and delimited through a structured and systematic set of steps or activities designed to achieve a specific objective or outcome. Some of them are:
Inputs and outputs: the process defines clear inputs (what is needed to start with) and desired outputs (what is to be achieved).
Document: instructions, guidelines, and any relevant forms or templates.
Clear steps: precise steps to be followed.
Efficiency: reducing unnecessary steps and optimizing the use of resources.
Consistency: consistently produces the same results when executed under similar conditions.
Roles and responsibilities: assignment of specific roles and responsibilities to individuals or teams at each step of the process.
Quality control: defining mechanisms for quality assurance.
Adaptability: flexibility, adaptation to changing circumstances or improvements.
Continuous improvement: periodically review and improve processes.
Thus, the possibilities for the application of AI in companies are immense. Examples include the optimization of industrial processes, customer interactions, collection processes, content generation, data exchange, document handling and retrieval, route optimization, logistics processes, etc.
- Choosing the right model
Once a process to be improved has been identified and selected, the next step is to find the AI model(s) best suited to the nature of the challenge. Sometimes, a single model will be sufficient to improve a particular process. Other times, some orchestration will be required.
Selecting models requires structuring data sets for training and testing, defining quality control and bias criteria, model feeding mechanisms, and location criteria. Based on the above, it is possible to define a matrix of potential suppliers and/or models to be evaluated, followed by a period of adaptation or training: prompting tests, adaptation tests, defining the criteria for retraining or fine-tuning (depending on the type of model fitting the process).
At this stage, it is desirable to seek the advice of an expert who is familiar with these models and who can carry out this selection task directly or guide you through the shortest route.
- Integrating the solution
Once we have verified that the model(s) fits the process and that there is indeed convergence and predictive or operational utility of the model, we can move on to the next stage, which is integration with other tools (ERP, omnichannel communication platforms, non-code systems, SCADA platforms and other multiple alternatives that will depend on the process selected).
This stage is generally carried out by the team leading the AI integration, together with internal teams or external third parties who know and can manage the tools to be integrated.
- Evaluating complementary technologies
An important element to consider in integration processes is that automation through AI may require complementary technologies (e.g., vector databases) and may render systems or areas in companies obsolete.
The AI implementation team must be able to identify the opportunities arising from the integration to leverage the highest value from this project for the organization.
- Articulating security
Once the mechanics of integration between existing systems and the new technologies required have been defined, the design of the security process associated with the technology and its complete operational cycle begins. Questions relating to the cycles and processes of adaptation or training of the models to be used, data validation mechanisms, fine-tuning, RAG or other mechanisms, training cycles, and frequencies need to be answered.
At this stage, collaboration between the internal IT team and the AI implementation team is essential. IT will provide the security framework required by the company, while the AI team will share the specifications derived from the integration.
What are the critical success factors?
For an AI integration project to be successful, identifying the process and the challenge it presents is key. It is common to fall in love with a problem or process that needs to be automated, but in this case, given the time, effort, and costs invested in the project, it is imperative to determine whether this process is one worth being automated.
The second key is to choose the right AI consultant to take the lead of this project. The consulting team will be able to guide you through each of the steps I have described, including the process selection. This will save you time and effort while ensuring that you implement the best alternative available on the market to achieve your goals.
Bear in mind that an automation project can take two to three months until released into a productive environment, depending on its scope and the areas involved. This period will be preceded by internal negotiation and adoption of the idea and project by obtaining the commitment of the management and the departments involved, as well as the corresponding budget.
Finally, continuous organizational improvement will be the key to sustaining the leadership achieved. Once the first process has been automated, it is advisable to revise how it can be improved with the new technologies arising periodically. Simultaneously, identifying new processes that can be improved with AI leverages the company's experience in this area which is now prepared to develop different projects in parallel. The entire organization learns to operate at a new speed and with a new level of automation, allowing for ever more ambitious projects.
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