AI (Artificial Intelligence) and ML (Machine Learning) can offer organisations breakthroughs in their production systems and even a competitive advantage if used thoughtfully and in the right context. The digital transformation and its multiple advances have generated pressure on companies, derived from the fear of being left behind, which in turn has resulted in a pre-willingness among leaders to implement these technologies in their companies.

But in most cases, even if adopted, the fundamental barriers remain and few companies have the basic components that allow AI to generate value at scale. Being clear about where the Artificial Intelligence opportunities are and having central and defined strategies to obtain the data that AI requires should be the starting point for any entity that decides to immerse itself in this transformation.

Therefore, before adopting an AI and ML strategy, companies should ask themselves the following questions:

1. What is the problem you plan to solve with AI?

The main thing in this case is to start by defining the problem. What is the company looking for? Is it a machine learning model that can solve it? Is it known specifically what AI systems will be used for?

It is important, on one hand, to detect which types of activities are being inefficient or human capital intensive, and on the other hand, to determine how AI and ML systems can mitigate these problems.

2. What is the company’s plan to turn AI into an opportunity?

How does the company plan to address the problem and implement the solution?

At this point it is essential to know how to reformulate the problem definition in an automatic learning problem and how to implement it in a way that avoids any kind of slowdown or loss of value during the transformation process.

3. Does the company need a temporary or permanent solution?

AI technologies must become part of the company’s core business and must be accompanied by a change of mentality on the part of the management team. The vast majority of success stories are supported by a digital transformation of the company at all levels.

Depending on whether an AI model is needed for a specific action or for the company’s daily processes, it will be decided to acquire a customised product, a standardised solution or a temporary service.

4. Does the company have the necessary data to feed the AI model?

The quality of the AI model is directly dependent on the quality and quantity of data available to the company. The use of AI implies training an accurate and meaningful data model that can feed the AI systems so that they learn to function on their own, therefore, having a quality historical data is key.

Does my company have enough data? Are the data sources that the AI will use are reliable? Does the company have a robust data architecture? In order to answer these questions, it is necessary to have a solid framework of objectives and KPIs (key performance indicators) and a robust data strategy to ensure that it is squeezed in the most valuable way possible.

5. Is this data digitised?

Do I have the data stored in digital systems? To be able to manage the data correctly, they must be digitised, centralised, organised and integrated in different digital tools (such as CRM’s, or ERP’s, SCADAS, etc.) or in databases, CSV files, Excels, etc. If this is not the case, the digitalisation and use of AI of these data can take a long time and sometimes an insurmountable investment.

6. Does the company have the necessary resources for the implementation?

The company must be realistic about whether it really has the necessary resources at the level of human and financial capital to absorb change. Where will we find the expert talent to deploy AI? What is the company’s budget for acquiring an ML model?

In order to achieve a smooth transition and a correct integration of the models in the internal systems, it is key to have a technical team that knows the company and also knows the developer or data scientist. In addition, these teams must be qualified to integrate the models to be implemented into the company’s systems.

On the other hand, the accuracy of the AI model will depend on the budget, equipment and time available to the company to develop it. All this will also determine whether the company chooses an on-demand service or the acquisition of its own model implemented by its team.

7. What are the consequences if AI fails?

AI models work through very sophisticated algorithms and statistical correlations, but there is always a margin of error. Does the company want to implement AI in a process with high variability and a low accuracy rate, or the opposite? What risks and how much investment would be lost if it didn’t work out?

Depending on which systems and data are available, the company must evaluate whether the accuracy of these models is expected to be high enough to proceed.

8. How will AI be integrated with the company’s overall strategy?

How will the company integrate IA with processes and people? Are there turning points where IA will collide with processes?

AI should not be implemented as a stand-alone technology, but as an integrated solution that enters into synergy with all areas of the company to maximise productivity and results. The company must ask itself if the AI model will be able to work together with the rest of the parties and identify what problems may arise.

9. How will this change affect the company’s workers?

To what extent will IA’s ability to automate the activities now performed by workers affect the size of the workforce? Workers can be very sceptical of change and the company must find ethical solutions so that they do not lose their value and motivation.

Effective change programs will focus on specific training and interventions to involve employees and managers in the company.

10. What are the expected returns from applying this technology?

How long will it take for the company to recover the investment? How much will the company’s costs be reduced once AI is implemented? Integrating AI and ML models in a company implies a cost and therefore an important investment.

For this reason, a realistic estimation must be made to determine the parameters of the return on investment. To carry out this plan, the possible performance indicators (KPI’s) should be established, so that the return can be measured and how much value the model is bringing to the company should be calculated.

Are you thinking of implementing AI in your company?

AI opens doors to countless possibilities for businesses, but if it is deployed simply as an experiment, if a specific problem is not identified and a plan of action is not created, then it will turn out to be a worthless proposition and management will see no return on investment.

From Nexus Integra we pave the way for the implementation of AI and ML technologies to be an assured success story. Nexus Integra, the integrated operations platform, offers a structured Big Data tool that provides data scientists with the quantity and quality of data needed for AI and Machine Learning applications, as well as the exploitation of the data in any of its applications; native or external.

The native application of Machine Learning allows for the management of different advanced algorithms and their easy introduction into the production process in real time. Nexus Integra as an integral operation center and Big Data platform allows to get the maximum value from the data.