4 Steps to a Successful Artificial Intelligence Solution Implementation

For most people, artificial intelligence is something straight from the (imminent) future, in which – in extreme anti-utopian visions – intelligent robots take over the world. In fact, AI-based solutions are already a part of our lives. We use many of them every day, often without even realizing it. So is artificial intelligence just another technology – similar to all other IT systems and applications? And what exactly does the process of implementing such a solution look like?

So how to balance the desire to use the most modern solutions, using AI/ML, with the obvious fear of engaging in projects that are highly likely to fail, and in addition will generate considerable costs?

The answer to this question is a properly designed process of implementing AI-based solutions, which is designed in a way that will most accurately determine the needs and expectations of customers and minimize the risk of project failure.

In the following part of the article, we present the main stages of the project of implementing an AI-based solution. For better illustration, we will use an example.

At the very beginning, we create a business hypothesis (hypothetical business case) which will be verified at the next stage of the process. Based on an interview with the customer, we define the problem, determine the initial assumptions of the project and effects that would be satisfactory for the customer in terms of quality, as well as specific return on investment (ROI) – measurable profits and benefits. We determine with the customer what effect of the AI solution he would consider a success.

It is worth noting that the benefits do not have to end with the savings resulting from the use of an AI solution in one specific process (in our example it would be the process of reading documents).

The calculations should also take into account indirect benefits – resulting from the fact that, for example, people involved in the process, which will now be handled by the AI solution, can devote their time to other tasks. We can also look for profits in other places in the company, in other processes that will be indirectly improved by using artificial intelligence.

After defining the problem, the expected results of implementing a solution using artificial intelligence, and the budget available to the client, we present the available options of the solution. Not every problem can or should be solved using artificial intelligence.

As we have already mentioned, projects using AI are burdened with risk, so we always try to find alternative solutions that might work better in a given case. Just because a client approaches us with an idea to solve their problem with the help of artificial intelligence does not yet mean that it is the best option. Due to the time-consuming character, costs, and high uncertainty of AI projects, we recommend such implementations only in cases where they are actually likely to benefit the customer much more than other alternatives.

It is worth noting, however, that much depends on the strategy of the customer – one company may not decide to use AI because it will be able to achieve a similar return on investment by more conventional means, while another, despite similar conditions will decide to go forward with the AI/ML implementation as they will consider the prestige that comes with having such solution an important factor in their business strategy.

Let’s assume that after verifying the initial assumptions, determining the budget and initial ROI, and considering alternative paths to solve the problem, the customer decides to implement an artificial intelligence-based solution.

At this point, the next stage begins (let’s call it Step 2). This is still an early stage of the project, where the business hypothesis and assumptions from step 1 are verified. We are still far from the actual construction of the AI solution.

In stage 2, we create a business case – a business justification for the implemented project that presents the assumptions and profitability of the implementation. We also verify the preliminary ROI calculated in the previous step – creating a business hypothesis. We analyze what the current costs of the project are and based on this information, we determine whether we are able to fit in the assumed budget.

The nature of AI projects makes it difficult to accurately define project costs. Such attempts are however necessary because the budget is one of the factors that greatly influence the decision to embark on an AI implementation. If the client doesn’t have adequate funds, moving on to subsequent stages of the project doesn’t make much sense.

If a limited budget is the only thing standing in the way of full implementation, another option is to use artificial intelligence only to a limited extent. This way, the customer stays within budget and gets a solution to some part of their problem. This way out can bring enough benefits to consider this option.

In this phase, the customer provides us with the data that we use to build the prototype AI model and that will be used by the AI in the production phase. It is at this point that we assess what value this data has in the context of the problem that we are trying to solve. We also define the means we will use to extract value from this data so it can be utilized properly by the AI solution. If at this stage, we are not so sure about these means, we conduct appropriate tests.

To increase the chances of the success of the project, it is very important that the customer takes care of not only providing a large amount of data but especially good quality data. It is a common misconception that having large amounts of data is enough to make AI models work well.

What is much more important is the quality of that data. It is so important that there are separate job positions for people whose main responsibility is to verify the quality of the data, which will be used in machine learning or AI models.

What exactly does good quality data mean? Among other things, it is important that the sample is representative of all the data collected, that there are no gaps in the data, that it comes from the same environment as the data used in the production environment, and that the data is not processed (processed data may carry less information than the original data).

In the framework of the created business case, we define the parameters, e.g. qualitative factors concerning the precision and sensitivity of the solution and response time. Furthermore, we specify quantitative assumptions and design the architecture: the technologies used, the environment in which the solution will operate (cloud, customer infrastructure, hybrid environment). Finally, we create the quotation.

As a summary of step 2, the customer receives a document – a kind of report on the basis of which they decide to implement or not to implement an AI-based solution.

This stage involves several days of work and an estimated cost of 60 000 – 100 000 PLN. Although it may seem a significant amount, it is still just a part of the total cost of implementing an AI solution, so it is important to conduct it as reliably as possible so that before making a decision, the customer has the most in-depth knowledge of their problem, solution, risks, benefits, and costs, but also additional benefits resulting indirectly from the implementation of the solution.

This will help avoid misunderstandings at later stages of the project, as well as reduce risks and possible financial losses. As we all know, the earlier the problem is detected, the less it costs to fix it. According to the 1:10:100 rule, detecting problems at the design stage is 10 times cheaper than detecting them at the implementation stage.

So we have reached the solution implementation stage! The business problem has been properly defined, the customer has decided to go towards an AI solution, the business case has been successfully validated. The customer has decided – let’s implement!

At this stage, we build the actual AI/ML system. To do this, we prepare learning and test data and create AI/ML models which we then test and tune. We also conduct performance and FAT tests.

After passing all the tests, the solution is made available to the customer and integrated with their business processes. Then we launch it in production – in the form in which it will be used by the end-users. All the time we also monitor its work and feed it with newly delivered data. In this phase, we also conduct training for the users of the solution.

Here it is once again worth emphasizing the specificity of systems that utilize artificial intelligence. Unlike standard IT solutions, whose effectiveness does not generally increase over time, training with AI models makes it possible to improve the quality of results and adapt to changing data. Somewhat like a brain, an AI model has the ability to learn from newly emerging information and flexibly adapt to changing conditions.

[More profits with reliable forecasts of energy production from renewables (globema.com)]

After the stage of implementation and launch of the production version of the solution, our work does not end. We make sure that the solution works properly, we provide advice and support, and, if need be, introduce changes.

Possible paths of an AI solution implementation project

Above we have described the typical stages of conducting an AI solution implementation project with a positive scenario, i.e. when the customer decides to implement AI, the business case passes the verification, and at no stage do we encounter contraindications to carrying out the implementation.

However, many projects do not go through all these stages, in fact, only a small percent does!

In the diagram, we present alternative paths of an AI project:

In order not to succumb to the myths about artificial intelligence, it is worth learning about the course of such a project – its stages, the risk associated with each of them, the data we must have at our disposal, as well as the degree of involvement on the part of the client.

Although of high risk, when implemented successfully, AI solutions greatly improve processes and bring enormous benefits. What’s more, through the process of learning, AI systems, somewhat like a brain, have the potential to become smarter and perform better over time.

Interested in more details?

Globema is a Research and Development Center, and our team consists of experienced analysts, data scientists, and IT specialists. Contact us about professional services in implementing Artificial Intelligence and Machine Learning solutions.

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