Polski
How to adopt AI in your business? Simple as it may seem, it is not. First of all, you need to explore, clean, and normalize your data. Then, you need dedicated systems that incorporate AI and will work on it. Finally, you need to educate your teams on how to utilize the new solutions effectively. Do you wish to learn more? Then, keep reading.
Table of Contents
- AI in Business: Most Common Challenges
- How to Implement AI in Your Business?
- How Can You Use AI in Your Business?
- Conclusions
AI in Business: Most Common Challenges
We shall begin by analyzing the challenges that businesses face when introducing AI, ones that can significantly hinder the effective implementation of artificial intelligence into currently existing systems. What are they in particular?
Data Bias
The first problem that you might encounter is historical bias in the data. Training your AI-based system on the information gathered over the years might result in decision-making and insights that are far from the truth. Why is it so?
Humans made decisions in the past, and since people are always somehow biased in their opinions, so is the historical data. As a result, such data can lead to tragic consequences, with lawsuits, lost customers, and lost revenue being the most important ones. Therefore, data needs to be cleaned and evaluated before artificial intelligence is fed with it.
Security
Whilst AI can be implemented to increase the overall security of a network or personal data, it also poses some risks. For instance, if an attacker reaches your AI-based system, they might inject the artificial intelligence with toxic data, corrupting the model and leading to incorrect statements and decisions.
Siloed Data
Another common issue that businesses face when wondering how to use AI is data silos. With numerous separate software being used for different purposes, the information is fractured and stored in each system responsible for acquiring it. Hence, to implement artificial intelligence effectively, it is necessary to integrate all the data into one storage space – let it be a cloud or a server – so that AI (but also human employees!) can use the most complete, up-to-date information.
Legal and Ethical Constraints
There are also legal and ethical obstacles. Institutions like the EU introduce laws that restrict the usage of AI, for instance, forbidding categorizing (or scoring) people or even using biometrics in security (for example, CCTV). The legislation is constantly changing, which makes it difficult to predict what direction AI will head towards and adds an extra risk to more innovative AI-driven applications.
Technological Illiteracy
Finally, there is the problem of people itself. Take, for example, ChatGPT – anyone can access it, but will everyone be able to create captivating content or answer a ticket with its help? No, because people need to know how to use it and how to create effective prompts.
The lack of technological literacy is what prevents many businesses from harnessing the most out of AI applications. After all, they are just tools – tools that require human input for maximum effectiveness.
How to Implement AI in Your Business?
No matter whether we talk about the use of AI in banking or any other industry, there are several steps that need to be taken in order to implement this technology effectively. Let’s look at them now.
Data
First of all, you need to organize your data. Integrate it with available solutions to avoid data silos. Select what particular kinds of data are indeed useful for your business (i.e., what segments and areas will benefit from the implementation of AI). Source the data and get ready to remold it for the sake of AI.
Data Engineering
Collecting, synthesizing, and selectioning data is just the beginning. Now, you need to engineer it so that it will be useful for the AI and so that you can avoid data bias.
This step involves exploring the data to clean and normalize it. You have to eliminate any factors that, in the course of machine learning, could lead to biased opinions, and any redundant, irrelevant information, while unifying the data architecture. It is also crucial that you ensure that the data is scalable, meaning that the growing quantities of data will not affect the performance of your system negatively.
Modeling
How to implement AI in your business if the data is ready? You have to create an AI model.
This step is not only about building the algorithms and artificial neural networks but also about training them through machine learning services, testing the model’s performance, evaluating it, and tuning the model to your needs and expectations. This should be a thorough process since an ineffective AI can do more harm than good. Also, remember about your data here – if the model is not working as intended, you might need to go back to your data engineering as the information might be the culprit behind the disappointing results.
Operationalizing
With your model ready, you may proceed to the registration and deployment of your AI-based system. Do not consider this the end of the journey – it is just a beginning. You still need to monitor your AI systems regularly and retrain them if needed.
Team Training
Along with your systems going live, you need to ensure that the employees are ready to work on them. Therefore, you need to implement training courses that will explain how to utilize the new AI tools effectively.
It might also be a good idea to launch a mentoring program – those who are more fluent in the new solution can act as mentors for those who find it more challenging to adapt to the new technology. This way, they will be able to exchange knowledge and hence learn to squeeze the maximum out of artificial intelligence more quickly.
How Can You Use AI in Your Business?
We have discussed the challenges and practical steps of implementing AI applications in businesses; now, we can examine the potential use cases of such solutions. There are a plethora of applications, so let’s examine the impact of AI from a certain perspective: banking. How can you use AI in your business in practice?
- Customer onboarding – AI can be combined with biometrics to compare data from ID and customer photos in order to accelerate customer onboarding and eKYC procedures.
- Active product recommendations – It is also possible to use customer data paired with AI to generate intelligent product recommendations – ones that are highly tailored to the needs and preferences of your customers.
- Customer segmentation – In banking, artificial intelligence is also utilized to segment customers based on their transactions, personal data, bank contracts, products, activities through online channels, etc.
- Preventing product churn – AI-powered tools are also capable of spotting anomalies that indicate the will to give up on certain banking products. Knowing this early on, enables you to introduce measures that will prevent this churn from happening.
Conclusions
Implementing AI in business is not a simple task – you need to overcome the challenges related to your data, your team, or legal and ethical constraints. Yet, if done properly, artificial intelligence may be leveraged to achieve significantly better results in your company. Hence, we recommend following our guidelines. Do you still feel that this is going to be a difficult journey? Then check our AI banking solutions – ready models that will help your financial institution thrive.
You might also read: Artificial Intelligence and Machine Learning in Banking
