In recent years, artificial intelligence (AI) has been gaining momentum in every industry. Healthcare, transportation, manufacturing, logistics, retailing and more have started with AI-based applications to improve productivity and performance.
According to PwC (PwC is the second largest professional service provider in the world and one of the Big Four auditors together with Deloitte, EY and KPMG), artificial intelligence will contribute $ 15.7 trillion to the global economy by 2030. Companies can, therefore, benefit enormously from investing in AI. Research showed that 85% of managers believe that AI can help their organization to stay ahead of the competition.
The best way to appreciate this emerging technology is to find out the practical use of it in your company. Here are some applications of AI applications that can help you.
In this article, we will discuss how you can start with artificial intelligence in your company, where to start, what to think about and how to limit the risk.
Where do you start with AI? A company can be quite complex and is connected by a lot of external elements such as; customers, community, partners, competitors and regulators. In addition to external elements, there are also internals such as; vision, strategy, plans, decisions, measures & measures, risk, priorities and governance. Finally, there are the Operational elements such as; organizational structure, processes, automation, data, time, money, and products.
Of course, you also have immaterial things such as leadership, culture, marketing and brand.
It’s pretty complex … And so the question we hear the most; Where should I start integrating AI into my company?
Step 1: Decide which business process you want to improve / what problem you want to solve
There are two ways to approach AI …
- The wrong way: Looking for an AI technology just because it’s ‘cool’ and then forcing a problem that can be solved with this technology. We do not recommend this method …
- The best way to approach AI is to find a problem in your organization, looking for an AI technology that helps solve that problem.
Artificial intelligence has multiple technological landscapes and multiple applications. You can solve/improve almost any business problem/business process with AI technologies.
Step 2: Measure your business problem vs. solution
There are thousands of places where AI can be applied in your company, so it is important to know what and why you choose what you choose. So you have a list of different business processes that you want to improve / problems that you want to solve. Match these problems/solutions to the value they can offer and the difficulty of implementation. Besides, you put it next to the degree of alignment with the strategy to make better choices.
For example, solutions that generate more revenue, reduce time spent on certain processes, increase margins, etc.
Every company has different business processes, so every company has a different focus. Do not build or have AI technology built without knowing what you want to achieve.
Step 3: Start small, but build and architect for growth
If you are just starting with AI, you need to consider between the most ideal solution (this often costs a lot of money and time to implement) or the starter solution (relatively inexpensive and can be implemented quickly). Of course, the most ideal solution sounds like music to your ears, and with enough money and access to the right ‘infrastructure’, you can implement this.
Unfortunately, this is not possible in many cases and a distinction must be made between the various problems that could be solved with AI. But how do we do that?
To make things a little easier for you, we have listed 5 criteria that you should always take into account when you want to solve a problem with AI technology:
- Timeframe → How much time does it take to implement this technology? 1 month, 6 months or one year?
- Data dependency (Data) → How dependent is this technology on the data (data) that you provide for it to function the way you want it to function?
- The internal organization required for implementation → What organizational structure is needed to implement this? Can you do it yourself? Is it a plug-n-play solution? Or do you work with engineers?
- Technology risk → AI is and remains a technology, and technology always has bugs and errors. So what if this technology no longer works after implementation … Is your entire company going flat? Or will it be an independent incident that does not affect the rest of the company?
- Approval → How often does it have to be approved and by whom? Does it only require approval from the manager? Or should it also go to the general manager, financial director and commercial director? Here are two examples:
Imagine selling personal care products online, for example for the face and in the hair. That someone is on the website or app and looks into the camera so that the app recognizes the face and/or hair. Here they select a product, and they can immediately see what the product looks like on their face or in their hair.
Mentioned above is the ideal solution and would be great for any visitor to use. Let’s check the criteria:
- Timeframe → This will certainly take a lot of time; it will take them at least 6 to 12 months to implement
- Very dependent on data → Because you first have to perform several tests, eg how you will integrate the data into your databases etc.
- Organizational structure → For this solution you need a software engineer …
- Risk → Quite high – If this is the most important way to attract visitors and convert them into paying customers, it is risky. Because if it no longer works, there will be no revenue.
- Approval → You need your approval from the director (s).
The above is the ideal solution, but as you can see there is a lot involved.
When you are just starting with AI, it is not the best solution for your company. So what you can do to get started with AI, for example, a chatbot is a better option for your e-commerce. A chatbot works as follows: A visitor asks a question on the website, and answers with the various answers that you have given to the chatbot in advance as ‘data’. The chatbot recognizes which words the visitor uses, what kind of question is asked and answers it correctly. If the chatbot cannot answer the question, someone from customer service must assist. As soon as he/she answers the question, the chatbot learns from this, so that he or she can give the correct answer next time.
Let’s check the 5 criteria for this solution:
- Time frame → a good idea, you can implement the chatbot within 4 to 6 weeks.
- Very dependent on data → You do not need that much data to implement it. You can enter the questions that visitors themselves enter with the corresponding answers. So you do not need much data for the chatbot to function properly.
- Organizational structure → For this solution you need a software engineer … There are even plug-n-play solutions that are good to start with (for now …)
- Risk → Quite low. If the chatbot no longer works tomorrow, you can easily restore it while the sales keep coming in (only customer service gets it busier)
- Approval → Depending on the company you work for, but you probably don’t need approval from the director to use a chatbot.
So the latter option would be better if you just started with AI.
Perfect! You have just discovered which problems or processes within your organization can be solved/optimized! But remember, AI is not something ‘magical’, cannot be implemented within a day and needs a lot of data to ‘learn’ to function properly.
How can you start with AI today?
How you implement AI depends entirely on you and your company! For people looking for Artificial Intelligence, the bad news is that there is no shortcut than testing. Businesses need to consider which Artificial Intelligence technology solves their problems, starting with the steps in this article.
By following those steps you will find hidden business opportunities. Evaluate your company to find out where you can use Artificial Intelligence, such as inventory management or chatbots.
Once you’ve done that, it’s important to find a reliable AI partner who supports you. Div tag helps organizations with the strategy, design, building and growth of an AI technology.
Finally, we recommend that you be an ‘ AI practitioner’. You can only learn by trying. It’s normal for organizations to do things wrong. When you implement solutions, you learn from your mistakes and you collect new and high-quality data. Your experience (and data) gives you an advantage over your competitors. Do you also want to be one step ahead of your competitors? Prevent errors and take your first step towards AI? We would like to get in touch with you to talk about possible solutions specific to your organization!