Adopting AI into your Business

It is undeniable artificial intelligence (AI) is growing at a tremendous pace and is transforming every aspect of society. Today, many corporates have started to leverage AI in their business to reduce costs of operation and increase productivity, among others. Other than the field of business, AI has already made huge impacts across industries like healthcare, retail and manufacturing. The hype around AI and their evolving speed have exacerbated the sense that we have to get started now in adopting AI into business or risk playing the catch-up game later.

Are you thinking about adopting AI systems into your business but are not sure about how to get started? Follow this step-by-step guide to make your business AI-ready for the future. 

Step 1: Get Familiar with Artificial Intelligence (AI)

Before you start to adopt AI into your business, familiarise yourself with AI. Understand which AI technology you could make the most of in your business. It is an essential foundation for decision making.

First and foremost, know how to differentiate between artificial intelligence (AI), machine learning (ML), and deep learning (DL). These words are often used interchangeably, yet the applications are quite different from each other.  

Artificial Intelligence (AI)

AI is the core that can be broken down into a wide variety of technologies such as machine learning, deep learning, computer vision, and more. It refers to the ability of computer-controlled machines to simulate human intelligence by thinking and acting like humans. It describes systems that can learn and solve problems by themselves. Wish to enhance your knowledge in AI? Enrol now in the AI for Everyone course.

Machine Learning (ML)

ML refers to the field of AI that uses a large number of datasets to train an algorithm to learn without the use of complex programming and rules. The study showed that the machine learns from data and makes decisions without human involvement.

Deep Learning (DL)

DL is also known as an artificial neural network, which refers to a form of machine learning that includes more layers in the learning process. It makes use of the concept of the human brain in training the algorithms. It can perform various functions, such as object detection, image recognition, image classification, etc. Wish to learn more about deep learning? Check out the Deep Learning in Computer Vision course here at CertifAI. 

Step 2: Identify the problem statement of your business.

The second step to adopting AI is to identify problems in your business. Once you are familiar with the basics of AI, you have to understand more about your business needs to identify the AI technology that can address your business situation. To define your business needs, assessing the existing business problems in your company is a must. While doing so, ask yourself a few questions:

a. What is your company doing?

To learn how you can apply AI capabilities to your existing services or products to achieve your business goals.

b. What are the outcomes that you want to achieve?

To identify your business goals so that you know which AI technology to use to achieve the outcomes.

c. What are the obstacles that your business might face to achieve your business goals?

To develop concrete AI solutions that can help you to deal with possible obstacles in striving for success

d. What problems do you want AI to solve? 

To identify existing problems in your business that you want to solve with the implementation of AI technology.

e. How can AI help your business to be successful? 

To have more concrete ideas about AI solutions and use cases that your business needs in achieving your desired outcomes.

Step 3: Prioritise the significance of the AI project.

Now, comes to the third step in adopting AI into your business. After you have identified various possible AI solutions for your business, how do you pick which one to use?  What you have to do at this point is to assess the potential business and financial values of your AI project. You can do so by considering the near-term goals and the concrete returns of each AI implementation to identify your objectives. This way, you could make better decisions in choosing an approach that best suits your business. 

However, you have to make sure your decision is not swayed by the current trends in the market which is popular today. Instead, base your decision on the additional value that could be gained by your business through AI.

Step 4: Evaluate your internal capabilities and address the gap.

At the forth step in adopting AI into your business, it is time for you to decide which approach works best for your business after prioritising the values. Before moving on, try to evaluate what your business is capable of and what it needs. It is essential to specify what you want your business to achieve and understand the gaps in its internal capabilities before adopting AI into your business.

The internal capability gap refers to the inconsistency that exists between your business goal (what you want to achieve) and business capability (what you are capable of actually accomplishing using your existing solutions within a specific time frame). 

A sweet reminder: 

Identifying capability gaps is essential to provide you with a better insight into the things that you need to acquire and the processes you need to go through before turning your business into a full-blown AI implementation. 

Step 5: Consult a domain expert and set up your pilot project.

Building an AI project is not a simple task as it requires a combination of particular skill sets and knowledge, as well as lots of experience to create algorithms that can train machines to learn and optimise your business workflows. Hence, developing and adopting AI into your business is an impossible mission if you do not have a highly-skilled developer team to help you in building the AI project. 

In that case, you have to consult and bring in domain specialists to set up your pilot project. A pilot project does not need a lot of time and people to complete, usually, the duration required is between two to three months and a team of four to five people. After you have identified your internal capacity gap, it is time to bring skilled people into the project or outsource the task to an expert who has the experience and skillsets crucial for your AI project to be successful. 

Business and AI experts

Another tip in building a powerful pilot project team is to merge both people who are good in business and those who are good at AI together. If you are still not sure how to do it, you may choose to seek advice and assistance from an agency specialised in AI. They have the knowledge and experience in assisting corporates in their AI-based business journey.

Upon completion of the pilot project, you will more or less have an idea of how you want the longer-term project to be. You will also be able to decide whether the value proposition is making sense and is effective for your business.

Step 6: Clean your data and start small.

The origin of AI is data. Hence, having high-quality data can build a strong foundation for your AI to perform better tasks. During this stage, what you can do is to integrate different data and ensure the datasets are free from information incongruency, are accurate and rich in the attributes needed for an algorithm to work. 

As it is your first AI project, be sure to start implementing AI to a small sample dataset instead of throwing all the datasets at it and praying for a miracle. Always have your project goals in mind and focus on a problem that you wish to solve in your business because the algorithms do not perform the tasks well without a clear direction on what you want to achieve. 

Low risk and low cost

Another thing to bear in mind about adopting AI into your business is to always start from projects that have low risk and low cost, and assess whether your approach is fit-to-scale. Otherwise, you have to alter your initial strategy before moving forward to avoid costly mistakes in the future.

After testing with the first sample, it is time to try out your AI on new datasets, collect feedback and expand accordingly. If the sample datasets have shown multiple wins in proving the value that lies within, you can now introduce your AI project strategically and get full support from the stakeholders. 

Step 7: Incorporate AI into your daily routine

Now, it is time to incorporate AI as part of your daily tasks. Your workers might be quite open to AI. However, they might be worried if they do not have the necessary skills to take advantage of the change and that the advancement of AI technology will replace their job roles one day. 

Fix misconception

Therefore, being transparent on how AI technology works to optimise and improve business workflows is important. It can help workers to visualise that AI is a tool that augments their daily roles and resolves issues in the workflow and fix the common misconception that AI is something that replaces them.

Build confidence in AI

After all, they might still need some encouragement to adapt to it. Help your team to build confidence towards AI by, again, starting small and expanding accordingly. This way they can experiment and learn how AI can help them to augment their tasks, which eventually prompts them to have more trust and confidence towards AI within a short time frame.

Step 8: Build your AI system with balance

The last step in adopting AI into your business is to build your AI system with balance. Integrating AI systems into your business is serious work that requires balance. So, what does “balance” mean? A variety of aspects are essential to building a balanced AI system, including having in-depth knowledge about the project that you want to develop, meeting the tech and research requirements, and balancing the overall budget spent to achieve the goals.

An AI system designed with the focus of achieving the goals without looking into the needs and limitations of the supporting software and hardware is known as an imbalanced or even a dysfunctional system that is unable to achieve desired objectives.

Optimisation of storage, GPU, networking and security

As AI requires swathes of data to perform its tasks, to achieve this balance, you have to optimise AI storage for data ingest, modelling and workflow. Make sure there is sufficient bandwidth for storage built into the system so that it fits in the large volumes of data required to form an accurate model that helps you to achieve your computing objectives. 

Similarly, improving the Graphics Processing Unit (GPU), networking and security safeguards are also the crucial criteria in building a balanced AI system.

AI is becoming increasingly useful. Do not delay implementing AI for your business and risk being left behind! Quickly take your first step into AI starting from now to leverage it to the fullest. 

Looking for an AI talent solution for your business? Please do not hesitate to contact us.

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