As AI continues to gain popularity, it’s becoming clear that the technology has the potential to revolutionize the business world. In fact, a study by McKinsey found that AI has the potential to create between $3.5 trillion and $5.8 trillion in value across various industries.
Despite the potential benefits of AI, the technology can be difficult to implement. In fact, a study by Dimensional Research found that 96% of companies face challenges when it comes to AI implementation.
The key to successfully implementing AI is to avoid making mistakes that can stall or derail your project. Here are 10 common mistakes to avoid when implementing AI.
1. Not having a clear business case
AI is a broad term that includes many technologies and tools that can be used to solve a variety of business problems. It’s important to have a clear understanding of the problem you want to solve and the value an AI solution will bring to your organization.
“AI is not a goal in and of itself,” said Matt Swanson, co-founder and CEO of Argolytics. “It’s a tool to help you achieve a goal. You need to be really clear about what that goal is.”
To determine the business case for an AI solution, you should start by understanding the problem you want to solve and then consider how AI can be used to solve that problem. In some cases, AI may not be the best tool for the job. In other cases, the value of an AI solution may not be worth the cost and effort to implement it.
2. Not selecting a suitable AI use case
Just because AI has the power to transform your business doesn’t mean it should be applied to every use case. If you’re new to AI, it’s best to start with a single, high-impact use case. This will allow you to develop your AI implementation skills and prove the value of AI to your organization.
When selecting your use case, it’s important to make sure it’s a good fit for AI. Look for use cases with high complexity, high volume, and high variability. These are the types of problems that AI is uniquely suited to solve.
You should also make sure that your use case aligns with your business goals. AI can be a powerful tool, but it’s not a silver bullet. Make sure that your use case will help you achieve your overall business objectives.
3. Not having the right data
Data is the lifeblood of AI, and without the right data, your AI model will not be able to make accurate predictions. You need to have a clear understanding of the data that is available to you and what it can tell you about your business.
In some cases, you may need to collect new data or modify existing data to make it more useful. This can be a complex and time-consuming process, but it is essential if you want your AI model to be successful.
It’s also important to note that you need to have a large amount of data to train an AI model effectively. If you don’t have enough data, your model may not be able to make accurate predictions.
4. Not having the right talent
Top talent is a key factor in AI success. Make sure your team has the right skills and experience to tackle AI projects.
“Having the right talent in place is crucial to the success of any AI project,” said Chris Chapo, chief data scientist at WhiteHat Security. “Data scientists, machine learning engineers, AI researchers, and AI developers are the key roles to have on your team.”
If you don’t have the right talent in-house, you may need to hire new employees or work with consultants or vendors. You can also train your existing team to develop AI expertise.
5. Not having a budget
You can’t just throw money at an AI problem and hope it goes away. You need to have a carefully crafted budget that takes into account all the costs associated with implementing AI in your business.
This includes the cost of the technology itself, as well as any additional equipment or software you may need to purchase. You’ll also need to factor in the cost of training your employees and any other expenses that may come up along the way.
By having a budget in place, you can ensure that you’re not overspending on AI implementation and that you’re able to track your progress along the way.
6. Not understanding the technology
AI is a broad term that encompasses a number of different technologies, including machine learning, natural language processing, and computer vision. It’s important to understand the different types of AI and what they can do.
For example, machine learning is a type of AI that allows computers to learn from data. It’s often used to automate tasks – say streamline collaboration with content workflows – make predictions, and provide recommendations. Natural language processing, on the other hand, is a type of AI that allows computers to understand and analyze human language. It’s often used in chatbots and virtual assistants.
7. Not having a clear implementation strategy
Artificial intelligence is not a technology that can simply be turned on and off. It is a highly complex and advanced technology that requires a clear and detailed implementation strategy to be successful.
Your organization needs to have a clear understanding of what you want to achieve with AI and how you are going to get there. This will require a detailed plan that outlines the different stages of the implementation process, as well as the resources and time required to achieve your goals.
For example, if you’re looking to use AI within your referral programs, it’s simply not enough to ask ChatGPT to create one. You need to use dedicated referral tools with AI, such as ReferralCandy, which allow you to create a loyalty program based on a prompt.
Without a clear implementation strategy, your AI project is likely to fail. Make sure you take the time to develop a detailed plan before you begin your implementation.
8. Not involving stakeholders
While it’s important to have a dedicated team to lead the AI implementation process, it’s equally important to involve stakeholders. These are the people who will be most affected by the changes. Make sure you have a clear plan for how to communicate with and involve stakeholders.
“Stakeholder involvement is key to the success of any project, and AI is no exception,” said Roberta Antunes, CEO of Hack. “The more involved stakeholders are, the more likely they are to support the project and help it succeed. This means that you need to communicate with stakeholders early and often, and you need to give them a voice in the process.”
If you’re not sure who your stakeholders are, consider anyone who will be affected by the changes, as well as anyone who has the power to influence the project. This might include employees, customers, suppliers, investors, regulators and community members.
9. Not having the right infrastructure
AI requires a lot of data. And, it requires a lot of computing power. Companies need to have the right infrastructure in place to make sure that their AI systems have access to the data and computing power they need.
This means that companies need to make sure that they have the right hardware and software in place to support their AI systems. It also means that they need to have the right data management systems in place to make sure that their AI systems have access to the data they need.
In addition, companies need to make sure that they have the right networking and storage infrastructure in place to make sure that their AI systems have access to the data and computing power they need.
10. Not having an AI governance framework
AI is a powerful tool, and like any powerful tool, it needs to be used responsibly. There are many ethical and legal questions surrounding AI, and it’s important for businesses to have a governance framework in place to address them.
“AI governance is the responsible and ethical use of AI technology,” said Chris Finneral, CEO and co-founder of Sketchy. “It’s about setting up processes and guidelines to ensure that AI technology is used in a way that aligns with your company’s values and goals.”
Finneral said an AI governance framework should be built around a few key questions: What are your company’s values? What are the potential risks of using AI in your business? How will you mitigate those risks? What are the legal and ethical guidelines you need to follow when using AI?
Conclusion
The success of AI projects is based on the quality of data used to train the algorithms. If you don’t have enough data, your models will not be accurate. If you have data that is not cleaned, your models will be inaccurate. If you have data that is biased, your models will be unfair. Addressing these issues will require a thoughtful approach to data collection, data governance, and data management.