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What are the main errors to avoid when starting in AI as a company?

We have listed some errors to avoid before planning AI implementation in your business

Have you considered implementing Artificial Intelligence in your business? If chances are you have, here are some errors to avoid before planning AI implementation.

Not having the right stakeholders on board

Artificial Intelligence can require some efforts, availability and desire to learn. If the employees working on your AI project are not ready to dedicate themselves to the project, it can be harder for you to succeed in accomplishing your business goal. In practice, not having the right employees involved in the project may lead to a lost momentum, and a loss of resources: no reactivity, no follow-up, no internal coordination etc.

Recommendation: Make sure you empower the right employees and enable them to have enough time to dedicate to the project.

Not having sufficient understanding of AI within your team

Employees who already understand AI will probably get excited about the adoption of AI within your organization. For the other concerned employees, you will need to take extra efforts in order to onboard them.

Recommendation: Having a first meeting (or workshop, or course) to get everyone on-board can be a good starting point to educate your employees. This can also be used to explain how the business can benefit from AI, and how employees themselves can boost their professional path with AI. Beyond the organizational impact, explain about the human impact.

Jumping on a use case without doing a proper analysis

Artificial Intelligence hype is real. AI is being mentioned every day in the media, and sometimes presented as the number one remedy to all business problems. That being said, there are many AI use cases depending on the business challenges of an organization, and not every use case can be applied by every company. A use case is a methodology used in system development to identify, clarify and organize system requirements with stakeholders’ mission and goals. Some companies decide to choose a use case without previous analysis of their data sources that can be used in AI, the match between the use case and their business strategy, and the value of the use case for their business. If you decide on a use case without enough analysis, you may not generate as much value as you could, and your organization may miss an opportunity to maximize its business potential.

Recommendation: It is important to spend the time to map the use cases that will bring you value. This can be done fast. If you are not sure about the possible AI use cases and how they can benefit your organization, we have formed a detailed explanation of 12 use cases of AI that have proven Return on Investment.

Not looking at how competitors or similar industry players are doing it (well or bad)

Odds are you can’t just call your competitors and ask how they are using Artificial Intelligence in their company, but you can find out about their experience with a specific AI use case. Chances are, not looking at competitors’ or comparable industries’ experience with AI may prevent you from learning from other’s errors or successes stories. As a result, you may lose an opportunity to learn from peers and integrate their experience in your business choices.

Recommendation: Artificial Intelligence is still an emerging technology, but with some external help or enough research, it is possible to review how similar use cases (if not extremely disruptive and unique) have been performed in your industry environment, and benefit from others’ experience.

Starting with a use case that is too complex

In the section above, we have defined what is a use case. In AI, some use cases are more complex than others, requiring different levels of Machine Learning and data science work. Starting with a use case that is technically, humanly or organizationally too complex can make your journey harder, as well as the journey of your collaborators and partners. Hence, starting with a use case that is too complex may compromise your chances of success.

Recommendation: As with any journey, it is often preferable to start with the low hanging fruits, and progressively intensify the integration of Artificial Intelligence within your organization.

Not getting the right expertise on board

Implementing technologies like Artificial Intelligence, Machine Learning and Data Science can require some knowledge and experience on a company level. If you feel like you are lacking information about the opportunities that arise from these technologies, you would need a partner that can guide you and explain to you the technical choices it proposes.

Some companies are wondering what is the right partner to help them on their AI journey, and there is no single answer to this question.

Recommendation: You should ensure that your partner has experience in what you are considering, and that is experienced in starting, testing and deploying AI-based solutions. This approach can save you time, money, credibility and much more!

If you are considering implementing AI-based solutions to your business problems and challenges, but you are not sure where and how to start, leave us a message and one of our experts will be in touch with you soon! We are a team of passionate AI and data science experts who master AI techniques and new technologies, specializing in a number of topics and use cases, from analysis to deployment.

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