Shifting to Artificial Intelligence is the ultimate goal for many organizations in the market today, but how hard are these organizations really working towards building the right foundation to make this happen? Developing and mastering the intelligent work style needed to support AI requires the creation of the right foundation In order to succeed, otherwise, organizations can face all kinds of issues.
From the data in your workplace to the machinery and even the talent you hire, everything needs to be correctly aligned to move an organization towards functioning with Artificial Intelligence.
Knowing how hard it is to make the transition to AI and have intelligent processes in place, we gathered the below information to guide you through the creation of the right AI foundation.
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4 Things to Look After Before Launching AI in your Organization
There are certain necessary factors you should address before launching AI within your organization. The true AI foundation includes the following:
1. Quality Data
Before you think of extracting sense from data, you need to make sure that you have quality data on your hands. Data is extremely important for the overall analytical process. Your analytics and insights would only be as good as the data that you have on your hands. Without quality data, your AI process can prove to be a liability or collapse entirely.
2. Access to Data
While quality data is crucial, you also need to have consistent access to data. The data coming into your organization should be consistent and accessible. Your data sources should also be in compliance with the laws of the land and shouldn’t breach some of the clear cut regulations in place for clients’ right protection and/or privacy.
|Read: GDPR and What It Means for Big Data|
3. Trust and Data Transparency
Trust and data transparency are two other factors that need to be taken into consideration. To begin with, trust should be at the top of your priorities. You need to create a connection of trust with your clients so that they know how their data is being used. Be transparent with your usage of data and don’t hide or distort facts.
|Read: How Businesses Can Navigate the Ethics of Big Data|
4. Finding the Right Talent
This, we believe, is the single most important factor for succeeding in the tough AI ecosystem of today. You need AI talent that can handle your data needs and lead you into the age of analytics. The right talent with the right capabilities and the right experience can help your AI campaign succeed. Additionally, the right AI talent can help realize future opportunities for you and assist you in conquering them. There are numerous opportunities and challenges in the AI market, and you need the top talent to conquer them.
The talent you hire should be:
SpecializedYour future talent should be specialized in certain facets of the AI market. Rather than putting their hands in everything within the AI market, the top talent should preferably keep them limited to certain niches like big data, machine learning or classification.
ComprehensiveThe specialization we mentioned above can only be achieved through unparalleled comprehension. Therefore, your future talent should be good at comprehension.
AttentiveThe right AI talent should have specific attention to detail and should be able to focus on the specifics. Without attention to detail, things can easily backfire.
Additionally, the talent of the future should know how to:
- Help in the provision and extraction of insights from AI data
- Apply AI across different scenarios
- Maintain an interaction between computers and humans
- Respond to the challenges around them
- Diversify the business around them
- Customize the demand of the system
- Apply AI knowledge from their academics into different complex situations
- Coordinate among platforms
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With these skills and knowledge, the top talent in the AI market can surely make a name for itself. Employers, however, will have to apply any one of the following three methods to meet their data needs.
Sourcing External TalentThere is a serious dearth of AI talent in the market, which is why you can go for external talent without really hiring them. Do your research on organizations that provide external AI talent, and work with them for additional resources.
Upskilling Existing TeamThis is a tough option, as you have to educate your current cohort and teach them the art of AI. Choose training sessions that provide your team with sufficient technical knowledge, communication skills, and product understanding to function effectively.
Source Directly from Post-Secondary InstitutionsFinally, you can source talent directly from universities and colleges as well. With this talent, you will have to put in extra effort, as they have the required passion and interest in data, but don’t have all the necessary technical skills or business experience. You will be required to train this talent and work alongside them to ensure that they have a grasp on the technical skills they need to perform effectively.
Strategies to Develop AI Talent
There are certain strategies you can follow to develop the right AI talent within your organization. For beginners, you ought to have a basic understanding of what you should expect from your data team. Expecting too much or too little can be detrimental, which is why you need to develop balanced expectations.
Additionally, you should have multiple teams working together to achieve the cumulative goals of your organization. With this method, employees can learn within the organization and impart in each other the lessons of AI.
Governed data should be available at all times, and your organizational culture should be driven by data. Data should sit at the center of your organizational processes, and everyone within the organization should realize the role that it plays.
In conclusion, you can benefit from creating a mix of the strategies mentioned here to extract the best results. Combine all these methods to make sure your foundation is strong for AI deployment. Finally, you need to have an end-to-end data management strategy for AI. Make sure that your staff are trained for the full data lifecycle and can do what is required of them to truly achieve their goals.
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