Most often we believe that the reason for resisting AI adoption is attributed to the ambiguity that is woven around it. However, this is not necessarily the case.

Challenges of AI Adoption

Artificial intelligence (AI) is finding its way into many industries and a growing number of companies already experience the benefits of using AI in their business processes. Even though AI is developing and gaining more popularity, organizations have trouble adopting AI.

Most often we believe that the reason for resisting AI adoption is attributed to the ambiguity that is woven around it. However, this is not necessarily the case. There are a few challenges that organizations face with this new technology.

In a survey published by Gartner, results showed that some of the top barriers for AI Adoption were related to people, data, or business alignment. Understandably, that every company is different. Hence, each organization will experience the process of AI adoption quite differently. In this article, I will highlight some of the challenges you may face in the process of implementing AI and ways in which you can stay prepared.

AI Adoption Challenges

Source: Gartner

Skill gap

To handle AI, you need a specific set of skills. A lack of these skills can often be a huge roadblock for you. Most early adopters face AI skill gaps and are looking to for people with the right expertise to leverage the capabilities of an AI platform. According to Deloitte’s global study of AI early adopters, 68 percent report a moderate-to-extreme AI skills gap. This skill gap arises probably because AI is a new technology that is quickly expanding.

ai skills shift

Source: Deloitte

According to the survey, the most-needed role is what I’d call AI solution builder – those who are involved in creating AI solutions based on the business requirements. These roles include:

  • AI researchers
  • Software developers
  • Data scientists
  • Project managers

The second most sought after role is the AI translator – those who play the bridge between the business’ decision makers and the technical team. These roles include:

  • Business leaders
  • Change management experts
  • UX designers
  • Subject-matter experts

The best way to be prepared is to invest in educating the organization and with data democratization. This way, analysts can play the role of citizen data scientists. People on the business team already have the required industry expertise. This means that the AI adoption will align perfectly with the overall business objectives. With the use of a robust AI platform, analysts and business teams can quick gain expertise and fill the skill gaps.

Hiring talent

It’s one thing to be filling the skill gap while it’s a whole other thing to understand that a certain level of AI adoption requires the expertise of data scientists. Scaling a data scientist team is crucial to building great AI models to meet the specific requirements of a business. What you’ll need is a data scientist unicorn or a full-stack data scientist.

But who is a data scientist unicorn? While is there is no standard definition of a full-stack data scientist, here are a couple of viewpoints that you can consider. A data scientist unicorn is

  • Someone capable of understanding and executing data analytics, model development, model deployment, and integration of AI with the business application or within the business process.
  • Someone who can understand the business requirements and can explain why an AI approach will generate better results than the existing business process.

Finding that someone is not just difficult but also expensive. Hence, it is necessary to understand that there is no universally perfect data talent. You can invest your time and money in hiring and grooming data scientist from the existing pool of talent. Or you can invest in an AI platform that does not require the expertise of a data scientist.

Building a foundation

A strong technical infrastructure is necessary to handle huge quantity of data. It should have the technical capability to process large sets of data effectively and in a timely manner. This can vary based on the size of the data and the business use case. If the architecture is unable to support this, you will not be able to succeed irrespective of how well the AI model is designed.

Hence, you need to put in enough care to design the data architecture. It needs to be stable enough to support your business’ requirements and use case. It should be agile so that you can scale the models as your business grows.

You need to remember that building a data architecture is not a one-time task. On the other hand, it is difficult to validate the returns from architectural upgrades. However, if you have a strong data team that works closely with the research and development team to define achievable goals and continue to improve as AI seeps into the organization, this will no longer be a challenge.

Collecting data

We’ve always stressed on the importance of data because without it, AI will cease to exist. Data is the foundation on which AI evolves. There is no dearth for data and data sources. However, it is crucial to feed your AI models with only qualitative data, without which the models will not be able to provide accurate insights.

To begin with, you need to know what data you already have and then compare it with the data that your AI model requires. To do this, you need to know what AI model you will be working on. This means, you need to list the types and categories of data you have – is the data you have structured or unstructured? What kind of information do you collect from your customers? Once you have figured out what you have, you’ll be able to identify what data is missing and take the necessary action to fill the data gap.

Data decision tree


The next concern is data labeling. Most of the AI platforms available deliver models using supervised learning. This means, your data should be labeled so that the model can be trained on it. There are a few approaches to data labelling that you can adopt. You can use synthetic labeling or data programming to label data.

data labeling approaches

Source: Neoteric


AI is a large part of our daily lives. We rely on algorithms each day to do various tasks efficiently. Hence, we understand how it works and how provides insights and predictions. This is important not just to comprehend the way AI works but also to be able to trust the technology. This in turn is important for progress. As you begin to tackle bigger and more complicated problems, you will encounter, what is commonly known as, the black box problem – the difficulty to explain what happens under the hood.

black box

Source: Artificial Intelligence Mania

Explainability has multiple facets. It isn’t just about the ability of the AI model to explain the process behind an output, but also refers to whether the entire process and intention around the model can be accounted for. Explainability should therefore comprise of three aspects:

  • The intent behind how the AI system impacts the users
  • The data sources that are used and how the outcomes are audited
  • How the data inputs result in specific outputs

Approaches such as LIME (local interpretable model-agnostic explanations) help to increase a model’s transparency.

Closing thoughts

We’ve discussed some of the common challenges that organizations face. Depending on the business requirements and use case, you might face certain other challenges not listed here. However, you need to remember that you cannot handle all the issues by yourself. Ensure that you familiarize yourself with AI so that you can recognize the issues that you need to watch out for. With a strategic approach, you will be able to experience a smoother AI implementation.

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