Identifying The Fundamental Challenges Involved In AI Adoption

It goes without saying Artificial intelligence (AI) is the most revolutionary concept to have surfaced in the 21st century.


It’s the hottest, most in-demand technology today, influencing every single industry it touches.


But if you notice, all conversations and discussions so far on the deployment of artificial intelligence for business and operations purposes have only been superficial. Some talk about the benefits of implementing them while others discuss how an AI module can increase productivity by 40%. But we hardly address the real challenges involved in incorporating them for our business purposes.

The challenges involved in AI adoption are real and without a strategy in place, AI can become more of a bane than a blessing.

And that’s exactly why we decided to come up with this article – to help understand the concerns surrounding implementing AI without proper planning. After reading, we are sure you’ll understand the broader picture and know how you can go about implementing AI for your business.

Let’s get started.

AI Adoption Challenges

Quality Data Availability

Artificial intelligence and machine learning are insignificant without data. Data is processed by AI modules for automation, inference, insight, and a variety of other unique purposes. That’s why one of the fundamental challenges lies in data collection and the availability of quality data. For quality data, you need to define quality touchpoints and have data sourcing strategies in place. The better the data quality, the better your output.

Data Annotation

Once you have data, you can’t simply feed it to an AI module. An AI module is useless without its human counterpart, training it from scratch with meta tags. This process begins with data annotation or labeling to train AI modules so that they can understand what each data point is and what it stands for.

Eliminating Bias and Assumptions

Like humans, AI systems are also prone to bias and operate under assumptions. And these two factors can alter the course of your AI-driven purposes. Biases and assumptions arise when you don’t define goals, parameters, conditions, scenarios and other factors that AI modules need to consider when processing data. These instruct the modules to be aware of probable prejudice and eliminate every instance as and when it arises. But without defining factors, AI will learn to form an opinion autonomously by exploring millions of opinions in the process skewing your results.

Lack Of Skilled Experts

In a lot of countries, AI is still an upcoming industry. It might only be a buzzword in several market segments and it is only gradually that prevailing talent pools and companies are waking up to the impact of AI. As this awakening happens, skilled AI experts will steadily yet slowly be made available in the market because of factors like upskilling, reskilling, specialization, experience gathering, and more.

This lack of skilled experts stalls the ambition of several businesses. At the other end of the spectrum also lies the factor that highly-skilled AI specialists often become unaffordable for businesses. This demand-supply gap is a major hurdle in AI adoption.

Lack of Clear Business Goals

Why does your business need AI in the first place? Are your AI implementation plans driven by peer or competitor pressure? Or is staying ahead of the curve a deciding factor for your AI adoption strategies?

If you’re planning to adopt AI for your business growth, the first element you need (even before you need adequate quality data) is a goal with AI. Is AI going to make your business scalable? Do you need it to understand your consumers better? Are you going to make way for dynamic pricing with AI integration?

Define proper goals and align AI strategies with your vision for optimum results. Without a defined goal in place, AI adoption will not only prove to be expensive but time-consuming as well.

Legal And Compliance

All the technicalities aside, AI requires proper and airtight compliance with regulations and legalities. With countries around the world taking notice of the impact of technology and its offerings, regulations, and compliances are being revisited and amended to tackle adverse effects of technology, to identify them, prevent them and pave the way for people’s security. Since AI processing involves data, factors like GDPR & HIPAA with special emphasis to individual privacy should be considered as well. So, before implementing AI, it is pivotal to have the appropriate security and compliance in place to maintain a good brand reputation.

Wrapping Up

Besides these, there are also challenges like recruiting the right talent, finding credible vendors, concerns involved in integrations such as tech infrastructure and more. When every factor is fixed, operations are on autopilot with AI. But until then, it is human intervention alone that can make AI work to its fullest abilities.

If you find all these too complicated and have faced similar issues in the past which has delayed your AI adoption, companies can always look to professionals like Shaip, who with their patented platform, skilled resources and robust processes have enabled Fortune 500 companies in their own AI journeys.

Let us know what you think are other practical challenges in AI adoption in your comments.

Author Bio

Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is a CEO and co-founder of Shaip, which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.