Why Your Organization Is Struggling to Adopt AI (And How to Fix It)

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Why Your Organization Is Struggling to Adopt AI (And How to Fix It)

Barely 10% of organizations manage to adopt AI. Find solutions to the top 4 AI obstacles.

· AI Zone ·
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Artificial intelligence (AI) offers a lot of promise today, but it’s still a bit of a black box for most people. There are many obstacles to adopting AI for today’s organizations, no matter your industry. Understanding what they are will help dispel some of the myths surrounding AI, its limits and potential, and risks to your people and business.

Over 73 % of organizations say that adoption is their biggest challenge with AI. In the same report, over 90% say they don’t have the people and processes to become the data-driven organization they need to be to adopt AI fully. 

How can today’s organizations change the way they think and behave around AI when faced with such obstacles? Let’s take a look at four of the biggest challenges and how you can overcome them to deploy AI in your organization.

You Have Ethics Concerns

AI is a powerful technology that can be a little scary to organizations that aren’t ready for it. It requires a structured implementation that should govern its capabilities and uses within an organization to remove the mystery surrounding the technology. 

A governance framework will help educate people on how the AI consumes, analyzes, and transmits data. It can also incorporate any additional privacy guidelines dictated by industries and government. — For example, ensuring that healthcare AI abides by all privacy laws and maintains data security at all times. 

Plus, AI is built by humans, meaning that all of our flaws and biases can be translated into the system without anyone noticing. This could lead to incorrect data analysis and application, causing irreparable harm to individuals or groups of people. 


To avoid the sci-fi movie future of an AI that takes over the world, tech organizations must balance ethics and privacy with the efficiency and other benefits AI brings. It requires that all levels of an organization participate in creating an AI governance system so all concerns can be addressed appropriately. 

You Have Culture and Talent Concerns

We’ve all seen the headlines about how "robots are coming for our jobs." In reality, AI is supporting people in their current jobs and is replacing people at few jobs right now. That doesn’t mean people aren’t scared when they hear that AI is coming to their organization. Many companies have done an excellent job of publicizing the arrival of AI and how it’ll be used in the organization. Still, it tends to be an ad hoc sort of thing, allowing misconceptions and wrong information to spread throughout the teams. 

This haphazard method of internal socialization is also restricting the chance data engineers and other technical roles have to train for upcoming AI deployments. The most recent LinkedIn Emerging Jobs Report shows that AI-related job openings grew 344% in the last three years, and it’s pushing the entire tech workforce to learn new skills as all IT jobs are interfacing with AI at some point. 


To keep pace, organizations will need to build a diverse and AI-knowledgeable workforce across roles and disciplines. Management teams will need to be aware of cross-training and upskilling opportunities and engage with training teams to create the learning pathways needed for that. 

You Have Transparency Concerns

Training is a useful tool to dispel the mysteries surrounding AI, but it’s only the first step. We’ve been conditioned by too many sci-fi movies and books to fully trust AI, especially when rolled out without fanfare or explanation. People’s experiences and understanding of AI are wildly different, so organizations must do everything they can to explain its current abilities, best uses, and how your particular flavor of it works.


To gain transparency and remove the black box notion of AI, organizations should fully document its inner workings. Show how an AI model reaches its results and publish its decision-making process so anyone can read and understand it. 

You Have Infrastructure Readiness Concerns

When deploying any new technology, organizations must ensure their tech stack and environment can handle it. This is particularly true for AI since it relies on large volumes of historical data and massive computing power. AI will expose poorly-organized data sources, underpowered infrastructure, collaboration issues, and testing inefficiencies. 

Before deploying your AI solution, it’s critical to take stock of your infrastructure and data systems. Further, many organizations haven’t practiced good data hygiene, leading to excessive prep time for the data as AI uses only clean data, leading to more delays in deploying your AI.


Invest in a strong infrastructure foundation with high-performing and scalable computing systems, high-volume storage, and high-powered GPU architectures. Ensure your tech team has the budget, resources, and people to prepare your organization for your AI. Spend time cleaning your data sources and setting up good data hygiene practices for future data collection, storage, and viewing. Some reports say that the failure to invest in robust infrastructure and proper data hygiene practices is responsible for the estimated 90 percent of AI that’s never put into production.

AI is the newest technology organizations want to embrace, but they’ve got to navigate some expected obstacles before they can enjoy it fully. Organizations that embrace it as a collaborative transformation that requires robust and necessary discussion across disciplines and industries will do it more successfully. 

Far from the evil entity from the sci-fi world, AI should be seen as a supporter and enabler of humanity, helping us tackle the complex challenges our future faces. 

ai, ai adoption, ai adoption challenges, ai ethics

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