June 6, 2023

Navigating the Risks of AI Implementation

At a time when digital transformation has become a major buzzword in the commercial sphere, companies everywhere are striving to keep up with emerging tech trends.


At a time when digital transformation has become a major buzzword in the commercial sphere, companies everywhere are striving to keep up with emerging tech trends. In an effort to solidify a place in the vanguard of their respective industries, these companies are seeking to optimize themselves and boost their success through the implementation of cutting-edge AI tools in sales, marketing, human resource management, and more. Understandably so, given the enormous potential of Artificial Intelligence for data processing, analysis, and automation.

In truth, though, succeeding with AI-powered business tools is often more challenging than it may initially seem, and there are a variety of risks to be aware of before deciding to embark on this journey. In this article, we'll explore some of the risks associated with the utilization of AI in business and posit solutions that can help you to implement AI-driven technologies successfully at your organization.

1. Shadow AI

Since Artificial Intelligence was first introduced to the business space, there has been a veritable boom in the development of tools that can leverage its power. Now, with such an extensive array of AI tools available to fulfil functions in every business department, shadow AI is becoming a pressing concern for ambitious companies everywhere. But what exactly is it?

Shadow AI is a term used to describe the use of AI tools that have not been approved or cleared by an organization's CTO (Chief Technology Officer), CIO (Chief Information Officer) or IT department. Effectively, shadow AI encompasses AI tools that employees have taken upon themselves to use in their daily responsibilities.

The risks of shadow AI are considerable. By allowing individuals to utilize a variety of different AI tools across an organization, you will eventually run into issues with data fragmentation due to the disjointed nature of how AI has been implemented. This can result in employees drawing on outdated or inaccurate data, leading to errors ranging from minor to potentially catastrophic depending on the situation.

To prevent setbacks resulting from shadow AI, it's wise to put measures in place to combat it before attempting to implement AI tools at your enterprise. Specifically, you should establish a robust AI governance framework within your organization. By outlining clear processes and procedures for adopting and utilizing AI tools, you can help to ensure that new tools are implemented smoothly as part of a cohesive whole. This will allow you to manage data assets more effectively and prevent fragmentation.

2. Data Privacy & Security

It is often said that an AI tool is only as good as the data you feed it. This is most certainly true, but little is said about the nature of that data, and whether or not AI tools can be trusted to handle it.

Companies often have access to a considerable amount of customer details in their databases, for instance, all of which would be considered sensitive. In the current climate, when people are more concerned than ever with how their data is utilized, the reputation of your enterprise hangs on your ability to safeguard sensitive data and provide an assurance of privacy and security to customers and clients. However, providing such an assurance can be difficult when using AI tools.

Data breaches are continually causing problems for companies worldwide, and AI-powered tools are the culprit in some cases. Depending on the encryption, authentication processes, and APIs (Application Programming Interfaces) a particular AI tool uses, sensitive data assets may be vulnerable to breaches. Additionally, tools may be misconfigured, or there may be vulnerabilities in the application's code that could put the privacy and security of sensitive data at risk. Finally, some AI tools may not operate in compliance with data regulations. This could put the reputation and future of your business in jeopardy, so you should mitigate against such issues.

To maintain a prevent damaging breaches, data security should be prioritized from the outset when seeking to implement new AI-drive tools. This means creating a team responsible for selecting AI technologies and establishing a thorough process by which those technologies should be evaluated before implementation. New tools should be comprehensively vetted to determine whether their encryption protocols are at the necessary standard, for instance, and whether they comply with data regulations such as those outlined by GDPR. In taking these measures, you can select secure AI tools for use and limit the risk of a breach. 

You can even bolster your levels of protection by learning about the ins-and-outs of AI security, which is an increasingly relevant market segment in its own right. It’s an example of how the cause for concern raised by AI’s proliferation is counterbalanced by the power it can provide in an anti-cybercrime context. Automating breach prevention and threat detection is becoming more scalable and efficient as a result.

3. Employee Skill & Resistance

To extract true value from new tools, it follows that you should seek to utilize them at every possible opportunity, as doing so will enable you to truly optimize internal processes for maximum efficiency. Of course, that sounds good to say, but it's one thing to introduce a new piece of AI technology at your business, but it's another thing entirely to use it effectively across an organization.

When businesses try to implement new AI technologies at a company-wide scale, they often encounter problems with employee skill levels. It stands to reason that this would be the case – after all, these are cutting-edge tools we are discussing, and not everyone who ought to be using a certain piece of software will have the exact competencies they require to use it optimally. If left unchecked, however, this issue can quickly get out of hand as employees grow frustrated and begin to resist the implementation of new tools. This can lead to tools being used incorrectly or cast aside altogether, thereby hindering your optimization efforts.

The solution here is to comprehensively onboard employees who are required to utilize AI tools in their daily operations. Ideally, this should begin before the implementation process begins, through the provision of learning materials and primers via the company network. Following this, employees should be provided with a robust real-time learning solution, such as a digital adoption platform, which can provide useful overlays with moment-to-moment guidance that enables employees to quickly reach competency with new AI tools. Additionally, it is wise to outline clear communication channels between employees and management, allowing for feedback and assistance throughout the onboarding process.

4. Machine Learning Biases

The incredible power of AI lies in its ability to process, analyse, and extrapolate from data to learn and provide solutions to different problems. However, it's important to note that this is a double-edged sword to some extent. Since AI can only be trained on the basis of historical data, machine learning biases present a significant risk.

Machine learning bias, also called AI bias, happens when an AI algorithm draws erroneous conclusions from historical data during the machine learning process, which leads it to produce biased results. This can give rise to all kinds of complications depending on where relevant AI tools are being implemented. For instance, it may result in discriminatory pricing outcomes for customers or clients, or unfair selection outcomes when considering job applicants. Such errors can have a considerable detrimental effect on a company's reputation, which can scupper attempts at growth and development.

To prevent machine learning biases, it's advised to be mindful of how AI tools are trained and maintained. This means setting out clear rules and procedures which aim to prevent AI from drawing erroneous conclusions. Diverse data should be provided to balance AI during the training process, while ethical guidelines should be put in place to ensure that AI tools are deployed properly. Additionally, AI tools should be closely monitored, and clear metrics should be established for regular bias assessments to take place.

By carefully evaluating AI models before selection, and then training, deploying, and assessing those models with accuracy in mind, you will be able to ensure that AI tools are implemented in a way that is both fair and transparent.


In the age of big data, analytics, and digital transformation, there is an undeniable allure to the potential that AI tools offer, as they can enable you to optimize your process in a way that other technologies simply can't.

At the same time, though, it's important not to be blinded by the possible upsides of using these tools. While AI has the power to help you drive your business to new heights, so too does it have the power to create confusion and damage your enterprise's reputation if utilized incorrectly. To gain maximum value from AI tools, proper implementation is paramount, and that means navigating the risks associated with the technology.

Shadow AI, Data Privacy, employee skill gaps, and machine learning biases are all present obstacles in AI implementation, but they are far from insurmountable. By carefully selecting, training and monitoring AI models and comprehensively onboarding employees, you can create an environment which is conducive to the use of Artificial Intelligence. As such, you will be able to effectively optimize processes, achieve greater efficiency and productivity, and ultimately power your enterprise to the forefront of its industry.

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Aryan Vaksh

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