AI Implementation Challenges to Consider

Artificial intelligence has plenty of applications in the modern business world to help improve, streamline or speed up processes.

With its benefits come a number of drawbacks and challenges to consider prior to its deployment in a business setting. As it’s a relatively new mainstream tool, careful consideration must be given to its use and implementation within an organisation.

Discussed below are some of the challenges that implementing artificial intelligence might bring to organisations.

Integration with Existing Systems

Integrating AI solutions with your organisation’s legacy systems and existing workflows can be a major obstacle.

Many organisations still rely on legacy systems to complete daily tasks, bringing with it its own set of challenges when AI is considered. Compatibility issues and data format mismatches can ultimately hinder seamless integration.

Costs may arise from developing APIs or connectors to facilitate data exchange and communication between different systems.

Internal research and analysis will need to be done regarding integrating new AI tools with older systems.

Data Security

AI tools handling sensitive data raises concerns about privacy and security. Clear internal guidelines need to be in place before any AI tool handles company data.

It’s also important to ensure the AI developer is trustworthy and accredited prior to being fed any sensitive data.

Implementing robust security measures and policies to protect data from breaches is important and ultimately limits risk.

Overestimation of Ability

While AI tools may greatly benefit workflows and help make processes more efficient, they aren’t failsafe tools. AI tools tend to rely on the quality of data it’s fed, meaning it’s only as good as what you give it.

AI models can produce biased or erroneous outputs, especially when trained on biased data or operating in unfamiliar contexts.

It’s important to understand and document AI’s limitations in certain situations and to avoid total reliance on it.


Costs associated with AI implementation often vary significantly depending on the complexity and scope of the project.

While AI implementation requires significant upfront and ongoing costs, it’s essential to consider the potential return on investment in terms of improved efficiency and innovation. A cost-benefit analysis may be required to assess whether the investment meets and aligns with the organisation’s requirements.

Maintenance & Support

AI systems will require plenty of ongoing maintenance, monitoring and support. This is necessary to address issues such as performance degradation, feature updates or changing requirements.

Throughout its use bugs and glitches may become more apparent. Similarly, AI tools need to be kept up to date with the latest security patches to ensure vulnerabilities are addressed and data remains safe.

Ongoing maintenance and support will need to be accounted for in budgeting prior to embarking on any AI implementation.


As artificial intelligence has only entered the mainstream over the past year or two, experience levels among the general workforce tend to be low. Acquiring specialised AI talent might be difficult due to the small pool along with high demand.

With tools like AI being in their infancy stage it also means that training resources might not be adequate for general employees.

Model Training

Artificial intelligence tools aren’t magic and require tinkering to adjust to your workflows. AI models need to continuously learn and adapt to evolving data patterns and user requirements.

Continuous monitoring, feedback loops, along with model retraining is essential for maintaining the relevance and performance of the AI over time.

Are you preparing to alter your data management strategy over the next few months? If so, contact us today on +353 1 8041298, or click on the link below to be brought to our contact form.


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