Top Considerations To Ensure Optimal AI Adoption

Is your organisation considering implementing AI tools? This blog explores some of the key things that organisations need to consider before introducing AI, including cloud readiness and data preparation.

The author of this page: Eamon Keane
Eamon Keane, Senior Software Engineer Apr 15, 2024

Successfully implementing AI can prove challenging for many reasons. In Salesforce’s latest State of IT report, IT leaders cited several challenges or barriers when it comes to implementing AI, with 66% flagging that employees lack the skills to use it successfully as one of the top barriers. This was further supported by a CompTIA Artificial Intelligence In Business report which also noted this as a top barrier alongside unclear ROI metrics and the complexity of AI systems.

Becoming AI-driven requires organisations to embrace a new way of working which needs to be carefully managed. Therefore, strategic planning, adequate resources and a commitment from the organisation’s leadership are required to implement AI in a way that will drive the most value. Below we explore some of the key things that organisations need to consider before starting to implement AI.

Cloud Readiness

When it comes to implementing AI, being a cloud-first organisation can make things a bit easier. AI needs data to work and cloud data storage eliminates the need for the business to build their own data centre. Take for example Microsoft cloud solutions, organisations can take full advantage of the cloud and Microsoft enterprise grade security standards without the high cost of ownership traditionally associated with on-prem server maintenance. Organisations can deploy AI solutions more quickly on the cloud with some cloud providers offering access to popular AI tools or plug-ins, making it easier for users to access the power of AI. Deploying AI on-premise can require more time and effort and cannot be changed as easily, so for organisations still discovering which AI tool is best for them, in most cases cloud deployment would be the better option. Further with the rates of cloud migration only continuing to increase, to stay competitive leading organisations should combine the power of cloud and AI.

Data Preparation

If your organisation wants to introduce AI, having high-quality data is essential. AI can only be as good as the data it works with. So if you have inaccurate, incomplete and unorganised data, then your AI initiative is more likely to fail. In a Workday survey, 77% of participants were concerned that organisational data is neither reliable nor timely enough to use with AI or machine learning. Further insufficient data quality and volume were noted as the top reason for AI initiatives falling short of expected outcomes.  

Often businesses see data preparation as an afterthought and not a prerequisite for the success of the project. Taking the time to clean your data can involve steps such as removing duplicates and errors which can be the result of human error or technical issues, adding extra information where required to make data more useful and correcting any formatting issues. If your business needs some guidance with data preparation, typically your technology partner can help or several tools can help aid the data cleaning process for organisations, even some AI tools.

Copyright Concerns & Lack of Common Sense

AI is good at recognising patterns, generating insights based on data or improving existing content. However, it’s important to note that when using Generative AI tools such as ChatGPT that pull content from other data sources, there can be a risk of copyright infringement when using this content. However, some vendors are already working to address this. Microsoft has announced a new Copilot Copyright Commitment that assures users they can use the output of what the Microsoft AI generates and if the customer is challenged, Microsoft will assume responsibility for the potential legal risks involved. While AI systems can perform complex tasks and make decisions based on data and algorithms, they lack the ability to apply common sense in various situations like humans do.  For example, there is a risk of AI hallucination which occurs when an AI produces outputs that are factually incorrect or that do not match any data that it has been trained on. Therefore AI tools still require consistent human input to help enhance them for future tasks in the organisation.

Potential For Bias & Ethical Concerns

There is potential for AI systems to amplify biases that are present in the data they are trained on. For example, if data used by the AI is over-representative of a particular gender or race for example, then the resulting AI predictions will be less reliable and may perpetuate discrimination or biases. It is also important that AI users keep in mind ethical concerns such as deep fakes, misinformation, privacy infringement and equitable access. While the ethics around AI continue to be debated and discussed, more guidelines and restrictions around the way we use AI may come into place, in particular the EU AI Act.

If you would like to learn more about preparing your business for AI adoption, read our previous blog on Unlocking Business Advantage with AI or get in touch to speak to an expert.

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