Page 151 - Cyber Defense eMagazine Annual RSA Edition for 2024

However, the report also found that only 18% of organisations believe they are digitally thriving. So, how
do you go about implementing Copilot when your organisation isn’t that digitally savvy? I’ve spoken to
many people who are almost afraid about implementing AI, but it doesn’t have to be a concern if you plan
and implement the right steps methodically. With many different AI versions, from Dynamics, Salesforce,
Azure, and Service Now the advice is the same.

Here are my top tips for being better prepared to implement AI tools and make them work well for your
organisation.

Ensure your data quality is high.

To obtain the right results from AI you need to make sure that the data it searches for information is of
good quality, relevant, and not out of date. Understanding first how the AI tool uses data and the potential
consequences of using old or wrong data is critical to success. AI is only as good at the information it
uses to answer your questions and importantly how you ask it questions.

It’s crucial to gain visibility of all your data sources and start to assess their quality. Look at what can be
discarded, archived, or what needs updating, and what is good to use straight away. There is no point in
AI trawling through lots of documents to help you create a new one if the information in the previous ones
is out of date.

Incredibly, your average person using technology creates at least 1.7 MB of data every second, so it’s
not surprising 47% of workers struggle to find the information needed to perform their jobs effectively.
Workers waste time every day looking for what they need, but AI can help with this if we keep only what
is relevant and up-to-date, and company data policies should address this issue.

It’s crucial to spend time assessing and ‘cleaning up’ your data before you start using AI. Your IT teams
will need to assess the quality of all your data across functions and departments.

It’s also important to be aware of ‘dark data.’ That is, data that is not necessarily immediately visible to
you but which is still accessible by AI. This could be data that has come across with a migration, for
example. Check your organisation has a policy to delete all data that is five or ten years old. Putting in
place the right permissions and reviewing your data constantly will ensure you adopt AI successfully.

Undertake a data assessment to make good decisions on what content to keep, remove, or archive. By
establishing what data is relevant and improving your data quality your AI results will improve.

Store your data in the right place.

It’s important to match your storage capabilities and platforms with your data needs so that you are not
wasting money on the wrong type of storage. You also need to be able to scale up and down as required
when you are running AI tools as they need extra compute power to run smoothly.

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