As we’ve covered in a recent blog post, more and more enterprises are adopting AI in some form or fashion. In general, there are four reasons why:
- Automation to handle tasks like data entry, basic natural language processing, record-keeping.
- Improving data analysis by efficiently sifting through large amounts of data to drive better business decisions.
- Internal communications such as monitoring employee usage habits, booking travel, ensuring staffing needs are met.
- Improving customer service via chatbots that are always available on demand.
Each of these provide clear benefits, which is why AI usage is on the rise. But, for many enterprises, knowing whether they’re reading to integrate AI into their workflows is not so clear.
Data quality, infrastructure needs, use cases—these essential elements can be hard to identify, especially for enterprises that are just getting their feet wet in AI.
If you’re looking to adopt AI, here’s a rundown of things you should know about your enterprise before jumping in:
Your infrastructure needs
Adopting AI is a process, and that process extends to your infrastructure as well.
Most enterprises start small, with a handful of engineers exploring AI projects on high-powered workstations with GPUs to accelerate the training of models.
Eventually, though, you’ll need to start thinking about your IT organization as a whole, and how it’s going to support multiple engineering teams conducting training models at once.
That means finding shared storage solutions that fit your needs and weighing the different compute options available.
Where you are going
The only reason to adopt AI is if you know you have a reason to do so. A specific problem you’re looking to solve, or an inefficiency you’re hoping to address.
While one or all of the four uses listed above will probably be on your list, it’s important to start with a small, impactful project and then chart out your path for greater AI adoption. Identify a use case and a section of data you want to use, then copy that data to a VM with a GPU in a public cloud in order to run a proof of concept.
Knowing where you are going and what you want to accomplish will also inform your path in storage solutions.
In general, enterprises just starting out with AI are often better suited for relying on a cloud provider since it’s easy to spin up GPU VMs and get them into the hands of engineers.
As your AI projects become more sophisticated, however, you may want to go on-premises or with a hybrid solution if scaling in the public cloud becomes too expensive.
What data you have
AI solutions are only as good as the data they have access to, so as you explore adopting AI you need to know:
- What data you currently have
- If there are any gaps in your data
- Where all your data is located
- Who needs access to what data
Once you have a thorough understanding of these four data elements—as well as everything else listed above—you’re likely ready to start the AI adoption process.
Gain even more clarity on AI adoption by downloading our free eBook The Enterprise Guide to Kicking Off the AI Adoption Process.
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