How NOT to adopt AI cloud technologies: 8 pitfalls to avoid

Simon Edward • 5 August 2024

Up your chances of successfully implementing AI cloud technologies with our guide to 8 common pitfalls.

AI, AI, AI – two letters that anyone in business has heard echoing down the corridors in the last few years. With ChatGTP now a household name, it seems like everyone wants a piece of the AI boom.

But AI cloud technologies are like any other cloud technology: they're only as good as how they're used. With businesses worldwide racing to integrate AI solutions, we're seeing more and more cases of people using a trawling net to catch a butterfly.

It doesn't have to be this way. There are situations where AI cloud technologies can boost performance and save money. To do this, however, you need a smart approach.

Here, then, are eight common pitfalls awaiting companies who want to integrate AI cloud solutions.

1. You know you want it, but you don't know why

Remember the dot com bubble? In a way, it never burst, with the internet now a crucial part of most people's lives. But along the way, absurd amounts of time and money were wasted. And why? Because people wanted to be part of the scene.

Yes, the shock of the new brings with it a palpable sense of peer pressure. You might think you're above it, but look – you're only human.

If you're looking to implement AI cloud technologies, you need to know how they'll help your business perform and grow. The alternative is a white elephant that keeps doing its business in the finance office.

So before you commit to anything, brainstorm possible usages and wished-for outcomes. Are you still convinced? Now it's time to get spending.

2. You don't have an adequate security posture

AI algorithms are only as good as their data sets. But too often, companies put their data to work and at risk in one fell swoop.

The security risks are real. An inadequate security posture can mean a new attack surface for hackers and new opportunities for internal error.

If you find yourself breaking into a sprint to implement AI, slow down. All you're doing is increasing the chance of data breaches, with all the reputational damage and regulatory consequences they bring.

3. You don't understand AI technology

AI is new. It's niche. It's complex. If you don't fully understand how it works or what it does, join the club.

This means you need to establish an internal knowledge base long before jumping on the AI train to nowhere. Things get complicated, fast – so make sure your team has the expertise required.

Skip this stage and you're heading for a tornado of communication breakdowns, the misallocation of resources and an AI cloud infrastructure that's far from optimised.

4. You choose the wrong provider

The cloud is now embedded in our business lives enough that most providers have similar offerings. They know what you want and they give it to you. AI cloud technologies have yet to reach this point, meaning that providers offer more or less advanced tools, frameworks and APIs.

This connects with our third point about the need for an internal knowledge base. You need to make an informed decision about your provider. Otherwise, you're storing up problems with performance, compatibility, security and resource allocation.

5. Your data is mismanaged

Show us a company whose data is decreasing and we'll show you a flying pig. Business data is always expanding and it's never been more important to organise, clean, label, format and duplicate it.

Mismanaging your data for an AI training model is like trying to cook up a feast with bad ingredients. The results will be unreliable and skewed and you'll be throwing money down the drain.

Remember: AI is only as good as the data you feed it. Manage it well and you can expect good results. Fall into this all-too-common trap and you may as well get Gary from HR to oversee your AI cloud technologies.

6. You're working for AI when AI should be working for you

At Ascend Cloud Solutions, we always say that cloud technologies are there to help your business grow. They're never purely technical. The same goes for AI cloud technologies. If they're not aligned with your long-term company goals, you're on a hiding to nothing – and an expensive hiding at that.

7. You don't spend your money well

Serverless options vary wildly in cost. Some services are pay-as-you-go. Others you pay for even when they're not being used. If you want to maximise your chances of a successful AI cloud deployment, make sure you shop around and balance performance with cost-effectiveness.

8. You don't integrate AI into your system

AI isn't a big box that you can set up in the corner and leave alone to do its AI thing. It needs to be integrated with your existing systems, applications and people.

There are two sides to this.

The first is technical. Are your tools, frameworks and platforms compatible with the AI cloud technology in question?

The second side is all about people. There needs to be effective collaboration and communication between your AI team and your existing cloud team.

Similarly, you need to be sure that people are effectively overseeing AI. This is vital if you want AI to help you make better decisions. Without effective human oversight, results could be skewed – and could even have ethical and moral consequences.

Conclusion

AI cloud technologies can help you identify, catalogue, interpret and manage data – data that can drive better decision-making. But to do so, it needs to work for you. And for it to work for you, you need a smart plan of action.

Rush into the AI boom and you could well be lumbered with an over-expensive, ineffective solution. Take your time and you could be unlocking new savings and efficiencies. We hope you make the right choice!

Are you looking for a cloud advisory consultant to help you get to grips with emerging AI technologies? Get in touch with Ascend Cloud Solutions today for a free, no-obligation consultation.

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