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The Rise (and Risks) of Edge AI.

by Steve Salvin & Paul Maker
The rise and risks of edge AI

The explosion of generative AI and large language models (LLMs) has everyone excited about AI and its use in business. And as these models get smaller, it gets even more exciting. In our recent blog, we talked about how Meta’s smaller Llama 2 models for business can run on standard hardware, yet still offer impressive reasoning power. As people start to recognise that bigger isn’t always better when it comes to LLMs, AI really is coming to the masses. But with everyday apps all rushing to launch their own AI widget or LLM integration, the risks of edge AI are on the rise and rapidly creating a complex enterprise AI landscape – leaving users and business leaders overwhelmed as they try to work out the best (and safest) next step.

Risks of edge AI

We're champions of AI and how it can help businesses solve their biggest problems. But we have two key concerns about the rise of AI at the edge:

User experience

The user sits at the heart of everything we create at Aiimi, and we believe the user experience must always be protected. We all interact with so many apps every day; if each one launches its own AI bot, suddenly the user is overwhelmed by notifications and it’s impossible to know which ones are important. AI should be able to provide the answers you need, but with all these competing voices, it’s reduced to a distraction; you can’t see the wood for the trees and thousands of alerts collectively dilute to nothing.

Data silos

Each app is only aware of the data contained within it, so the AI insights it can deliver will only ever be as good as that data. Let’s say you ask a question of your enterprise messaging app. It might use the most cutting-edge AI to find an answer, but if the right answer doesn’t live within that single app, it won’t be able to find it. It’s an extension of the problem we see with data silos across an organisation, where each team only has access to their own data. In fact, it compounds that exact problem, by putting an AI gate in front of the existing app silos of data and experience.

Combined, we believe these two factors can seriously weaken the power of AI for business.

Get the full picture

Everyone wants a slice of the AI pie, but if each app takes just one slice, their view is limited to that small section of your data. The user is bombarded by noise and the apps can't get to the right answer. It’s the Wild West as far as data privacy and security are concerned, and there’s no cohesive governance if nothing is connected.

So, if we understand the risks of edge AI, what’s the alternative? How can businesses take control, without missing the boat on enterprise AI?

We've been here before. When businesses started to recognise that data silos were a problem, they put CIOs and CDOs in place to create a more joined-up view of their world. In the same way, to bring AI into the enterprise now, you need one clear, connected view of your entire data picture. Instead of each app running AI at the edge, independently and only within its own parameters, all these different tools should be tapping into one shared AI-as-a-service platform that’s been created using all your enterprise data, wherever it lives, with existing governance and permissions maintained. When everything is connected and controlled, and AI-as-a-service is layered across the whole enterprise, the user gets the answer they need.

Data matters more than ever

In the old world, if an engineer saw 15 alarms sounding, their intuition and experience would tell them which was the critical one to respond to. The minute you start divesting some of that responsibility to AI, data quality and governance become more important than ever. You need to be able to rely on its accuracy, and you must be able to evidence where the answer came from and why.

If we leave AI at the edge, we risk reducing the technology to a distraction and driving an ever-more siloed data landscape, ultimately putting more obstacles between the user and the answer they need. Being successful with AI in business will never be about doing the newest thing, fastest; it always comes back to having solid foundations of data quality and governance in place, and an enterprise AI platform that can see and interconnect your entire data picture – including all the answers you’ll ever need.

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