As you might have been witnessing through our social media presence, we have recently focused our design efforts at Grus into machine learning hardware and AI applications on the edge.

Designing customized machine learning hardware is exciting, since there is whole new wave of supply coming from chip designers, which allows AI applications to run on the edge, rather than in the cloud or in servers.

Relevant market research is supportive. If you have read our blog post "Who will pick the strawberries?: 3 use cases of machine learning on the edge in agriculture" you might have also taken a look at the McKinsey article that we cited, which claims that edge computing use cases could create more than $200 billion in hardware value by 2025.

Edge applications of AI allows for data to be used right where it appears, such as on the street, in a parking lot, in a manufacturing facility, or in a hospital. Putting AI applications on the very edge makes decision-making possible there and then. We like to refer to it as "wherever attention and intuition is required".

In an era where data is the new currency, not making use of the data your business constantly generates is crazy. Right? Or at least that's what everybody says. But in reality, most companies lag behind when incorporating the AI-way-of-thinking into their business.

A recent MIT Sloan article that we read gives us insight about how to implement AI into your business.

1. Consider using AI for more revenue rather than less cost

Most companies do not look into AI solutions in order to find new ways to create new products and services.

More-advanced organizations have already started to make a strategic and more customer-centric shift towards AI. This means revenue increase for them.  

In fact, AI could act as a super power when introducing new products and services to interest customers. Think of new revenue channels.

Instead, what usually happens is, companies consider AI solutions to reduce costs. This makes the whole shift towards AI less exciting and more lethargic.

Takeaway: In order to effectively implement AI into your business, consider how you can blend it into your strategy by redefining your revenue channels with new products and services that can provide more added value to your customers.

2. Build an infrastructure for data collection and usage

AI does not have one definition. This is a massive challenge when trying to make sense of it.

Because of the hype, most people are excited about AI but do not how they can make use of it, or whether it is even feasible for them.

The main question is: do you have sufficient data for AI implementation?

Takeaway: Having a proper infrastructure for data access and management is vital for AI implementation.

3. Connect teams who understand AI to teams who develop strategy

In a vast majority of companies, teams who develop corporate strategies do not have a deep understanding of AI technologies.

And teams who understand AI technologies do not have a voice in developing corporate strategies.

Takeaway: Connecting these teams and aiming "strategy with AI" rather than "strategy for AI" is vital for successful AI implementation.

At Grus we design application-specific machine learning hardware for diverse business needs. Check out our website and book your free consultation session to discuss your AI needs.

Resource: https://sloanreview.mit.edu/article/three-steps-to-implement-ai/