How AWS wants to partner
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Insight into AWS’ approach to partnering
We spend a lot of time talking about Microsoft on this show, but there’s another platform we’ve been neglecting: Amazon. I wanted to get a better sense of Amazon’s approach to the managed services space – specifically with Amazon Web Services – so I welcomed Ben Schreiner onto a bonus episode of the Business of Tech late last year.
Schreiner’s work at AWS is focused on helping small and medium-sized businesses make the most of their technology, which means he’s well-versed in the MSP/customer relationship. Ready to learn a bit more about AWS’s vision for the modern tech services provider? Schreiner’s explanation might surprise you.
Here’s what he shared:
How AWS Helps Customers
Shreiner kicked off our conversation with a simple explanation of the AWS/MSP/customer ecosystem:
“We focus on helping small and medium businesses grow by leveraging technology. And specifically for the managed service providers, we’re trying to help them offer new products and services to their end customers via AWS.”
Fair enough. But what does that actually mean? When I think of AWS, I think lots of great developer tools, lots of components that go into building solutions – usually for mid-market enterprise companies that have internal developers.
Shreiner confirmed that many folks on the platform are MSPs, but they aren’t the traditional providers. They’re usually more based, or even born in, the cloud.
According to Shreiner, the benefits these types of MSPs get by being on AWS are innovation, agility, and a faster way of finding tools that improve operating margins or the ability to offer new products and services. The cloud enables MSPs to scale up and down quite quickly and balance expenses and revenue.
Breaking Down the Service Piece
I asked Shreiner to lay out the actual types of service offerings a born-in-cloud provider can provide with AWS. He explained that it’s all the traditional stuff (e-commerce platforms, email, desktop support, help desk), but where AWS takes things a step further is integration work:
“A big challenge among small and medium businesses that seek the help of managed service providers is stitching together the software that they’ve purchased. You’re seeing more and more small and medium businesses take advantage of software as a service, but if that software isn’t being run by the managed service provider, you now have data in some other cloud or in some other data source, and not with the traditional managed service providers environment. So pulling all that together is one of the value adds, again, of being in the cloud.”
Again, in many ways, an MSP using AWS is like any other traditional MSP, but with access to over 300 cloud-based services, the provider isn’t limited by off-the-shelf software. According to Shreiner, this is really helpful for data analytics.
“For example, reports and reporting capabilities might be limited by the software that’s off the shelf, but the data is there, or the data is in several different places. But the managed service providers pull that together in a data warehouse on the customer’s behalf and then lay cloud analytics on top of it, so that the end customer now has dashboards and metrics that weren’t available in the traditional software itself,” he said.
We all know that customers struggle with data analytics, so this piece definitely caught my eye.
Another piece that you may not have considered using AWS for is backup and recovery – apparently, this is something most providers start utilizing right out the gate. Again, Shreiner highlighted the cloud’s flexibility as a selling point here.
Finally, there’s a third piece that Shreiner believes MSPs get from AWS: innovation on behalf of customers. He mentioned AWS’s digital innovation program here, where people like himself help customers work backward from their customer’s needs to solve problems. For example, Amazon Go stores: they started with long lines at self-checkout as the problem, then landed at their contact-free technology system as the solution.
Amazon on Machine Learning + Generative AI
This led us to the elephant in the room: AI and machine learning (AIML). I asked Shreiner to walk us through Amazon’s thinking when it comes to this space, and he broke down the AWS philosophy to three distinct layers:
Hardware: chips, storage, network, everything in the foundational layer related to AIML. AWS is focused on making these things affordable and cost-effective for customers.
Interacting with models: AWS believes in choice and doesn’t think one model will work for everyone. So, they want customers to be able to freely test and compare different models.
Interacting with purpose-built AI capabilities: the ultimate goal is for customers to use AIML tools without having to think about what model they’re using or how to run it.
I also asked Shreiner to share where the biggest adoption success stories are coming from right now. To him, it’s clear that software enhancement is having the best luck with AIML. However, he encouraged listeners to think about customer service as another area where AIML can ramp things up – for example, injecting it into a call center with sentiment analysis.
This brought us back to data for a moment; I asked what the data requirement would be to implement AIML in an example like the call center, and he believes that it starts with tracking down all of your data sources, finding a way to consolidate them, and making sure that data is secure, encrypted, not shared publicly by mistake.
Turns out, this is where AWS’s AI strategy branches off from competitors like Microsoft and Google:
“Our differentiated approach all along has been focused on security and privacy, first and foremost, followed by choice, right? The others that you mentioned have models, but they’re all from the same source, right? They’re all derivatives or evolutions of their own models. And that’s their approach. Ours is actually to invite and make available many other models,” he said.
He also reminded listeners here about the importance of responsible AI – he encourages everyone to check out the IDC’s article on responsible AI research, which Amazon apparently sponsored.
Before wrapping things up, I asked Shreiner for his take on the wine analogy I like so much (models are the grapes, tools are the wine, the MSP is the sommelier). He agreed, but also added on the analogy of a wine blend – he encourages MSPs to start with the problem to find the AI solution (and/or blend of solutions), instead of starting with a tool and looking for problems.