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How Our Innovation Sprints with AWS and Google Push Our Talent and Tech Partners Forward

AI AI, Experience 5 min read
Profile picture for user Iran Reyes

Written by
Iran Reyes
VP, Global Head of Engineering, Experience.Monks

Innovation sprints

“Given that AI technology is evolving rapidly, it’s extremely valuable to have a safe space to experiment with these technologies at an early stage,” our Executive Technical Director Andy McDonald tells me. So, we’ve created this safe space: innovation sprints are all about learning by doing and giving our talent the opportunity to get hands-on experience with building brand-new tools and technologies—and not just for our own gain, but to help push innovation forward at the world’s most impactful tech companies.

And opportunities there are. In the last few months alone, we’ve completed three innovation sprints in collaboration with some of our key cloud partners. First, we joined forces with Amazon Web Services (AWS) to host a challenge across time zones to create internal AI tools using Amazon SageMaker. A few weeks later, Google gave us, in our capacity as a large Workspace customer, the chance to play with Vertex AI and push the technology to its limits in two multi-day events focused on experimentation.

From the outset, the purpose of these sprints is to benefit our cloud partners next to our own business, as we collaborate on solving key industry challenges, developing use cases that drive brand results, and strengthening our partnerships.

Seizing every opportunity to tweak our expertise. 

The setup of innovation sprints is as follows: together with our partners—AWS and Google in this case—we come up with a challenge. Hereafter, our talent dedicates their time and creative chops to come up with ideas and execute on them by using the partner’s AI technologies. 

The needs of brands are at the heart of every sprint. Privacy, for one, is always a bright golden thread, as most brands are highly concerned with making sure everything is safe and sound. Collaborating with AWS and Google to develop AI tools guaranteed we were operating in a privacy-safe environment within their cloud computing platforms. For instance, when you’re deploying a project to Vertex AI, it’s going to be sort of sandboxed within your own hosting environment, which means it’s only pulling data from a knowledge base that you control. As for Amazon SageMaker, this service is GDPR-compliant. 

When it comes to AI-driven projects fully hosted by our partners, our Technical Architect and one of our AWS Certified Solution Architects Ben Moody says, “We used to tackle AI projects with high-level tools like Amazon Rekognition and Transcribe, among others. With Amazon SageMaker, we can be entirely flexible, covering any custom AI solution, high AI data privacy needs, and low latency requirements.” 

In developing AI-driven solutions for brands, it’s critical to know all the capabilities as well as limitations of the tools you’re working with. As our Senior Creative Technologist Angelica Ortiz highlights, “We use a lot of the latest tools from our cloud partners, and these innovation sprints are a great opportunity to formally dedicate the time towards testing their capabilities.” Such early-stage testing enables us to truly understand the limits of what we are pushing certain tools to do—and as a result, McDonald says, “New ideas get spun around all the different ways we could use a technology, which is 100% going to show up in our client work.” 

Accelerating experimentation to drive results for brands.  

As anyone who works in the field of technology knows, experimentation has a ripple effect. Whether you run into a roadblock or discover a new possibility, you’re always expanding your knowledge and skills. But these ripples have a much further reach than just the individual creative, designer or developer. By experimenting with building our own AI tools in partnership with leading technology brands, we’re able to create truly crafted, custom-made solutions for our clients. Let’s be honest, massive AI tools can’t really do that (yet).

“With tools like SageMaker and Vertex AI being made available to us, we’re really able to supercharge our experimentation processes,” says McDonald. “And then, we can feed these generative AI learnings back into our existing projects and new pitches for AWS and Google as our clients.” As it turns out, most of the solutions our Monks come up with during these innovation sprints are transferable and can be wrapped up and applied to various other scenarios. 

Feedback is a very powerful ripple. Once a sprint comes to a halt, we always share our learnings with the aim to help our cloud partners improve their tooling. For example, Moody says, “During the AI challenge with AWS, we had ten teams with members across different time zones, and so we quickly noticed that it was not easy to set up a seamless MLOps and Monitoring strategy. Since our team was lucky to have direct contact with AWS, they supported us right away and provided learning resources for future production iterations.” 

Similarly, innovation sprints allow us to offer our cloud partners an exciting new take on their technologies. “They’ve been working on their products for years and years, so we can provide fresh perspectives—and sometimes even discover bugs in the system—by applying what we know using their tech. While they help us learn more about these technologies, we give them valuable feedback on how they can improve their products and services, so it’s really a win-win situation,” Ortiz says. And as a fun bonus, it often makes our partners excited to explore uncharted territory together.

Nurturing partner relationships is an ongoing process.  

This feeling of excitement to keep experimenting has been echoed by every participant and organizer of our recent innovation sprints, including myself. Now, all there’s left for us to do is to keep carving out the time, so that we can continually develop our creative ideas inspired by the technologies that our cloud partners kindly make available to us. Our Technical Solutions Engineer Sarah Sheppard highlighted that it’s great to “finally get the time to build some momentum on something. We have so many day-to-day things that can slow us down, so to actually set aside time and create space so we can keep our ideas moving forward—I think that's the best thing these sprints do for us.” Her team, for example, had several weeks to flesh out ideas, while getting trained on the AI tools. “This made the whole experience feel like a sprint, as we tried to do as much as we could in the allocated time,” adds Sheppard. 

Ultimately, this time spent on experimenting with existing technologies and creating new applications allows us to not only drive technical solutions for our cloud partners (and ourselves), but also push our partnerships forward. As Sheppard tells me, “One of the best parts of these sprints has been working with our partners and seeing where their heads are at in a totally different context. Instead of reaching out about a fire that needs putting out, I was now messaging them to say I had some cool ideas and if we could work on it together.” In the end, when it comes to team play, you always want to make sure that you add some fun to the game.  


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The website has been translated to English with the help of Humans and AI