Sage has an AI team. Why would you care?
The Internet is currently cluttered with posts on how Artificial Intelligence (AI) is going to change the world and software as we know it. Meanwhile, your marketing team can’t wait for you to release an AI-backed feature so they can update their landing page with “AI powered technology”. So, when I sat down and began writing this piece introducing Sage AI, I asked myself the question, “Why would anyone care?”
An exciting mission
Sage AI is a cross functional team delivering AI projects across Sage. We bring together experts distributed across 7 countries with various skill sets and embrace continuous delivery for machine learning. Our mission is to build composable AI solutions that transform Sage into an AI-enabled technology business.
Before you roll your eyes at a corporate mission statement, let me set some context. At Sage we have an impressive portfolio of best-in-class Accounting, Financial, and HR software backed with data sets ideal for AI disruption. The reality is that much of transactional accounting can be fully automated. Whether it’s eliminating the month-end-close, automatically capturing financial transactions, or providing bold financial insights Sage astutely identified the importance AI will play in delivering value to our customers.
Production or it didn’t happen
You can do all the planning in the world, but it’s not until your model is in front of a user that you can truly evaluate whether your hard work has any value.
This understanding is key to how Sage AI approaches its transformation mission. Within our team we have an internal mantra of “Production or it didn’t happen.” By this we mean that until the AI software we build makes it into the hands of customers, the work doesn’t have any use or meaning. We subscribe to this because our team believes the best way to empower our customers is through delivery.
There are many benefits that come with this customer centric mindset. For example, it means that our ML practitioners work as efficiently as possible to deliver their first baseline model without over-engineering. It means that issues that may impede releasing are tackled up front. It also encourages our Engineers to leverage automation as much as possible and defines a team that is driven to think creatively when faced with the challenges of innovating enterprise software.
Team > sum(parts)
Companies often underestimate what it takes to deliver machine learning at scale. To combat this risk, we designed Sage AI as a full stack machine learning department. Meaning we have the necessary capabilities and importantly the accountability for taking an idea all the way through to production and beyond.
This means we bring together a range of roles such as Product Managers searching for that critical customer need that AI will solve, Data Scientists evaluating various modeling techniques and our ML Engineers and Cloud infrastructure team building out our AI platform. As a result, we believe the Sage AI team is far greater than the sum of its individual parts.
I hope this post has resonated with any budding technologists out there, to give you a glimpse of the culture we have built at Sage AI and incited your curiosity. ML is a rapidly evolving space both in general, and at Sage. If you have been inspired to contribute to Sage’s evolving success in the ML space, parse our career opportunities hub to see how you can get involved.
The team continues to work hard delivering innovative products and I am looking forward to sharing our journey with you.
