September 6, 2021 • 1 min read
Robotic Process Automation is one natural path of Artificial Intelligent adoption at enterprises, bringing tangible business benefits without massive investments. With the adoption of RPA and intelligent automation growing, the demand for fast-to-implement machine learning solutions is reaching new heights.
Enterprise RPA centres of excellence (CoE) have successfully used the Aito.ai to push the boundaries of cost-efficient automation. In a typical case, Aito.ai extends the capabilities of the user's UiPath automation platform by providing the prediction and decision features over simple API, powered by Aito's machine learning operating in the cloud.
To further advance the adoption and provide solutions to market at scale, Aito.ai has become an Advanced Technology Partner with UiPath. As a result, Aito's machine learning solution will be available to a broad audience of users, and the technical integrations will become easier and more accessible.
"We are insanely excited to work with a market leader like UiPath and grow the ecosystem. The AI use cases that our users implement today using robotic process automation are no-hype: they bring instant business benefits for players in various industries. We strongly believe this is the way towards widespread use of AI." says Tommi Holmgren, Aito's Chief Product Officer.
One of the first to adopt Aito's predictions as part of their accounts payable automation was Finnish logistics giant Posti. Their purchase invoice automation was implemented using Aito.ai and UiPath by their intelligent automation team without the involvement from the data science team.
Tero Byman, intelligent automation manager at Posti, sums up their first year with Aito in production: "We’ve been delighted users of Aito for over a year. While the implementation project was easy and fast with Aito, the real benefits come during the operations phase. The bot runs and predicts critical details for processing the invoices day in day out, and Aito does not require us to spend time with machine learning model retraining and deployments. It all just works. We can focus on automating the next process, not on maintenance."