How we work

We’ve seen that the challenges facing those adopting industrial AI can be steep. As a result, knowing exactly where and how to get started can be intimidating. We’ve found the following points to be important for enterprise business and technology leaders to keep in mind.

Establish the team

As with any other enterprise initiative, making progress with industrial AI requires ensuring that the right team is in place. The following roles should be represented on an organization’s initial AI teams:

• Executive champion

• Domain experts

• Data scientists

• Software developers

Start with a business problem

While we generally advise against the pursuit of technology for technology’s sake, we believe the impact of AI to be so dramatic that it is important to prioritize exploratory AI investments. However, even when the initial investment decision is driven by a desire to build AI competency, as soon as a commitment to invest has been made, it becomes critical to shift focus to business problems.

Know your “good enough”

AI uses data to train a statistical model to identify trends, make predictions or plans, or take actions. This training process is inherently one where you start with low accuracy and continue to push forward until you’ve achieved acceptable accuracy. Read that again. The goal is not perfection, it is acceptable accuracy. As in many other endeavors, training an AI system to achieve 90% accuracy from nothing can be much easier than getting the next 5% accuracy out of the system. For this reason, it is important to know when you’ve achieved “good enough” for a given project. In speech recognition, 90% accuracy is mostly useless, whereas a system that identifies 90% of the defects missed by human inspectors can be of great value to the business.

Keep it Simple

Albert Einstein famously said, “everything should be made as simple as possible, but not simpler.” That’s certainly the case with industrial AI. If business goals are truly driving your AI projects, your team’s excitement about various AI techniques and technologies should inform, but not drive, their ultimate technology direction. There are many amazing new machine learning approaches and technologies coming online, and it can be tempting for teams to gravitate towards the latest techniques getting all the press. But the adage “if all you have is a hammer, everything looks like a nail” applies here.

Think hybrid intelligence

Reading the press, you’d get the impression that AI is about to put humans out of work. In fact, the reality is that today’s AI is largely dependent on human intelligence to function. To ensure the success of your industrial AI projects, it’s critical to incorporate the knowledge of your employees and other humans in designing and training the systems, establishing best practices, and, once deployed, in managing, overseeing and correcting them. Silicon Valley won’t tell you this, but some of the most successful AI systems in production are hybrid AI systems. Hybrid AI seeks not to replace human labor, but to eliminate the most boring and repetitive aspects of it, allowing humans to spend more time on complex requests. Even looking to the future, a realistic scenario for many AI systems still anticipates significant human involvement, with humans handling perhaps the most complex 10% of requests.

Our Goal

Robotic process automation (RPA) is an emerging form of business process automation technology. This is non immersive technology that resides on tasks users typically conduct inside given business process through application’s GUI thus allowing to use existing investments, or applications and seamlessly bridge to your backend environment without any need for coding and using APIs. As artificial intelligence (AI) or cognitive services are integral part of new software robots we have really strong foundation to introduce successful business process transformation and automation of your business. All answers to these questions or any other RPA related, sharing best practices and lessons learned are typically addressed through RPA Proof of Concept (PoC) tailored to your needs.

Join RPA journey and unleash full business potential your company has.

2016 - 2020

YEAR by YEAR GROWTH

Despite its long history, enterprise AI is a nascent and fast moving field. Even for those who work in it every day, it can be challenging to keep up with all the latest advances in technology and approaches. AiPath was able to put into fast forward motion implementations with its agile approach and empowerment of your workforce. This has allowed us to scale and move forward in the matter of days and weeks, and not moths and years.