How to successfully implement an AI system

During the rapid investment in intelligent interfaces, many companies are struggling to become AI-based companies due to the organizational and technical challenges that arise during deployment. IPL's UK and executive director Martin Linstrom, a digital artificial intelligence colleague like human, who learns and improves over time, explores five best practices for successful artificial intelligence deployment.

Where do I start when I introduce my AI system to my business?

The most important thing to remember when starting AI deployment is that you need to have business results. This should remain at the core of the decision through travel. The most effective way to achieve this is to ask your staff how AI can make your day-to-day work easier and more fun, and ultimately provide the best user experience for your employees.

19659003] Calculate the estimated cost savings you can achieve with a successful AI deployment and use it as a starting point for your investment to minimize error costs or shorten your expectations. In addition to improving efficiency, cost savings must be based on productivity gains that can be leveraged in other areas of the business by securing staff in management tasks. This allows companies to avoid making excessive investments before seeing initial results, reduce potential ROI if changes need to be made, and enable companies to switch to potentially other viable alternatives.

Why should companies invest in artificial intelligence-enhanced digital colleagues instead of bot bots?

Before advising on the solution your business needs to invest in, it is important to set the goals you want to achieve first. Digital colleagues can provide a much better level of customer service, but they need more resources.

Most chat bots are not scalable, and once deployed they are designed to answer FAQs based on a fixed set of rules, so they can not be integrated into other business areas. Unlike your digital colleagues, they can not understand complex questions or perform multiple tasks at once.

However, the biggest advantage of cognitive AI solutions is that machine learning can lead to continuous improvement over time without improving work and increasing investment. The fact that it is scalable across a broader range of businesses means that in the long run, it will deliver even greater value to the company.

How can an employee use the new AI system?

Employee involvement is very important in relation to digital conversions. New AI deployments should be tested in a small group of passionate employees or super-users who provide honest feedback on system languages ​​and interfaces to help define the most effective user experience for the business.

It's a good idea to start with a super user group because if you test successfully, you can create a positive outlook for your new technology among your peers.

Companies that do not include early-stage employees The AI ​​deployment risk makes AI systems feel compelled by their employees, preventing them from being widely adopted among employees.

How should companies expand their AI deployment?

The advice for a company trying to implement a new AI use case is to continue testing with a short cycle of 60 to 90 days. It is important to continually develop and finalize your solution before you can reproduce it elsewhere in your business.

Once the technology is proved, we propose to experiment in two directions. First, extend the proof of concept to perform the same activity on a larger scale with limit iterations. Second, extend the scope of AI implementation. You can achieve this by experimenting with new areas of your business with new technologies and new initiatives. The second direction is more dangerous, but it is crucial to become a digital company. We must aim to harmonize the two factors that are appropriate for the business culture.

Martin Linstrom, UK and Managing Director IPsoft