Artificial intelligence today is not AI from a decade ago. Companies across all industries are finding that the technology is finally, truly helping to drive efficiency and develop new services. However, implementing these technologies at scale and proving value is still a challenge.
According to Chris Dobbrow, vice president of business development at Augury, there are common pitfalls companies can fall into during an implementation project.
“Some companies lack an overall digital strategy, which is essential for aligning with organisational goals and methods,” says Dobbrow. “Another problem is not thinking about scale. Assuming the test is successful, it’s critical to plan out the impact, including the benefits it would deliver for the business and its employees. Finally, a lot of companies lack success metrics, meaning they can’t prove a solution’s effectiveness to key stakeholders.”
Dobbrow recommends an approach that commits to deploying technology on a large scale from the outset in order to prove future value.
“Going out in search of a technology only is a mistake,” he says. “With 80 per cent of pilots in purgatory, you have to find a purpose-built solution for your specific problem. Once you do that, and have a track record of its success in other organisations, you have an opportunity for quick value and fast scaling.
“Instead of starting with one small production line or a few machines, which won’t help you to understand what scaling looks like, you go big at the beginning. You can start with several sites with varying degrees of maturity to prove the value of a solution.”
Augury provides support throughout the implementation and value creation process as companies roll out machine and process health solutions for production efficiency and optimisation. As a trusted provider within most manufacturing environments, Azure is a fundamental success factor in securely scaling Augury’s Machine Health solution to thousands of critical assets in dozens of locations.
“Our view is that a successfully scaled solution is 30 per cent technology and 70 per cent people,” says Dobbrow. “We’re upfront about the importance of change management and the impact that key personas will see in their workflow, and we support them throughout that transition. We invest in people to provide that support, deliver our experience and drive value. That comes in the form of deep AI and machine learning expertise, as well as the playbook for implementation we’ve developed working with customers of different levels of maturity.”
This article was originally published in the Spring 2023 issue of Technology Record. To get future issues delivered directly to your inbox, sign up for a free subscription.