Doubt. Uncertainty. A decade ago, magazines and trade pubs used these words to describe manufacturers’ initial reactions to cloud computing. How could we ever send our data to the cloud? Is it safe? Is it stable? Is it necessary?
Manufacturing has often been at the forefront of innovation. Computers and programming cropped up in factories the world over long before mass adoption in the public sector. Manufacturing leaders don’t have a “fear of change” but rather an understanding that, with change, comes the responsibility not only to maintain the pace of operations but to elevate it.
We’re seeing this today with AI.
Connected Concerns
Artificial intelligence has already augmented many systems and platforms within manufacturing’s digital ecosystem. It has been integrated into scheduling tools, CMMS, warehouse management platforms—the list goes on. This budding adoption is happening despite awareness that manufacturing remains one of the most targeted industries for cyberattacks, with 24 percent of surveyed organizations reporting an impact from data theft.
Like other technological advancements, AI isn’t something you can simply not adopt. What is being discussed, however, is strategy: How do we implement AI to improve our processes and workflows while minimizing the risk of data exposure or compromise?
The keyword here is minimizing. Anytime a business connects to a system outside its four walls, there is always a degree of risk. The art of successful risk mitigation boils down to two salient components: workforce adoption and purposeful implementation.
Planning For (and With) the People
The most successful manufacturing leaders approach AI through the lens of workforce adoption. Any trustworthy AI vendor will have baked industry-standard safety measures into their products, but even the best protections can fail if the people using the product (in this case, shop floor teams) do not follow best practices.
Safety often erodes when users are not trained on the precautions they can/should be taking. It is paramount that business leaders have an onboarding strategy for the people who will be interacting with a given technology. This strategy should include:
- Reviewing best practices for password generation and storing.
- Outlining how to interact with a system that will have access to sensitive information.
- Cautioning against interacting with the system on personal devices and/or on public WIFI.
While these items may seem commonplace, they can be the weakest links in the data integrity chain. Standards such as ISO 27001 provide strong guidance in this area.
Planning with Purpose
Before onboarding, though, businesses should consider why they want to implement AI, where they plan to roll it out, and who will interact with it. Rather than trying to bring the tech to every facet of their operation all at once, leaders should plan for a phased rollout. An incremental approach that prioritizes strategic impact over hype will not only contribute to ROI but also shore up safety.
If, for example, leaders want to adopt AI to help improve performance, they should proactively communicate with team members responsible for performance. Their questions, input, and insight should help guide the conversation.
After all, AI is a tool—if its role is misunderstood or the people responsible for using it are not aligned with its purpose or how they are meant to interact with it, efficacy plummets. So too does cybersecurity.
Today, risk is just another business factor. Being competitive means using the digital tools shaping your industry in alignment with your risk tolerance. Some companies want to push the envelope and use AI to augment as many systems as possible—even those that may be more involved in the exchange of sensitive data. Other companies are less aggressive and are seeking to use AI to enhance day-to-day tasks such as reporting, scheduling, and communication.
As with any software deployment, there is no “one size fits all.” When it comes to AI, business leaders will need to be purposeful in their adoption. Determining potential gains, weighing risks, understanding organizational preparedness, and establishing a road map that prioritizes improvement and training are all key to maximizing ROI and bolstering cybersecurity.
