The solution? When you’re shopping for an enterprise LLM, make sure it includes secure data retrieval, data masking, and zero retention. We’ll explain what these terms mean below.
With secure data retrieval, are enforced in every interaction to ensure only those with clearance have access to the data. This lets you bring in the data you need to build contextual prompts without worry that the LLM will save the information.
Next is data masking, which automatically anonymizes sensitive data to protect private information and comply with security requirements. This is particularly useful in ensuring you’ve eliminated all personally identifiable information like names, phone numbers and addresses, when writing AI prompts.
You also need to ensure that no customer data is stored outside your systems. When data masking is in effect, Generative AI prompts and outputs are never stored in the enterprise LLM, and are not learned by the LLM. They just disappear.
Start your enterprise LLM journey today
An enterprise LLM consisting of all your organization’s proprietary america phone number list data may eventually be the most powerful tool you have to serve customers even better, uncover buried intelligence, operate with unprecedented levels of efficiency, and a lot more.
Thankfully, these tools and techniques can help you meet the common challenges that may come up at the beginning of your enterprise LLM journey.
Design strategy considerations:
recognize field service workers for who they are: customer-facing representatives of your organization at the doorstep.
Consider giving your field workers the opportunity to “score” flexibility points that let them bypass workflows, update knowledge articles, and see more of their schedules (if you drip feed schedules).
Limit the use of tools that lead to micromanagement and undermine worker autonomy.
3. Ensure your metrics are meaningful and reasonable
customer satisfaction scores (csat) is a tidy metric for field service leaders. But most of the workers we spoke with said that it’s negligible since so few end customers respond to surveys. They also told us they spend significant time researching and prepping for appointments, and documenting appointment outcomes, but often feel this work “doesn’t count.”
design strategy considerations:
find ways to reward the ancillary tasks that don’t show up in traditional metrics.
Recognize and incentivize – it needn’t be monetary! – the time field service workers spend upskilling and training their colleagues.
Routinely evaluate what you measure and why, and include technician perspectives when designing new metrics and dashboards.
4. Deploy technology to meet your field service workers’ needs
the mobile workers we spoke to said the most important tool they need is the right information at the right time. That may mean knowing what assets are available and in what location. It could be access to knowledge articles about a particular site or project. Regardless, the goal should be to reduce friction and increase efficiency for your workforce.