AI is everywhere, so at Naviant, we spend a lot of time talking about AI best practices and use cases to help our customers understand its value.
But sometimes a little perspective shift can help a concept really click.
So, for this blog, we’re sharing how Naviant customers like you are starting to use AI in their organizations to solve common problems, and their overall outlook on the technology itself.
The customers in question are IT leaders representing both commercial and government organizations who were panelists at the recent Reno, NV stop of Naviant’s Intelligent Automation Day conference series. Here are four ways these organizations are embracing AI and automation—while keeping their eyes wide open.
4 Ways IT Leaders Are Using AI and Automation to Solve Real Problems
1. Automating the Mundane to Focus on What Matters
For our first panelist, a Senior Deputy CIO for a government institution, AI isn’t about replacing people. It’s about freeing them up for more.
“We look at it as a partner,” she said. “More of the ability to take care of some of the, I don’t want to say mundane, but the smaller tasks that take up time. And allow people to provide that service better.”
Her strategy: Her team is piloting Microsoft Copilot internally, and while she couldn’t share specifics just yet, she emphasized the value of having easily accessible written “guidelines” and “restrictions” in place to ensure safe use.
“Copilot is nice because we can put guidelines, we can put restrictions on what is used, what’s put in there,” she said.
Her ultimate goal is to make steady, cautious progress in integrating AI into employees’ work lives in thoughtful ways that make their work lives more enjoyable and help them accomplish more.
The Lesson: Successful, responsible AI adoption starts with transparency. If you’re introducing AI tools like Copilot, start with internal pilots and pair them with clear usage guidelines to build confidence and ensure safe adoption.
2. Using AI to Improve Customer Experience Without Sacrificing Trust
Our second panelist, an Administrator for a government agency’s research and project management office, is leading an AI initiative that includes AI-powered chatbots and document recognition tools.
The agency has started using ABBYY for document classification and Einstein ChatBot within Salesforce to support customer interactions.
She added, “We’ve used some very simple AI in Teams, too. The staff gets really excited about the automated summaries. It’s a huge time saver.”
But it’s not without obstacles, as she’s also navigating the delicate balance between innovation and public trust every step of the way. This is the change management element that comes along with any intelligent automation journey, but especially when AI is involved.
“We deal with a lot of PII [Personally Identifiable Information],” she said. “If you talk to our business staff, they’re so excited about AI, but we have to be careful.”
Along with setting strict boundaries, she’s committed to making her intent clear: “Our intent is not to replace humans with AI. We want to strategically use AI and automation to enhance their day to day and improve the customer experience.”
The Lesson: If you’re exploring AI in a regulated or public-facing setting, consider how clearly communicating your intent and starting with internal use cases can help build trust and momentum.
3. Laying the Groundwork for Responsible AI Adoption
Before diving into generative AI, our third panelist, a Director of IT at a commercial organization, is focused on data quality and foundational infrastructure.
“One of the first things out of the gate we’ll be doing is implementing data quality,” he said. “Which is one of the first things you do with these new tools, to train them on your data, and then they will continue to improve.”
Click Bond already uses machine learning in inspection processes and is planning to explore AI for supply chain optimization. “Supply chain is a process. There’s a lot of dependencies on it. And potentially a lot of bottlenecks,” Childs explained. “It seems the logical place to start… without disrupting our manufacturing process.”
The Lesson: Every AI initiative must start with high quality data, so before launching generative AI, invest in data quality and identify operational areas where AI can add value without introducing risk.
4. Balancing Optimism with Caution, and Educating Along the Way
All three panelists expressed optimism about AI, but also a healthy dose of realism.
“I’m an optimist,” said the Administrator. “Because I know we’re surrounded by good people who have the best interests of our constituents at heart.”
The IT Director echoed that sentiment: “We see AI as an opportunity for our elevator workforce looking at a low-value process and ultimately training employees to learn a greater set of capabilities.”
But they also emphasized the importance of education and critical thinking. “It’s okay to have set results, but you also have to digest those and ensure they actually are accurate,” the IT Director said. “That’s probably where… the caution comes in.”
The Senior Deputy CIO and Operations Officer summed it up best: “I’m a realist at heart. But I’m also an optimist. I think there’s just a lot that still needs to be learned and folks need to be educated on what to do with it.”
The Time to Dive in is Now, But Not Without Caution
These leaders’ experiences prove that success with AI starts with high-quality data and thoughtful policy, but also a clearly expressed purpose. And above all, it’s vital to bring your people along for the journey.
