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HDI Service & Support World
April 25-29, 2027
Caesars PalaceLas Vegas, NV
Your Support Team Is the AI Frontline

Tony North has been in this industry since 1994. In his lightning talk at HDI’s Service & Support World, he covered a lot of ground in what he’s seen in his career: real-world disasters, governance frameworks, ethics training and the most underutilized asset in any AI deployment. Spoiler: it’s the person answering your support queue.

“Trust is the foundation of AI,” North says. “If people don’t trust it, they won’t use it.”

That sounds obvious, but most organizations treat “trust” as something to do at the end of the AI rollout. North says it has to be the first design principle, baked into how the system is built, how it’s explained and how it’s governed.

He shared three examples of what happens when you skip the trust step:

  • Amazon: In 2018, the company rolled out an AI-powered hiring tool built on a decade of historical data, but the data was overwhelmingly male. The system penalized resumes that included the word “women.” Even though Amazon was actively trying to build diverse teams, the tool worked against them.
  • The city of San Francisco: In 2023, the city deployed an AI system to manage employee benefits. It incorrectly denied benefits to 14% of employees. When they dug into why, they found the system had been trained on English-dominated historical data. Anyone who submitted a request in another language got flagged.
  • Delta Airlines: In 2024, the CrowdStrike outage hit Delta’s AI-dependent scheduling system. It collapsed because there was no manual backup process. The company lost $5 million in revenue and 7,000 flights were canceled.

The Delta example illustrates North’s main point. While executives were calculating losses, it was the support teams who staffed the airports, rebooked passengers, reviewed logs, removed corrupted files, rebooted systems and managed the chaos.

“Support teams are the first to see and solve AI issues,” North says. “They’re the first ones to talk with customers when AI issues occur. They’re the first ones to detect issues. They are the AI frontline.”

The San Francisco and Amazon examples share a common failure: governance came after the fact, if it came at all. North encouraged attendees to build a classification system before the AI tool or software goes live. This means classifying every type of intake your system will handle: by risk, by automation, by prediction, by decision support, by generative content. Every category gets its own rules.

“You’ll be able to explain everything that comes through your AI system,” North says. “And most important, you'll be able to solve problems much quicker.”

North’s favorite example of AI done well is from the city of Amsterdam. It’s the first government in the world to publicly publish its AI algorithms. It’s a complete catalog of every AI system the city uses, what decisions it influences and how residents can challenge it.

Finally, North encouraged attendees to train every staff member on AI ethics. He wants to see more conversations on fairness, accountability, transparency, privacy and safety.

“We want employees to make the right decision for the right reason, and not be told what to do,” North says. “It’s a little similar when we talk about AI systems. We want a culture of trust.”

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Bridging the Gap: 5 Tips for Cross-Functional Collaboration That Enables AI Transformation

Ask ten executives who owns AI at their company, and you’ll get ten different answers. IT says it’s not their call. Legal gets blamed for slowing everything down. HR figures it’s someone else’s department. Meanwhile, teams are buying tools nobody signed off on, duplicating work and hoping it all sorts itself out. Sound familiar?

Lisa Duerre spent the last year studying why that happens. As part of an applied research project for her leadership consulting collective, RLD Group, she studied where AI adoption breaks down inside organizations, and where it works. The findings from RLD Group’s research helped inform a collaboration on the CONVERSATIONS WORTH HAVING®: The Human Accelerator for Artificial Intelligence Quick Start Guide, which is available as a digital download.

Duerre views organizations through what she calls an I–WE–US leadership framework, defined like this:

  • I: individual judgment and accountability

  • WE: workflows and cross-functional coordination

  • US: governance, decision rights and organizational measures

“All three levels are contributing to the breakdown or the alignment, whether people realize it or not,” Duerre says. “AI is amplifying whatever’s already true in your system. The teams that were disconnected before AI showed up are more disconnected now. The ones that talked to each other are moving faster, together.”

If your company is ready to collaborate better with AI tools, Duerre shared the following tips. Take a look.

Form a cross-functional AI committee

Duerre’s background is in HR, and she says most HR leaders assume AI ownership belongs to IT. It doesn’t, at least not exclusively.

“Ownership needs to sit at the system level,” Duerre says. “Each function carries a piece of it, based on what they do, how well they understand that part of the business and how their work depends on everyone else’s. AI is flattening how we work. You can’t just keep it in your own business unit anymore. You have to look all around you.”

For starters, she suggests building a cross-functional AI committee instead of having one department make all the AI decisions. Legal, IT, cybersecurity and HR should be on the committee, Duerre says.

“If you have a C in front of your title, you should be on that committee,” Duerre says. “That’s how I look at it, because it’s a system-level solution.”

During these meetings, Duerre says you’ll find out that some departments are racing ahead with AI and others are holding back.

“Both sides need to name the trade-offs aloud,” Duerre says. “With teams moving too cautiously, you have to talk about the opportunity cost of falling behind. With teams sprinting ahead, you have to ask them what happens if they don’t bring everyone else along with them.”

Figure out how to use AI strategically

Most companies spent the past two years telling employees to use AI with anything, without much strategy behind it. Duerre says that’s starting to catch up with organizations as finance teams scrutinize the cost.

Her rule of thumb: if you can’t articulate the goal and how you’ll measure success, don’t roll it out yet.

“Teams that use AI well have a strategy behind it,” Duerre says. “They’ve kicked the tires on what they’re trying to solve it for. You need to ask yourself, ‘Which business outcome are we trying to improve, and what must be aligned for AI to create measurable value?’”

Here are a few examples of how to use AI strategically:

  • A company could select a workflow that regularly creates delays, redesign it with AI and test the new approach. Then, measure whether it improves time, cost, quality or capacity.

  • Use AI to support early sales outreach and qualification across markets and languages. AI can help a business reach and assess more potential opportunities, while people remain responsible for understanding the customer and building trust.

  • Flag patterns in customer complaints across multiple channels with AI, so leadership can see recurring problems before it shows up in satisfaction scores.

Check-in regularly during an AI rollout

Duerre recommends a minimum weekly check-in during any AI rollout, sometimes daily depending on complexity. But the format matters more than the frequency. Status updates don’t cut it.

“Ask, ‘What are we learning and what are we surprised by?’ That’s a question that helps you with your check-ins, versus, ‘It’s in three products now and we’ve tested six,’” Duerre says. “That doesn't help, because you’re having these meetings to figure out what’s working and why. If you ask more strategic questions, you can move even faster.”

Publish AI guardrails

Employees who don’t know what’s allowed with AI will either freeze or go around the system entirely, Duerre says. She recommends publishing clear, specific guardrails on what’s okay and what’s not. Come up with some real examples, and pair them with an intake process that doesn’t require writing a thesis to get an approval for using it.

"The approval path should be lightweight, not bureaucratic,” Duerre says. “Something like, ‘If you’re going to use AI, here’s the path. And if it needs approval, here’s three or four quick questions for you to answer.’”

Take employee anxiety about AI seriously

“AI is just a tool” is a phrase Duerre hears at nearly every conference she attends, but she doesn’t buy it.

“Saying it’s a tool is underselling what’s happening at companies right now,” Duerre says. “AI is changing how we work. It’s changing how we lead teams.”

Duerre wants leaders to remember that a lot of employees are fearful of AI. Pew Research Center found 52% of U.S. workers are worried about the future impact of AI in the workplace.

Employees who feel AI is being “done to them,” instead of built alongside them are especially anxious, she says.

“Leaders need to recognize that anxiety is contagious,” Duerre says. “As a leader, this is your opportunity to show up as the safe, steady person who is showing what you’re learning with AI. And don’t be afraid to show how you’ve failed using AI, too.”

Duerre asks every executive she works with: “Who am I with AI?” and encourages them to pass this mindset question along to their employees, too.

“AI is now your teammate,” Duerre says. “Phrasing it as, ‘who am I with AI?’ is different than, ‘what’s going to happen to me with AI?’ You really want your team to feel empowered with AI and show them how it can help accelerate their career.”

Put these ideas into action

Rewiring your organization for AI requires more than the right tools. It takes shared language, practical frameworks, and a willingness to keep learning. Here are a few resources to help you take the next step.

  • Enterprise AI Playbook: Practical frameworks and executive discussion questions to help IT, HR, and business leaders align around AI that delivers measurable value.

  • Work-First AI Use Case Assessment: Identify the workflows where AI can have the greatest impact before you invest in new tools.

  • The REWIRED Brief: Get weekly insights, real-world case studies, and practical advice on leading AI transformation.