A few weeks ago, we opened our Reimagining Work with AI webinar by asking a simple question: If you could hand one task in your life over to AI, what would it be?

For those of you who were there, you'll remember that one of our guest speakers, Laken Masterson, landed on taking out the garbage. At first, she doubted AI could do something so physical, but the more she sat with it, the more she questioned that assumption. Once she broke the chore down into steps, she found that AI could draft the model for a garbage-hauling robot, design its remote control, and even run its quality checks.

It was a funny answer, but it captured the spirit of the hour: the biggest opportunity with AI isn't doing today's work faster; it's questioning whether that work should exist at all.

That same instinct to reimagine showed up in every AI adoption story we heard from our guest speakers’ organizations, and it clearly resonated with many of you. Your questions filled the chat fast, so we've gathered the ones we couldn't get to live and answered them for you below.

Helping people solve the right problems with AI

Tara Dignam, Learning Solutions Designer at Autodesk (timestamp: 7:43)

Tara joined Autodesk to lead the learning side of the company's AI transformation. Her team began with tools and prompt engineering, then evolved its approach as it learned that people needed help deciding where to apply AI. This led her team to build a framework called PRIME.

Tara's story sparked plenty of curiosity about how to make that shift, from choosing what to reimagine to helping leaders let go of old habits. Here's what she had to say:

Q: What are the elements of Autodesk's PRIME framework, and what criteria did you use to triage what to Reimagine?

A: Autodesk’s PRIME framework was developed to encourage people to experiment with AI safely. It stands for:

• • P - Pick a workflow

  • R - Reimagine a workflow

  • I - Implement guardrails

  • M - Meaningfully experiment

  • E - Evaluate and Iterate

During the webinar, Tara explained that the "Implement guardrails" step is where the human value really comes in. It's the point where teams set clear markers for human judgment by deciding "what's safe to do, what's not safe to do, what's the downstream effects of this."

When it came to deciding what to reimagine, Tara said, "We really left that up to each team to decide. We asked them to focus on a business impact that could be improved."

Q: What might people need to unlearn to be open to making this conceptual shift from replacement thinking towards reimagination?

A: "People may need to unlearn the idea that good leadership means having the answers or staying in control," Tara said. "I saw some reticence where people were uncomfortable letting go of established ways of working." That discomfort often shows up in leaders, since AI has become the great equalizer by putting knowledge in everyone's hands. "A lot of leaders find it hard with AI being used in various ways across their team and them not being the only people with the most knowledge," she explained. "It breaks down hierarchy in teams. Leaders who embrace that and offer to be a coach to their teams flourish." In Tara's experience, teams whose leaders shift from expert to facilitator of learning and exploration are progressing faster than those maintaining traditional command-and-control.

Q: Should we use AI to only do what we've always done, only faster, or seize the chance to make work more human?

A: "Automation can end up removing the humanity we're trying to create. I think the real opportunity is not just to do the same work faster, but to rethink the work itself," Tara said. "If we can create more trust and connection, then people will bring more of themselves to work." At Autodesk, building that trust starts with making it safe to fail. As Tara explained, "we want to spotlight the experiments that went awry... because we find that there's the most learning in those." When people share what didn't work, they break down silos, learn from each other, and see that the best ideas for reimagining work don't just come from developers; they come from everyone.

Building new ways of working with AI through subtraction and addition

Laken Masterson, VP of Operations at Indeed (timestamp: 22:19)

Laken has spent sixteen years at Indeed, watching the organization grow from the world's leading job site into an AI-powered, two-sided marketplace connecting job seekers and employers. Indeed has used AI for nearly two decades, and today it's built into everything from recruiter tools like AI sourcing and screening assistants to the way teams make decisions internally. But Indeed holds firm on one principle: "hiring is human." AI can surface the best candidates, but the final decision always belongs to people.

Inside Laken's operations team, that people-first approach extends to how AI gets built into solutions through AI Explorers, a program where a small group of volunteers partner with Indeed's internal AI team to build people-first AI solutions.

That same human-centered approach shapes how Indeed protects its users' data and how it puts its own people in charge of building with AI. Here's what Laken had to say:

Q: How do we ensure data security while embracing AI in our work?

A: "One of the main things we care about is ensuring job seeker trust with our platform," Laken said, which is why Indeed has entire teams dedicated to security and responsible AI. Those teams have even written a constitution for how Indeed uses AI, and "every single thing that we build references the constitution." If your organization doesn't have that yet, Laken's advice is simple: "put someone in charge of trust and security and have them provide the guidance to the rest of the organization."

Q: Tell us a little more about Indeed’s AI Explorer Program? What is its framework or program elements?

A: Indeed’s AI Explorer Program is operation-specific. It pairs a small team of builders, captains, and executive sponsors with Indeed’s internal AI team for four to five months. Anyone can submit an idea, and leaders prioritize the ones that will do the most for the client experience, time, cost, and effort before the team builds them. "We're ultimately building product managers in the AI world who are also operators," Laken explained. The key, she said, is dedicated time and structure, because simply telling people to go use AI, "you don't get anywhere."

Moving an organization towards AI reimagination, deliberately

Jamie Li, AVP of Learning and Workforce Capability at Manulife (Time stamp: 38:06)

Manulife is a leading international financial services provider headquartered in Toronto, with operations across Canada, Asia, and the United States. When its AI journey began in 2024, Jamie's team wove learning directly into the company's broader AI transformation. Their AI champions network started as a way to harness the energy of early adopters and quickly grew into a community of peer-led change agents. As people got more comfortable, the conversation shifted from how AI could help them work faster to whether they should do the work differently altogether.

Moving an organization that size takes both momentum and time. Here's what Jamie had to say:

Q: What helped Manulife move so quickly on AI adoption?

A: "From the very beginning, our work stream was part of the larger AI transformation," Jamie said. That meant buy-in was there from day one, so there weren’t "wasting time trying to convince people.” Instead, they actually had the opposite problem: keeping up with all the requests coming in. To manage that demand, her team works in three-week sprints: they agree on priorities up front, check in along the way to course correct, and end each sprint with a demo.

"So while large, we acted nimble," Jamie explained. "It was messy sometimes, but... we just kept the lines of communication open and very transparent on where we are, where it's working, where it's not."

Q: How much time do people pour into this AI work?

A: "It really is dependent on the individual and the role that they're playing and where they are in their own personal journey," Jamie said. Manulife sets aside dedicated learning time through their well-being initiative, Fuel Up, which gives employees four hours of dedicated learning together on the second Friday of each month. This also includes bite-sized learning for client-facing roles that are harder to step away from. For champions, the role has evolved from something organic into a recognized part of their job.

Across all three stories, the lesson was the same: the real work of AI adoption isn't about the tools, but about giving people the confidence, time, and permission to question how things have always been done. Whether it's a chore at home or a process at work, the best ideas start when someone stops to ask what could be different. If you have more questions we haven't answered yet, reach out to us at marketing@experiencepoint.com and we'd be happy to keep the conversation going.