🧭 Flatter, Agile Structures
Jess described how mid-size companies are abandoning rigid hierarchies for faster, flatter structures. Shirley expanded this, noting that “AI-native” startups are born lean, while larger firms are having to re-engineer for agility.
“For large tech companies, they kind of have to reengineer their entire org structure to get there. And for… AI native startups… the entire org is from day one kind of engineered towards maximum efficiency, maximum focus on impact.”
What you could do next
- Review where layers of approval or process slow work down, and remove one.
- Try forming smaller, multi-skilled teams that own an outcome end-to-end.
🚀 From Panic to Ownership
When Shirley first joined Monday.com, she was told only to “decrease customer acquisition cost by 20%” - with no instructions on how. Instead of panicking, she interviewed dozens of users to understand their needs, eventually hitting the goal.
“I interviewed dozens of users to understand their motivations, to understand their underlying needs… and that was the way that eventually we were able to decrease customer acquisition cost by twenty percent.”
It taught her that autonomy, curiosity, and problem-solving drive better results than control.
What you could do next
- Set clear goals but leave the “how” up to the team.
- Build learning into performance reviews - reward experimentation, not perfection.
🔁 Continuous Change over One-Off Transformation
Jess explained that organizations must move beyond one-off transformation programs and start thinking in cycles of continuous improvement.
“Organizations are moving away from the ‘here's my one-and-done transformation that will do me for the next ten years’ and starting to realize … that actually change is continuous, and it's a continuous improvement mindset.”
Shirley shared how Monday.com embodied this mindset through “AI Month,” pausing all normal work to let teams experiment and reimagine their ways of working.
“Seven hundred people stopping everything for a month, which is extremely radical… giving room for employees to experiment, to bring new ideas, and to really bake AI in the core of the work and not just as a nice add-on.”
What you could do next
- Swap annual transformation projects for rolling quarterly improvements.
- Schedule focused “innovation sprints” to test new ideas without distractions.
🎯 Strategy Before Tools
Both speakers cautioned against chasing shiny AI solutions without clarity on the problem they’re solving. Shirley emphasized that tools are secondary to understanding the business challenge.
“The problem is not AI. It's not the problem. Problem is maybe to scale with less people or to save time on mundane tasks or to gain better visibility on data.”
“Seventy-five percent of organizations are gonna dabble in AI this year. Eighty percent of those projects will fail because there’s no real strategy aligned to it.”
What you could do next
- Write a simple one-page AI strategy that links specific business goals to use cases.
- Measure outcomes in time saved, errors reduced, or insights gained - not tools adopted.
⚙️ Empower Teams to Own Solutions
Shirley’s Dream Team story brought this to life. She asked her customer success team to find inefficiencies and fix them - no top-down roadmap. They used Intercom to automate ticket handling and cut implementation time using AI-driven prompts.
“We asked our CS to really analyze where they're not efficient enough… they implemented Intercom… agent now answers nine percent of our tickets and with great feedback and satisfaction rate.”
“Now all the implementation in Dream Team happens with a prompt… it fundamentally reduced implementation time… for our customers because our CS representatives are not a barrier anymore.”
What you could do next
- Give teams the freedom (and time) to identify and fix inefficiencies.
- Share their improvements widely - turn small experiments into repeatable playbooks.
💬 Confidence and Psychological Safety
Jess pointed out that simply telling people to “go experiment” isn’t enough - they need direction and support to feel safe trying new things.
“You can block out half an hour in people's diaries and say this is your experimental half an hour, but people don't really know where to start. You will find quite quickly it's going to be incredibly tactical and actually not particularly game changing.”
What you could do next
- Provide prompts or example use cases to guide experimentation.
- Celebrate small wins and learnings publicly to build collective confidence.
🧠 Hiring for Learning Agility
Shirley shared that after returning from maternity leave, she found her role had already changed - proof that knowledge dates fast. She now hires for curiosity and adaptability, not memorized expertise.
“You don't hire necessarily for knowledge anymore. Knowledge has been commoditized… focus should be on how to solve problems, how to stay curious, how to learn, to unlearn things.”
“One of our candidates asked if they can use Cursor and ChatGPT… our first response was no… but then we understood, of course yes. That’s the new reality. But we need to change the question.”
What you could do next
- Refresh interviews to test for curiosity and learning ability, not trivia.
- Encourage candidates to use AI tools in take-homes to simulate real work.
🔮 The Builders’ Era
Both speakers believe the next wave of organizations will be defined by smaller, high-impact teams that design, build, and deliver autonomously.
“The next era won’t be defined by the biggest org charts… it will be the builders… small teams, lean teams that really do end-to-end work.”
What you could do next
- Create compact “builder teams” that own problems from insight to outcome.
- Invest in internal AI literacy so every employee can create, not just consume.