Leaders keep describing the same feeling: unsure if they're keeping up, unsure if they're doing it right. AI is only the most recent trigger. Before it came the Great Resignation. Before that, COVID-19. Each wave hit fast, and left boards asking for more, employees expecting more, and leaders with less certainty than before.
AI didn't create the
overwhelm.
It's the latest cause of it.
Start with people.
The technology comes second.
Most AI rollouts start with a tool. Pick the software. Announce the plan. Train people. Wait for adoption. Employees experience it as something that happened to them.
We start somewhere else. We help teams map their own work first: what takes the most time, what creates the most value, and how people actually feel doing it. That map shows exactly where automation should start.
Usually it's the work that's high friction or what’s pulling people away from what matters most, to the business and to them.
This targets the tasks that add the least value and gives leadership a defensible answer on where AI investment should go first.
We map the work before we touch the tools.
The map tells you where AI should start.
Tool first vs. people first
Typical approach
Tool first
Pick the software. Announce it. Train people. Measure adoption. The rollout is something IT manages and employees receive.
11fold's approach
People first
Map the work with the people doing it. Find where friction is highest and value to the person is lowest. Target automation there first.
What this builds
Trust, and a real AI roadmap
People trust changes they helped design. Leadership gets a roadmap grounded in where the friction actually is, not where someone assumed it would be.
The Bubble Chart:
how we find where AI should start
Map
Each person maps their own work
Every employee plots their tasks on two axes: business value created, and how closely the task fits the team's actual mission. Bubble size shows attention spent. Color shows how the person feels doing it: green for energizing, yellow for neutral, red for draining.
Synthesize
The maps get combined across the org
Individual maps roll up into one picture. Tasks that are high attention, low value, and draining across many people become the clearest automation targets. Tasks that are high value and genuinely rewarding stay human, and are protected.
One diagnostic,
a sharper question:
The synthesis sorts work into a clear call: automate it, augment it, or eliminate it. People also add a fourth marker, blue, for work they'd like to do more of. That becomes input for what roles look like once AI is integrated into the workflow.
Automate or eliminate.
Protect everything else.
Case Study:
How Creative Force used the
11fold Bubble Chart
to identify the best tasks to automate with AI
Creative Force's People team, led by VP of People Astrid Riber Poulsen, had already been leading teams through the 11fold Bubble Chart activity. When AI came up, Director of AI Operations Aaron Horwath didn't start with a list of tools. He pulled the Bubble Chart data the team already had and used it to find the tasks that were both low value and low joy across the organization. Those became the first automation targets.
The Approach
8 months → 10 hours
The Impact
A customer product that once needed 20 people and eight months shipped in 10 hours with three to four, once AI took the grunt work.
Eliminated 44% of wasted time
We identified that 44% of engineering managers’ time was wasted waiting for others’ decisions, providing the data needed to streamline processes & governance.
Adoption that stuck
Aiming AI at the tasks people liked least built the buy-in that most AI rollouts never get.
The Bubble Chart offered some true “Aha” moments with regards to my team.
Eva Tjhie, Head of Customer Success, Creative Force
“
Ready to find out where
AI should start
in your organization?
We'll walk your team through the same diagnostic Creative Force used. No tool pitch first. Just a clear look at where the friction actually is.