01
AI value was never created
The use case, task, workflow or human-AI fit was wrong from the start.
Typical intervention: Stop it or redesign it.
AI adoption diagnostics
You invested in AI.
We show you where the value is getting lost.
GrundMind turns employee, manager and workflow evidence into a clear diagnosis of where AI value is being created, lost or blocked. The full survey takes one hour per participant and is completed online through a secure invitation link.
The Four Pain Points of AI Adoption
Missing ROI
We invested in AI, why can’t we prove the return?
Value Leakage
Where is the value actually being lost?
Team Gap
Where do leadership expectations and team reality diverge?
Cognitive Fit
People use AI in different ways. Different work needs different patterns.
Missing ROI
“No visible ROI” can describe four different problems.
GrundMind separates different failure mechanisms so leadership does not respond to every AI problem with the same answer: more training, more licenses, or another pilot.
02
AI value was created, then consumed
Verification, correction, context gathering and broken handoffs eat the expected gain.
Typical intervention: Fix the workflow, not the AI.
03
AI value was created, but never captured
People save time, but the capacity disappears back into the same workload.
Typical intervention: Redesign roles and reallocate the gain.
04
AI value exists, but nobody can prove it
There was no baseline, KPI, ownership or measurement mechanism.
Typical intervention: Instrument the value.
Low ROI
AI value is not delivered, captured or proven.
Value Leakage
Find where expected AI gains leak back out of the work.
GrundMind measures extra active work created by specific AI friction using separate quantitative evidence. Value Leakage is not inferred from Fit scores and Fit ratings are never converted into time or money.
Counting boundary
Count extra active work only, not passive waiting, baseline task time, theoretical savings, or somebody else’s time.
Expected gain
Where value should land
Value loss
Where value drains
01
Verification and correction
Extra active work spent checking, repairing or rewriting AI-supported output.
02
Context rebuilding
Time spent searching, copying and reconstructing information the AI or workflow lacks.
03
Duplicate work and rework
Work repeated because AI output does not survive the next handoff, review or system step.
Quantitative evidence
Frequency × extra duration
Reported result
Extra active hours
Manager–Team Alignment
What leadership believes.
What teams actually experience.
Manager-Team Alignment compares two separate evidence sources. GrundMind shows where leadership perception and employee experience align, diverge, or lack enough evidence, without averaging the contradiction away.
When leadership diagnoses the wrong problem, it invests in the wrong solution.
A perception gap can become a value gap through misplaced training, tooling, governance or workflow interventions.
Leadership view
“Practical AI guidance is available.”
“The workflow has been enabled.”
“Review and data rules are clear.”
Perception gap
Team experience
“The guidance is too generic for our work.”
“We still copy, search and rebuild context manually.”
“We are not always sure what is allowed or who approves.”
Human–AI interaction patterns
People work with AI in different ways.
Departments have different needs.
The right fit depends on the work.
GrundMind identifies four recurring Human-AI interaction patterns. None is inherently better than another. What matters is whether the interaction pattern fits what the work requires.
Does the way people work with AI fit what the role actually requires?
01
High-consequence finance work
Exploratory AI use may require tighter verification, auditability and control.
02
R&D and ambiguous problem solving
Iterative, exploratory interaction can create value where uncertainty is part of the work.
03
Low-AI or highly physical workflows
Low appetite for AI may be entirely appropriate if the work has little relevant AI leverage.
Illustrative role-fit logic, not normative personality ranking. Role demands, consequence of error, auditability and judgment requirements matter.
Co-thinker
Explores broadly and uses AI as an active thought partner.
Calibrator
Engages confidently once evidence and verification are clear.
Controller
Works best with structure, checkpoints and clear human control.
AI-averse
Uses AI selectively where value, safety and certainty are proven.
Evidence before conclusions
Map the terrain. Trace the evidence.
Auditable conclusions you can trace back to their source.
The system is designed not to manufacture certainty. Different perspectives retain their source, evidence thresholds protect against unsupported conclusions, and insufficient evidence is shown as insufficient evidence.
Inputs
Employees
Managers
Workflow evidence
Context
GrundMind deterministic logic
Does the evidence support this conclusion?
Do the different perspectives tell the same story?
Auditable outputs
Team diagnosis
Manager–Team alignment
Corporate diagnosis
Manager evidence stays separate from team evidence.
Missing evidence is not treated as failure.
Conclusions retain the evidence source behind them.
Start small. Learn something real.
Start with one team.
Get one evidence base. Make one clear decision.
Run a focused GrundMind pilot using employee and manager evidence, identify the real value and friction pattern, and decide what should happen before you scale.