01
Value Leakage Map
Where value is lost between licence, use, trust, workflow and business outcome.
After AI is already in use
You invested in AI. Now see whether it is actually creating value.
Understand where AI is creating value, where value is leaking and what is blocking adoption across your workflows and teams.
AI Adoption Diagnostic
Recognition
This may sound familiar.
“Licences are paid for, but usage is patchy and hard to explain.”
“Some teams love the tools. Others quietly stopped using them.”
“We see activity metrics, but not business impact.”
“AI output is being checked and reworked more than expected.”
“Managers and employees describe adoption very differently.”
“We do not know what to scale, fix or stop.”
What we help you answer
The questions you need answered before you act.
- 01
Are employees actually using AI?
- 02
Is it changing the work that matters?
- 03
Where is productivity being lost?
- 04
Which workflows benefit most?
- 05
Where is AI increasing rework or risk?
- 06
What should we scale, fix or stop?
How the diagnostic works
Your team provides the evidence. We do the heavy lifting.
- 01
Scope
We define the decision you need to make and the teams, workflows or business areas that matter.
- 02
Gather evidence
Employees and managers provide structured input independently through secure online surveys and targeted evidence collection.
- 03
We analyse
GrundMind identifies gaps, contradictions, bottlenecks, readiness issues, perception differences and areas of value leakage.
- 04
You get decisions
Leadership receives a concise, prioritized diagnostic with clear actions and sequencing.
Minimal time from your team. Maximum insight for leadership.
Focused online input from the people involved. No calendar full of workshops.
What we examine
What we look at.
A focused set of dimensions, chosen for the decision in front of you. Not a methodology tour.
Business Value Fit
Whether AI use connects to outcomes the business measures, or only to activity.
Workflow Fit
Where AI sits inside the real flow of work, and where it sits beside it.
Cognitive Fit
How different people think, decide and trust when working with AI, and where that blocks use.
Governance & Risk Fit
Whether verification, ownership and escalation work in practice.
Role & Capability Fit
Whether roles, skills and expectations have adjusted to the new way of working.
What you receive
What leadership receives
02
AI Adoption Map
Who is using AI, for what, and how deeply it has entered the work.
03
Perception Gap Analysis
Where leadership, managers and employees see adoption differently.
04
Prioritized AI Roadmap
What to scale, fix or stop, in order.
What you get
A clear view of what is working, what is getting in the way, and what to do next.
GrundMind turns team and workflow evidence into a practical leadership diagnosis. You see the strengths worth preserving, the barriers absorbing value, the changes that should be made, and a sequenced path forward.
Exact outputs depend on the scope and the evidence available. Missing evidence remains missing rather than being turned into a false conclusion.
From evidence to action
What is working
Strengths · Fit · use cases
What is getting in the way
Barriers · leakage · team gaps
What should change
Adapted workflows · support
What to do next
Actions · owners · 30/60/90
Know what is working
See the strengths worth preserving and scaling.
GrundMind identifies where business value, workflows, Human-AI interaction, governance and capability already support useful AI work. It also surfaces the use cases that are worth pursuing rather than assuming every task needs more AI.
Illustrative team result
Primary strength
82.9
Cognitive Fit
Business Value75
Workflow33.3
Cognitive Fit82.9
Governance & Trust75
Role & Capability75
Strong foundation
Human-AI readiness
Opportunity
High-value use cases
Know what is getting in the way
Find where useful AI work is losing value.
Barriers are kept distinct. Missing ROI, Value Leakage, Team Gap and workflow friction answer different questions, so leadership can respond to the actual constraint rather than defaulting to more training, more licences or another pilot.
Illustrative result
Where useful work loses value
110.9 h
active-work leakage / month
Context
Retrieval friction
friction
AI work
Manual repetition
friction
Handoff
Coordination
friction
Verification
Excess checking
friction
Outcome
Value retained
Context retrievalReworkManual repetitionExcess verificationHandoff coordination
Missing ROI: Value created but consumed
Know what should change
Turn findings into adapted workflows and practical support.
Recommendations connect the evidence to concrete changes in workflow, governance, Human-AI handoffs, verification, capability and management support. The aim is not simply to increase AI use, but to make useful AI-supported work function better.
Current workflow
01Context scattered
02AI outside the tool flow
03Unclear handoffs
04Heavy verification
Adapted workflow
01Reliable context
02Clear AI entry point
03Named human handoff
04Risk-based verification
Workflow
Remove friction
Governance
Make controls usable
Capability
Support the real work
Know what to do next
Leave with a sequenced plan, not a list of observations.
Recommended actions are translated into a 30/60/90-day roadmap with priorities, ownership, timing and measurable conditions for success. Leadership can see what to address first, what to test, and what should only be scaled once the evidence improves.
First 30 days
01
Understand
Prioritize
Map workflows
Set baselines
Days 31–60
02
Redesign
Adapt workflows
Set controls
Enable teams
Days 61–90
03
Measure
Run pilots
Reassess
Scale selectively
Owner
Timing
Success measure
Clear reports, built for decisions.
Results are delivered at the appropriate individual, team, manager and corporate level. Evidence remains attributable, privacy boundaries remain intact, and findings are reviewed before client delivery.
Business impact
Why this matters
- Identify where value is leaking and why
- Identify avoidable rework created by AI use
- Decide what to scale with evidence rather than anecdotes
- Reduce risk from unverified output inside critical work
- Improve allocation of licences, training and attention
Why GrundMind
A different kind of consulting
Research-based, not opinion-based
Our methodology is grounded in the research foundation of the Grund Institute and structured evidence, rather than generic consulting assumptions.
Small, dedicated senior team
A focused group of experienced people works directly on the engagement. No oversized project team and no unnecessary layers.
Minimal time from your team
We are designed to get the evidence we need without filling calendars with workshops.
We do the heavy lifting
We collect, structure, compare, analyse and synthesize the evidence. Your people provide focused input. We do the consulting work.
Multiple perspectives, not averages
We compare employee, manager and workflow perspectives to expose gaps that averages hide.
Decision-ready output
Leadership receives priorities, actions and evidence, not a long slide deck with vague observations.
Evidence before intervention.
Who this is for
Best suited for
- Knowledge-intensive companies
- Professional services
- Technology
- Financial services
- Engineering
- Logistics
When to use this diagnostic
When companies typically use this
- After an AI rollout when ROI remains unclear
- Before rolling AI out across more teams
- When licence renewals need a business justification
- When adoption numbers look fine but results do not
The AI journey
- PrepareAI Investment Readiness
- AdoptYou are hereAI Adoption Diagnostic
- ScaleAI-Ready Knowledge
EU AI Act Readiness is relevant at every stage of the AI lifecycle. Learn more
Next step
Not sure whether this is the right starting point?
Tell us what you are trying to decide or change. We will help you identify the right diagnostic.
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.
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.
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.