Active licenses are not adoption. We measure the human and organizational factors that actually drive your return on AI — and show your leadership team where intervention will yield the most.
Up to a 90-minute introductory consultation · no obligation · for AI-transformation decision-makers
Who works on what — the input to transformation.
What happens to people when they meet AI.
Effectiveness, quality, and durability of change.
Adopting AI means changing how work happens — not just writing an email three seconds faster. The 2025 data is clear: technology spend alone isn't enough.
of enterprise generative-AI deployments produce no measurable impact on the bottom line (P&L).
MIT · Project NANDA · 2025
of AI's potential value is left on the table when the technology lands on a weak talent foundation.
EY Work Reimagined 2025
of employees use AI in ways that transform how they work — even though 88% use it.
EY Work Reimagined 2025
of organizations turn AI adoption into transformational business results.
EY Work Reimagined 2025
Under BCG's 10-20-70 rule, only about 10% of the value in an AI transformation comes from algorithms, another ~20% from technology and data, and a full 70% from people and process. That 70% is exactly what usually goes unmeasured.
Expecting results without securing the resources and without overseeing people's experience leads to the wrong conclusion — that "AI transformation just doesn't work here." Usually the problem lies elsewhere — and it can be pinpointed.
Collaboration.tech assesses interconnected areas that shape AI adoption. Each one feeds the next — and each is a distinct lever for intervention.
Who works on what — the input to transformation.
What happens to people when they meet AI.
Effectiveness, quality, and durability of change.
A starting list grounded in research. We tune the final set to your organization during the workshop.
The input to transformation
When they meet AI
The effect of change
Five stages, one goal: an honest picture of what's happening in your AI transformation.
A review of the company data you already hold — the starting point for the diagnosis.
Together we translate your transformation goals into concrete areas to measure.
We build a measurement instrument fitted to your organization's reality.
A test on a chosen area — back office, field, or warehouse.
A 12–18-minute survey per respondent, segmented by role.
Four deliverables that turn "we think" into "we know where to act."
A report across selected areas, segmented by department, role, tenure, and customer-facing role. You see not the average, but exactly where the problem is.
Which actions will yield the most:
Groups at elevated risk — technostress, burnout, and turnover — flagged, with recommendations for proactive interventions.
Repeating the same check at 6 and 12 months shows whether the transformation is advancing in the variables that truly matter.
We measure people and their collaboration — not license counts. Because it's people, not tools, who decide whether change holds.
Collaboration is a social process, and it happens only between people. It requires three conditions: aligned goals, compatible attitudes, and mutual knowledge of one another's competencies. AI meets none of them.
So we don't ask "how do people collaborate with AI." The question is: what happens to people and to their collaboration when AI enters the process — and that is exactly what we measure.
Wekselberg V., Wasilewski J., "Cooperation, Collaboration, Coordination, Groupthink," Difin 2021 (English ed. 2023).
Employees are increasingly wary of AI, and regulation is entering its enforcement phase. Organizations that set a baseline now gain on two fronts.
Due-diligence documentation for the EU AI Act and its human-oversight requirement.
A baseline to measure your own progress against — instead of guessing.
Dates per the provisional political agreement on the Digital Omnibus package (7 May 2026).
How we measure the human and organizational factors that drive your return on AI.
A satisfaction survey measures how people feel about a tool. This check measures the factors that predict your return on AI: organizational readiness, the skills gap, technostress, fear of job replacement, and the real impact on productivity, quality, and retention.
About 10–12 weeks. It includes a review of your company data, a workshop to tune the instrument, building the instrument, a pilot, and the full check — a 12–18-minute survey per respondent.
Yes, indirectly. AI systems used in employment decisions are classed as high-risk. Under the Digital Omnibus agreement (7 May 2026), their obligations were deferred to 2 December 2027. Setting a baseline now creates due-diligence documentation and a basis for measuring progress.
Collaboration is a social process between people — it requires aligned goals, compatible attitudes, and mutual knowledge of competencies. AI meets none of these; it is a tool in the coordination layer.
For boards, C-level leaders, and decision-makers responsible for AI transformation — especially in organizations that have already invested in AI tools but aren't seeing the expected return.
Yes. Repeating the same check at 6 and 12 months shows whether the transformation is advancing in the variables that truly matter.
Up to 90 minutes for AI-transformation decision-makers. We'll discuss your goals, translate them into concrete adoption areas, and agree on a timeline.
The empirical data on this page has been independently verified at the source.