AI adoption and ROI
How much is AI really delivering in your company?
Delphis measures real adoption and compares expected with realized impact, process by process.
A demo on your use cases, with our team and no commitment.
AI tools overview
Last 30 daysMonthly spend
€48,200
Unused seats
31.4%
Estimated savings
€6,870
- Microsoft Copilot612 seats, 71% used
- ChatGPT Enterprise384 seats, 58% used
- Claude288 seats, 83% used
ROI by use case, expected and realized
- Internal request handlingexpected 62%, realized 48%
- Document summarizationexpected 40%, realized 53%
The problem
More tools, less visibility.
- 01
Licenses that multiply.
Each department adds its own tool. Spend grows by layering, with barely used seats and tools that do the same thing.
- 02
Usage that stays in the shadows.
Part of the usage happens on personal accounts, outside any data control and invisible to vendor consoles.
- 03
A return no one measures.
You know which tools were bought, almost never how far AI has entered the processes and what result it produced against expectations.
- 04
Rules that add up.
GDPR, NIS2 and the AI Act look at the same tools from different angles, and working out what applies to what takes time and expertise.
How it works
Four pillars, one data model.
Demo data, not real 01
Adoption
Measures how far AI has really entered the work, by department, role and use case.
Demo data, not real 02
ROI and impact
Compares expected with realized results on the processes where AI was introduced and derives the return, use case by use case.
Demo data, not real 03
Spend
Rebuilds the cost of every tool, finds unused licenses and overlaps and estimates the savings from a consolidation.
Demo data, not real 04
Governance
Keeps the register of use cases, maps regulatory obligations for each tool and flags what is missing.
Independent of AI vendors and built to work with Microsoft Copilot, ChatGPT, Claude and local models.
Spend rationalization
How much do unused seats and overlaps weigh?
An order of magnitude in thirty seconds, with your company's numbers. Delphis replaces estimates with real usage data, tool by tool. The saving is only part of the return: the other part is what the remaining AI produces, which is what we measure use case by use case.
Indicative estimate
€97,500
of potential savings per year, about 33% of a spend of €300,000
- Unused seats
- €75,000
- Overlaps between tools
- €22,500
AI ROI
AI ROI is not estimated, it is measured.
Knowing which tools you bought does not tell you how far AI has entered the work or what it is producing. Delphis links the full cost of each use case to a baseline measured beforehand and a comparison afterwards, and keeps three levels of measurement apart, because each needs a different method.
How a use case's ROI is reached
- 1
Full cost
Licenses, usage, integration, training and management time, reconciled with real usage.
- 2
Baseline before
A measurement of the bounded process before AI is introduced.
- 3
Comparison after
The realized result against the expected one, on the same process.
- 4
ROI
Net benefit against cost, use case by use case.
On broad, loosely defined processes the measure is less reliable: we say so and treat it as an estimate.
- 01
Real adoption
Usage data read from vendor APIs is traced back to users, roles and departments. Questionnaires bring out use on personal accounts, which company consoles cannot see.
- 02
Impact on processes
Measured well on bounded processes: a baseline before AI is introduced and a comparison after, expected against realized results, use case by use case.
- 03
Impact on people
Surveys and sentiment, always in aggregate by role or department, never as an assessment of the individual employee.
The result is a documented answer to a simple question: what changed, where, and against what expectation.
Learn more about adoption and ROIWho it is for
One view, many readers.
- CIO
- Spend control and tool rationalization.
- Chief AI Officer
- Use case portfolio, adoption status and priorities.
- CEO
- A clear reading of the return on AI investment.
- HR
- Training, AI literacy and impact on people.
- DPO and CISO
- The map of regulatory obligations for each tool.
Getting started
Start with a pilot project.
The platform is already operational. To get started we run a pilot with a limited number of companies at a time. Participants get a first map of the AI tools in use and their cost, access to the platform on reserved terms and a direct channel with the team to shape its evolution.
Contact
Request a demo
A demo on your use cases, with our team and no commitment.
FAQ
Frequently asked questions
How is AI ROI measured?
We start from a baseline on well-bounded processes and compare expected impact with realized impact after AI was introduced. On broad, loosely defined processes the measure is less reliable, and we say so.
How much can you save on AI licenses?
It depends on how many seats are unused and how much the tools overlap. The calculator on the page gives an order of magnitude on values you enter; in a demo we rebuild the estimate on your real data.
Is the platform already available?
Yes, it is operational. We start it with a pilot project, with a limited number of companies at a time. Whoever requests a demo is contacted to assess scope and timing together.
Which AI tools does it work with?
It works with Microsoft Copilot, ChatGPT and Claude, extending to local models.
Is it tied to an AI vendor?
It is independent of all model vendors, a necessary condition for giving impartial advice.
How is data about people handled?
Always in aggregate form by role or department, never as an assessment of individual employees.
Does it replace legal advice on compliance?
It helps map obligations and gaps, and should be used alongside the opinion of your legal advisors.