AI audit & strategy for businesses
Building solid foundations before any AI project
Most artificial intelligence projects fail not for technical reasons, but for lack of framing: a poorly defined use case, unusable data, a misjudged ROI, or an underestimated integration. Our approach is to secure these points upstream, before any investment. Three situations come up frequently:
You're unsure where to start
You know artificial intelligence can transform your business, but you don't know where to begin to avoid a misstep.
Your data exists, but is it usable
Your data is rich but scattered. You don't know whether it can truly serve as the basis for a well-run AI project.
A disappointing AI experience behind you
You may have already attempted a software or AI project that failed to deliver on its promises, and you want to start again on a sound footing.
Our 3-phase methodology
Our audit method combines strategic analysis, operational immersion, and discussions with teams to identify the gaps between theoretical processes and on-the-ground reality. It's in these friction zones that the most relevant automation and optimization opportunities emerge. Structured by sector or department, the audit fits into your existing organization to strengthen operations without disrupting the tools and methods already in place.
Operational & data analysis
Understanding your company in its operational reality: its business, its IT system, its tools, its data, its market position. Across your organization, we assess your data maturity, identify opportunities and risks, and concretely explore your data to judge its quality and its potential for exploitation.
At this stage, we're not yet talking about algorithms or models. We identify the first opportunity leads to explore, systematically confronting management's perception with the actual operation observed.
Use cases & trade-offs
Not all the leads identified are equal. Together with your leadership, we co-build the priority use cases that combine strong business value, technical feasibility, and strategic alignment. For each lead, we check case by case the conditions required for implementation: data availability, compatibility with your information system, regulatory requirements (nFADP, GDPR), the expected level of performance, and internal governance.
For each viable use case, we estimate the impact, the costs, and the return on investment. The goal isn't to produce a theoretical list of projects, but to retain a limited number of these priority cases, arbitrated according to their operational value and their ability to be deployed effectively within your organization.
Roadmap & action plan
The third phase turns the priority use cases into framed, planned projects. For each one, we detail the work required: data preparation, annotation, architecture choices, POC development plan, integration, and deployment.
We build a realistic roadmap that accounts for technical dependencies, potential blockers, and your teams' capacity to absorb change. The final deliverable isn't a theoretical analysis: it's a concrete, sequenced, and costed action plan, immediately actionable to move into the development phase.
Report and deliverables
At the end of the engagement, you receive a single consolidated report structured in 6 sections, which forms the decision-making foundation for your next artificial intelligence investments.
Department-by-department diagnosis
A map of the processes, tools, and flows analyzed for each audited department.
Cross-functional flow analysis
Interactions between departments and with your external partners (clients, suppliers, subcontractors): breakpoints, re-entry of data, and inefficiencies in the flow of information.
AI maturity assessment
An analysis of the data, tooling, and governance prerequisites for your organization.
Prioritized AI use cases
Each case documented individually: description, required data, technical feasibility, estimated gain in hours and in CHF, and implementation timeline.
Budget and ROI estimate
Estimated implementation costs and calculated return on investment for each retained use case.
Deployment roadmap
A structured roadmap with dependencies, milestones, and budget, directly usable to move into the development phase.
A final summary report is presented at a dedicated debrief meeting, attended by leadership and business stakeholders.
Frequently asked questions
We answer them with full transparency — that's also what sets an audit apart from a sales pitch.
The audit generally takes from a few days to two weeks, depending on the scope to cover — mainly the number of departments or functional units to audit.
Each department is the subject of an interview with its head, an on-site observation session, and a dedicated synthesis. Added to this are two sessions with leadership: one at the start of the engagement to frame the objectives and understand the strategic priorities, and another at the end to present the report and the roadmap.
The total duration is assessed after the first framing meeting, which is not billed.
The audit is for any organization that wants to adopt artificial intelligence in a structured way, without setting off in a poorly framed direction. It's particularly suited to companies that don't know where to start, to those that have already attempted an AI project without convincing results, or to leadership teams that sense optimization opportunities but lack the elements to prioritize and cost them. We work mainly with organizations of 10 to 100 people, across a variety of sectors.
The budget for an AI audit depends mainly on the scope to cover, the size of the organization, the availability of your documentation, and the accessibility of your data. We assess these factors during the initial framing meeting, which is not billed.
- The scope of the audit, that is, the number of departments to cover and the depth of analysis wanted for each.
- The size and complexity of the organization, bearing in mind that a structure with distributed teams or a matrix organization takes more time than a more linear one.
- The availability of documentation, since already-formalized processes put down on paper significantly speed up the investigation phase, whereas reconstructing everything from interviews and on-site observation is a more substantial effort.
- The quality and accessibility of your data, as well-identified and accessible sources reduce the time needed to explore and assess them.
Yes, the audit can cover several departments at the same time, and it's actually the approach we recommend when the organization allows it. Auditing several departments in parallel makes it possible to identify cross-functional opportunities, the flows of information between departments, and the inefficiencies that are only visible when perspectives are cross-referenced. We structure the audit by department or functional unit, with interviews and observations dedicated to each, followed by a cross-functional synthesis that highlights the interactions and breakpoints between teams.
No, you don't need to already have structured and organized data. Assessing your data maturity is an integral part of the audit. If your data is scattered or imperfect, that's precisely what we identify and qualify.
Yes, your data stays confidential, and that's a non-negotiable point for us. We sign a confidentiality agreement from the framing phase, before any access to your information.
No data is exfiltrated, shared, or used outside the strict scope of the engagement. For sensitive sectors or those subject to specific regulatory requirements, we work exclusively on your own environments or on dedicated Swiss infrastructure, without any transfer to external systems.
Yes, we can work with your IT teams or your current provider. We design our recommendations so they fit into your existing environment and respect your tools, your systems, and your organization already in place. Implementation can be handled by your internal teams, by a provider of your choice, or jointly with us.