Support
Recurring requests, ticket triage, response preparation, knowledge gaps, escalations.
Example
A digital employee reads new tickets, recognizes topic and urgency, suggests answers, and flags cases that need human expertise.
thirdmind · AI Compass
AI Compass for the right next step.
The AI Compass meets you wherever you are: without a clear AI idea, with several possible use cases, or with a concrete process that needs to be sharpened in a workshop.
- Orientation, workshop, and prioritization in one offer
- Assessment by benefit, feasibility, data situation, and risk
- Recommendation: start internally, build a pilot, or set the idea aside
Starting point
The AI Compass starts where you are right now.
Many companies already have enough AI ideas: automate support, maintain CRM data, prepare offers, check documents, make internal knowledge usable.
The decision is which of these is mature enough, important enough, and controllable enough to start. This is where the AI Compass separates useful AI use cases from nice ideas.
If no use case is clear yet, we create orientation. If a concrete process is already on the table, we use the workshop part of the Compass to sharpen the task, data, systems, edge cases, rights, and approvals.
AI CompassDecision basis
What we clarify
- which processes have real AI levers
- which benefits can be realistically verified
- which data, systems and rights are relevant
- where risks, approvals, or control limits arise
- what needs to be clarified in a concrete process workshop
- what can be implemented internally and where external help makes sense
- how each use case should be ranked by business impact and effort
Result
The result is not a list of ideas. It is a basis for decisions.
Sequence
Orientation, workshop, and decision in five assessment areas.
Some teams first need orientation. Others already know which workflow hurts and need to check whether it can become a useful pilot. The AI Compass combines both: potential analysis, process workshop, and a recommendation for the next step.
We examine concrete processes, data situation, feasibility, risk, and internal effort. After that, it is clearer whether you start internally, build a pilot, or set a topic aside deliberately.
Starting point
We clarify whether you first need orientation, want to compare several use cases, or already bring a concrete process into the workshop.
- relevant areas
- existing ideas
- concrete process or open search field
- teams involved
- decision goal
Process map
We make visible where recurring work arises, which steps are manual today, and which handovers are critical.
- recurring tasks
- manual handovers
- typical examples
- inputs and outputs
- edge cases
- first use case candidates
Value rating
We evaluate which use cases or process steps have enough operational value to deserve more than a neat AI demo.
- frequency
- Time spent
- Quality risk
- Team relief
- Connection to the business goal
Feasibility
We check which data, systems, rights, and control points are necessary for an idea to become a resilient AI process.
- Data sources
- System access
- Roles and rights
- Approvals
- Human-in-the-Loop
- typical edge cases
Next step
We distinguish what your team can handle internally, which process deserves a pilot, and where professional architecture or implementation makes sense.
- internal implementation options
- workshop result and open clarification needs
- sensible MVP scope
- Need for integration
- Build-or-buy assessment
- external support needs
Result
A decision basis that can be reused internally.
The result can be used directly by management, departments, and IT. It shows which AI use case makes sense first, which concrete process is ready for a pilot, and what still needs to be clarified before that.
- Process map with relevant AI levers
- prioritized use case matrix
- workshop result for a concrete process if the process is already clear
- Assessment by benefit, feasibility, risk, and data situation
- Build-or-buy assessment
- Recommendation for the first digital employee
- useful scope for a pilot or internal start
- risks, assumptions, and open questions
- decision basis for management, departments, and IT
Typical topics
Typical topics in the AI Compass.
Sales
Lead research, account preparation, CRM maintenance, follow-ups, offer preparation.
Example
A digital employee prepares accounts before a first meeting, researches relevant information, and creates meeting notes for sales.
Operations
Document review, email triage, data comparison, internal coordination, recurring approvals.
Example
A digital employee checks incoming documents against defined criteria and only prepares the cases where a decision is necessary.
References
What other teams took away from the AI Compass.
IMV
For IMV, several automation fields were assessed, including documents, knowledge access, communication, and administrative standard processes.
- Collected and prioritized automation fields
- Derived a recommendation for useful first implementations
Innotec
At Innotec, existing AI experiments, process ideas, and operational bottlenecks were translated into a prioritized implementation logic.
- Structured use cases from billing, field sales, IT, sales, and back office
- Separated a quick win, lighthouse topic, and MVP scope for the next step
LCI
For LCI, sales and meeting processes were structured around a possible AI coworker, with a focus on research, outreach, offer work, and follow-up.
- Prioritized sales research, outreach, and offer workflows
- Mapped meeting documentation and follow-ups as the next work area
Continue working
With us, internally or with existing partners.
The AI Compass is designed to stand on its own. You can then implement with thirdmind, continue internally, involve your existing IT team, or work with another implementation partner.
Pilot
Start with thirdmind or partners
If the use case, data situation, and control model are solid enough, the result becomes a limited first implementation step.
How a project with us worksInternal
Continue with your own team
If the organization already has the right resources, the result gives your team a decision and scope basis.
No
Choose not to implement
If value, data, or risk do not fit, a clear no is a useful result.
Some results lead to a pilot. Some lead to internal implementation. Some lead to a deliberate no. That is part of the value.
Our aim is to leave you with a basis you can use without us. You know which use case makes sense, which data and systems are relevant, what scope is realistic, and where the risks are.
Frame
The framework depends on your processes.
We deliberately do not publish a flat rate. Before a potential analysis, we check the scope, data situation, and internal stakeholders. We then propose a sensible framework.
We will discuss the specific framework after the fit check. The AI Compass should be large enough to make reliable decisions and small enough not to become a major project itself.
AI CompassScoped individually
Included
- Fit check
- Process and use case analysis
- workshop or deep-dive session for concrete processes
- System and data testing at a conceptual level
- Evaluation of possible use cases
- Build or Buy Assessment
- Final document
- Recommendation for the next step
Frequently asked questions
What teams usually ask before an AI Compass.
What is an AI potential analysis?
An AI potential analysis checks which processes are suitable for AI, which data and systems are affected, which risks arise, and which next step makes sense. In the AI Compass, that can lead to orientation, a concrete process workshop, or a scope for the first pilot.
Is a digital employee already being built in the AI Compass?
No. The AI Compass evaluates use cases, clarifies concrete processes, and shows whether implementation is worthwhile. The build happens later, either in a separate sprint or internally.
Do we have to continue working with thirdmind after that?
No. The AI Compass is structured so you can use the result independently: internally, with your existing IT, with another implementation partner, or with thirdmind.
How much time do we need internally?
We usually need a kickoff, one or two process or workshop conversations, relevant example materials, and a results call. The exact effort depends on whether we start broadly with orientation or go deeper into one concrete process.
Do we need perfect data?
No. The data situation is part of the analysis. The AI Compass often shows which data is already usable, what is missing, and which use case is clean enough to start.
Is the workshop part of the AI Compass?
Yes. If a concrete process is already clear, we work on it workshop-style inside the AI Compass: task, data, systems, edge cases, rights, approvals, and MVP scope. If no use case is clear yet, we start more broadly with orientation and prioritization.
What size company is this suitable for?
The AI Compass is best suited for medium-sized companies and B2B teams with operational processes in which repetition, manual checking or information searches take up a lot of time.
Can we start implementing it straight away?
If the use case is clear enough after the AI Compass, yes. The result can also be that you should first clarify data, rights, or process responsibility internally.
Next step
The best start is a short check of your current situation.
Tell us about the workflow and systems involved. We will discuss a useful next step together.
Request AI CompassIn 90 seconds, we clarify which next step makes sense.