Ideation workshops quickly produce dozens of use cases: summarizing documents, assisting support, generating offers, searching knowledge, or automating checks. The difficulty is not in finding ideas. It is in selecting those that deserve investment and can be adopted.
A serious prioritization avoids two biases: choosing the most spectacular project and choosing only the easiest one. The portfolio must balance quick wins, common foundations, and strategic bets, while excluding uses whose risk or lack of data makes their value unlikely.
Start from business problems
A use case should be formulated as an improvement of a task, not as 'using an LLM.' For example: reduce the time to qualify a request while keeping the error rate below a threshold, or help a technician find a procedure with its version.
The initial form specifies:
- user;- trigger;- current task;- volume;- irritants;- expected result;- concerned decision or action;- available data;- proof of success.
This formulation sometimes makes it possible to observe that a rule, a standard search, or an interface improvement is sufficient.
Distinguish four levels of assistance
Information
The system retrieves, organizes, or summarizes. The user decides and acts.
Recommendation
The system offers an option or a diagnosis. The decision remains human and the evidence is visible.
Draft
The system prepares content or a reversible operation that the user modifies and validates.
Action
The system triggers an operation. Rights, approvals, idempotence, and audit become critical.
The level of autonomy influences the risk and cost of validation.
Evaluate on six dimensions
1. Value
Measure frequency, time, cost, revenue, quality, satisfaction, or avoided risk. The gain must be related to an observable behavior.
2. Data
Do the sources exist, are they accessible, up to date, structured, authorized, and representative? Can the rights be reproduced?
3. Technical feasibility
Is the task testable? Is the required level of quality achievable? Are the integrations and latency compatible?
4. Adoption
Can the process change? Do users have an interest in validating and correcting? Does the tool integrate into their environment?
5. Risk
Data, decision, security, regulation, reputation, and financial impact are evaluated separately. A high risk should not be drowned in an average.
6. Economy
Discovery, integration, modeling, operation, human validation, and maintenance costs are compared to the expected gain.
Funnel for selecting AI use cases from the business problem to industrialization.
Calculate an ROI without false precision
A simple calculation for a time saving can start from:
volume × temps économisé × coût chargé × taux d’adoption × taux de réussite
It is necessary to remove the validation time, errors, the cost of models, and operations. The assumptions are presented in ranges and tested with a cautious, central, and ambitious scenario.
For quality, income, or avoided risk, define proxies: reopening rate, conversion, delay, error, compliance, or satisfaction. When the value cannot be monetized, it remains an explicit criterion instead of being invented.
Favor the waiting period until the proof
A good first project allows testing a hypothesis in a few weeks with a representative set. The prototype should measure quality and behavior, not just display an interface.
A case requiring six integrations, a data overhaul, and a regulatory change before the first learning must be broken down. A research or draft step can precede the autonomous action.
Identify the common foundations
Several cases may depend on the same abilities:
- identity and rights;- data catalog;- documentary ingestion;- model platform;- evaluation;- observability;- governance;- business connectors.
The portfolio must finance these foundations when they serve multiple projects, without creating an abstract platform before any use.
Build waves
Wave 1: low-risk learning
Assisted research, synthesis with citations, classification or draft. Users correct and errors are observable.
Wave 2: integration into the process
AI is integrated into the business tool, uses the rights, and produces reversible actions. Adoption and operational cost are measured.
Wave 3: controlled automation
Certain stable actions are carried out with appropriate limits and approvals. The audit and compensation mechanisms are proven.
Wave 4: transformation
The process has been redesigned around AI capabilities, with mature governance and a platform.
Define an experimentation protocol
Each pilot has:
- hypothesis;- population ;- dataset;- current reference;- metrics;- stop thresholds;- duration;- responsible;- safety plan;- decision at the end.
The pilot is not free. It must produce a reusable asset: evaluation set, pipeline, policy, or understanding of the process.
Measure actual adoption
The number of openings is not enough. Follow:
- share of eligible tasks using the tool;- rate of accepted suggestions;- degree of modification;- total time, including validation;- errors detected;- bypasses;- satisfaction and trust;- reasons for non-use.
Low adoption can come from poor integration, a lack of trust, or a little useful use case.
Integrate the human cost
An answer sometimes needs to be reviewed by an expert. The cost of this validation and the associated fatigue can cancel out the gain. The interface must focus attention on uncertain points, display sources, and make corrections quick.
Human feedback fuels evaluation and improvement, but it must not be collected without purpose or protection.
Manage risks from the selection stage
A high-risk case may be relevant, but it requires more evidence, control, and skills. The assessment must include rights, bias, safety, decisions, transparency, and applicable obligations.
Some uses are excluded or limited. Technical feasibility is not a sufficient justification.
Choose build, buy, or combination
A market solution can accelerate generic uses. Custom development is justified when data, integrations, or differentiations are central. A combination can use existing models and services with its own orchestration.
This decision comes after the description of the case, not before.
Manage a portfolio
A regular review updates value, risk, cost, adoption, and dependencies. Projects that do not meet their criteria are stopped or reformulated. Lessons are shared and reusable components cataloged.
The portfolio prevents each team from creating its own assistant, policy, and provider without consistency.
Start with a useful proof
The best roadmap links problems to measurable experiences. It is willing to abandon an idea and invest in the foundations that will accelerate the next ones.
Partitech can lead the workshops, build the matrix, prototype the priority cases, and industrialize those that demonstrate their value. The goal is a results-driven portfolio, not an accumulation of demonstrations.
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