Quick introduction: the right scope for a useful pilot
Deciding which features to launch first when deploying an AI avatar on a screen or kiosk in a store requires balancing business impact, technical feasibility and operational constraints. Without a framework, teams risk opening too many workstreams at once or, conversely, starting with overly ambitious use cases that delay go‑live.
This article provides a practical, actionable framework to choose and prioritise use cases: a simple impact vs complexity matrix, a ready‑to‑use qualitative scoring grid, sector examples (retail, tourism, hospitality, public services) with quick‑wins and more strategic cases, and a precise list of technical and organisational dependencies to check before running a pilot.
The decision matrix: commercial impact versus operational complexity
The matrix compares the expected commercial impact of a use case with the complexity required to implement it. It helps decision‑making by placing each use case in one of four quadrants:
Prioritise first the use cases with high impact and low complexity (quick‑wins). For high‑impact but high‑complexity cases, plan a roadmap and allocate dedicated resources. Low‑impact and low‑complexity cases can be implemented if spare resources exist or used as experimentation ground. Low‑impact and high‑complexity cases should be delayed or archived.
The value of this matrix is to guide trade‑offs without requiring perfect initial metrics: a documented qualitative assessment is often sufficient to select the first tests.
Ready‑to‑use qualitative scoring grid
To move from the matrix to a concrete selection, use a simple grid of criteria that can be assessed qualitatively. For each use case, rate the elements below using levels such as high / medium / low. Document your assumptions.
Reading these criteria together will allow you to justify a choice to management and to prioritise reproducibly.
Commercial impact: contribution to customer experience, visibility of offers, lead generation or facilitation of purchase.
Technical feasibility: need for real‑time integrations (POS, inventory, booking), availability of a structured knowledge base, complexity of RAG or function calls.
Operational constraints: need for specific operating procedures, staff training, content update processes and quality control.
Content maturity: existence and quality of product sheets, FAQs, opening hours, procedures; ease of multilingual updates.
Risks and compliance: sensitive information in content, confidentiality constraints, risks related to provided advice (avoid topics with high liability).
Expected user experience: voice vs touch needs, multilingual scenarios, accessibility.
How to apply the scoring in practice
Assign each criterion a qualitative level (high / medium / low) and note key observations. A use case assessed mostly 'high' for impact and 'low' or 'medium' for technical feasibility naturally positions itself as a priority.
For cases with high impact but low feasibility, document precisely the dependencies that make implementation difficult (e.g. unavailable APIs, missing content). These cases belong to a technical roadmap. For low‑impact, high‑complexity cases, consider pausing until the context improves.
Quick‑wins and maturity by sector: retail, tourism, hospitality and public services
Priorities vary by sector. Below is an operational ranking of frequent use cases and their relative maturity. Use these indications as a starting point: adapt them to local specifics and business objectives.
Retail - Quick‑wins: in‑store wayfinding, aisle FAQs and opening hours, basic product search. Dependencies: up‑to‑date product knowledge base, screen or kiosk placement plan. These use cases can work without real‑time integration and rely on static or semi‑dynamic content.
Retail - Strategic cases: in‑aisle assistance (deep product guidance), personalised promotions tied to the basket. Dependencies: POS/inventory integrations and marketing workflows (specific integration required).
Tourism - Quick‑wins: opening hours, access information, simple routes and multilingual answers. Dependencies: content validated by the tourist office, multilingual configuration.
Tourism - Strategic cases: ticketing or guided booking via the avatar. Dependencies: connection to booking systems (requires specific integration and careful UX choices).
Hospitality - Quick‑wins: information on services, breakfast hours, practical instructions and directions to facilities. Dependencies: validated internal content, adapted voice and language.
Hospitality - Strategic cases: assisted check‑in, service request management via the avatar. Dependencies: direct integration with the PMS or management system (specific integration). Pay attention to data governance for personal information if sensitive exchanges are handled (verify privacy aspects according to the chosen configuration).
Technical and organisational dependencies to verify before launching a pilot
Before final selection, validate a precise set of dependencies. Some relate to avatar configuration, others require specific integrations. Document and prioritise these checks to avoid blockers during the pilot.
Availability and quality of business content: product sheets, FAQs, opening hours, procedures. Clear, regularly updated content eases deployment.
Connectivity and integrations: identify which data must be consulted in real time and whether APIs or webhooks are necessary (e.g. inventory, POS, booking) – these elements require specific integration.
Avatar platform and configuration: define the persona, languages to activate (SANIA can communicate in more than 100 languages) and interaction mode (touch and/or voice depending on configuration).
Hardware and environment: check screen/kiosk placement, audio quality for voice use, network coverage and 24/7 maintenance.
Operations and runbook: define who updates content, who checks sensitive responses and how to handle operational incidents.
Governance and compliance: who validates content, what information must not be exposed, and how to handle sensitive requests (plan escalation to staff when needed).
Operational readiness checklist before starting
A clear checklist prevents launching a pilot with unresolved dependencies. Tick these items or document an action plan for those still open.
Business objectives defined and use cases prioritised with qualitative justification.
Required content identified, validated by business teams and organised for ingestion into the knowledge base.
List of required integrations established and technical contacts identified for potential APIs.
Choice of interaction mode (touch, voice or hybrid) and hardware validation according to the hardware guide.
24/7 operations plan, runbooks and the person(s) responsible for content maintenance.
Multilingual UAT plan and validation protocols before production launch.
Common mistakes and watch points
Several recurring mistakes appear when deploying an AI avatar in a public space. Anticipating them reduces risks and speeds up achieving meaningful results.
First mistake: trying to do too much from the start. An overly broad scope complicates implementation and dilutes value. Second mistake: neglecting content quality. The avatar depends on a clear, regularly updated knowledge base. Third mistake: overlooking multilingual UX and voice/human scenarios; testing in real conditions is essential.
Other watch points: plan editorial maintenance, define clear escalation processes (without assuming automatic handover), protect sensitive information and run tests in noisy environments if voice interaction is used.
Recommended method to launch an operational pilot
An iterative, documented approach limits risk and produces actionable lessons for later industrialisation. Adopt a narrow scope composed of one or two priority use cases as quick‑wins, then expand gradually based on findings.
Clearly define pilot objectives and expected outcomes in terms of user experience and operations.
Select priority use cases using the matrix and the qualitative scoring grid.
Prepare and validate business content in necessary languages and configure the avatar persona.
Verify technical dependencies and set up essential integrations if required.
Conduct user tests in real conditions (touch and voice) and adjust UX and content before public launch.
Document incidents, feedback and improvement plans for industrialisation.
Conclusion and practical next steps
Choosing the right use cases for an in‑store AI avatar relies on a pragmatic trade‑off between commercial impact and operational complexity. A simple matrix, a qualitative scoring grid and a readiness checklist enable rapid progress from diagnosis to a useful pilot.
SANIA can form the basis of a conversational solution on screen or kiosk, available 24/7 and communicating in more than 100 languages. It relies on an organisation‑specific knowledge base and can be configured for touch and/or voice interaction depending on the project. To assess which use cases to prioritise in your context and consider a pilot, request a demonstration tailored to your sector.