Product layout in stores can be optimised to improve sales yield, customer satisfaction and operational effectiveness.

Predict product demand trends to inform production decisions. One challenge is to combine internal data sources with 3rd party (e.g. distributor) and external data sets (e.g. social media) to build a complete picture for data analysis.

Analyse vending machine demands and product placement opportunity for optimal product mix. This can be a key channel in specific markets and segments.

Provide clothing and beauty product proposition online based on subscriber preferences. Typically consumer fills in a questionnaire and responds to photo prompts - this is combined with other customer data, including sales, to be analysed to prompt customer product mix proposition. The customers acceptance or rejection of these reinforces the learning loop. Often there remains a human in the loop - especially initially.

Inventory Management - helping company calculate how many units are required where and when. This is precisely the sort of complex, data-rich, prediction-heavy closed loop where AI can potentially excel if the data tracking is accurate - so long as the unexpected does not intervene.

Deliver products to consumers on the move. One option is to stock a variety of products in a self-driving store - demand for the products and the highest relevant footfall location being predicted from multiple data sources. This may be the future of ice cream vans... but it is currently the combination of a lot of AI technologies so realisation may be some way off.

Optimise in-store product assortment to maximise sales. Store assortment choices may vary by location, customer demographics and seasonality for example.

Analysing the basket of products that consumers purchase - typically in one go but also on a broader timeline - provides ample data to enable predictions on potential cross-selling and repeat purchases.

Potential optimal store locations can be modelled based on data fields such as footfall, customer demographics and consumption patterns, competitor siting and so on.

Classify visual information from in-store video to collect data on product placement, consumer response and activity and other key data points for use in market research

Enhanced customer's retail experience and service through chatbots able to provide access to wide ranges of knowledge at scale.

Leveraging machine vision to enable customers to search products by image or video to immediately access the information they need.

Predict cannibalisation risk to support overall market proposition - thus ensuring that product management is better positioned to optimise yield and revenue capture.

Self-checkout systems, also known as cashierless, cashier-free or automated checkouts, have increasing functionality built in to them - some of it increasingly powered by AI.

To ensure legal compliance with variosu age restrictions technology can abe used to analyse facial features and determine or predict a person's age. This can help retailers automate the selling of age-restricted items, such as alcohol or tobacco.

Use video, images or robots in the retail area to audit and analyse shelf space usage. Optimising shelf space drives retail sales, can reduce risk of running out of stock and it can also be use to prioritise shrinkage risk areas.

Monitoring and predicting shrinkage enables retailers and consumer product companies to crack down on potential value loss.

Support multi-lingual service provision - whether to foreign visitors or minority communities - through conversational agents / chatbots. This may be deployed through a tool like Alexa in a hotel room or built in to public sector offices or online channels.

Inventory management / dynamic pricing to maximise capacity fill rates and return. In a (seasonally) high fixed capacity, time-sensitive business optimising this is a key driver of profitability.

An automated assistant can make a reservation for a user - initially this has been rolled out for restaurants. Natural sounding voice interfaces helps smooth the process - but does raise potential concerns about human / robot interactions. The multi-lingual usage opportunity - for example in the tourism industry - will be considerable.

Table management and reservations is a key function for hospitality providers - often accessed through multiple distribution and booking engines. Ensuring accurate and timely information is key to optimising customer throughput and satisfaction.

The roll out of peer to peer transport apps creates potential security risks, particularly in markets where personal checks on users and drivers are less developed. Automated systems will highlight beahvioural anomalies that may indicate a developing security or health situation and trigger both back-up enquiries and potentially further action.

Promos/upgrades/offers are a key part of the travel industry's customer relationship (and capacity utilisation) approach. Optimising these with ML reduces risk of mis-allocated (or mis-aligned) incentives.

Typically using video or camera technology to spot individuals whose activity is at risk of causing accidents or other problems. Relevant images will be flagged for potential further intervention.

Workforce management (also applies to lots of verticals) - hospitality industry workforce is frequently characterised by high turnover, casual working and seasonal demand. This complicates the challenge of maintaining performance.

Using personalised taste and health feedback machine learning can help recommend (and, in the future, produce) personalised dishes. More broadly it can help recommend a personal diet plan with further inputs, e.g. seasonal or regional foodstuff availability. Tying this to shopping, food distribution or travel recommendation channels helps close the supply loop.