AI Use Cases 

Monitor employees engagement with chatbot for better productivity measurement. This may be a role better carried out by managers but this can provide additional oversight and insight.

Recommends specific training based on performance review data

When job seeking there is difficulty in reflecting on and improving a CV after having worked on it for a lot of time: candidates can use technology to do that for them before they apply for jobs. The technology will have been trained on successful CVs and resumes to identify how the content has been articulated. Therefore, such a system can compare the input with its database and identify areas for improvement. Increasingly this is effectively about reverse engineering recruitment algorithms.

Identify the characteristics and behaviours of the most successful and effective employees. This is an area of potential algorithmic bias risk.

Predict migration volume based on different data sources and indicators. This can aid country policymakers and NGOs to prepare for changing migration patterns.

Predict potential issues in the audit process by assessing and cross-correlating financial data, seeking out potential discrepancies and highlighting potential risks. Typically this would act as an initial triage approach allowing expensive audit personnel to focus on key areas and higher value tasks.

Automate reconciliation of financial statements of related legal entities - typically used for sophisticated and complex businesses operating across multiple legal authorities. This matters for providing an audit trail.

Automating the creation of financial records - for example where there are multiple legal entities where data needs to be populated - or automatically aggregating such data sets is an area where automation offers the potential to be a significant time saver.

Determine risks in currency movements and / or risks in cash flow to help manage risk situation - including understanding reserves required from a prudent management (or regulatory) perspective.

Determine optimal moment to bill accounts for missing payments - using historic and comparable data to optimise chances of maintaining paying relationship with a subscriber or other repeat customer whose payment has failed.

Predicting the optimal approach to paying suppliers given the characteristics of the counter-party and the contract.

Determine the risk of a customer not being able to keep to repayment schedule.

Using a combination of known future cash movements with machine learning analysis of historic (and comparative) cash flow events to predict future cash positions of an organisation. This is preferably only fully relied upon in conjunction with experienced finance professionals to ensure its likely accuracy.

Global distribution and connectivity relieson increasingly accurate mapping to optimise supply chains, distribution and access. Using satellite and other remotely captured images it is increasingly possible to build accurate maps at scale and provide addressing schemes in underserved areas.

Match expectations from both sides of a 2-sided online market. Typically this goes beyond simple price matching and includes a variety of other variables that may have different levels of weighting to market participants.

Tailor debt collection processes by identifying which practices are most effective for different segments of customers. This is a sensitive process with occasional risks.

Predict risk of churn for individual customers or clients and recommend intervention strategy - this may be involuntary (ie due to bankruptcy) or voluntary (ie switching accounts) churn

Call routing (i.e. determining wait times) based on caller id history, time of day, call volumes, products owned, churn risk, LTV, etc. Route calls to most capable agent available and ideally leading to fewer agent-handled calls - hopefully leading to increased customer satisfaction and reduced handling costs.

Use AI to provide real time translation services. This has both B2C and B2B applications. Current depth of access on traditional language pairs - e.g. English: Spanish - are being extended to other languages, although many cross-translations pass through English.

Improve IVR effectiveness through deploying voice to text and natural language processing (NLP) to better capture and enable response to customer queries. Understanding customer pre and in-call intent helps reduce time to serve and potential customer problems. This leverages Natural Language Processing (NLP) and machine learning to estimate and manage customer's intent on calls. In-call assessment enables multiple functionalities: e.g. call routing, issue triage, automated responses.

Automate customer service voice conversations through a conversational agent chatbot enabling high volume, fast reaction customer support. Unexpected questions will likely 'break' the chatbot system so consumers need to be clear that they are interacting with a machine.

Monitor overall customer contacts to identify major trends in their questions, concerns, etc.

Leverage Natural Language Processing and machine vision to build approach to automatic responses to customer requests that come through social media channels.

Advanced analytics on call data to uncover insights to improve customer satisfaction and increase effectiveness

Automate customer service text conversations through a chatbot enabling high volume, fast reaction customer support. Unexpected questions will likely 'break' the chatbot system so consumers need to be clear that they are interacting with a machine.

Understand customer sentiment and customer value to prioritise inbound emails for response and trigger potential replies.

Bots will listen in on agents' calls suggesting best practice answers to improve customer satisfaction. Putting the right data on the operator's screen to ensure they are prepared with the context of the call to speed resolution and maximise likelihood of customer satisfaction.

Ensure that high-volume, low value-add end-user issues are handled with the support of AI for voice recognition / security purposes.

Accelerate identity verification (e.g. photos, biometric reading) for new and existing customers. This enables offering of enhanced services and greater security.

Analyse how customers are interacting with a deployed chatbot - typically this will generate performance metrics but also customer, service and business insights.