AI Use Cases and Case Studies
by Business Function

Overall corporate management will have to become more AI savvy - especially on the mix of human and agent organisational structuring. Immediate applications will support better resources allocation and project tracking whilst reducing paperwork but judgement will remain a distinguishing feature for success in this function.

Already a high profile usage area for AI thanks to the widespread deployment of chatbots or conversational agents to assist in customer service interactions. Whilst the headline potential for reducing staffing requirements in call centres is striking issues remain with actual conversational performance and practical delivery of operational cost reductions. Deployment for specific use cases that require limited flexibility, transparency on AI usage and improved NLP will make them a critical part of most organisations’ customer service toolkit.

The key enabler of AI in an organisation. Increasingly able to use AI to - for example - build better quality data sets. Data engineering, science and management will be a growth area for most organisations.

Automation of key financial processes will be a potentially significant reducer of “red tape” both within the function but also for those that interact with them. For example, part-automation of the expenses process will have a positive impact on everyone involved. Increasingly Finance staff members will find that judgement is the critical success factor for interacting with colleagues as the collation and audit of financial data is streamlined and potentially made more accessible to other functions.

Human resources is seeing significant experimentation in new applications of AI. These range from automation of key internal processes to AI-driven interviewing supposedly better able to eliminate unconscious bias. Key technological improvements in speech and facial recognition and NLP are driving much of this - but an awareness of the growing legal risks of data protection and algorithmic bias needs to sit behind all these deployments.

This function has a huge role to play as organisations deploy AI - both from a technological delivery but also a data access perspective, Shiny new AI tools that cannot integrate with legacy data systems are likely to be a clear challenge for organisations seeking to capture real business benefits from their investment, and a potential driver of concerns about over-hype in the industry.

Automated document processing and production has the potential to open up lower cost access to these services, including for SMEs. This may help alleviate some of the challenges from rising legal and compliance expectations in most industries.

Artificial intelligence offers the potential to shave further productivity out of existing manufacturing processes. The potential impact on employment from the increased deployment of robots is a concern but not necessarily a new one by historic standards. The opportunity for businesses to use AI and related technologies such as Iot and 3D printing to re-examine their manufacturing footprint is likely to be a critical task in the years ahead.

Marketing is already one of the areas most impacted by AI - indeed it is the function with the most current use cases. The rise of digital marketing across data platforms such as Google and Facebook has long depended on algorithms to process, the application of machine learning processes to this has been inevitable. Hence marketing companies have become the biggest investors in AI talent - reaping big rewards for often small shifts in performance captured. AI use cases now exist in multiple fields - including traditionally offline ones like market research and product promotion. Meanwhile chatbots are fast becoming the new face of companies online - alongside the websites or apps that traditionally dominated.

Complex operations requiring coordination of multiple elements and data streams - including from the growing array of sensors in the Internet of Things - will increasingly use AI to optimise results. Reducing costs and improving speed and service will always matter - and AI offers the potential to reduce the traditional trade-offs between speed, focus and scale that business models typically face.

AI will both power new products - drones, consumer apps and devices, transport vehicles - but also empower other technologies - virtual and augmented reality data processing, Internet of Things sensor scaling - but also change the research and development process itself - new ways of analysing drug compounds or sorting through research literature. R&D teams will need to pick and choose the projects but there is the potential for a major boost to productivity.

Sorting through the relevant data and spotting issues is a task that AI can potentially excel in. the combination of flexibility, speed and scalability at manageable cost lies behind growing implementation in areas such as credit scoring or transaction checking. However, for the function a new set of risks and concerns are emerging around transparency and inherent bias potential in ‘black box’ algorithms.

Sales management can see that high volume sales channels, traditionally call centres or online, may start to migrate to new AI-powered tools like text chatbots or conversational agents. These offer the potential of delivering sales at lower cost and potentially higher reliability; but that the tools resistance to be being “broken” will depend on further NLP development. The data to watch will be on conversion rates and ticket size.

Despite lurid headlines predicting the rise of General AI, strategy’s focus on judgement will means that AI is a long way from transforming the role, although key elements such as research and data preparation will increasingly use AI-powered tools.The bigger challenge for the strategy team is thinking through what AI will do for (and to) the business - and the competitive space - that they operate in. Key questions on focus, potential for achieving scale and organising for innovation and transformation will all be on the table. 

The growing availability of data, driven by sensors and market feedback loops, will require increasingly sophisticated data management tools to meet rising consumer and distributor expectations, hence AI. In a world where Amazon increasingly looks like the model to follow, and where traditional distribution is suffering firms will need to invest in robotics, automation and new transport options to stay at the top of the game. AI will potentially also deliver the next steps in the ongoing dance of procurement - especially when it comes to inventory management.