Corporate Management

Companies, how to get off on the right foot with artificial intelligence

Ai applications are now at the centre of the expectations and concerns of all business function managers

17 May 2024

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The topic of Artificial Intelligence (AI) is the hype of the moment. Since it has been dominating the media for many months, it is not surprising that it is at the centre of the expectations and concerns of all business function managers. Its potential for positive impact is enormous, but so are the challenges and risks it poses.

On the one hand, AI already permeates many aspects of modern life. Just think of the role of the virtual assistant, with Siri and Alexa. Or the autonomous driving cars. Or the generation of films with virtual actors.

On the other hand, alongside its promise, AI raises ethical and social concerns: from fears of job losses and the need for retraining programmes to issues of privacy and data security. Moreover, the idea that generative AI (GenAI) surpasses human intelligence raises questions of consciousness and autonomy for humanity itself.

Basically, AI refers to machines with cognitive functions such as learning and problem-solving, typically associated with human intelligence. This includes various techniques, including machine learning, natural language processing, computer vision and robotics. The roots of AI can be traced back to the 1950s, when pioneers such as Alan Turing laid the theoretical foundation for artificial intelligence. Early artificial intelligence systems were rudimentary, limited by computing power and lack of data. However, as technology advanced, so did AI. The advent of neural networks and deep learning algorithms revolutionised the field, allowing machines to process large amounts of data and learn from it.

By embracing AI with a clear understanding of its capabilities, limitations and ethical considerations, its innovative and transformative power can be harnessed to create a more prosperous future for all.

What is clear is that everyone understands its importance and potential. The issue leaders continue to grapple with, however, is identifying and prioritising use cases for technology, particularly when budgets are tight. All surveys converge on how pervasive this issue is and how uncertainty reigns as to where to start.

A relatively quick and easy way to start is to identify a business problem and ask the question of how artificial intelligence can help solve it better or faster. In this way, a number of potential use cases can be generated to generate interest and buy-in within the organisation for what the technology can offer.

Another crucial point is data management, which is frequently mentioned as the main obstacle to the implementation of artificial intelligence. As tempting as it is to think of artificial intelligence as the answer to all technological problems, it is clear that the challenge lies in the field of data availability, reliability and management: technology has always needed data to function effectively. In terms of investment, at least three quarters should be allocated to infrastructure and data management projects.

Without claiming to be exhaustive, here are some use cases of AI in different business functions:

Marketing and Sales

AI is used in the analysis of customer data to identify market segments and targets and in the development of intelligent chatbots to interact with consumers. Machine learning algorithms are then able to predict customer preferences and customise offers. KLM uses a virtual assistant on Facebook Messenger to provide flight information, make reservations and provide assistance during travel. Amazon leverages machine learning algorithms to personalise users' shopping suggestions and improve their online shopping experience. Netflix, through AI algorithms suggests films and TV series based on users' tastes and preferences. Similarly, Spotify creates customised playlists based on musical tastes.

Research and Development

AI is being used to accelerate the discovery of new drugs and chemicals. For example, the pharmaceutical company Pfizer uses AI to analyse large amounts of drug data and identify potential candidates for new treatments, thus reducing the time and cost required to bring a new drug to market.

Production and Logistics

Among the AI applications developed is the supply chain planning and optimisation of production processes as well as advanced factory automation. DHL uses AI algorithms to optimise parcel delivery routes and improve the efficiency of the logistics process. Tesla uses intelligent robots to automate assembly tasks in electric vehicle production lines. Siemens introduced automatic monitoring and control of product quality through machine vision techniques. Unilever uses robots and AI algorithms to automate the product packaging process, enabling higher speed and accuracy than manual work. IBM Watson worked with Maersk to develop a system that can monitor the flow of goods in real time, manage financial transactions and identify potential problems in supply chains.

Procurement

AI can automate the entire procurement process, from requisitioning to pay and supplier performance analysis. In addition, AI can analyse large amounts of data from different sources, such as market data, industry databases, historical procurement data and customer feedback for the search and selection of new suppliers, insights into market trends, pricing and contract terms. Leading e-procurement platforms such as Coupa, IBM Watson, Ivalua, Jaggaer, SAP Ariba, among others, are equipping themselves with AI engines that analyse many data sources, including contracts, market trends, historical spend data and automate the data loading and billing process.

Finance

Artificial intelligence algorithms such as Ant Financial are used in lending for credit scoring and risk analysis. BlackRock uses machine learning algorithms to analyse and predict financial market movements and make investment decisions.

Human Resources Management

Through AI, IBM can screen CVs and automatically select candidates based on the key competencies required. Unilever uses intelligent chatbots to guide candidates through the selection process and handle frequently asked questions about job opportunities and company benefits. H&M relies on AI to analyse employee performance data and provide personalised feedback for continuous improvement.

These are just a few examples of how AI can be used in different business functions. The potential of AI is constantly evolving, always offering new opportunities.

Navigating this technological landscape, however, involves entering a relatively unfamiliar and recently experienced context. It may be confusing, but its promise of alleviating the mundane and raising the strategic level is an attractive proposition.

This can only be realised after the first step has been taken. There are no established models, nor experiences that can be immediately reapplied to different areas. Hence, it is appropriate to proceed with tailor-made implementations on a specific business need, through applications designed as Proof of Concept around delimited and defined use cases, with relatively low investment.

*Senior Executive Advisor - NTTData