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Updated: May 14

Artificial intelligence (AI) brings great value to business in a number of ways. The benefits include: Increased Efficiency and Automation; Enhanced Decision Making; Improved Customer Experience and Innovation and New Products.


AI can automate repetitive tasks, freeing up time for more strategic work. This can lead to significant cost savings and improved productivity. For instance, AI-powered chatbots can handle customer service inquiries, while AI algorithms can analyse data to streamline operations.

AI is particularly effective at repetitive tasks and automating entire workflows. Repetitive tasks are prone to human error but well tested AI can deliver the same quality results minimising errors and reducing operational costs.

AI can analyse documents such as contract or loan applications, verify content against complex rules and make both rule and risk based decisions. AI can also be used to identify exceptional applications that require human attention. These steps can be used to streamline processes providing faster and more consistent responses to customers.

AI can be used to gather data in a variety of ways including soliciting information from customers in a natural series of questions. For example, a travel booking chatbot might start asking what type of holiday the customer was after before discussing possible destination, then accommodation.

AI can be used to manage inventory, production schedules or shipping routes to optimise the supply chain. This helps businesses reduce costs, minimises occurrences of being out of stock and ensures timely delivery of goods.

AI can continuously adapt to changing data with machine learning (ML). ML algorithms can learn from data and improve their performance over time. This means that AI-powered automation can continuously adapt and become more efficient as it processes more data. This might be used to adapt to changing customer behaviour or market conditions. It could be used to monitor and adapt internal processes to increase efficiency, minimise response times or maximise re-use.

AI can be applied to predictive maintenance. Using data about component age and operating metrics an AI can predict potential failure risk and schedule preventative maintenance. These scheduled maintenance slots can avoid peak operating times reducing disruption and dramatically reducing costly unexpected failures.

This approach can be applied to the automation of order processing through to customer support. AI can work 24/7 freeing up staff to focus on strategic direction and higher value work.

No AI can completely replace the expertise and experience of humans but can work collaboratively. Examples include AI assistants scheduling and summarising meetings, writing or summarising documents and emails. This increases employee efficiency and boosts productivity.

Different solutions can make the most of AI-human collaboration with human-in-the-loop and human-over-the-loop. In the former collaborations, AI will defer one or more steps in a process to a human expert. In the latter, an AI will provide the human with summarised information and the option to intervene if required.

By automating tasks and streamlining workflows, AI can significantly improve business efficiency. This translates to reduced costs, improved productivity of the workforce and the business, faster response times and better scalability.


AI can analyse large amounts of data quickly to identify patterns and trends that are not practical with other techniques. This supports businesses in making better, data driven decisions about everything from product development to marketing campaigns. AI is already in use for risk assessment and fraud detection.

Traditional data analysis often involves sifting through large datasets, which can be time-consuming and lead to overlooking crucial details. AI algorithms can examine massive amounts of structured data (e.g. sales data) and unstructured data (e.g. contracts or social media posts) faster and more comprehensively than humans.

AI can identify subtle patterns and trends in data that might escape human attention. These patterns can provide valuable insights into customer behaviour, buying habits or potential risks. For instance, AI can analyse customer purchase history to predict future buying patterns and tailor marketing campaigns accordingly.

AI can be used for predictive analytics, a technique that uses data and algorithms to forecast future outcomes. This allows businesses to make data-driven decisions about everything from inventory management to product development. For example, an e-commerce platform might use AI to predict demand for specific products during peak seasons, ensuring stock levels to meet but do not excessively exceed customer demand.

AI can be applied to financial data to identify potential risks, such as fraudulent transactions or loan defaults. This allows businesses to take proactive measures to mitigate these risks, protecting the business from losses. For example, banks might leverage AI to analyse loan applications and flag those with a high probability of default.

AI can go beyond just analysing numbers. AI can analyse customer reviews, social media sentiment and other forms of unstructured data to gain insights into customer preferences and satisfaction. This allows businesses to make better decisions about product development, marketing strategies and customer service.

All of the above can be done faster with AI providing insight to support decision making faster supporting timely decision making and business agility.


AI can personalise customer experience by providing targeted recommendations and support. AI chatbots can answer customer questions 24/7 and AI can be used to suggest products or services that customers are most likely to be interested in.

The customer’s experience defines how they perceive the business. Bad customer experience can tarnish a brand and take a long time to recover. Good customer experience can elevate a brand beyond the competition and help drive sales.

As already discussed, AI can analyse customer data, including purchase history, browsing behaviour and past interactions, to understand individual preferences. This allows businesses to tailor product recommendations, marketing messages and support interactions to each customer. For instance, an online retailer might use AI to recommend products similar to those a customer has previously purchased.

AI-powered chatbots can provide instant customer support around the clock, answering frequently asked questions, troubleshooting common issues and directing customers to helpful resources. This allows customers to get the help they need whenever they need it, improving overall satisfaction.

AI can be used to predict potential issues before they arise. For example, an AI system might identify a customer who is likely to cancel a subscription based on their recent activity. The business can then proactively reach out to the customer with retention offers.

AI can categorise customer sentiment. This allows businesses to identify areas where they are excelling and areas where they can improve. AI can also be used to create automated feedback mechanisms, including surveys or chatbots, to gather customer feedback in a timely and efficient manner.

Virtual assistants and chatbots use natural language when engaging with customers. These interactions provide better and more open ended experiences for customers in answering questions, completing tasks or providing personalised recommendations.


AI can be used to develop new products and services that would not be possible without it. For example, AI is being used to develop self-driving cars and new medical treatments. AI is already proving to be a game-changer in the world of innovation, acting as a powerful tool to generate new ideas and develop groundbreaking products.

AI excels at pattern recognition in a wide variety of data. It can sift through scientific research papers, material properties and market trends, to identify hidden connections and potential applications that are just not practical any other way. This can lead to the entirely new application of materials, more effective drug combinations or innovative product designs. AI algorithms are already being used on genomic data to identify new targets for drug development with the potential for breakthroughs in personalised medicine.

AI can be used to generate new ideas and concepts by drawing inspiration from existing data. AI applied to existing products, customer preferences or even creative text formats can yield fresh ideas for product features or entirely new inventions. For example, AI has been used to design new fashion styles or generate musical compositions with unique characteristics.

AI can speed up early product development by creating AI simulations and virtual prototypes of new products, allowing for rapid testing and iteration. This can significantly reduce the time and cost associated with traditional product development cycles. AI can then analyse the results of the simulations and tests to identify areas for improvement, helping to refine the product before it goes into production.

AI can personalise products and services to individual customer needs. This allows for the creation of highly customised products or even products that can learn and adapt to user preferences over time. AI can be used to optimise the manufacturing process for these customised products, making mass adoption more feasible.

In brief, AI provides businesses with the ability to offer their customers more capability with less effort.

If you have a Gen AI application idea and would like to see how our AI and Data Solutions team can help to grow your business, let’s talk!


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