Debunking the AI myth: four real-world use cases for AI in the enterprise - The EE

Debunking the AI myth: four real-world use cases for AI in the enterprise

Anita Weppenaar of SoftwareONE

A report released over the summer shows the number of AI companies in the UK has surged 600% in the last decade, from 180 in 2011 to more than 1,300 today. The rise in AI adoption has resulted as a way for companies to keep up with markets and competitors one of the main challenges for modern businesses, says Anita Weppenaar, SoftwareONE cloud services practice lead for UK, IE and South Africa

In Gartner’s 2021 CIO survey, 69% of boards are accelerating digital business initiatives to improve business operations and get new, high quality products and services to customers faster than ever before.

As many look at adopting AI for assistance in achieving these goals European businesses’ spending on the technology is set to reach $12bn (€10.23bn) this year. However, for many companies, AI can be seen as a difficult endeavour or even a pipedream, especially for organisations still wrestling with upgrading legacy IT. Knowing how and where to apply AI within the business can be another huge barrier to adoption, as many companies simply don’t have data science skills in-house.

AI can also seem very daunting for businesses due to the hype surrounding it. Much of the conversation revolves around robots, super computers and driverless cars, but businesses must remember to think about AI in real-terms, and consider its practical applications.

This can include implementing AI on a smaller-scale to improve internal business processes. Adopting AI to improve just one process or workflow can enhance data analysis, optimise business decision-making, and achieve greater outcomes at a quicker pace. To help debunk the myths and understand its use in the enterprise, here are four real-world use cases for AI:

  • Better customer experience – AI can be used to analyse customer activity and trends, see where pain points are, and identify any additional products they may be interested in. For example, if users are regularly engaging with certain features, or experiencing performance issues on specific web pages, AI can immediately identify the problem and provide the business with suggestions to auto-resolve it. Similarly, user patterns can be analysed to predict future growth driving opportunities, by providing businesses with recommendations on new services customers may like to see.
  • Improve business efficiency – AI can also be used to remove toil and manual work from business processes. Over 40% of workers spend at least a quarter of their week on repetitive tasks, with email, data collection, and data entry occupying the most time. AI can perform those operations in real-time, as well as learn from previous patterns to suggest how business processes can be optimised. This can result in huge time savings for employees, who are then able to refocus their efforts on tasks that drive additional value for the business, such as app development or customer service.
  • Increase data security – AI can monitor user activity and learn to detect potential security threats, both internally and externally. For instance, an AI-based security solution would know when employees log into a cloud solution, which device they used, and from which location they accessed the cloud data. If one night, a user logs into their account at 3am from another country, the AI would flag this activity as suspicious and can alert the organisation’s security team.
  • Identify new business opportunities – AI can analyse market, customer, and company data to find patterns that can lead to new opportunities and revenue streams. This could include using AI to automatically assess the validity of form-fills to identify quality leads. An official company email will be immediately sorted as a high-quality lead, while a “” email address will be sorted as low quality, meaning organisations spend less time cleaning datasets, and more time capitalising on hot leads.

The most important thing when it comes to adopting AI is creating a clear roadmap, identifying which processes could most benefit from its implementation, what value this can bring, and how it can realistically be achieved. Whether AI strategies are devised and carried out by an in-house team or a technology partner, it’s crucial to understand your organisation’s needs and specific use case for AI.

This can help inform the decision to either custom-build AI for a specific process, or to adopt a tool from one of the major tech vendors think Azure or AWS that has AI built-in, so you can transfer the benefits over to your own business.

The author is Anita Weppenaar, SoftwareONE cloud services practice lead for UK, IE and South Africa

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