The change implications of AI use in businesses

The blog was focused on the most well-known of these, ChatGPT. 

Since then, the pace of AI evolution hasn’t slowed; if anything, it is getting faster with every passing month, and becoming steadily more integrated into almost every aspect of our lives – including businesses.    

In essence, the adoption of AI is a litmus test for the innovative nature of businesses. Those who embrace it – and do so early – have a head start against competitors who have not done so. But it’s not enough to embrace the new shiny thing and assume that this is all that needs to be done. Proactivity is key to staying ahead of the curve and working out how not to squander that early adoption advantage.   

Here are ten things that businesses should think about when it comes to AI.  

  1. Understanding AI’s Effect on Their Industry

  • Sector-specific applications: no business operates in isolation, and benchmarking how AI is being used in a specific sector can be very enlightening. Is it being applied to automation, customer service (e.g., chatbots), data analysis, product recommendations – or are there other innovations you may not have considered? The outcome of such an analysis can give you a competitive edge; for example, streamlining operations, enhancing product/service offerings or improving customer experience. 

  1. Data Strategy

  • Data quality: the effective use of AI means businesses will need a lot of high quality data. This will require infrastructure that is up to the job when it comes to collecting, cleaning and storing that information.  
  • Data privacy and compliance: one of the main considerations when talking about a data strategy is the UK GDPR (General Data Protection Regulation). Businesses need to familiarise themselves with what the Regulation requires; collection of data, ensuring compliance with privacy laws and data protection requirements will all be covered. Ignorance is no defence.  
  • Data security: the cyber sector is constantly evolving and the value placed in AI systems means strong cybersecurity measures are needed to protect against data breaches or attacks that could compromise AI systems and exploit their vulnerabilities.  

  1. AI Skills and Talent

  • Upgrading skills: such is the speed of change in the AI sector that many employees may not have the skills needed to work with the new systems. A programme of upskilling or reskilling may need to be initiated – including training in data analysis, machine learning or AI tools specific to the industry. 
  • Hiring AI experts: if the upskilling/reskilling can’t answer all the gaps in expertise, external recruitment – on a permanent or consultancy basis – might be needed. This could cover, for example, data scientists, machine learning engineers or project managers. 

  1. AI Integration and Automation

  • Automation of repetitive tasks: Many businesses have repetitive processes which, at present, have to be carried out by employees. To improve efficiency and reduce operational costs, these could be carried out by AI; for example, process automation for finance, HR, or customer service. 
  • Customer service: chatbots and virtual assistants are becoming more common in many businesses, and these can be used to provide 24/7 customer support when employees aren’t available or act as a first line of response to help reduce workload.  

  1. Ethical AI Use

  • Transparency and fairness: One of the reported issues with the use of AI in business is that bias can creep into processes; whether this is intentional or unintentional, the end result is the same. Businesses need to make sure that the AI systems they use are fair, unbiased, and transparent. This is particularly important if the AI system is being used in decision-making, hiring or lending practices, where there could be consequences if the outcome is challenged by a third party. 
  • Ethical considerations: although, for many businesses, the use of AI seems like a matter for themselves only, it can have wider consequences on employment and society; therefore, it’s important that it is used responsibly. 

  1. Regulation and Legal Framework

  • Regulatory compliance: It’s not just the AI sector that is evolving rapidly; the government’s stance of AI regulation will also evolve depending on social and commercial concerns, as well as the degree to which the development and use of AI will affect the economy as a whole.  

https://www.gov.uk/government/publications/ai-opportunities-action-plan-government-response/ai-opportunities-action-plan-government-response 

Businesses need to ensure that they keep up to date with the current thinking on AI regulation – it will be easier to react to something if you are aware of it beforehand, particularly with regard to data use, AI safety, and accountability. 

  • Liability and accountability: there are many legal implications surrounding the use of AI, including the potential for AI errors, misuse or harm. Businesses need to ensure they have access to reliable legal expertise and guidance to ensure they remain compliant.  

  1. AI Adoption Roadmap

  • Start small: If businesses haven’t started to use AI, it’s not a good idea to go all in immediately. Piloting AI in specific areas of the business is a good idea; this will help to identify both the benefits and challenges it can present, and the strategy can be refined and developed before it’s rolled out across the business as a whole.  
  • AI maturity: AI adoption will be most effective if the organisation is ready for it. Systems and processes need to be mature and robust enough to handle AI integration. If they’re not, a process of strengthening and updating them can be initiated.  

  1. AI in Customer Experience

  • Predictive analytics: AI, thanks to its ability to crunch the numbers at a rate far greater than human input, can accurately predict customer behaviours and this can help businesses forecast demand, enhance customer service and improve sales margins. 

  1. Cost Considerations

  • Investment in AI technology: for businesses who are considering adopting AI solutions for the efficiencies that can be delivered, it’s worth bearing in mind that it will require significant upfront investment in tools, platforms, and training. This investment needs to be factored into annual and longer term budgeting and the return on investment figured in.  
  • Scalability: it’s important to scale the AI solution to business growth so it can evolve naturally with the company.  

  1. Building AI Partnerships

  • Collaboration with AI providers: Few businesses have the technical expertise to go it alone; therefore, strategic partnerships with AI solution providers, consultants or cloud providers may help to create and deploy the AI technology and systems that will best suit their needs.  
  • Research and development: As well as AI solution providers, universities, research institutions or innovation hubs can also be excellent sources of insight and support, helping businesses to keep up to date with the latest developments in AI technology advancements. 

If you have any questions or need advice on setting up a company policy for the use of AI in your workplace, please contact our Cyber, Data and Information Law team.