12 top tips for using AI in retail and consumer businesses

Last year, we set out our top ten tips for retailers entering the metaverse. This year, AI is the hot topic in retail and pretty much everywhere else! AI is redefining the retail and consumer industry. It can improve consume engagement, aid decision-making, curate tailored promotions, improve efficiencies, and reduce costs. So what do retailers and consumer brands need to be mindful of when deploying AI?

Build a team of experts

AI is only a tool – your business needs talented individuals to develop use and ensure AI systems function as intended, do not produce discriminatory outcomes, and achieve objectives. Any individual or team coding or training AI should be aware of biases that could be inadvertently introduced. It also helps to have external experts to advise on all aspects of AI and its use within your business.

Beware the black box

The inability to see how deep learning systems make their decisions is known as the “black box" problem. This opacity in decision making is problematic in several ways, including causing difficulties in diagnosing and fixing issues and its potential to reflect or amplify societal or dataset biases without the retailer deploying the AI knowing.  Businesses must be aware of how its AI systems work and how AI-assisted decisions are made; it's crucial to demonstrate to customers and regulators that AI is used responsibly and appropriately. 

Consider the data

An AI solution is only as good as the data that trains it. Larger, high-quality data sets produce more accurate results. If you are licensing AI, question the quality and types of data the model was trained on. If you are training AI, consider if you can acquire additional data to refine the model or use synthetic or anonymised data. Ensure that you are clear on data ownership ingested by AI – is it within a ‘walled garden’ or shared with the provider’s other users? 

Set up guardrails   

Implement policies to ensure the business uses AI appropriately (including free-to-use AI). Policies should address data management, roles and responsibilities, and any human intervention, amongst others. Customer-facing teams should be trained on procedures to deal with concerns from customers arising out of AI systems e.g. AI hallucinations or unexpected results.

Define your use case

Identify specific processes which are prime for AI investment and development. Bear in mind that the use of AI in high-stakes environments must be robust, fair and transparent. A clear strategy will reduce the risk of wasted costs and time and avoid reputational damage or other harm. Current areas of focus for retailers and consumer brands engaging with AI are marketing and customer engagement, logistics and supply chain.  

Prioritise data privacy

AI systems ingesting personal data should ensure privacy by design and default. Bake in data protection principles from the design phase and throughout the AI lifecycle. A data protection impact assessment will be required for most AI systems using personal data. Where human intervention is required (eg, to oversee automated decision-making), the AI interface should be designed to support this.

Establish trust with your customers

Build customer trust in any customer-facing AI by ensuring that AI systems and decisions are explainable and transparent. Be clear in your customer communications about the purpose of the AI, how it works, and the potential implications for personal data. Be aware that customer perceptions of AI vary considerably, and different demographics will respond to new AI products differently.     

Protect your contractual position

Ensure you have robust contracts with any AI provider that include provisions regarding confidentiality, IP ownership, and liability allocation. A strong contract governance framework will also help to identify risks early on and prevent them from becoming a greater issue. Also, future-proof contracts to allow amendments in response to developments in AI regulation. 

Be flexible

Track the progress of each project and analyse the metrics of any AI solution. Document what worked well and what could be improved. Prepare to pivot if it appears that better efficiencies and ROI can be obtained with modified requirements.  

Be clear on IP ownership 

IP issues need to be considered regarding the input data for training and the output of any AI system. It is currently unclear in the UK as to whether AI developers’ use of IP-protected works to train AI models is unauthorised, and therefore infringing use. When it comes to outputs, current legislation suggests two different options as to who’s the legal author: it could be the AI developer or the AI user who added the prompts. Until this is resolved by legislators, the courts or by an AI developer providing full legal and financial responsibility to users, retailers and consumer brands should ensure they have a licence to use the works and that the use licence addresses IP ownership for generated work.

Stay up to date with legal developments

AI regulation is in its infancy, with various approaches being taken indifferent nations. Keep up to date with legislative developments and regulatory guidance, in particular, the AI White Paper published earlier this year, the EU AI Act (currently in negotiations), and guidance from the UK Information Commissioner on the interplay between AI and GDPR. International agreements, such as the Bletchley Declaration signed at the UK’s recent AI Summit and the G7 Code of Conduct, mark increased cooperation between governments on regulating AI tools and AI safety.

Wait and see

There is value in keeping an eye on what your competitors are doing, as you might experiment with AI yourself. In the short term, we expect AI solution providers to continue to consolidate and refine their offerings, at which time retailers and consumer brands will likely be better equipped to integrate AI more comprehensively into their business.