If you’re wondering whether you’re missing out on Artificial Intelligence (AI), rest assured: your competitors are likely already exploring it, but there’s still time to make it work for your business. The good news? AI offers significant commercial and operational benefits, and you don’t need to dive in all at once.
How do I know this? At Gardner Leader, we’re expanding our use of AI to enhance our offering. We’ve found that a department-specific approach, rather than a companywide rollout, minimizes disruption while addressing unique team needs, but we are also keeping our eye on our overall strategy. With this in mind, we have drafted our AI Framework and Policy document, and businesses should consider starting this process. We are here to help.
If your business is a small to medium-sized business, starting small and scaling gradually is often the smartest strategy. Chances are, one or two people on your team already have an interest in AI. Lean on those individuals (and others suited to the task) and give them time and a modest budget to explore how AI could benefit your business.
However, AI isn’t just about opportunities; it comes with its own set of risks. To navigate the risks and maximize the opportunities, here are some key considerations:
Data Privacy
It is critical for businesses to comply with the General Data Protection Regulations (GDPR). GDPR sets strict rules about how personal data can be used, and it’s crucial to ensure that you and your AI provider comply with these regulations. Relying on assurances given during the sales process is not enough; you should always check that appropriate data protection clauses are included in contracts and that privacy policies are transparent. Mapping the scope of data collection and understanding how data is processed is crucial. For instance, when data is processed through AI, it is important to ask where the data is sent and who it is shared with. Regular data mapping and conducting Data Protection Impact Assessments (DPIAs) are important when sensitive or large-scale data or systematic processing is involved. And don’t forget to check how your data is being used; for example, will it be used to train the AI? You might need to dig into the contractual details or raise the issue with your provider to uncover this.
Trusted AI Providers
Not all Artificial Intelligence providers are created equal, so it’s crucial to work with reputable vendors. Ensure your contracts clearly define liability. For example, a UK client of mine almost signed with an AI provider registered to a serviced office outside the EU. While the contract included carefully negotiated liability provisions, the provider had no real track record or assets. In practice, recovering losses from such a vendor would have been very difficult, and the arrangement created particular data protection considerations. This highlights the importance of thorough due diligence by you and your lawyer, and knowing what to look for in your contracts. Choose providers with a solid reputation, proven reliability, and clear accountability.
Ethical Challenges
Artificial Intelligence can introduce ethical risks, such as bias in hiring tools or a lack of transparency in decision-making. These issues aren’t just legal risks—they can damage your reputation and hurt the people affected. Build accountability into your systems from the outset.
Oversight Is Critical
Artificial Intelligence is powerful, but it’s not infallible. Automated tools can make mistakes, from misclassifying data to missing critical real-world complexities. Always validate AI outputs, especially when they impact significant business decisions. This is a good example of where an AI Framework and Policy can be so helpful.
Explore Industry-Specific Applications
It is important to research the functionality of Artificial Intelligence thoroughly and understand the full scope of the AI at the start; this is important in terms of overall risk assessment. Look for AI which can be tailored to unique challenges in your industry. For example:
- Life Sciences: Use AI for drug discovery, predicting patient outcomes, or automating regulatory compliance processes.
- Logistics and Supply Chain: Optimize delivery routes, schedule return deliveries, forecast demand, and reduce inefficiencies in real-time operations.
- Retail: Personalize customer experiences with AI-driven product recommendations and inventory management.
- Healthcare: Implement AI for personalized medicine and predictive analytics to improve patient care.
- Finance: Utilize AI for fraud detection and risk management.
Highlighting these sector specific benefits can help you better define use cases that deliver measurable value; but for most smaller businesses automation is the starting point.
Good Data In, Good Results Out
AI relies on high-quality data. Outdated, inaccurate, or biased datasets will lead to poor outcomes. For example, if your AI system is trained on outdated data, it might not recognize emerging trends or risks. Bad data can cause biased decisions, under-representing certain groups. Errors in combining data from different sources make datasets unreliable, and cleaning bad data is costly and delays decisions. High-quality data is therefore crucial for reliable AI, and prioritizing data quality ensures better AI performance.
Define Clear Objectives
Don’t adopt AI just for the sake of it. Define specific, measurable goals that align with your business priorities. Consider phased trials or short pilot projects with limited users to refine your approach while keeping risks and costs manageable. Also, consider checking with your insurer if you have disclosure obligations in your insurance contract or there are any exclusions to consider.
Protect Your Intellectual Property
If you’re developing custom AI applications, safeguard your intellectual property (IP) from day one. Clear contractual terms can prevent disputes and protect your business’s innovations. See my article on the UK’s Data (Use and Access) Bill.
Stay Ahead of Regulatory Changes
Artificial Intelligence regulation is evolving rapidly, especially in jurisdictions like the EU with the AI Act. Although the UK Government has in recent years indicated a relatively light touch on AI regulation, as per the article above, the Bill has recently been with the House of Lords, where they are suggesting amendments to better protect copyright holders, and the UK Government is proposing to set up an AI Authority. Further, the EU AI Act sets a global benchmark, influencing AI regulation beyond Europe and is likely to influence UK regulation going forward. Preparing early not only ensures compliance but can also position businesses as AI deployment. Stay informed about new compliance requirements to avoid costly surprises and ensure your AI systems meet legal standards.
Artificial Intelligence adoption can transform your business, but only if you approach it thoughtfully. Start small, involve your team, and plan carefully. With the right mix of experimentation, robust legal guidance, and strategic implementation, you can unlock AI’s potential while staying ahead of the risks. Remember, it’s not too late to begin, but the clock is ticking, and your competitors aren’t waiting.
At Gardner Leader, we are happy to help with your AI implementation questions, including Policy adoption. Please do contact James Fox and our Commercial team. Please also keep up to date with our monthly AI soundbites.