Cien S.

Understand your industry, the regulations in place at where you are operating in, the types of data your handling, the infrastructure of your product and systems and how your data is being managed and manage user consent. If you have all the necessary measurements in place, you’ll be able to provide your users the most tailored experiences. Having all of the necessary elements in place will allow you to grow meaningfully.

AI implementation will most always include educating clients, customers and stakeholders. There is a common misunderstanding of the capabilities and limitations of what the existing available technology can and can't do. Most importantly, when deploying a solution, build your implementation around the problem you are solving, impact you are projecting to make and the benefits (plus risks) of the AI project.

Employee resistance is to be expected. If you've worked in organisations before, you will likely have experienced how averse people are to change and how deeply ingrained habits and processes can be. It's important to recognise that this resistance often stems from fear, concerns about job security, or simply the discomfort of leaving a familiar routine. Leaders must approach change with empathy, clear communication, and inclusive strategies that involve employees in the transformation process. Providing adequate training, addressing concerns transparently, and demonstrating the benefits of change can help ease the transition and build a culture that embraces innovation rather than fears it.

Run POCs. The promise of AI and cost-effective experimentation is exactly that ... running POCs will be cheaper than ever before and allow organisations to explore innovative ideas with minimal risk. The ability to quickly iterate, test hypotheses, and validate concepts means that businesses can confidently make informed decisions. This accelerates the innovation cycle and democratizes access to cutting-edge technology, enabling smaller companies to compete on a level playing field with larger enterprises. Ultimately, the cost-effectiveness of AI-driven POCs empowers organisations to be more agile, responsive, and adaptive in a rapidly evolving market landscape.

To be AI - driven, means being data - driven. Test and iteration means you’re able to measure the efficacy of your tests. The criteria of your tests should include: - quality - consistency Your AI project won’t likely be perfect (our data being inherently biased and all) but having clear ACs and DoDs will allow you to push out a project that’s iterative. If you’re in high risk industries (and even if you’re not), ensure your stakeholders are aware of the risks of the project so you can handhold them through your delays. It’s important that your data is clean, ready and your AI project compliant when you release it.

Understand what data you have how you are currently managing your data and how you are recording consent. Are you processing sensitive data? PII? Look at your Privacy Policy and Ts and Cs and find out what you've covered. Then validate if you've captured consent and what the consent covers. Depending on where your data is stored and where your customers are located, work through it to ensure that you meet the relevant regulations in the locations you operate in. This not only allows you to show commitment to ethical standards but as well as ensure the scalability of your products.