AI development may draw from standard software development processes, but from the customer's perspective, AI solutions are very different from anything they've relied on before. While traditional enterprise software often includes AI in applications such as security, help and autocomplete, the programs themselves are essentially deterministic. The user understands the logic that connects each step of the process and, barring a major update, that logic doesn't change much from day-to-day.
AI is different. The process by which it evaluates data is often a black box, and even when it's not, the clients may not have a good understanding of how it derives its conclusion. Additionally, as the AI receives further training and tuning, it evolves in ways software doesn't.
As a result, clients are often hesitant to trust a new AI project, no matter how impressive its claimed benefits — who wouldn't be cautious about trusting core business processes to a product they don't understand? As a recent Forbes article points out, it's the AI developer's responsibility to earn their trust. Here are some takeaways on how to do that.
AI isn't the right fit for all projects. If standard software does the job just as well, or if the benefits of AI for a particular use case aren't strong enough to justify the disruption of a new system, move on.
It's important to be realistic about the costs, risks and benefits of AI. No AI is 100% accurate. If it fails, can a human correct it, or will the failure go undetected? And what is the consequence if that happens? An AI system recommending the wrong product to a shopper is a small risk. An AI system failing to prevent a car accident or warn that medical equipment has failed is much more significant.
AI development is never finished. You need to continually train and improve your models to keep up with changes in the real world. It takes a lot of patience to establish a successful track record and earn your client's trust. Start with a small pilot to demonstrate value to the client before rolling out a more ambitious project. Take time to check-in and answer your client's questions. If you show them that your solution is trustworthy for day-to-day operation, they'll trust you.
Complacency is always a liability, but in AI and ML, the consequences are particularly severe. To prevent model drift, your system needs to be constantly trained on new data and tuned and re-tuned to address changing real-world conditions. Both software mechanisms and business processes are needed to check and recheck your AI model for weaknesses and a culture of continuous improvement to ensure you retain the trust you've worked so hard to build.
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