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Top 7 AI Solutions Design Challenges And How To Overcome Them

Designing and developing ethical AI solutions in healthcare, and other regulated industries can lead to improved efficiencies, enhanced patient/customer experience, and increasingly accurate outcomes.

Top 7 AI Solutions Design Challenges And How To Overcome Them

However, challenges like navigating privacy regulations and incorporating SDOH (Social determinants of health) in your data can make designing trustworthy AI in an already complicated environment even more difficult.

Read this article and uncover:

  • Some of the top challenges we’ve repeatedly seen when working with leaders in regulated industries.
  • Strategies to prevent and/or overcome these challenges.
  • How to design trustworthy AI in healthcare by partnering with a team of experts you can rely on.

Many AI-powered solutions never make it to the design phase and an even greater number are halted too soon. The reason? Organizations often feel unprepared to start an AI endeavor or there’s been some other sort of communication gap hindering success. Before designing a solution, it’s critical to know some of the top challenges regulated industry leaders face when considering AI.

Creating the “perfect” data governance structure.

While data governance and quality are critical, most organizations expect that data must be perfect before getting to applications. The reality? Organizations uncover the most about their data when they start prototyping. You may learn that you need to capture data in a different way or discover insights that lead to new questions and data requirements.

Justifying data quality with clear ROI.

Rather than letting data governance limit AI applications, let AI prototyping and iterative learning guide data quality and governance processes. This, in turn, allows you to justify data quality with clear ROI attributed to real use cases.

Mitigating risk and eliminating all bias.

Even after removing PHI and other protected information from datasets, AI solutions can still unintentionally result in biased outcomes. Risk mitigation in AI can never be a checklist, but that doesn’t mean you should avoid it. Engage in the process, facilitate difficult conversations around potential risks, and be ready to course-correct if things don’t go according to plan.

Setting and managing realistic expectations.

Designing, developing, and implementing AI solutions requires patience and realistic expectations. The best way to correct and improve an AI-powered solution is by learning from its failures. It’s imperative that your organization develop a strategy with clear goals and intended values for your AI solution before design so that results are measured properly and realistically.

Establishing KPIs related to bias and failure.

Despite our best intentions, data contains biases encoded in historic human decision-making and is limited by geographic diversity in many cases. Even features like purchasing activity may map back to a protected attribute, like gender, and result in a model associating these features with a negative pattern. To proactively address potential bias and failure, establish acceptable margins for error in your strategy and outline what to do if these margins differ significantly for specific protected classes like age or gender.

Developing an AI strategy with ROI in mind.

AI can increase revenue, improve efficiencies, streamline processes, and more. But to achieve these results, organizations must build a strategy that proactively accounts for the AI solution itself and actions that an organization may need to take to realize the full ROI and benefits of the AI system.

Considering the human and AI interaction.

Are you trying to automate an existing process? If so, consider what aspects of that process still need to be owned by humans. Is there a task that is limited to sampling like quality control in the contact center? Or is there a task where the information load is so great that it can’t be handled today by humans alone?

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