Part 2 of a 2-part series on AI for insurance and other high-volume data businesses. To check out Part 1, please click here.
These unprecedented times amplify the need for companies to stay agile enough to adapt their strategy at the drop of a hat. AI offers the type of precise data-crunching tools that give businesses the up-to-the-moment insights they need to make smart decisions, no matter what the world throws their way.
These insights are especially helpful for industries that rely on huge amounts of evolving data to make smart decisions, like those in the insurance sector. These days, AI is already transforming the way insurers manage claims, launch products, support their staff, and serve their customers in an ethical way. The potential of just how far AI can go remains to be seen.
Being the first to venture into new technological waters can feel intimidating. Balancing the potential of new technology against budget and organizational constraints is something we often help our clients navigate here at Apply Digital. When it comes to AI, there are few things a company can do to prepare itself for AI integration in the near future, even if their R&D allowance hasn’t quite caught up to the idea.
#1: Start planning
Consider how the data you collect today could be used by AI and machine learning tomorrow. What are you actually capturing with your data?
If processing wasn’t an issue, what other data points would you track to get a more in-depth impression of your customers, claims, and products? Think about which procedural and technological constraints are limiting the way you collect data, and what technology solutions are at hand to bridge these gaps.
Technology is only as smart as the people behind it. What do you need to do to make sure you have the support of key decision-makers at every level to begin to integrate AI into your operations?
Break down silos by including AI preparations in planning across departments.
#2: Predict the benefits and weigh the costs
Approach this as testing out hypotheses.
Start by looking at several projects that would benefit from faster, smarter, and comprehensive data analysis. Weigh the extra effort of integrating AI with the benefit to the company as a whole. Pick the metrics you’d use as indicators of business success. Then, use these insights to predict whether or not AI would be the right tool to move each project forward in the long run.
#3: Communication and collaboration are key
Keep the lines of communication open. Make sure your staff knows that AI won’t ‘replace’ them, but rather will make their jobs easier, give them an opportunity to learn, and help the company grow and succeed.
Check-in regularly with senior managers as well as teams from across departments. AI-integration is a company-wide project and requires the support of everyone from actuaries and project managers to product developers and the IT division.
#4: Prioritize ethics
AI is based on data sets inputted by people, and so is subject to the same unconscious social biases driving the way we typically collect and sort information.
That’s why it’s so critical that companies introduce policies designed to catch and balance for these pre-existing biases. A few ways to do this include organizing an advisory group, appointing a Chief Data Officer, and providing company-wide training and guidelines on responsible data practices.
It’s also important to be transparent with your customers about the use of AI. Share with them that you’re using AI, and let them know that the final decisions are still being made by the human behind the screen.
Having all of these tools in place will mean you’re ready to roll the minute it becomes the right time for your organization to integrate AI.