Is AI adoption more difficult in healthcare?

I built and led data & AI teams in pharma for 6 years, one of the most highly regulated industries. Prior to that, I led teams and developed data & AI capabilities in tech for 21 years. It was a big change when I moved from tech to pharma. This experience was “enhanced” by the parallel move from the more progressive USA and to the more risk-averse Germany.

Are there differences in AI adoption across industries?

Yes, but not for the reasons most people think.

Aside from tech and pharma, I have seen it also in other industries. It’s believed that different laws and regulations across industries impact AI adoption. More regulated industries are supposedly limited in their use of data and AI.

It’s not that. It’s the mindset of the people and organizations in those industries.

When I joined the pharma company I was surprised about two things. First, I was surprised how much more data a pharma company in Germany had than I expected, especially in the commercial setting. And second, how little of that was being used to drive business decisions.

That was driven partly by the data mindset in the organization and partly due to the strong data privacy regulations in Germany. Although this was years before the General Data Protection Regulation (GDPR) was implemented in the European Union, Germany had strong data privacy rules already before then.

The more I tried to understand the supposedly data-protection-driven limitations, the more I realized how much more data use was possible than internally allowed. I also realized how a mutually respectful cooperation with data privacy (and legal) teams can benefit an organization to be more data- and AI-driven. More on that in a second.

Let’s keep in mind what the role of the data privacy and legal teams is. Their job is to minimize risk to the company. And what is the best way to minimize risk associated with using data and AI? Yes, the best way to achieve that is to not use any data or AI at all. Can you blame them? That’s their job.

Let that sink in.

Grab a coffee or tea and then come back to read this again: The ideal solution from a data privacy perspective would be to NOT use any data at all.

The mindset of what is risky differs across industries. It’s true that the more regulations an industry needs to follow, the more things could go wrong. That leads to higher risk aversion. But it’s seldom true regulations that limit data and AI use, it’s mainly the mindset in the industry.

How to be successful with AI in highly regulated industries?

Successful data- and AI-driven companies find the right balance, in any industry. They find the right balance between using as much data as financially beneficial for the company. And as little as possible to avoid potential fines and reputation damages.

In Europe, GDPR even has a clause allowing for the use of personally identifiable information: legitimate reason for specific business purposes, including vital interests. Financially successful companies leverage data for business decisions, so using data, including personally identifiable information, is of vital interest.

This is how I approached it too.

My team and I started small with developing customer profiles. Initially, this was considered illegal by data privacy. But I was able to find a precedent of a customer profile established decades ago and commonly used across the pharma industry. That led to the next step of uncovering other internally imposed restrictions on data use.

Whenever possible, I have discussed the benefits of using data and AI for the company with the data privacy and legal colleagues. At the end, the collaboration with data privacy became mutually respectful. My data science team has involved data privacy and legal right from the start whenever we had a new idea for a data implementation or an AI solution.

This collaboration exceeded even my own expectations. It got to the point where our data privacy and legal partners were proactively recommending suggestions to address data privacy concerns once they understood the business objectives. Instead of giving up on AI solutions that would have been considered not doable just a few years before, many times we together found a feasible approach.

In whatever industry you are, if your company wants to implement data and AI solutions, don’t fall into the trap of believing that you are restricted due to GDPR or the EU AI Act. Or similar regulations in the US, UK, and other countries.

If it works for pharma, it will work in your industry.

Successful AI adoption depends on people and their mindset. And that’s not only the end users, but also all relevant stakeholders. Make sure to involve them – like in this case, data privacy and legal – right from the start.

And if they are tainted by the historic risk aversion in the industry, start explaining the business benefits and potential trade-offs of using more data and AI. You may be surprised how far you get.