AI is obviously a buzzword of the age now. Apart from the hype, I think one key take away is that AI and data are inextricably linked. The value of data has soared phenomenally over the past few years. The models we have in AI, the use cases are all contingent on good quality data and loads of it.
Now. This is especially relevant in the life science and healthcare space, which is estimated more than 30% of global data being healthcare associated data. Med tech companies and others have responded in three typical ways. The first response is what I'd like to call the passive response.
This is in which the company perceives AI as something happening afar and once it's mature they will consume it, purchase it, Basically a consumer product relationship. This response fails to understand the level of expertise required even to make a purchase of said AI or even understand the level of commitment and complexity associated with executing on it or rolling it out within the company or even externally. The second response is what I'd call the reactive response. And these companies are not passive.
They want to be market leaders in AI adoption and they typically set out maybe a centralized AI team, possibly with IT personnel, some enthusiastic commercial leaders, and they begin soliciting possible use cases from different divisions. Ultimately, they end up with a laundry list of possible initiatives and mishmash of projects and a usually incoherent strategy, if you can call it a strategy. I've had companies, when queried about their AI strategy, very boastfully say we have 360 plus initiatives going on. Now that's a laudable thing to do, but is it the right thing?
Is it truly capturing the potential of the company? Is it actively playing a role in the AI narrative? Which brings me to the third group, which I'd like to call the proactive proof. Now these companies understand the value of the data they have and they are investing heavily in agile, expertise LED teams where they understand that an IT person doesn't equal a data scientist.
Where enthusiasm is no replacement for expertise. Multiple teams creating frameworks for vetting different projects, investing heavily in data infrastructure and access. They understand that having that data doesn't mean having access to it, especially in an industry which was built upon extreme data regulation. Life sciences is often governed by multiple firewalls, whether physical, software or regulatory, that limit access to data, and addressing those head on is key to those companies.
They're also investing heavily in AI literacy. They understand that there will be workforce disruption, some skill sets will become redundant, but they are not unaware of how they can improve on the existing skill sets through data literacy and AI literacy programs. And they're also investing in internal ecosystems which can create their own AI use cases and investing heavily in those as well. These are not buy ONS, add-ons licensing only because they want to play an active role in the AI narrative, not just a passive 1, not a reactive one.
And I think this group of companies are the ones that are going to lead the pack and change that narrative.
