Despite a lot of media attention on Artificial Intelligence, some recent interviews Eularis conducted with Pharma executives revealed very little understanding of the impact of AI on their businesses. Below are some quotes from them when asked about the role of AI in Pharma:
“It doesn’t have one, does it?” — EU Commercial Vice President
“Never used and never heard of it in Pharma analytics.” — Global Associate Brand Director
“Just not part of my vocabulary.” — Marketing Director and Business Unit Head
What was fascinating about these responses was the limited understanding of how these apply to what they described as their biggest challenges.
It is time for the ignorant to pay attention – not only CEOs, but also marketing and analytics teams who still do promotional response modeling and use other inferior methods because if they do not, they will soon be out of a job.
Herewith some questions that Pharma execs need answers to if they are to survive in their roles in the upcoming years.
1. What’s AI all about?
AI is the field in which mathematical approaches intersect with high level computer approaches, andit really is far beyond anything else for analysis in terms of speed and accuracy. It is, in essence, computational mathematics, and there are many branches of it. However, the big game-changer has been machine learning in which the machine can change its algorithms as new data comes in – so it is essentially learning.
CEOs have often seen many fads come and go, so it is understandable that they probably view this with the same ‘wait and see’ attitude as they have with previous approaches. In the last few years, the growth in accuracy and application of AI has been logarithmic, and that continues. What Eularis did last year with machine learning algorithms has already been replaced with newer improved models, which we are busy incorporating into our own system. Things are moving at breakneck speed. AI is just now ‘coming into its own’; the impact on Pharma business is going to be profound.
2. How does AI apply to Pharma?
From discovery throughout the business, the impact of AI will be enormous. Take a simple AI example. The process to go from discovery to market for one chemical entity used to take up to 15 years and cost close to $1 Billion. Since then, teams are implementing all sorts of capabilities to slash a year off development; however, it is still a long and costly process with little change.
At present, some companies are already using Artificial Intelligence powered clinical reporting tools that write a regulatory compliant clinical study report that is around 90% complete. The time it takes to do this? Less than an hour! When people do this, it takes around 6 weeks. Therefore, this saves at least 250 man hours and around $25,000.
This is amazing when you consider that the AI behind this saving is relatively simple Artificial Intelligence algorithms, and yet even simple AI algorithms far exceed anything that humans can do. Consider if this was used by the top 10 drugs alone; more than $5 Billion in revenue would have been saved.
Think about more complex applications, such as within research on gene mutations. AI can churn through huge amounts of data, find valuable information and potentially take a process that took years down to months or even weeks. One type of machine learning technique has been used to file patents and grants in Japan and the US.
AI powered software is already now being used in Pharma to screen 10’s of millions of potential molecules in a few hours. It’s also more accurate than other software based approaches in identifying promising leads for drug discovery, saving large amounts of time and money for companies implementing these. If your company is not using AI, they are seriously already behind.
3. Can AI make a difference to my biggest challenges?
For epidemic outbreak prediction, discovery, R&D, clinical trials, pricing and reimbursement, sales and marketing (including segmentation, targeting, messaging and positioning, strategy, channel optimization, budget allocation, sales force effectiveness, customer engagement) and patient adherence – to name but a few areas – AI has already been proven to make a marked difference in results to other techniques that are being used.
Consider epic drug launch failures, such as Zaltrap, due to poor pricing and reimbursement decisions. Eularis are now using AI to create far stronger pricing models based on payer value and real patient outcomes than has ever been previously possible. Ask us how!
4. Can AI make a different to my revenue and profit?
More than you can imagine currently. Almost every area of the company can be optimized and improved using AI for vast changes to revenue and profit. As an example, by using our Artificial Intelligence powered Sales Rep Suggestion Engine alongside our automated detail aid tools, one client sales team generated 43% increase in sales when using this AI tool compared with the teams in the company that were not using it.
5. How fast is AI moving? Do I have time to wait and see how it evolves?
The pace of change in this field is super fast, so many may be tempted to wait. However, the reality is that waiting too long will essentially kill a company. The challenges Pharma face are large, and the sooner AI is understood and utilized at potential, the sooner Pharma will begin to grow far more than many are currently able to realize.
6. How deep should we use AI?
Many companies are only using AI for narrow applications – maybe writing a clinical study report or using it to predict customer behavior. Others are going deep and looking at much broader uses. Of course, it can be used in sales and marketing for all aspects, including pricing, patient adherence, customer engagement, customer loyalty, product positioning and messaging, channel optimization and budget allocation. However, did you also know that it can be utilized across all areas of the company – in discovery, R&D, clinical trials, pricing and reimbursement, HR (it can even predict which employees have the potential to commit fraud), legal and real-time analytics on all functions.
7. Can HR save hiring additional staff?
It is interesting how much intellectual human work is already being superseded by AI. For instance, it is getting stronger results than pathologists in predicting Cancer cell growth when both have access to the same clinical tests. It is also being used in legal defense cases now where the AI is able to more successfully identify legal defenses than humans.
8. Who should lead the effort?
This is an interesting question. For example, in analytics projects we were always approached by sales and marketing, business analytics or business insights teams. However, now we are beginning to get more and more projects driven by a CEO or the IT teams. The IT teams are coming to us because many internal teams are requesting AI and ask IT to help. It is not really an IT function, despite the fact that IT is an integral part of it.
So, IT teams are turning to Eularis now for help in delivering what their internal customers need.
The increase in CEOs turning to us for help is an encouraging sign that some are innovative and seeing what the future holds for them if they do not implement this. I personally believe, given how widespread across the organization this should be used, it really should be a CEO initiative.
9. What would success look like?
Success will be delivering the right message and service to the right customer at the right time and delivering benefits of customer satisfaction, customer loyalty, customer evangelism (see my blog on this by clicking here), and the resulting rapidly growing revenue and profit.
10. Is Applying AI in Pharma Different From Other Industries?
Pharma is different in many ways. To get the most from a tool, you need to understand the industry you are in and be able to strategically marry the inherent challenges faced (including a strong understanding of the regulations, compliance, etc.) with the AI solutions possible. Without a strong understanding of the industry, this can be more difficult to achieve.
Imagine if you were creating a budget allocation tool for your portfolio of Oncology brands or HIV brands. If the team were from outside Pharma, they may not understand combination therapy, or lines of treatment, and might simply be looking at these as different products, as they would in other industries. However, there are far more complex interplays in Pharma than other sectors. The combination of deep industry expertise alongside the AI expertise is critical for success.
A client in the US told us that they had brought in several companies before us to apply AI and those companies were able to demonstrate impressive results in other industries but were unable to achieve the same in Pharma as they didn’t understand the industry or requirements well enough; they kept assuming one industry was the same as another.
However, there will be growing pains along the way. One thing that springs to mind is infrastructure. Traditional IT systems and networks today within Pharma companies are small and have a relatively simple structure. However, to accommodate utilization of Artificial Intelligence (which crashes most typically used computers and servers) requires a more complex IT ecosystem that unites heterogeneous data and devices with varying protocols, architecture and infrastructure. This is not easy for most internal IT departments to accomplish without help.
