Machine learning and artificial intelligence (AI) are often used interchangeably, but there are subtle differences. Machine learning is a subtype of AI, meaning that AI is a more encompassing term. But they are similar and refer to the ability of a machine to “learn” without being directly programmed. Ordinary programs (which themselves can be extremely complex) take in specific data points and do pre-programmed things to it. With AI, the machine is programmed not just to carry out a series of commands, but also to learn from what results. Using often-dissimilar data, machine learning picks up things like subtle or unexpected data trends. In other words, the programs themselves can change when they’re exposed to new data.
Chances are, you have already interacted with machine learning without realizing it. Your Facebook News Feed, for example, is personalized to you because of machine learning that has paid attention to what you stop and read, and what you like.
What’s a Reasonable Expectation for Machine Learning?
You can expect plenty of machine learning, but not quite everything. A mental task that the average person could do with a split second’s worth of thought is the kind of thing AI is good for. But even that is quite a bit! Language translation, preventative maintenance, financial approvals, and simpler tasks like photo matching are all within the scope of machine learning.
A lot of machine learning is based on known use cases done by humans, and some of it is tasks that people simply didn’t think were possible. Maybe data didn’t use to exist, or maybe it was in a format that couldn’t be analyzed with traditional programming. But the combination of AI and big data have changed all that. Healthcare and legal services are two major fields in which machine learning is expected to play an increasingly important role.
Machine Learning Captures Knowledge So It Won’t Be Lost During Transitions
One application of machine learning that should prove to be especially useful for businesses is the creation of a knowledge base of evidence-based insights that will carry over during times of transition, like a major change in management. No longer will decades of industry knowledge disappear when a valued employee retires, because machine learning can make sure it is kept and used to educate up-and-coming replacements.
But machine learning by itself can’t do much. It has to have large quantities of data to work with. Fortunately, that data doesn’t necessarily have to be organized and housed in rigid databases to be of use. When companies break down the walls that tend to grow up and create data “silos,” and create instead massive “lakes” of data, it can all feed into AI, potentially yielding insights that might never have been possible before.
Machines Will Not Take Over for Your Pharma Sales Reps
You don’t have to worry about machines taking over the jobs of your pharma sales reps, however. What it is far more likely to do is things like analyze email marketing campaigns and make recommendations about how often to target which customers, and with what information, making marketing efforts increasingly easy to personalize. Another possibility within the field of pharmaceuticals is drug discovery, based on AI engines chewing through masses of data from different research projects, potentially over many years’ worth of information. Unexpected new uses of existing medicines may result.
Computers and algorithms can crunch data, but they can’t tell from his tone of voice that the physician is in an irritable mood and probably won’t want to set up a sales call today. We haven’t figured out the many ways humans learn and pick up on instantaneous cues, so AI can’t do everything people do. But it’s making progress.
In short, pharma sales trainers should think of machine learning as a co-pilot rather than an autopilot when it comes to sales training. Your judgment and the judgment and decision-making skills of the reps you train are still absolutely essential to successful sales training. There may come a point when your reps to use machine learning to improve their results. AI might for example, analyze what type of learning content is the most helpful (meaning the type that results in sales) so that they can tap into more of that type of content when dealing with customers.
Identifying “Pareto Potential”
You’re probably familiar with the Pareto Principle, or the “80/20 rule.” It basically says that 80% of your sales come from your top 20% of best customers. It’s applied in a lot of other situations too. For example, your top 20% of sales reps may bring in 80% of your deals by dollar value. Artificial intelligence is expected to be able to take that to a deeper, more accurate level. With greater quantities of more varied information from which to learn, algorithms will grow “smarter” over time. You could potentially learn which 10% of combinations of effort bring in 90% of your revenue, for instance. Eventually, the result could be an ability to make small adjustments to content or processes in order to get outsized positive results, i.e., doing more with less and doing it better!
The Future of Machine Learning
Artificial intelligence and machine learning haven’t reached maturity yet in the way that, say, the cloud has. In fact, you’ll see a backlash against machine learning as people realize that it won’t produce the sci-fi reality we may have first imagined. Nonetheless, within a couple of years, as machine learning gains maturity and more use cases are identified and tested, expect it to broaden and deepen in scientific fields like pharmaceuticals. Eventually, AI will be an everyday business tool like the others that you use for training your reps, analyzing your data, and generally maximizing efficiency among your team.
Please feel free to browse through our blog content for additional information on machine learning and topics like learning theory, gamification, and others that have a measurable effect on pharma sales training.
