Optimizing Your Go-To-Market Strategy Using Human-Machine Teaming

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David Ehrlich is the CEO at the helm of Aktana, a provider of AI-driven support to sales professionals in the life science space. The technology helps reps deliver more value to the healthcare providers they serve while reducing typical costs associated with in-person sales teams.

Digital companies have always automated go-to-market processes, but it is not second nature in more traditional industries. In life sciences, the pharmaceutical or medical device sales rep has always played an outsized role in speaking with doctors, educating them about offerings, and driving adoption of new products.

However, it’s expensive to maintain an in-person salesforce. If each salesperson’s time isn’t optimized, the organization is leaving money on the table. And in a field like life sciences, that lack of optimization can have even broader consequences. When sales reps cannot reach the right doctors with the right solutions, patients may miss out on treatment options that can significantly improve their lives.

It’s important to note that you won’t hear David advocating for the end of in-person sales. He recognizes the valuable role salespeople play in building trust and maintaining relationships with providers. But there’s also room to fine-tune their approach.

That’s where the idea of human-machine teaming comes in. The best automated go-to-market processes, David says, allow the machines to do what they do best while freeing humans up to shine in their own way.

He provides an example of a sales representative working with oncologists. The sales rep has a fantastic second-line cancer treatment to offer, but for the doctor to find the suggestion relevant, the timing needs to be correct. 

If they don’t have a patient on first-line treatment right now, there’s no need to explore second-line therapies. If they have a patient that’s completed first-line treatment with suboptimal results, the doctor has likely already found an alternative.

Aktana’s software combs through anonymized patient data and identifies doctors who are treating patients midway through first-line treatment that are seeing poor results. These doctors are most likely to need that second-line option right now, so this is the ideal time for the sales representative to approach them with a solution.

On their own, the sales representative knows the product, but they might not know when to talk about it with the doctor. Teamed with the machine, they speak with the right doctor at the right time, making the sale and (even more importantly) providing a viable second-line option for the patient.

Of course, people tend to bristle at the suggestion of incorporating AI into their existing processes. David shares tactics for addressing any potential hostility expressed by sales reps toward the program. He says the key lies in following these three guidelines:

  • Give a recommendation. It likely won’t be well-received if the machine dictates what the salesperson must do. Instead, the person must have the power to accept or reject the suggestion.
  • Share the reasoning. Remember when your parents used to reply, “because I said so,” when you asked why they were enforcing a rule? That tactic didn’t work then, and it won’t work now. Share the logic behind the suggestion with the person.
  • Make it easy. If your sales team needs to open up another program to get the suggestion, they’ll likely just maintain the status quo. Instead, incorporate suggestions into the existing workflow to make it frictionless to adopt this new working method.

Even the best technology used correctly won’t deliver overnight success. David notes that it usually takes about nine to twelve months to see ROI after implementing the technology. During that time, there’s lots of testing and learning to fine-tune the AI and how your team interacts with it. As with most things in life, the more you put in, the more you get out.