Alan Kalton and Graham Rapier
Aktana presents a three-part series focused on dispelling common AI misconceptions and how to engineer success that scales. In Part 2, we discuss the myths surrounding costs of a global AI implementation, job replacement and the ideal time to scale up your efforts.
It isn’t a lack of interest causing some organizations to decelerate their AI initiatives. It’s a lack of clear information—and objectives. This series aims to change that.
In Part 1, we started breaking down some of the most persistent myths about implementing AI on a global scale, specifically around losing local autonomy and not having enough data to start. Today, we’ll continue to deconstruct the pernicious presumptions that may be stalling your digital transformation efforts.
Myth 3: “AI on a global scale is a luxury that we can’t afford right now.”
With all projects, there must be a balance between investment and return. An AI investment is no different and should be balanced to provide the greatest value within the parameters of every situation. We all need transportation, for instance, but we do not all need a luxury SUV. To illustrate, here are three common life sciences scenarios.
- Scenario 1 – In major markets or with leading brands that have already invested significantly in data and technology, it makes sense that a more considerable up-front investment in more complex AI will result in higher returns and impact.
- Scenario 2 – In a slightly smaller market or with a growing brand, a high investment in AI may be out of balance with its potential impact; however, there are opportunities to standardize the AI platform to scale. Here, a modular approach allows companies to configure the same AI platform to different use cases for multiple regions and scenarios.
- Scenario 3 – Some companies deploy the same technology in each region. In this case, it is critical to delicately balance the organization’s overall technology investment with its specific AI investment to provide the best value.
Right-size a modular AI solution to fit the needs of each region
In 2020, Aktana deployed a highly bespoke AI solution for a Spain-based pharmaceutical company that significantly impacted their commercial success. Happy with the results in Spain, the company sought to scale the solution globally. However, replicating the same premium system proved cost-prohibitive when multiplied across many smaller markets – most of which did not warrant the same robust capabilities. Aktana’s modular platform provided a flexible, standard template that could be deployed fast and efficiently but be uniquely configured to accommodate local complexity. It was governed from the top-down across all regions and designed to evolve accordingly in each market, at its own speed.
Today, AI is a requirement for commercial success—not a luxury. By leveraging a flexible AI platform tailored to various situations, pharmaceutical companies can leverage this valuable technology regardless of their size or budget. The key is to consider all variables at the start. The foundation can be the same, like the wheels and chassis on a car, with various modules layered on top to right-size the platform for different markets and brands.
Myth 4: “AI will displace human creativity and intuition.”
Undoubtedly, AI is incredible. It is exceptional at rapidly finding answers to complex questions, but its usefulness depends on humans asking the right ones. AI deployment is not an either/or initiative – man or machine. Success requires a balance of both, especially the active involvement of people, even as the system simplifies their daily workflow to drive efficiencies.
For instance, AI adds tremendous value in analyzing zettabytes of data to mine insights—a task it can accomplish 1000 times faster than a team of analysts. But before AI can get to work, humans must gather the correct data and define the patterns for identification. AI sees patterns in numbers to draw conclusions that inform the commercial strategy but does not excel at the creativity needed to define it.
You can’t fully leverage AI without investing in internal talent
Companies should invest in their internal talent with training opportunities to optimize new skill sets and fully leverage the advanced technologies that will become a necessary part of their toolkit from this point forward. “You will always need human intervention because things constantly change – the customers, channels, strategies. Eventually, AI will automate more tasks, but that just means humans can divert their efforts to more creative and strategic work,” said Eriksson.
Myth 5: “There are too many unknowns in the market to scale AI.”
COVID-19 shifted the commercial life sciences landscape dramatically. Digital channel adoption and agility is now a requirement, driving many companies to dive into the AI pool, but often only in a major market. These companies want an immediate solution to help them manage the increased commercial complexity and improve engagement with HCPs. However, this same complexity is causing companies to wait to roll out AI on a global scale.
Here’s why this is a mistake. Pharma’s digital transformation could backfire if these omnichannel HCP interactions are not carefully coordinated. The combination of more emails, virtual meetings, and scientific e-papers from medical science liaisons all sent to the same physician at the same time could result in a negative interaction – diluting the message, appearing impersonal, and making HCPs feel spammed. In fact, the number of emails sent to HCPs by sales reps increased by more than 300%, and the frequency rose 82% from the same period in 2019 to 2020 (per Aktana North America Data).
AI is the only way to personalize the customer experience at scale
Now consider the impact on a global level if left unmanaged. AI is the only way to quickly cut through channel complexity to help commercial teams orchestrate their engagement with HCPs. Omnichannel engagement and AI must go hand-in-hand to optimize digital’s advantage and prevent unintended damage to customer relationships. It is not one or the other. AI enables the leap to a fundamentally different HCP experience, making it a critical part of the global commercial strategy where there’s even additional complexity.
“That’s the whole point of AI in commercial life sciences, right? It helps orchestrate the best interaction with every customer,” added Eriksson. “One customer may not be familiar with our product, and another HCP writes many scripts – each customer is on his or her own journey, and it’s our commercial teams who need to guide them from one point to the other, but they need AI to do it well.”
Do not let perfection paralyze AI initiatives
More than half of companies expect to see AI as a piece of digital engagement nearly ubiquitous throughout healthcare – including the pharmaceutical industry – by 2025. Do not be left behind, waiting until all of the perceived requirements are in place to get started. Establish careful governance over the AI initiative and scale it globally. The platform will learn and evolve in concert with the business.
AI, by definition, is a technology that continuously learns. It is okay not to have all the answers at launch or a textbook technology infrastructure in place worldwide. With a flexible and open platform at its core, AI can be deployed globally to balance the economies of scale in each market and yield the maximum return on investment.
Eriksson concluded, “Most companies are at the start of the AI journey, but COVID-19 suddenly accelerated the use of digital channels – and we won’t go back. They will complement the communications mix but also complicate it. Only AI can help to simplify execution. It’s the future.”
Now that we’ve put the most common AI myths to rest, let’s explore how to build a system that’s built to go global from the start.