Top 5 Most Perpetuated AI Myths, Debunked

Alan Kalton

It is scientifically proven that teenagers are clumsy. The main reason teens are so awkward is that they are growing fast–for example, a 13-year-old’s legs can grow 1cm a month! The brain must continuously recalibrate precise calculations about movements, and when it misfires, teens stumble. Similarly, COVID-19 has led to a sudden growth spurt for Artificial Intelligence (AI) technology in commercial life sciences. According to ReportLinker, the global AI in pharma market is expected to grow from $0.91 billion in 2020 to $1.27 billion in 2021 at a compound annual growth rate (CAGR) of 39%.And like the newfound height and strength of a growing teenager, AI may feel all “arms and legs” for organizations anxious to leverage its great promises in a post-pandemic, digital-first world.

Though enthusiastic about AI, some companies are hesitant. Unsure how to maneuver in this rapidly changing environment, they doubt their ability to create a more robust data analytics infrastructure without faltering. Misperceptions about what’s required to scale AI globally are causing some to decelerate their AI initiatives—to take more measured steps one brand or region at a time while competitors race to deploy multi-national projects.

This piecemeal, slow-roll approach, a stark departure from today’s rush to digital transformation, should be replaced by a paced, global strategy. Advanced AI technology continuously learns and improves quickly, whether a company has a massive database or a small one, a world-class centralized CRM platform or different point solutions from region to region. Regardless of the starting point, AI is the fastest way to reach the destination: a superior omnichannel customer experience.

Here are the industry’s biggest AI misperceptions debunked—and the truth exposed.

Myth 1: “Local technology autonomy and complexity hinders global AI scalability.”

One-size-fits-all AI fits no one. Advanced AI platforms are built to accommodate each organization’s global technology landscape, regardless of complexity – even enabling data aggregation across geographies.

Whether it is a CRM, marketing automation system, or data warehouse, every technology platform will have varying levels of central control and autonomy compared to local systems configured for each country. Even a single system deployed globally may be used differently in different countries, so the key is to map out the full technology landscape and identify where AI can add value across a broad spectrum of use cases within that ecosystem.

Bayer is a leader in AI innovation. In partnership with Merck, Bayer recently received FDA breakthrough designation to AI software for pattern recognition to detect a rare form of pulmonary hypertension called CTEPH (or chronic thromboembolic pulmonary hypertension) which affects about five million people worldwide. The company is also leveraging AI to support the commercialization of products across its portfolio in many key markets globally. The solution provides its sales and marketing team with valuable HCP insights and next-best-actions.

“Every region wants and needs some degree of autonomy,” said Klas Eriksson, global head of Scale Analytics Solutions at Bayer. “We deployed a common CRM system and business rules for aggregating data in the same way for every country. Our open AI platform also includes a self-service component for local flexibility in data reporting.”

Eriksson continued, “The technology is complex, but, like anything, success requires change management. Articulate the changes, especially the benefits, to all teams. As important, align related business processes–such as how commercial teams segment customers and input data–globally before you deploy. It’s not that everyone has to work the same, but broad-strokes commonality across regions makes deploying any new technology easier as long as the solution offers some flexibility.”

AI technology should maintain a symbiotic relationship with sales and marketing automation systems, feeding off each other to achieve a common goal. An API-driven, open AI platform makes this possible, seamlessly integrating with all systems and databases regardless of geography. In this way, AI is a bridge, not a wall, for connecting the global technology landscape.

Myth 2: “I don’t have the right data foundation to take advantage of AI.”

While it’s true that quality AI outputs are impossible without quality data, AI does not require a complete data set to start providing value. Often, companies need far less data than they expect to achieve the desired outcome. And while some life sciences companies have robust historical data, many others do not. Either way, AI creates better data over time by encouraging users to collect quality information about their interactions with healthcare professionals (HCPs) to naturally build an ever-growing database.

To start, AI leverages any existing data, reinforces good data input, and evolves continuously – eliminating data quality concerns as a barrier to deployment. The key to overcoming this misperception is to build an AI solution tuned to each company’s data starting point.

For example, if a company already has a marketing automation system that captures HCP responses, AI can immediately begin drawing insights that will trigger actions across all commercial teams. Encouraged, field teams will then input more data into the system, generating ever-more nuanced suggestions. The more results reinforce this behavior, the more users will input good data, and the faster the data grows. All the while, AI is learning from new inputs and becoming increasingly effective.

“There may be a specific way one market wants to segment customers. We are here to enhance customer engagement, so we must be flexible enough to accommodate. Work from a common playbook but also be able to adapt,” added Eriksson.

AI can make recommendations simply based on customer profile information, too. As teams add information into the system, the value compounds continuously to enable richer customer experiences. You can start the AI journey with basic data as the cornerstone, building a framework for ongoing data quality improvement while the system reinforces and rewards a “value in, more value out” process.

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.

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 their 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.

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).

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

The global pharmaceutical AI market is expected to reach $5.94 billion in 2025 at a CAGR of 47%.In fact, many say that we are in the early stages of the fourth industrial revolution defined by the intersection of digital, data, and biological innovations. 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 the 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.”

Alan Kalton, Vice President of Services and GM, Aktana Europe