In this report, we look at the applications of artificial intelligence (AI) in drugstore retail.

  • AI, or machine intelligence, is human-like or intelligent behavior exhibited by computers and machines.
  • In 2016, software and IT services firms invested the most in developing AI programs and tools.
  • AI investment and adoption in retail has been lower than in the software sector and much lower than in the healthcare sector.
  • Some 35% of global executives think that the healthcare sector needs further convincing that spending on AI can produce valuable return on investment.


AI is revolutionizing the world and its application in retail has transformed the sector as we know it. Many shoppers now receive daily emails from retailers that feature an impeccable collection of products they like. Supply chain managers and store managers have become more efficient at managing inventory. And nearly every online store seems to have a customer service assistant available at all times. All of this is partially or wholly possible due to AI systems that retailers have implemented.

In this report, we explore what AI is (and is not), where it is being used, how it is being deployed and how it can be deployed in drugstore retail, and its future outlook with regard to wider applications.

FGRT’s previous overview of AI in retail can be found here. In this report, we look at AI from a healthcare and drugstore retail angle and analyze its potential applications in the sector. First, we explore what AI is and what it is not.

What Is AI?

AI, or machine intelligence, is human-like or intelligent behavior exhibited by computers and machines. It is enabled by a computer program or set of programs that help machines make autonomous decisions through cognitive functions similar to those typically exhibited by human beings, such as learning, decision making and problem solving.

AI is not simply about robotics or automation, although some people use these terms synonymously. Robots are machines that can carry out difficult or repetitive tasks, but they do not all have AI capabilities. A robot may be able to move objects or transport itself independently via a built-in program and sensors on its body, but this is simply automation, not AI. On the other hand, a robot with AI capabilities would be able to assess its environment autonomously and make decisions regarding the most efficient path to take to a particular destination.

AI Is Used Across a Range of Industries and Sectors

AI is not specific to any single industry or sector and its applications are almost endless. Software and IT services firms have invested the lion’s share of funds that have gone toward developing AI programs and tools, according to 2016 data from AI market research firm TechEmergence. But it is likely that the tools developed as a result of these firms’ investment have been used across other sectors.

Apart from tech-heavy sectors, such as the Internet and telecommunications sectors, retail firms have been some of the biggest investors in AI. The technology is expected to have far-reaching impacts in hospitals and healthcare, but AI adoption in the healthcare sector has been slow relative to a number of other sectors, as shown in the graph below.


Earlier in 2017, TechEmergence surveyed over 50 executives who head global healthcare firms that are using AI. The survey results shed some light on why healthcare firms have been slow to adopt AI technologies:

  • Some 35% of the executives surveyed said that they think the healthcare sector needs to be further convinced that AI or machine-learning investments will show a return.
  • Nearly 20% of those polled said that healthcare providers recognize the value AI offers, but lack the technology skills to make the products a reality.
  • About 12% of respondents said that they think healthcare providers are convinced of the benefits of using AI, but that they do not have the resources to purchase or develop AI products and services.

Naturally, it takes longer to develop AI and other technology applications in healthcare than it does in other sectors because healthcare applications involve several specific risks and regulatory stages. But there are AI applications that have been developed for use in the broader retail sector that drugstore retailers can implement as well.

We have identified three main applications of AI in retail, including the drugstore retail segment: personalization, customer service and inventory management.

1. Personalization: AI helps retailers provide personalized service to each customer through tailored communications, shopping recommendations, e-commerce and m-commerce portals, and promotions.

Use case in drugstore retail: Drugstore retailers send online customers emails that contain offers, promotions and discounts that are similar to those sent by retailers in other segments. These emails are usually tailored to include products that the customer may be interested in, based on the customer’s shopping and browsing history on the retailer’s website and in-store purchase history.

CVS sends such personalized emails to customers who are enrolled in its ExtraCare loyalty program. But, according to a review of the company’s loyalty program published in the Harvard Business Review in 2015, CVS creates personalized emails in this case based on an analysis of the behavior of customers from a test group versus those from a control group. This method, also called A/B testing, involves investigating hypotheses by emailing deals and offers to customers who have similar profiles and assessing key performance indicators such as conversion rate and basket size. The Harvard Business Review article noted that if CVS’s program used machine learning or AI, the company could further leverage the data it collects and get better results.

2. Customer service: AI is also used to run chatbots that mimic interactions with human customer-care and sales staff. Most chatbots tend to autonomously resolve customer queries and direct more difficult queries to human staff after asking customers a series of questions designed to assess the complexity of their issue.

Use case in drugstore retail: Drugstore pharmacies may not always have adequate staff to answer all patient or customer queries. And patients may want answers to questions immediately, but be unable to go to a pharmacy to speak with a staff member in person. In such instances, chatbot apps or chat services from pharmacies’ websites and apps can provide initial assistance to customers. is an app that helps users find information about their health through its chat interface. For example, if a user has lab tests done, she can ask the chatbot questions about the results’ meaning instead of having to wait for a doctor to call to explain the results.

Another example of chatbot use in drugstore retail is Walgreens’ partnership with telehealth firm MDLive, which helps patients interact with physicians through video chat. The app uses “health bot technology” developed by Microsoft “to help triage patient inquiries before [patients] interact with a doctor via video.”

While the healthcare sector may be slow to adopt other AI applications, chatbots seem relatively welcome in the sector. A survey of 6,000 global respondents conducted by tech company Pega in April 2017 identified the sectors in which consumers are “comfortable with a company using AI to give you better customer service.” Respondents were allowed to choose multiple responses to the question. Fully 34% stated that they were most comfortable with online retailers using AI chatbots to provide customer service, followed by 27% who stated that they would be comfortable with them being used in healthcare customer service.


3. Inventory management: AI-powered data analytics help retailers predict what customers are likely to buy in the near future. This enables retailers to maximize the probability of having the right items in stock as customers order them, which results in faster fulfillment and leaner inventory operations.

Use case in drugstore retail: Apart from stocking the right nonmedical and over-the-counter products, drugstores need to have a supply of medications based on prescription and refill requirements. AI can help retailers keep an account of the product and medication inventory of each store and how frequently these items are sold. AI can analyze such data and help store managers order the right quantities.

There are several inventory management software applications that were created specifically for drugstores and pharmacy practices, such as McKesson’s and Chetu’s pharmacy management applications. But not all of these use AI or machine learning, and pharmacy retail management solutions can take a cue from AI firm Blue Yonder and German online and catalog retailer Otto Group.

Blue Yonder developed a replenishment optimization service that helps retailers predict what customers will buy in the future. It helps Otto stock what its customers are likely to buy, enabling the retailer to achieve faster deliveries and reduce returns. The Blue Yonder service can reportedly predict with 90% accuracy what will be sold within a 30-day period, and it allows Otto to dramatically reduce its delivery schedule for partner products, from seven days to one or two days, often enabling direct delivery from the supplier to the customer without the need for the item to pass through Otto’s warehouse.

Other companies that are developing AI solutions for use in the healthcare space include IBM, DeepMind and CareSkore.

  • Care management solution: Tech giant IBM and drugstore retailer CVS announced in 2015 that they will use predictive analytics and IBM’s Watson AI platform to manage the care of patients with chronic disease. The service helps caregivers across CVS access insights from various health information sources such as medical health records, pharmacy and medical claims information, environmental data, and fitness devices to help patients meet their health goals.
  • AI platforms that facilitate early and precise diagnosis: Researchers at Stanford University created an AI program that taps a database of 130,000 images of moles, rashes and lesions to identify skin cancer. Google’s AI firm, DeepMind, uses machine learning to scan patients’ eyes and spot conditions such as age-related macular degeneration early. DeepMind is working with the UK’s National Health Service to fight blindness.
  • AI platforms that facilitate preventative care: CareSkore uses clinical, socioeconomic, demographic and behavioral data to create patient profiles and help doctors and insurance providers provide better preventative care.

What the Future Might Hold

AI has a long way to go before it becomes conventional to use it across all industries, and it may be particularly challenging to incorporate in applications for healthcare and drugstore retail. While it may seem harmless to use AI to predict a customer’s next purchase at a drugstore and help stores stock up on products and prescription and over-the-counter medications, implementing AI technology also means that companies will store customer data for analysis. And ensuring the security of vast amounts of patient-specific data on servers is a looming concern for regulators and users alike, especially in the wake of a much-publicized case in which the UK’s National Health Service illegally provided certain patient information to DeepMind.

Another challenge is the type of jobs that AI and bots will be tasked with performing. Machines may be better equipped to identify or predict conditions and perform complex or repetitive tasks tirelessly and with precision, but they are far from being able to replicate the human touch of caregiving. Moreover, in the US, regulations governing caregiving and pharmacy and drugstore retail vary across states, so identifying areas for potential AI intervention and implementing reforms maybe complicated. However, situations and challenges such as those discussed here reveal loopholes and formerly unaddressed aspects of AI, all of which help developers create more sophisticated technologies and refine existing ones.


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