Integrating artificial intelligence into cannabis sales

According to PWC’s Global Artificial Intelligence Study: Exploiting the AI ​​Revolution report, artificial intelligence (AI) could contribute as much as $15.7 trillion to the global economy by 2030 and increase global GDP by 14%. Automation, artificial intelligence, and machine learning are already becoming commonplace in the cannabis industry as more innovative technologies are introduced throughout the supply chain.

Selling marijuana is just one stop on the seed-to-sale journey, where artificial intelligence is having a significant impact. From speeding up processes to reducing errors to saving money, artificial intelligence is changing the way cannabis companies operate and sell.

However, integrating artificial intelligence into the cannabis industry is not without its challenges. Let’s take a closer look at some of the specific impacts of artificial intelligence on the cannabis supply chain and the key roadblocks organizations are facing from the growth of automation, machine learning, predictive analytics, and artificial intelligence.

Artificial intelligence in B2C cannabis sales

In recent years, artificial intelligence and machine learning have become indispensable for companies that want to show consumers the most relevant products and services when they visit company websites. When artificial intelligence works behind the scenes to match visitors to the items they are most likely to buy and display those items in real time, sales and revenue will increase.

Additionally, a growing number of AI-powered apps, chatbots, and websites are launching to help marijuana customers find the products they need. This helps cannabis companies improve their sales and customer relationships both online and in physical dispensaries and retail stores.

For example, Potbot is a mobile app available on Apple’s App Store and Google Play Store that uses artificial intelligence to sort through tens of thousands of cannabis strains, read peer-reviewed medical journals to analyze studies on cannabinoids, and that information with dozens of symptoms like asthma and insomnia to find out which type is best for treating the specific condition.

Blinx AI uses artificial intelligence in its pharmaceutical application to analyze the amount of THC in cannabis products. The AI ​​looks for patterns and similarities between cannabis strains. The data helps medical marijuana patients understand compound concentrations, monitor their dosages, and have more control over their medications.

Another example is Lucid Green’s QR code technology, LucidID. Cannabis brands can add a custom QR code to their product packaging that consumers can scan and instantly access information on potency, customer and patient ratings, dosage recommendations, expected effects, lot numbers, lab testing, and more.

For pharmacies and retailers, Budster connects directly to the point-of-sale system and uses artificial intelligence to assess the health of the business and determine the true value of each customer. It also offers AI-generated offers and business insights to increase customer retention, sales, and revenue.

Artificial intelligence in B2B cannabis sales

B2B sales in the cannabis industry have also changed in recent years thanks to artificial intelligence, predictive analytics, and machine learning. As a result, workflows have been streamlined, tasks automated, and sales teams can focus more time on revenue-generating activities.

For businesses that sell to cannabis license holders, the Cannabiz Media License Database is the only sales, customer relationship management (CRM) and marketing tool that leverages AI and machine learning to help sales reps do their job more efficiently and successfully .

AI is already being implemented at all stages of the cannabis supply chain. For growers, tools like Bloom Automation and Budscout improve processes and results. Bloom Automation uses patented artificial intelligence, machine learning, and computer vision to help cannabis companies automate many tasks in the grow room. For example, the team at Bloom Automation developed an algorithm to robotically trim cannabis branches quickly and precisely.

Budscout helps growers increase sales and revenue by detecting problems early. The Budscout robot monitors the plants every hour and reports environmental indicators. Using a proprietary algorithm, the technology can detect crop health problems up to 14 days earlier than a human. In addition, Budscout uses artificial intelligence to measure the size and amount of buds on plants and predicts the expected yield many months in advance.

Sales and business data to reduce investment risk

With sales playing such a crucial role in a cannabis company’s success, it’s important to consider how artificial intelligence is affecting investors and risk management in the industry.

VantagePoint, a software company that offers programs to predict stock market changes, integrated cannabis stocks in the United States and Canada into its platform in 2019. Using artificial intelligence, VantagePoint identifies patterns in the data that can be used to make more accurate forecasts and investment decisions.

Adherence Compliance, which offers a mobile and cloud app for marijuana legal and financial compliance, uses artificial intelligence, predictive analytics, and machine learning algorithms to help cannabis company stakeholders assess business risk.

Challenges of artificial intelligence

Companies operating in the cannabis industry face similar challenges to integrating artificial intelligence into their operations as companies in other industries. The top three challenges relate to people, data collection, and data reliability and security.

The companies that can successfully navigate these changes will be able to take advantage of artificial intelligence, automation, predictive analytics and machine learning for growth and cost savings.

people challenges

With new technologies, including technologies that use artificial intelligence, there is a learning curve for employees. For companies, this means training costs and potentially hiring costs if the required talent isn’t already in the workforce. Many companies could face significant training costs as well as inevitable employee turnover when implementing artificial intelligence technology.

Data collection challenges

Artificial intelligence technology needs data to successfully integrate it into the day-to-day operations of a business. Finding the budget to collect, standardize, and effectively index this data is a major problem for many organizations. However, the benefits it brings are worth it in the long run.

Data reliability and security challenges

Data is often only useful when it’s current, and for many organizations, continuously collecting data to ensure it’s reliable for decision-making is a major challenge. Additionally, privacy and security concerns add to the complexity of management adds another layer of artificial intelligence technologies used in business operations.

Despite these challenges, artificial intelligence technology is the future, and cannabis companies that begin to integrate it into their operations could be in for a big hit in the years to come, when competitors who have yet to leverage artificial intelligence technology , falling behind.

Diploma

Artificial intelligence and machine learning are becoming critical parts of a cannabis company’s business strategy, and for good reason. AI offers measurable benefits through increased productivity and improved employee decision-making.

In addition, the data used in artificial intelligence technology enables companies to offer more personalized consumer experiences and higher quality products and services. This is as true of the cannabis industry as it is of other industries harnessing the power of artificial intelligence.

One thing is certain: when a company has access to artificial intelligence, it has a significant competitive advantage in the marketplace over companies that don’t have the same (or better) real-time predictive data.

Originally published on 6/7/18. Updated 3/10/23.