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Gartner Predicts the Near and Not So Obvious Future of AI in the Enterprise

By Svetlana Sicular | April 12, 2021 | 0 Comments

PredictsAI in the EnterpriseAI

While Gartner is known for its magic quadrants, I prefer crystal balls. Once a year, we make predictions. They are sparkly and imaginative, and are just more fun for analysts. Register to hear our predictions and also ask questions at a webinar Gartner Predicts the Future and Impacts of AI Beyond 2021 on April 14th at 10 am EDT.  If you ask questions here in the Comments, I would be happy to answer too.

Many predicts do not seem to be related to AI on the surface, and those are most interesting. Let me share with you several of them – about supermarkets,  restaurants and drug discovery. I won’t dive deep in the reasoning behind the predicts, but will just will outline connections between them and AI.


By 2023, five Tier 1 grocery retailers will have adopted hybrid store models, installing “go-style” smart check-out formats within their larger superstores.
Max Hammond and Kelsie Marian, Predicts 2021: Retail — Redefining the Physical to Capitalize on Digital

  • Adoption of the AI technology has grown in the two years since Amazon and Alibaba introduced the first go-style, smart check-out experiences. Several vendors have also jumped into the solution market, offering solutions that use computer vision to track customer purchases across the entire store, through smart carts or at self-check-out.
  • Go-style, smart check-out is best optimized for quick shopping trips for a smaller number of items, rather than for a customer’s large weekly shopping. As retailers leverage AI to better understand customer needs and drive more accurate assortments, it will become even more apparent which items are most suitable for go-style, smart check-out.
  • But smart check-out solutions are limited by the physical footprints of the stores, making it difficult for them to scale above 2,000 square feet, which continues to limit adoption for many Tier 1 retailers. So, while the technology can scale to large formats, the opportunity for grocery retailers to create a go-style store within a larger store will offer customers a choice based on specific customer journey – this is AI, and also BI.


By 2024, two of the top 10 global quick service restaurant brands will develop a collaborative food delivery marketplace to compete with third-party delivery aggregators.
Max Hammond, Predicts 2021: Retail — Redefining the Physical to Capitalize on Digital

  • Some restaurant chains will have their own Uber-like delivery.  What can be more AI than Uber?
  • New delivery channels will bring to these chains new customers who shifted from competing restaurants lacking delivery capabilities. AI’s job here is to find and reach these new customer segments who would appreciate the new delivery models.
  • The rapid development of autonomous vehicles (AI!) and drone technology has the potential to transform the entire delivery model. Concepts are currently in development worldwide by brands such as Chipotle, Domino’s and McDonald’s, and third-party aggregators such as Uber Eats and Just Eat.


By 2023, nearly half of new product lead candidates will come from preclinical R&D research portfolios that have invested in AI- and quantum-generated drug discovery initiatives.
Michael Shanler, Predicts 2021: Life Science Companies Must Quickly Adapt as Digital Expectations Change

  • New and more sophisticated forms of AI and computational methods, including investments in cloud drug development platforms and quantum computing, are poised to place formerly intractable problems in drug discovery within reach to solve.
  • Pharma investments and early successful POCs signal that AI will soon become the dominant modality for accelerating preclinical portfolios. COVID-19 has accelerated many AI programs for drug research.
  • AI is already a proven technology in life science. For example, the new compound DSP-1181 developed for treating patients with obsessive-compulsive disorder (OCD) was developed by Exscientia in collaboration with Sumitomo Dainippon Pharma. AI was used to help select and refine the lead and downstream synthesis methodology. It is entering human trials after 12 months of preclinical work. True success will be declared after completion of clinical trials; however, this is a positive step in the application of AI for early drug discovery.


Follow Svetlana on Twitter @Sve_Sic


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