With 2019 in full swing, the excitement for data and AI driven innovation continues. Over the past few years, we’ve seen leading innovators – like Riot Games, Regeneron and Shell – become early adopters of the latest machine learning and AI technologies, building and deploying AI applications into production.
But with great promise comes great challenges, as AI is not for the faint of heart. A recent CIO.com survey stated that even though 90% of organizations have budget for AI projects most face significant challenges including:
- Complexity of data: With data and data sources growing each day, it’s becoming increasingly complicated to access and prepare data for downstream analytics. 96% of companies cite data-related problems as their #1 blocker to AI success.
- Siloed teams hurt productivity: Cross-team collaboration is necessary for smooth, efficient analytics workflows. Unfortunately, this is not the norm. 80% of companies cite collaboration across data science and engineering teams as a top roadblock on AI projects.
- Explosion of ML technologies and frameworks: The many tools now available to support AI projects have proven to be a valuable resource. In fact, organizations are using on average 7 different ML tools to power their AI use cases. However, this boon of tools is a blessing and a curse. Data science and engineering teams face significant operational challenges managing complex AI workflows.
Achieving AI nirvana is an uphill journey. But those who make it to the top will find newfound strategies and competitive differentiation. The key is to not address each of the challenges in isolation, but to take a holistic or unified approach to analytics.
In this fourth and final installment of our blog series focused on AI trends, we explore and celebrate some of the companies who were able to overcome these challenges to deliver AI-powered innovation.
How do you predict AI will impact your business in 2019?
Expanding Across Markets with AI-driven Services
“Our AI technology has given Quby a strategic differentiator within the energy sector. We are offering services based on AI, such as our Energy Waste Checker, in multiple European countries, to hundreds of thousands of users on a daily basis. We’ve transitioned a solely IoT hardware-based business model towards a services-based model.
In 2019 these AI implementations will allow us to expand our services to new partners and allow us to move away from the energy sector to new markets such as the insurance and assisted living sectors.”
- Stephen Galsworthy, Head of Data Science at Quby
Improving Efficiencies through Automation
“At Nielsen, we are already using AI to accelerate automation and drive innovation – so, this journey will continue in 2019. While much of the benefit of AI discussions is on automation and augmentation, we believe that the continued digitizing our business will drive significant innovation for both Nielsen, our partners and our clients.”
- Mainak Mazumdar, Chief Research Officer at Nielsen
Connecting Better with Customers
“In my position as Chief Algorithm Officer, I have been tasked with ensuring that AI and ML are embedded through every facet of Overstock, from marketing to product sourcing to CRM. I think we underestimate how massive the internet is and how searching for a product can become overwhelming for customers that face millions of products on thousands of pages of content on each site.
Integrating ML and AI makes online shopping more discoverable. This technology makes it possible for each person to create their unique taste profile and bridges the gap between technology and the customer, creating a clear connection in every part of the ecommerce journey.”
- Kamelia Aryafar, Chief Algorithm Officer at Overstock
- Read the full results of our survey conducted with CIO.com: 2018 Trend Report: Enterprise AI Adoption
- Download the Unified Analytics for Dummies ebook to learn how to bridge the gap between data and AI to drive business innovation