Oct 3, 2020

Lux: Digital Transformation for the New Consumer Journey

AI
covid-19
Digital Transformation
IoT
Scott Birch
2 min
With hundreds of billions at stake, Lux Research outlines what companies need to know about AI and IoT’s impact on the consumer journey
With hundreds of billions at stake, Lux Research outlines what companies need to know about AI and IoT’s impact on the consumer journey...

Accelerated by COVID-19, emerging digital technologies, especially AI and IoT, are fundamentally changing the consumer journey in the consumer product market. A new report from Lux Research, “The Digital Transformation of the Consumer Journey,” outlines how companies can apply digital technology to enhance the consumer journey for their benefit.

Traditional digital solutions like digital marketing and ecommerce have laid the groundwork for the disruptive potential of AI and IoT. 

“Emerging AI and IoT technologies, however, are able to further move the needle in consumer personalisation through data collection, thus creating deeper value for CPGs and their supply chains,” says Jerrold Wang, Lux Research Analyst and lead author of the report.

The report looks at specific examples of how digital transformation impacts the consumer journey across the consumer product market, including categories like food, beverage, and nutrition, clothing and shoes, cosmetics and personal care, sporting goods, and furniture and household items. Among the most commonly used emerging digital technologies are AI technologies for human-machine interaction like computer vision, voice recognition, and natural language processing, smart cameras and sensors, and augmented reality (AR).

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“Though these digital technologies are empowering the five segments of the consumer journey – awareness, consideration, purchase, use, and retention – the hot spots of technology innovation and deployment are focused in the consideration and use segments,” explains Wang. 

Driven by AI-enabled personalised product recommendations and automated product utilisation, respectively, these two segments offer most of the opportunities for disruption. Innovations for these segments will enable customised value-added services and build consumer lock-in. In addition, these two segments have a direct impact on product sales and consumer retention, driving revenue from both new and existing consumers.

Lux Research states that companies in the consumer product market risk being left behind if they don’t develop AI and IoT strategies immediately. As personalisation has become a consumer expectation, market leaders like L’Oréal and P&G have been adopting digitally enabled products throughout the consumer journey – not to mention smaller companies launching new solutions on a nearly daily basis.
 

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

AI
CloudComputing
Data
ML
3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.

We take a look at some of the benefits and drawbacks of AI in cloud computing. 
 

The benefits of AI in cloud computing

 

Lower costs

A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.

Deeper insights 

AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.

Improved data management

AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments. 
 

Intelligent automation 

Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks. 

Increased security 

With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly. 
 

The drawbacks of AI in cloud computing

 

Data privacy 

 Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.

Connectivity concerns 

IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity. 

While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.

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